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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 | ||
| ||
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 | ||
| ||
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 | ||
| ||
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 | ||
| ||
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 | ||
| ||
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 | ||
| ||
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 | ||
| ||
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 / / 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 / / 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 | ||
| ||