top

  Info

  • Utilizzare la checkbox di selezione a fianco di ciascun documento per attivare le funzionalità di stampa, invio email, download nei formati disponibili del (i) record.

  Info

  • Utilizzare questo link per rimuovere la selezione effettuata.

Biblioteca

MARC Lista (tabellare)
Artificial Neural Networks and Machine Learning – ICANN 2019: Deep Learning [[electronic resource] ] : 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17–19, 2019, Proceedings, Part II / / edited by Igor V. Tetko, Věra Kůrková, Pavel Karpov, Fabian Theis
Artificial Neural Networks and Machine Learning – ICANN 2019: Deep Learning [[electronic resource] ] : 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17–19, 2019, Proceedings, Part II / / edited by Igor V. Tetko, Věra Kůrková, Pavel Karpov, Fabian Theis
Edizione [1st ed. 2019.]
Pubbl/distr/stampa Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019
Descrizione fisica 1 online resource (XXX, 807 p. 294 illus., 193 illus. in color.)
Disciplina 006.3
Collana Theoretical Computer Science and General Issues
Soggetto topico Artificial intelligence
Computer vision
Computer engineering
Computer networks
Algorithms
Data protection
Artificial Intelligence
Computer Vision
Computer Engineering and Networks
Computer Communication Networks
Data and Information Security
ISBN 3-030-30484-1
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Adaptive Graph Fusion for Unsupervised Feature Selection -- Unsupervised Feature Selection via Local Total-order Preservation -- Discrete Stochastic Search and its Application to Feature-Selection for Deep Relational Machines -- Joint Dictionary Learning for Unsupervised Feature Selection -- Comparison between Filter Criteria for Feature Selection in Regression -- CancelOut: A layer for feature selection in deep neural networks -- Adaptive-L2 Batch Neural Gas -- Application of Self Organizing Map to Preprocessing Input Vectors for Convolutional Neural Network -- Hierarchical Reinforcement Learning with Unlimited Recursive Subroutine Calls -- Automatic Augmentation by Hill Climbing -- Learning Camera-invariant Representation for Person Re-identification -- PA-RetinaNet: Path Augmented RetinaNet for Dense Object Detection -- Singular Value Decomposition and Neural Networks -- PCI: Principal Component Initialization for Deep Autoencoders -- Improving Weight Initialization of ReLU and Output Layers -- Post-synaptic potential regularization has potential -- A Novel Modification on the Levenberg-Marquardt Algorithm for Avoiding Overfitting in Neural Network Training -- Sign Based Derivative Filtering for Stochastic Gradient Descent -- Architecture-aware Bayesian Optimization for Neural Network Tuning -- Non-Convergence and Limit Cycles in the Adam Optimizer -- Learning Internal Dense But External Sparse Structures of Deep Convolutional Neural Network -- Using feature entropy to guide filter pruning for efficient convolutional networks -- Simultaneously Learning Architectures and Features of Deep Neural Networks -- Learning Sparse Hidden States in Long Short-Term Memory -- Multi-objective Pruning for CNNs using Genetic Algorithm -- Dynamically Sacrificing Accuracy for Reduced Computation: Cascaded Inference Based on Softmax Confidence -- Light-Weight Edge Enhanced Network for On-orbit Semantic Segmentation -- Local Normalization Based BN Layer Pruning -- On Practical Approach to Uniform Quantization of Non-redundant Neural Networks -- Residual learning for FC kernels of convolutional network -- A Novel Neural Network-based Symbolic Regression Method: Neuro-Encoded Expression Programming -- Compute-efficient neural network architecture optimization by a genetic algorithm -- Controlling Model Complexity in Probabilistic Model-Based Dynamic Optimization of Neural Network Structures -- Predictive Uncertainty Estimation with Temporal Convolutional Networks for Dynamic Evolutionary Optimization -- Sparse Recurrent Mixture Density Networks for Forecasting High Variability Time Series with Confidence Estimates -- A multitask learning neural network for short-term traffic speed prediction and confidence estimation -- Central-diffused Instance Generation Method in Class Incremental Learning -- Marginal Replay vs Conditional Replay for Continual Learning -- Simplified computation and interpretation of Fisher matrices in incremental learning with deep neural networks -- Active Learning for Image Recognition using a Visualization-Based User Interface -- Basic Evaluation Scenarios for Incrementally Trained Classifiers -- Embedding Complexity of Learned Representations in Neural Networks -- Joint Metric Learning on Riemannian Manifold of Global Gaussian Distributions -- Multi-Task Sparse Regression Metric Learning for Heterogeneous Classification -- Fast Approximate Geodesics for Deep Generative Models -- Spatial Attention Network for Few-Shot Learning -- Routine Modeling with Time Series Metric Learning -- Leveraging Domain Knowledge for Reinforcement Learning using MMC Architectures -- Conditions for Unnecessary Logical Constraints in Kernel Machines -- HiSeqGAN: Hierarchical Sequence Synthesis and Prediction -- DeepEX: Bridging the Gap Between Knowledge and Data Driven Techniques for Time Series Forecasting -- Transferable Adversarial Cycle Alignment for Domain Adaption -- Evaluation of domain adaptation approaches for robust classification of heterogeneous biological data sets -- Named Entity Recognition for Chinese Social Media with Domain Adversarial Training and Language Modeling -- Deep Domain Knowledge Distillation for Person Re-identification -- A study on catastrophic forgetting in deep LSTM networks -- A Label-specific Attention-based Network with Regularized Loss for Multi-label Classification -- An Empirical Study of Multi-domain and Multi-task Learning in Chinese Named Entity Recognition -- Filter Method Ensemble with Neural Networks -- Dynamic Centroid Insertion and Adjustment for Data Sets with Multiple Imbalanced Classes -- Increasing the Generalisaton Capacity of Conditional VAEs -- Playing the Large Margin Preference Game.
Record Nr. UNISA-996466303903316
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Artificial Neural Networks and Machine Learning – ICANN 2019: Deep Learning : 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17–19, 2019, Proceedings, Part II / / edited by Igor V. Tetko, Věra Kůrková, Pavel Karpov, Fabian Theis
Artificial Neural Networks and Machine Learning – ICANN 2019: Deep Learning : 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17–19, 2019, Proceedings, Part II / / edited by Igor V. Tetko, Věra Kůrková, Pavel Karpov, Fabian Theis
Edizione [1st ed. 2019.]
Pubbl/distr/stampa Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019
Descrizione fisica 1 online resource (XXX, 807 p. 294 illus., 193 illus. in color.)
Disciplina 006.3
006.32
Collana Theoretical Computer Science and General Issues
Soggetto topico Artificial intelligence
Computer vision
Computer engineering
Computer networks
Algorithms
Data protection
Artificial Intelligence
Computer Vision
Computer Engineering and Networks
Computer Communication Networks
Data and Information Security
ISBN 3-030-30484-1
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Adaptive Graph Fusion for Unsupervised Feature Selection -- Unsupervised Feature Selection via Local Total-order Preservation -- Discrete Stochastic Search and its Application to Feature-Selection for Deep Relational Machines -- Joint Dictionary Learning for Unsupervised Feature Selection -- Comparison between Filter Criteria for Feature Selection in Regression -- CancelOut: A layer for feature selection in deep neural networks -- Adaptive-L2 Batch Neural Gas -- Application of Self Organizing Map to Preprocessing Input Vectors for Convolutional Neural Network -- Hierarchical Reinforcement Learning with Unlimited Recursive Subroutine Calls -- Automatic Augmentation by Hill Climbing -- Learning Camera-invariant Representation for Person Re-identification -- PA-RetinaNet: Path Augmented RetinaNet for Dense Object Detection -- Singular Value Decomposition and Neural Networks -- PCI: Principal Component Initialization for Deep Autoencoders -- Improving Weight Initialization of ReLU and Output Layers -- Post-synaptic potential regularization has potential -- A Novel Modification on the Levenberg-Marquardt Algorithm for Avoiding Overfitting in Neural Network Training -- Sign Based Derivative Filtering for Stochastic Gradient Descent -- Architecture-aware Bayesian Optimization for Neural Network Tuning -- Non-Convergence and Limit Cycles in the Adam Optimizer -- Learning Internal Dense But External Sparse Structures of Deep Convolutional Neural Network -- Using feature entropy to guide filter pruning for efficient convolutional networks -- Simultaneously Learning Architectures and Features of Deep Neural Networks -- Learning Sparse Hidden States in Long Short-Term Memory -- Multi-objective Pruning for CNNs using Genetic Algorithm -- Dynamically Sacrificing Accuracy for Reduced Computation: Cascaded Inference Based on Softmax Confidence -- Light-Weight Edge Enhanced Network for On-orbit Semantic Segmentation -- Local Normalization Based BN Layer Pruning -- On Practical Approach to Uniform Quantization of Non-redundant Neural Networks -- Residual learning for FC kernels of convolutional network -- A Novel Neural Network-based Symbolic Regression Method: Neuro-Encoded Expression Programming -- Compute-efficient neural network architecture optimization by a genetic algorithm -- Controlling Model Complexity in Probabilistic Model-Based Dynamic Optimization of Neural Network Structures -- Predictive Uncertainty Estimation with Temporal Convolutional Networks for Dynamic Evolutionary Optimization -- Sparse Recurrent Mixture Density Networks for Forecasting High Variability Time Series with Confidence Estimates -- A multitask learning neural network for short-term traffic speed prediction and confidence estimation -- Central-diffused Instance Generation Method in Class Incremental Learning -- Marginal Replay vs Conditional Replay for Continual Learning -- Simplified computation and interpretation of Fisher matrices in incremental learning with deep neural networks -- Active Learning for Image Recognition using a Visualization-Based User Interface -- Basic Evaluation Scenarios for Incrementally Trained Classifiers -- Embedding Complexity of Learned Representations in Neural Networks -- Joint Metric Learning on Riemannian Manifold of Global Gaussian Distributions -- Multi-Task Sparse Regression Metric Learning for Heterogeneous Classification -- Fast Approximate Geodesics for Deep Generative Models -- Spatial Attention Network for Few-Shot Learning -- Routine Modeling with Time Series Metric Learning -- Leveraging Domain Knowledge for Reinforcement Learning using MMC Architectures -- Conditions for Unnecessary Logical Constraints in Kernel Machines -- HiSeqGAN: Hierarchical Sequence Synthesis and Prediction -- DeepEX: Bridging the Gap Between Knowledge and Data Driven Techniques for Time Series Forecasting -- Transferable Adversarial Cycle Alignment for Domain Adaption -- Evaluation of domain adaptation approaches for robust classification of heterogeneous biological data sets -- Named Entity Recognition for Chinese Social Media with Domain Adversarial Training and Language Modeling -- Deep Domain Knowledge Distillation for Person Re-identification -- A study on catastrophic forgetting in deep LSTM networks -- A Label-specific Attention-based Network with Regularized Loss for Multi-label Classification -- An Empirical Study of Multi-domain and Multi-task Learning in Chinese Named Entity Recognition -- Filter Method Ensemble with Neural Networks -- Dynamic Centroid Insertion and Adjustment for Data Sets with Multiple Imbalanced Classes -- Increasing the Generalisaton Capacity of Conditional VAEs -- Playing the Large Margin Preference Game.
Record Nr. UNINA-9910349283803321
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Artificial Neural Networks and Machine Learning – ICANN 2019: Image Processing [[electronic resource] ] : 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17–19, 2019, Proceedings, Part III / / edited by Igor V. Tetko, Věra Kůrková, Pavel Karpov, Fabian Theis
Artificial Neural Networks and Machine Learning – ICANN 2019: Image Processing [[electronic resource] ] : 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17–19, 2019, Proceedings, Part III / / edited by Igor V. Tetko, Věra Kůrková, Pavel Karpov, Fabian Theis
Edizione [1st ed. 2019.]
Pubbl/distr/stampa Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019
Descrizione fisica 1 online resource (XXX, 733 p. 417 illus., 273 illus. in color.)
Disciplina 006.3
Collana Theoretical Computer Science and General Issues
Soggetto topico Artificial intelligence
Computer vision
Computer engineering
Computer networks
Algorithms
Data protection
Artificial Intelligence
Computer Vision
Computer Engineering and Networks
Computer Communication Networks
Data and Information Security
ISBN 3-030-30508-2
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Unsharp Masking Layer: Injecting Prior Knowledge in Convolutional Networks for Image Classification -- Distortion Estimation Through Explicit Modeling of the Refractive Surface -- Eye Movement-based Analysis on Methodologies and Efficiency in the Process of Image Noise Evaluation -- IBDNet: Lightweight Network for On-orbit Image Blind Denoising -- Aggregating Rich Deep Semantic Features for Fine-Grained Place Classification -- Improving Reliability of Object Detection for Lunar Craters using Monte Carlo Dropout -- An improved convolutional neural network for steganalysis in the scenario of reuse of the stego-key -- A New Learning-based One Shot Detection Framework For Natural Images -- Dense Receptive Field Network: A Backbone Network for Object Detection -- Referring Expression Comprehension via Co-attention and Visual Context -- Comparison between U-Net and U-ReNet models in OCR tasks -- Severe Convective Weather Classification in Remote Sensing Images by Semantic Segmentation -- Action Recognition Based on Divide-and-conquer -- An Adaptive Feature Channel Weighting Scheme for Correlation Tracking -- In-silico staining from bright-field and fluorescent images using deep learning -- A lightweight neural network for hard exudate segmentation of fundus image -- Attentional Residual Dense Factorized Network for Real-time Semantic Segmentation -- Random drop loss for tiny object segmentation: Application to lesion segmentation in fundus images -- Flow2Seg: Motion-Aided Semantic Segmentation -- COCO_TS Dataset: Pixel-level Annotations Based on Weak Supervision for Scene Text Segmentation -- Learning Deep Structured Multi-Scale Features for crisp and occlusion edge detection -- Graph-Boosted Attentive Network for Semantic Body Parsing -- A Global-Local Architecture Constrained by Multiple Attributes for Person Re-identification -- Recurrent Connections Aid Occluded Object Recognition by Discounting Occluders -- Learning Relational-Structural Networks for Robust Face Alignment -- An Efficient 3D-NAS Method for Video-based Gesture Recognition -- Robustness of deep LSTM networks in freehand gesture recognition -- Delving into the Impact of Saliency Detector: A GeminiNet for Accurate Saliency Detection -- FCN Salient Object Detection Using Region Cropping -- Object-Level Salience Detection By Progressively Enhanced Network -- Action unit assisted Facial Expression Recognition -- Discriminative Feature Learning using Two-stage Training Strategy for Facial Expression Recognition -- Action Units Classification using ClusWiSARD -- Automatic Estimation of Dog Age: The DogAge Dataset and Challenge -- Neural Network 3D Body Pose Tracking and Prediction for Motion-to-Photon Latency Compensation in Distributed Virtual Reality -- Variational Deep Embedding with Regularized Student-t Mixture Model -- A mixture-of-experts model for vehicle prediction using an online learning approach -- An Application of Convolutional Neural Networks for Analyzing Dogs' Sleep Patterns -- On the Inability of Markov Models to Capture Criticality in Human Mobility -- LSTM with Uniqueness Attention for Human Activity Recognition -- Comparative Research on SOM with Torus and Sphere Topologies for Peculiarity Classification of Flat Finishing Skill Training -- Generative Creativity: Adversarial Learning for Bionic Design -- Self-attention StarGAN for Multi-domain Image-to-image Translation -- Generative Adversarial Networks for Operational Scenario Planing of Renewable Energy Farms: A Study on Wind and Photovoltaic -- Constraint-Based Visual Generation -- Text to Image Synthesis based on Multiple Discrimination -- Disentangling Latent Factors of Variational Auto-Encoder with Whitening -- Training Discriminative Models to Evaluate Generative Ones -- Scene Graph Generation via Convolutional Message Passing and Class-aware Memory Embeddings -- Change Detection in Satellite Images using Reconstruction Errors of Joint Autoencoders -- Physical Adversarial Attacks by Projecting Perturbations -- Improved Forward-backward Propagation to Generate Adversarial Examples -- Incremental Learning of GAN for Detecting Multiple Adversarial Attacks -- Evaluating Defensive Distillation For Defending Text Processing Neural Networks Against Adversarial Examples -- DCT:Differential Combination Testing of Deep Learning Systems -- Restoration as a Defense Against Adversarial Perturbations for Spam Image Detection -- HLR: Generating Adversarial Examples by High-Level Representations.
Record Nr. UNISA-996466305603316
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Artificial Neural Networks and Machine Learning – ICANN 2019: Image Processing : 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17–19, 2019, Proceedings, Part III / / edited by Igor V. Tetko, Věra Kůrková, Pavel Karpov, Fabian Theis
Artificial Neural Networks and Machine Learning – ICANN 2019: Image Processing : 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17–19, 2019, Proceedings, Part III / / edited by Igor V. Tetko, Věra Kůrková, Pavel Karpov, Fabian Theis
Edizione [1st ed. 2019.]
Pubbl/distr/stampa Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019
Descrizione fisica 1 online resource (XXX, 733 p. 417 illus., 273 illus. in color.)
Disciplina 006.3
006.32
Collana Theoretical Computer Science and General Issues
Soggetto topico Artificial intelligence
Computer vision
Computer engineering
Computer networks
Algorithms
Data protection
Artificial Intelligence
Computer Vision
Computer Engineering and Networks
Computer Communication Networks
Data and Information Security
ISBN 3-030-30508-2
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Unsharp Masking Layer: Injecting Prior Knowledge in Convolutional Networks for Image Classification -- Distortion Estimation Through Explicit Modeling of the Refractive Surface -- Eye Movement-based Analysis on Methodologies and Efficiency in the Process of Image Noise Evaluation -- IBDNet: Lightweight Network for On-orbit Image Blind Denoising -- Aggregating Rich Deep Semantic Features for Fine-Grained Place Classification -- Improving Reliability of Object Detection for Lunar Craters using Monte Carlo Dropout -- An improved convolutional neural network for steganalysis in the scenario of reuse of the stego-key -- A New Learning-based One Shot Detection Framework For Natural Images -- Dense Receptive Field Network: A Backbone Network for Object Detection -- Referring Expression Comprehension via Co-attention and Visual Context -- Comparison between U-Net and U-ReNet models in OCR tasks -- Severe Convective Weather Classification in Remote Sensing Images by Semantic Segmentation -- Action Recognition Based on Divide-and-conquer -- An Adaptive Feature Channel Weighting Scheme for Correlation Tracking -- In-silico staining from bright-field and fluorescent images using deep learning -- A lightweight neural network for hard exudate segmentation of fundus image -- Attentional Residual Dense Factorized Network for Real-time Semantic Segmentation -- Random drop loss for tiny object segmentation: Application to lesion segmentation in fundus images -- Flow2Seg: Motion-Aided Semantic Segmentation -- COCO_TS Dataset: Pixel-level Annotations Based on Weak Supervision for Scene Text Segmentation -- Learning Deep Structured Multi-Scale Features for crisp and occlusion edge detection -- Graph-Boosted Attentive Network for Semantic Body Parsing -- A Global-Local Architecture Constrained by Multiple Attributes for Person Re-identification -- Recurrent Connections Aid Occluded Object Recognition by Discounting Occluders -- Learning Relational-Structural Networks for Robust Face Alignment -- An Efficient 3D-NAS Method for Video-based Gesture Recognition -- Robustness of deep LSTM networks in freehand gesture recognition -- Delving into the Impact of Saliency Detector: A GeminiNet for Accurate Saliency Detection -- FCN Salient Object Detection Using Region Cropping -- Object-Level Salience Detection By Progressively Enhanced Network -- Action unit assisted Facial Expression Recognition -- Discriminative Feature Learning using Two-stage Training Strategy for Facial Expression Recognition -- Action Units Classification using ClusWiSARD -- Automatic Estimation of Dog Age: The DogAge Dataset and Challenge -- Neural Network 3D Body Pose Tracking and Prediction for Motion-to-Photon Latency Compensation in Distributed Virtual Reality -- Variational Deep Embedding with Regularized Student-t Mixture Model -- A mixture-of-experts model for vehicle prediction using an online learning approach -- An Application of Convolutional Neural Networks for Analyzing Dogs' Sleep Patterns -- On the Inability of Markov Models to Capture Criticality in Human Mobility -- LSTM with Uniqueness Attention for Human Activity Recognition -- Comparative Research on SOM with Torus and Sphere Topologies for Peculiarity Classification of Flat Finishing Skill Training -- Generative Creativity: Adversarial Learning for Bionic Design -- Self-attention StarGAN for Multi-domain Image-to-image Translation -- Generative Adversarial Networks for Operational Scenario Planing of Renewable Energy Farms: A Study on Wind and Photovoltaic -- Constraint-Based Visual Generation -- Text to Image Synthesis based on Multiple Discrimination -- Disentangling Latent Factors of Variational Auto-Encoder with Whitening -- Training Discriminative Models to Evaluate Generative Ones -- Scene Graph Generation via Convolutional Message Passing and Class-aware Memory Embeddings -- Change Detection in Satellite Images using Reconstruction Errors of Joint Autoencoders -- Physical Adversarial Attacks by Projecting Perturbations -- Improved Forward-backward Propagation to Generate Adversarial Examples -- Incremental Learning of GAN for Detecting Multiple Adversarial Attacks -- Evaluating Defensive Distillation For Defending Text Processing Neural Networks Against Adversarial Examples -- DCT:Differential Combination Testing of Deep Learning Systems -- Restoration as a Defense Against Adversarial Perturbations for Spam Image Detection -- HLR: Generating Adversarial Examples by High-Level Representations.
Record Nr. UNINA-9910349299803321
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Artificial Neural Networks and Machine Learning – ICANN 2019: Text and Time Series [[electronic resource] ] : 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17–19, 2019, Proceedings, Part IV / / edited by Igor V. Tetko, Věra Kůrková, Pavel Karpov, Fabian Theis
Artificial Neural Networks and Machine Learning – ICANN 2019: Text and Time Series [[electronic resource] ] : 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17–19, 2019, Proceedings, Part IV / / edited by Igor V. Tetko, Věra Kůrková, Pavel Karpov, Fabian Theis
Edizione [1st ed. 2019.]
Pubbl/distr/stampa Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019
Descrizione fisica 1 online resource (XXX, 761 p. 339 illus., 198 illus. in color.)
Disciplina 006.3
Collana Theoretical Computer Science and General Issues
Soggetto topico Artificial intelligence
Computer vision
Computer engineering
Computer networks
Algorithms
Data protection
Artificial Intelligence
Computer Vision
Computer Engineering and Networks
Computer Communication Networks
Data and Information Security
ISBN 3-030-30490-6
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto An ensemble model for winning a Chinese machine reading comprehension competition -- Dependent Multilevel Interaction Network for Natural Language Inference -- Learning to Explain Chinese Slang Words -- Attention-Based Improved BLSTM-CNN for Relation Classification -- An Improved Method of Applying a Machine Translation Model to a Chinese Word Segmentation Task -- Interdependence Model for Multi-label Classification -- Combining deep learning and (structural) feature-based classification methods for copyright-protected PDF documents -- Collaborative Attention Network with Word and N-gram Sequences Modeling for Sentiment Classification -- Targeted Sentiment Classification with Attentional Encoder Network -- Capturing User and Product Information for Sentiment Classification via Hierarchical Separated Attention and Neural Collaborative Filtering -- Imbalanced Sentiment Classification Enhanced with Discourse Marker -- Revising Attention with Position for Aspect-level Sentiment Classification -- Surrounding-Based Attention Networks for Aspect-Level Sentiment Classification -- Mid Roll Advertisement Placement using Multi Modal Emotion Analysis -- DCAR: Deep Collaborative Autoencoder for Recommendation with Implicit Feedback -- Jointly Learning to Detect Emotions and Predict Facebook Reactions -- Discriminative Feature Learning for Speech Emotion Recognition -- A Judicial Sentencing Method Based on Fused Deep Neural Networks -- SECaps: A Sequence Enhanced Capsule Model for Charge Prediction -- Learning to Predict Charges for Judgment with Legal Graph -- A Recurrent Attention Network for Judgment Prediction -- Symmetrical Adversarial Training Nets: A Novel Model For Text Generation -- A Novel Image Captioning Method based on Generative Adversarial Networks -- Quality-Diversity Summarization with Unsupervised Autoencoders -- Conditional GANs for Image Captioning with Sentiments -- Neural Poetry: Learning to Generate Poems using Syllables -- Exploring the Advantages of Corpus in Neural Machine Translation of Agglutinative Language -- RL extraction of syntax-based chunks for sentence compression -- Robust Sound Event Classification with Local Time-Frequency Information and Convolutional Neural Networks -- Neuro-Spectral Audio Synthesis: Exploiting characteristics of the Discrete Fourier Transform in the real-time simulation of musical instruments using parallel Neural Networks -- Ensemble of Convolutional Neural Networks for P300 Speller in Brain Computer Interface -- Deep Recurrent Neural Networks with Nonlinear Masking Layers and Two-Level Estimation for Speech Separation -- Auto-Lag Networks for Real Valued Sequence to Sequence Prediction -- LSTM Prediction on Sudden Occurrence of Maintenance Operation of Air-conditioners in Real-time Pricing Adaptive Control -- Dynamic Ensemble Using Previous and Predicted Future Performance for Multi-Step-Ahead Solar Power Forecasting -- Timage – A Robust Time Series Classification Pipeline -- Prediction of the Next Sensor Event and its Time of Occurrence in Smart Homes -- Multi-task Learning Method for Hierarchical Time Series Forecasting -- Demand-prediction architecture for distribution businesses based on multiple RNNs with alternative weight update -- A Study of Deep Learning for Network Traffic Data Forecasting -- Composite Quantile Regression Long Short-Term Memory Network -- Short-Term Temperature Forecasting on a Several Hours Horizon -- Using Long Short-Term Memory for Wavefront Prediction in Adaptive Optics -- Incorporating Adaptive RNN-based Action Inference and Sensory Perception -- Quality of Prediction of Daily Relativistic Electrons Flux at Geostationary Orbit by Machine Learning Methods -- Soft Subspace Growing Neural Gas for DataStream Clustering -- Region Prediction from Hungarian Folk Music Using Convolutional Neural Networks -- Merging DBSCAN and Density Peak for Robust Clustering -- Market basket analysis using Boltzmann machines -- Dimensionality Reduction for Clustering and Cluster Tracking of Cytometry Data -- Improving Deep Image Clustering With Spatial Transformer Layers -- Collaborative Non-negative Matrix Factorization -- Cosine Similarity Drift Detector -- Unsupervised anomaly detection using optimal transport for predictive maintenance -- Robust Gait Authentication Using Autoencoder and Decision Tree -- MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networks -- Intrusion Detection via Wide & Deep Model -- Towards Attention based Vulnerability Discovery using Source Code Representation -- Convolutional Recurrent Neural Networks for Computer Network Analysis.
Record Nr. UNISA-996466300903316
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Artificial Neural Networks and Machine Learning – ICANN 2019: Text and Time Series : 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17–19, 2019, Proceedings, Part IV / / edited by Igor V. Tetko, Věra Kůrková, Pavel Karpov, Fabian Theis
Artificial Neural Networks and Machine Learning – ICANN 2019: Text and Time Series : 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17–19, 2019, Proceedings, Part IV / / edited by Igor V. Tetko, Věra Kůrková, Pavel Karpov, Fabian Theis
Edizione [1st ed. 2019.]
Pubbl/distr/stampa Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019
Descrizione fisica 1 online resource (XXX, 761 p. 339 illus., 198 illus. in color.)
Disciplina 006.3
006.32
Collana Theoretical Computer Science and General Issues
Soggetto topico Artificial intelligence
Computer vision
Computer engineering
Computer networks
Algorithms
Data protection
Artificial Intelligence
Computer Vision
Computer Engineering and Networks
Computer Communication Networks
Data and Information Security
ISBN 3-030-30490-6
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto An ensemble model for winning a Chinese machine reading comprehension competition -- Dependent Multilevel Interaction Network for Natural Language Inference -- Learning to Explain Chinese Slang Words -- Attention-Based Improved BLSTM-CNN for Relation Classification -- An Improved Method of Applying a Machine Translation Model to a Chinese Word Segmentation Task -- Interdependence Model for Multi-label Classification -- Combining deep learning and (structural) feature-based classification methods for copyright-protected PDF documents -- Collaborative Attention Network with Word and N-gram Sequences Modeling for Sentiment Classification -- Targeted Sentiment Classification with Attentional Encoder Network -- Capturing User and Product Information for Sentiment Classification via Hierarchical Separated Attention and Neural Collaborative Filtering -- Imbalanced Sentiment Classification Enhanced with Discourse Marker -- Revising Attention with Position for Aspect-level Sentiment Classification -- Surrounding-Based Attention Networks for Aspect-Level Sentiment Classification -- Mid Roll Advertisement Placement using Multi Modal Emotion Analysis -- DCAR: Deep Collaborative Autoencoder for Recommendation with Implicit Feedback -- Jointly Learning to Detect Emotions and Predict Facebook Reactions -- Discriminative Feature Learning for Speech Emotion Recognition -- A Judicial Sentencing Method Based on Fused Deep Neural Networks -- SECaps: A Sequence Enhanced Capsule Model for Charge Prediction -- Learning to Predict Charges for Judgment with Legal Graph -- A Recurrent Attention Network for Judgment Prediction -- Symmetrical Adversarial Training Nets: A Novel Model For Text Generation -- A Novel Image Captioning Method based on Generative Adversarial Networks -- Quality-Diversity Summarization with Unsupervised Autoencoders -- Conditional GANs for Image Captioning with Sentiments -- Neural Poetry: Learning to Generate Poems using Syllables -- Exploring the Advantages of Corpus in Neural Machine Translation of Agglutinative Language -- RL extraction of syntax-based chunks for sentence compression -- Robust Sound Event Classification with Local Time-Frequency Information and Convolutional Neural Networks -- Neuro-Spectral Audio Synthesis: Exploiting characteristics of the Discrete Fourier Transform in the real-time simulation of musical instruments using parallel Neural Networks -- Ensemble of Convolutional Neural Networks for P300 Speller in Brain Computer Interface -- Deep Recurrent Neural Networks with Nonlinear Masking Layers and Two-Level Estimation for Speech Separation -- Auto-Lag Networks for Real Valued Sequence to Sequence Prediction -- LSTM Prediction on Sudden Occurrence of Maintenance Operation of Air-conditioners in Real-time Pricing Adaptive Control -- Dynamic Ensemble Using Previous and Predicted Future Performance for Multi-Step-Ahead Solar Power Forecasting -- Timage – A Robust Time Series Classification Pipeline -- Prediction of the Next Sensor Event and its Time of Occurrence in Smart Homes -- Multi-task Learning Method for Hierarchical Time Series Forecasting -- Demand-prediction architecture for distribution businesses based on multiple RNNs with alternative weight update -- A Study of Deep Learning for Network Traffic Data Forecasting -- Composite Quantile Regression Long Short-Term Memory Network -- Short-Term Temperature Forecasting on a Several Hours Horizon -- Using Long Short-Term Memory for Wavefront Prediction in Adaptive Optics -- Incorporating Adaptive RNN-based Action Inference and Sensory Perception -- Quality of Prediction of Daily Relativistic Electrons Flux at Geostationary Orbit by Machine Learning Methods -- Soft Subspace Growing Neural Gas for DataStream Clustering -- Region Prediction from Hungarian Folk Music Using Convolutional Neural Networks -- Merging DBSCAN and Density Peak for Robust Clustering -- Market basket analysis using Boltzmann machines -- Dimensionality Reduction for Clustering and Cluster Tracking of Cytometry Data -- Improving Deep Image Clustering With Spatial Transformer Layers -- Collaborative Non-negative Matrix Factorization -- Cosine Similarity Drift Detector -- Unsupervised anomaly detection using optimal transport for predictive maintenance -- Robust Gait Authentication Using Autoencoder and Decision Tree -- MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networks -- Intrusion Detection via Wide & Deep Model -- Towards Attention based Vulnerability Discovery using Source Code Representation -- Convolutional Recurrent Neural Networks for Computer Network Analysis.
Record Nr. UNINA-9910349299703321
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Artificial Neural Networks and Machine Learning – ICANN 2019: Theoretical Neural Computation [[electronic resource] ] : 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17–19, 2019, Proceedings, Part I / / edited by Igor V. Tetko, Věra Kůrková, Pavel Karpov, Fabian Theis
Artificial Neural Networks and Machine Learning – ICANN 2019: Theoretical Neural Computation [[electronic resource] ] : 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17–19, 2019, Proceedings, Part I / / edited by Igor V. Tetko, Věra Kůrková, Pavel Karpov, Fabian Theis
Edizione [1st ed. 2019.]
Pubbl/distr/stampa Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019
Descrizione fisica 1 online resource (XXX, 839 p. 372 illus., 242 illus. in color.)
Disciplina 006.31
Collana Theoretical Computer Science and General Issues
Soggetto topico Artificial intelligence
Computer vision
Computer engineering
Computer networks
Algorithms
Data protection
Artificial Intelligence
Computer Vision
Computer Engineering and Networks
Computer Communication Networks
Data and Information Security
ISBN 3-030-30487-6
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Bidirectional associative memory with block coding: A comparison of iterative retrieval methods -- Stability analysis of a generalised class of BAM neural networks with mixed delays -- Dissipativity Analysis of a Class of Competitive Neural Networks with Proportional Delays -- A Nonlinear Fokker-Planck Description of Continuous Neural Network Dynamics -- Multi-modal associative storage and retrieval using Hopfield auto-associative memory network -- Chaotic Complex-Valued Associative Memory with Adaptive Scaling Factor Independent of Multi-Values -- A Comparative Analysis of Preprocessing Methods for Single-Trial Event Related Potential Detection -- Sleep State Analysis using Calcium Imaging Data by Non-negative Matrix Factorization -- Detection of directional information flow induced by TMS based on symbolic transfer entropy -- Brain-Inspired Hardware for Artificial Intelligence: Accelerated Learning in a Physical-Model Spiking Neural Network -- Distinguishing Violinists and Pianists based on their Brain Signals -- Research on Image-to-Image Translation with Capsule Network -- Multi-View Capsule Network -- Advanced Capsule Networks via Context Awareness -- DDRM-CapsNet: Capsule Network based on Deep Dynamic Routing Mechanism for complex data -- Squeezed Very Deep Convolutional Neural Networks for Text Classification -- NeuroPower: Designing Energy Efficient Convolutional Neural Network Architecture for Embedded Systems -- Swap kernel regression -- Model-Agnostic Explanations for Decisions using Minimal Patterns -- NARPCA: Neural Accumulate-Retract PCA for Low-latency High-throughput Processing on Datastreams -- An Evaluation of Various Regression Models for the Prediction of Two-Terminal Network Reliability -- Capsule Generative Models -- Evaluating CNNs on the Gestalt Principle of Closure -- Recovering Localized Adversarial Attacks -- On the Interpretation of Recurrent Neural Networks as Finite State Machines -- Neural field model for measuring and reproducing time intervals -- Widely Linear Complex-valued Autoencoder: Dealing with Noncircularity in Generative-Discriminative Models -- NatCSNN: A Convolutional Spiking Neural Network for recognition of objects extracted from natural images -- Deep Semantic Asymmetric Hashing -- A Neural Network for Semi-Supervised Learning on Manifolds -- Counting with Analog Neurons -- On the Bounds of Function Approximations -- Probabilistic Bounds for Approximation by Neural Networks -- Tree Memory Networks for Sequence Processing -- On Deep Set Learning and the Choice of Aggregations -- Hilbert Vector Convolutional Neural Network : 2D Neural Network on 1D Data -- The Same Size Dilated Attention Network for Keypoint Detection -- Gradient-Based Learning of Compositional Dynamics with Modular RNNs -- Transfer Learning with Sparse Associative Memories -- Linear Memory Networks -- A Multi-Armed Bandit Algorithm Available in Stationary or Non-Stationary Environments Using Self-Organizing Maps -- Cooperation and Coordination Regimes by Deep Q-Learning in Multi-agent Task Executions -- Boosting Reinforcement Learning with Unsupervised Feature Extraction -- A multi-objective Reinforcement Learning algorithm for JSSP -- A Reinforcement Learning Approach for Sequential Spatial Transformer Networks -- Deep Recurrent Policy Networks for Planning under Partial Observability -- Mixed-Reality Deep Reinforcement Learning for a Reach-to-grasp Task -- FMNet: Multi-Agent Cooperation by Communicating with Featured Message Network -- Inferring Event-Predictive Goal-Directed Object Manipulations in REPRISE -- On Unsupervised Learning of Traversal Cost and Terrain Types Identification using Self-Organizing Maps -- Scaffolding Haptic Attention with Controller Gating -- Benchmarking Incremental Regressors in Traversal Cost Assessment -- CPG driven RBF Network Control with Reinforcement Learning for Gait Optimization of a Dung Beetle-like Robot -- Training Delays in Spiking Neural Networks -- An Izhikevich Model Neuron MOS Circuit for Low Voltage Operation -- UAV Detection: A STDP trained Deep Convolutional Spiking Neural Network Retina-Neuromorphic Approach -- Autonoumous Learning Paradigm for Spiking Neural Networks -- Multi-objective Spiking Neural Network Hardware Mapping Based on Immune Genetic Algorithm -- The Importance of Self-excitation in Spiking Neural Networks Evolved to Recognize Temporal Patterns -- Estimating and factoring the dropout induced distribution with Gaussian mixture model -- Sequence disambiguation with synaptic traces in associative neural networks -- Robust Optimal-Size Implementation of Finite State Automata with Synfire Ring-Based Neural Networks -- A Neural Circuit Model of Adaptive Robust Tracking Control for Continuous-Time Nonlinear Systems.
Record Nr. UNISA-996466301803316
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Artificial Neural Networks and Machine Learning – ICANN 2019: Theoretical Neural Computation : 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17–19, 2019, Proceedings, Part I / / edited by Igor V. Tetko, Věra Kůrková, Pavel Karpov, Fabian Theis
Artificial Neural Networks and Machine Learning – ICANN 2019: Theoretical Neural Computation : 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17–19, 2019, Proceedings, Part I / / edited by Igor V. Tetko, Věra Kůrková, Pavel Karpov, Fabian Theis
Edizione [1st ed. 2019.]
Pubbl/distr/stampa Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019
Descrizione fisica 1 online resource (XXX, 839 p. 372 illus., 242 illus. in color.)
Disciplina 006.31
006.32
Collana Theoretical Computer Science and General Issues
Soggetto topico Artificial intelligence
Computer vision
Computer engineering
Computer networks
Algorithms
Data protection
Artificial Intelligence
Computer Vision
Computer Engineering and Networks
Computer Communication Networks
Data and Information Security
ISBN 3-030-30487-6
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Bidirectional associative memory with block coding: A comparison of iterative retrieval methods -- Stability analysis of a generalised class of BAM neural networks with mixed delays -- Dissipativity Analysis of a Class of Competitive Neural Networks with Proportional Delays -- A Nonlinear Fokker-Planck Description of Continuous Neural Network Dynamics -- Multi-modal associative storage and retrieval using Hopfield auto-associative memory network -- Chaotic Complex-Valued Associative Memory with Adaptive Scaling Factor Independent of Multi-Values -- A Comparative Analysis of Preprocessing Methods for Single-Trial Event Related Potential Detection -- Sleep State Analysis using Calcium Imaging Data by Non-negative Matrix Factorization -- Detection of directional information flow induced by TMS based on symbolic transfer entropy -- Brain-Inspired Hardware for Artificial Intelligence: Accelerated Learning in a Physical-Model Spiking Neural Network -- Distinguishing Violinists and Pianists based on their Brain Signals -- Research on Image-to-Image Translation with Capsule Network -- Multi-View Capsule Network -- Advanced Capsule Networks via Context Awareness -- DDRM-CapsNet: Capsule Network based on Deep Dynamic Routing Mechanism for complex data -- Squeezed Very Deep Convolutional Neural Networks for Text Classification -- NeuroPower: Designing Energy Efficient Convolutional Neural Network Architecture for Embedded Systems -- Swap kernel regression -- Model-Agnostic Explanations for Decisions using Minimal Patterns -- NARPCA: Neural Accumulate-Retract PCA for Low-latency High-throughput Processing on Datastreams -- An Evaluation of Various Regression Models for the Prediction of Two-Terminal Network Reliability -- Capsule Generative Models -- Evaluating CNNs on the Gestalt Principle of Closure -- Recovering Localized Adversarial Attacks -- On the Interpretation of Recurrent Neural Networks as Finite State Machines -- Neural field model for measuring and reproducing time intervals -- Widely Linear Complex-valued Autoencoder: Dealing with Noncircularity in Generative-Discriminative Models -- NatCSNN: A Convolutional Spiking Neural Network for recognition of objects extracted from natural images -- Deep Semantic Asymmetric Hashing -- A Neural Network for Semi-Supervised Learning on Manifolds -- Counting with Analog Neurons -- On the Bounds of Function Approximations -- Probabilistic Bounds for Approximation by Neural Networks -- Tree Memory Networks for Sequence Processing -- On Deep Set Learning and the Choice of Aggregations -- Hilbert Vector Convolutional Neural Network : 2D Neural Network on 1D Data -- The Same Size Dilated Attention Network for Keypoint Detection -- Gradient-Based Learning of Compositional Dynamics with Modular RNNs -- Transfer Learning with Sparse Associative Memories -- Linear Memory Networks -- A Multi-Armed Bandit Algorithm Available in Stationary or Non-Stationary Environments Using Self-Organizing Maps -- Cooperation and Coordination Regimes by Deep Q-Learning in Multi-agent Task Executions -- Boosting Reinforcement Learning with Unsupervised Feature Extraction -- A multi-objective Reinforcement Learning algorithm for JSSP -- A Reinforcement Learning Approach for Sequential Spatial Transformer Networks -- Deep Recurrent Policy Networks for Planning under Partial Observability -- Mixed-Reality Deep Reinforcement Learning for a Reach-to-grasp Task -- FMNet: Multi-Agent Cooperation by Communicating with Featured Message Network -- Inferring Event-Predictive Goal-Directed Object Manipulations in REPRISE -- On Unsupervised Learning of Traversal Cost and Terrain Types Identification using Self-Organizing Maps -- Scaffolding Haptic Attention with Controller Gating -- Benchmarking Incremental Regressors in Traversal Cost Assessment -- CPG driven RBF Network Control with Reinforcement Learning for Gait Optimization of a Dung Beetle-like Robot -- Training Delays in Spiking Neural Networks -- An Izhikevich Model Neuron MOS Circuit for Low Voltage Operation -- UAV Detection: A STDP trained Deep Convolutional Spiking Neural Network Retina-Neuromorphic Approach -- Autonoumous Learning Paradigm for Spiking Neural Networks -- Multi-objective Spiking Neural Network Hardware Mapping Based on Immune Genetic Algorithm -- The Importance of Self-excitation in Spiking Neural Networks Evolved to Recognize Temporal Patterns -- Estimating and factoring the dropout induced distribution with Gaussian mixture model -- Sequence disambiguation with synaptic traces in associative neural networks -- Robust Optimal-Size Implementation of Finite State Automata with Synfire Ring-Based Neural Networks -- A Neural Circuit Model of Adaptive Robust Tracking Control for Continuous-Time Nonlinear Systems.
Record Nr. UNINA-9910349283703321
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Artificial Neural Networks and Machine Learning – ICANN 2019: Workshop and Special Sessions [[electronic resource] ] : 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17–19, 2019, Proceedings / / edited by Igor V. Tetko, Věra Kůrková, Pavel Karpov, Fabian Theis
Artificial Neural Networks and Machine Learning – ICANN 2019: Workshop and Special Sessions [[electronic resource] ] : 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17–19, 2019, Proceedings / / edited by Igor V. Tetko, Věra Kůrková, Pavel Karpov, Fabian Theis
Edizione [1st ed. 2019.]
Pubbl/distr/stampa Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019
Descrizione fisica 1 online resource (XXXII, 852 p. 295 illus., 211 illus. in color.)
Disciplina 006.32
Collana Theoretical Computer Science and General Issues
Soggetto topico Artificial intelligence
Computer vision
Algorithms
Computer networks
Artificial intelligence—Data processing
Computer science—Mathematics
Discrete mathematics
Artificial Intelligence
Computer Vision
Computer Communication Networks
Data Science
Discrete Mathematics in Computer Science
ISBN 3-030-30493-0
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto A Reservoir Computing Framework for Continuous Gesture Recognition -- Using conceptors to transfer between long-term and short-term Memory -- Bistable Perception in Conceptor Networks -- Continual Learning exploiting Structure of Fractal Reservoir Computing -- Continuous Blood Pressure Estimation through Optimized Echo State Networks -- Reservoir Topology in Deep Echo State Networks -- Multiple Pattern Generations and Chaotic Itinerant dynamics in Reservoir Computing -- Echo State Network with Adversarial Training -- Hyper-spherical reservoirs for Echo State Networks -- Echo State vs. LSTM Networks for Word Sense Disambiguation -- Echo State Networks for Named Entity Recognition -- Efficient Cross-Validation of Echo State Networks -- Echo State Property of Neuronal Cell Cultures -- Overview on the PHRESCO project: PHotonic REServoir COmputing -- Classification of Human Actions in Videos with a Large-Scale Photonic Reservoir Computer -- A power-effcient architecture for on-chip reservoir computing -- Time Series Processing with VCSEL-based Reservoir Computer -- Optoelectronic reservoir computing using a mixed digital-analog hardware implementation -- Comparison of Feature Extraction Techniques for Handwritten Digit Recognition with a Photonic Reservoir Computer -- Polarization dynamics of VCSELs improves reservoir computing performance. -- Reservoir-size dependent learning in analogue neural networks -- Transferring reservoir computing: formulation and application to fluid physics -- Investigation of EEG-based Graph-theoretic Analysis for Automatic Diagnosis of Alcohol Use Disorder -- EchoQuan-Net: Direct Quantification of Echo Sequence for Left Ventricle Multidimensional Indices via Global-Local Learning, Geometric Adjustment, and multi-target relation learning -- An attention-based ID-CNNs-CRF model for named entity recognition on clinical electronic medical records -- Deep Text Prior: Weakly Supervised Learning for Assertion Classification -- Inter-region Synchronization Analysis based on Heterogeneous Matrix Similarity Measurement -- Bi-ResNet: Fully automated classification of unregistered contralateral mammograms -- Pattern Recognition for COPD Diagnostics Using an Artificial Neural Network and Its Potential Integration on Hardware-based Neuromorphic Platforms -- Quantifying Structural Heterogeneity of Healthy and Cancerous Mitochondria using a Combined Segmentation and Classification USK-Net -- Breast Cancer Classification on Histopathological Images Affected by Data Imbalance Using Active Learning and Deep Convolutional Neural Network -- Measuring the Angle of Hallux Valgus Using Segmentation of Bones on X-ray Images -- Human Body Posture Recognition Using Wearable Devices -- Collaborative Denoising Autoencoder for High Glycated Haemoglobin Prediction -- On Chow-Liu forest based regularization of deep belief networks -- Prototypes within Minimum Enclosing Balls -- Exploring Local Transformation Shared Weights in Convolutional Neural Networks -- The Good, the Bad and the Ugly: augmenting a black-box model with expert knowledge -- Hierarchical Attentional Hybrid Neural Networks for Document Classification -- Reinforcement learning informed by optimal control -- Explainable Anomaly Detection via Feature-Based Localization -- Bayesian Automatic Relevance Determination for Feature selection in Credit Default Modelling -- TSXplain: Demystification of DNN Decisions for Time-Series using Natural Language and Statistical Features -- DeepMimic: Mentor-Student Unlabeled Data Based Training -- Evaluation of tag clusterings for user profiling in movie recommendation -- A Sparse Filtering-based Approach for Non-Blind Deep Image Denoising -- Hybrid Attention Driven Text-to-Image Synthesis via Generative Adversarial Networks -- Hypernetwork functional image representation -- Instance-based Segmentation for Boundary Detection of Neuropathic Ulcers through Mask-RCNN -- Capsule Networks for attention under occlusion -- IP-GAN: Learning Identity and Pose Disentanglement in Generative Adversarial Networks -- Hypernetwork Knowledge Graph Embeddings -- Signed Graph Attention Networks -- Graph Classification with 2D Convolutional Neural Networks -- Community Detection via Joint Graph Convolutional Network Embedding in Attribute Network -- Temporal Coding of Neural Stimuli -- Heterogeneous Information Network Embedding with Meta-path-based Graph Attention Networks -- Dual-FOFE-net Neural Models for Entity Linking with PageRank -- Spatial-Temporal Graph Convolutional Networks for Sign Language Recognition -- Graph Convolutional Networks Improve the Prediction of Cancer Driver Genes -- CNN-Based Semantic Change Detection in Satellite Imagery -- Axiomatic Kernels on Graphs for Support Vector Machines -- Multitask Learning on Graph Neural Networks: Learning Multiple Graph Centrality Measures with a Unified Network -- Neural Network Guided Tree-Search Policies for Synthesis Planning -- LSTM and 1-D Convolutional Neural Networks for predictive monitoring of the anaerobic digestion process -- Progressive Docking - a Deep Learning Based Approach for Accelerated Virtual Screening -- Predictive Power of Time-series Based Machine Learning Models for DMPK Measurements in Drug Discovery -- Improving Deep Generative Models with Randomized SMILES -- Attention and Edge Memory Convolution for Bioactivity Prediction -- Application of materials informatics tools to the analysis of combinatorial libraries of all metal-oxides photovoltaic cells -- Analysis and Modelling of False Positives in GPCR Assays -- Characterization of Quantum Derived Electronic Properties of Molecules: A Computational Intelligence Approach -- Using an Autoencoder for Dimensionality Reduction in Quantum Dynamics -- Conformational Oversampling as Data Augmentation for Molecules -- Prediction of the Atomization Energy of Molecules Using Coulomb Matrix and Atomic Composition in a Bayesian Regularized Neural Networks -- Deep Neural Network Architecture for Drug-Target Interaction Prediction -- Mol-CycleGAN - a generative model for molecular optimization -- A TRANSFORMER MODEL FOR RETROSYNTHESIS -- Augmentation is What You Need! -- Diversify Libraries Using Generative Topographic Mapping -- Detection of Frequent-Hitters across various HTS Technologies -- Message Passing Neural Networks scoring functions for structure-based drug discovery.
Record Nr. UNISA-996466303303316
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Artificial Neural Networks and Machine Learning – ICANN 2019: Workshop and Special Sessions : 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17–19, 2019, Proceedings: Workshop and Special Sessions / / edited by Igor V. Tetko, Věra Kůrková, Pavel Karpov, Fabian Theis
Artificial Neural Networks and Machine Learning – ICANN 2019: Workshop and Special Sessions : 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17–19, 2019, Proceedings: Workshop and Special Sessions / / edited by Igor V. Tetko, Věra Kůrková, Pavel Karpov, Fabian Theis
Edizione [1st ed. 2019.]
Pubbl/distr/stampa Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019
Descrizione fisica 1 online resource (XXXII, 852 p. 295 illus., 211 illus. in color.)
Disciplina 006.32
Collana Theoretical Computer Science and General Issues
Soggetto topico Artificial intelligence
Computer vision
Algorithms
Computer networks
Artificial intelligence—Data processing
Computer science—Mathematics
Discrete mathematics
Artificial Intelligence
Computer Vision
Computer Communication Networks
Data Science
Discrete Mathematics in Computer Science
ISBN 3-030-30493-0
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto A Reservoir Computing Framework for Continuous Gesture Recognition -- Using conceptors to transfer between long-term and short-term Memory -- Bistable Perception in Conceptor Networks -- Continual Learning exploiting Structure of Fractal Reservoir Computing -- Continuous Blood Pressure Estimation through Optimized Echo State Networks -- Reservoir Topology in Deep Echo State Networks -- Multiple Pattern Generations and Chaotic Itinerant dynamics in Reservoir Computing -- Echo State Network with Adversarial Training -- Hyper-spherical reservoirs for Echo State Networks -- Echo State vs. LSTM Networks for Word Sense Disambiguation -- Echo State Networks for Named Entity Recognition -- Efficient Cross-Validation of Echo State Networks -- Echo State Property of Neuronal Cell Cultures -- Overview on the PHRESCO project: PHotonic REServoir COmputing -- Classification of Human Actions in Videos with a Large-Scale Photonic Reservoir Computer -- A power-effcient architecture for on-chip reservoir computing -- Time Series Processing with VCSEL-based Reservoir Computer -- Optoelectronic reservoir computing using a mixed digital-analog hardware implementation -- Comparison of Feature Extraction Techniques for Handwritten Digit Recognition with a Photonic Reservoir Computer -- Polarization dynamics of VCSELs improves reservoir computing performance. -- Reservoir-size dependent learning in analogue neural networks -- Transferring reservoir computing: formulation and application to fluid physics -- Investigation of EEG-based Graph-theoretic Analysis for Automatic Diagnosis of Alcohol Use Disorder -- EchoQuan-Net: Direct Quantification of Echo Sequence for Left Ventricle Multidimensional Indices via Global-Local Learning, Geometric Adjustment, and multi-target relation learning -- An attention-based ID-CNNs-CRF model for named entity recognition on clinical electronic medical records -- Deep Text Prior: Weakly Supervised Learning for Assertion Classification -- Inter-region Synchronization Analysis based on Heterogeneous Matrix Similarity Measurement -- Bi-ResNet: Fully automated classification of unregistered contralateral mammograms -- Pattern Recognition for COPD Diagnostics Using an Artificial Neural Network and Its Potential Integration on Hardware-based Neuromorphic Platforms -- Quantifying Structural Heterogeneity of Healthy and Cancerous Mitochondria using a Combined Segmentation and Classification USK-Net -- Breast Cancer Classification on Histopathological Images Affected by Data Imbalance Using Active Learning and Deep Convolutional Neural Network -- Measuring the Angle of Hallux Valgus Using Segmentation of Bones on X-ray Images -- Human Body Posture Recognition Using Wearable Devices -- Collaborative Denoising Autoencoder for High Glycated Haemoglobin Prediction -- On Chow-Liu forest based regularization of deep belief networks -- Prototypes within Minimum Enclosing Balls -- Exploring Local Transformation Shared Weights in Convolutional Neural Networks -- The Good, the Bad and the Ugly: augmenting a black-box model with expert knowledge -- Hierarchical Attentional Hybrid Neural Networks for Document Classification -- Reinforcement learning informed by optimal control -- Explainable Anomaly Detection via Feature-Based Localization -- Bayesian Automatic Relevance Determination for Feature selection in Credit Default Modelling -- TSXplain: Demystification of DNN Decisions for Time-Series using Natural Language and Statistical Features -- DeepMimic: Mentor-Student Unlabeled Data Based Training -- Evaluation of tag clusterings for user profiling in movie recommendation -- A Sparse Filtering-based Approach for Non-Blind Deep Image Denoising -- Hybrid Attention Driven Text-to-Image Synthesis via Generative Adversarial Networks -- Hypernetwork functional image representation -- Instance-based Segmentation for Boundary Detection of Neuropathic Ulcers through Mask-RCNN -- Capsule Networks for attention under occlusion -- IP-GAN: Learning Identity and Pose Disentanglement in Generative Adversarial Networks -- Hypernetwork Knowledge Graph Embeddings -- Signed Graph Attention Networks -- Graph Classification with 2D Convolutional Neural Networks -- Community Detection via Joint Graph Convolutional Network Embedding in Attribute Network -- Temporal Coding of Neural Stimuli -- Heterogeneous Information Network Embedding with Meta-path-based Graph Attention Networks -- Dual-FOFE-net Neural Models for Entity Linking with PageRank -- Spatial-Temporal Graph Convolutional Networks for Sign Language Recognition -- Graph Convolutional Networks Improve the Prediction of Cancer Driver Genes -- CNN-Based Semantic Change Detection in Satellite Imagery -- Axiomatic Kernels on Graphs for Support Vector Machines -- Multitask Learning on Graph Neural Networks: Learning Multiple Graph Centrality Measures with a Unified Network -- Neural Network Guided Tree-Search Policies for Synthesis Planning -- LSTM and 1-D Convolutional Neural Networks for predictive monitoring of the anaerobic digestion process -- Progressive Docking - a Deep Learning Based Approach for Accelerated Virtual Screening -- Predictive Power of Time-series Based Machine Learning Models for DMPK Measurements in Drug Discovery -- Improving Deep Generative Models with Randomized SMILES -- Attention and Edge Memory Convolution for Bioactivity Prediction -- Application of materials informatics tools to the analysis of combinatorial libraries of all metal-oxides photovoltaic cells -- Analysis and Modelling of False Positives in GPCR Assays -- Characterization of Quantum Derived Electronic Properties of Molecules: A Computational Intelligence Approach -- Using an Autoencoder for Dimensionality Reduction in Quantum Dynamics -- Conformational Oversampling as Data Augmentation for Molecules -- Prediction of the Atomization Energy of Molecules Using Coulomb Matrix and Atomic Composition in a Bayesian Regularized Neural Networks -- Deep Neural Network Architecture for Drug-Target Interaction Prediction -- Mol-CycleGAN - a generative model for molecular optimization -- A TRANSFORMER MODEL FOR RETROSYNTHESIS -- Augmentation is What You Need! -- Diversify Libraries Using Generative Topographic Mapping -- Detection of Frequent-Hitters across various HTS Technologies -- Message Passing Neural Networks scoring functions for structure-based drug discovery.
Record Nr. UNINA-9910349298703321
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui