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Artificial Neural Networks and Machine Learning -- ICANN 2014 [[electronic resource] ] : 24th International Conference on Artificial Neural Networks, Hamburg, Germany, September 15-19, 2014, Proceedings / / edited by Stefan Wermter, Cornelius Weber, Wlodzislaw Duch, Timo Honkela, Petia Koprinkova-Hristova, Sven Magg, Günther Palm, Allessandro E.P. Villa
| Artificial Neural Networks and Machine Learning -- ICANN 2014 [[electronic resource] ] : 24th International Conference on Artificial Neural Networks, Hamburg, Germany, September 15-19, 2014, Proceedings / / edited by Stefan Wermter, Cornelius Weber, Wlodzislaw Duch, Timo Honkela, Petia Koprinkova-Hristova, Sven Magg, Günther Palm, Allessandro E.P. Villa |
| Edizione | [1st ed. 2014.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2014 |
| Descrizione fisica | 1 online resource (XXV, 852 p. 338 illus.) |
| Disciplina | 006.3 |
| Collana | Theoretical Computer Science and General Issues |
| Soggetto topico |
Artificial intelligence
Computer science Algorithms Pattern recognition systems Application software Computer vision Artificial Intelligence Theory of Computation Automated Pattern Recognition Computer and Information Systems Applications Computer Vision |
| ISBN | 3-319-11179-5 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Recurrent Networks -- Sequence Learning -- Echo State Networks -- Recurrent Network Theory -- Competitive Learning and Self-Organisation.- Clustering and Classification -- Trees and Graphs -- Human-Machine Interaction -- Deep Networks.- Theory -- Optimization -- Layered Networks -- Reinforcement Learning and Action -- Vision -- Detection and Recognition -- Invariances and Shape Recovery -- Attention and Pose Estimation -- Supervised Learning -- Ensembles -- Regression -- Classification -- Dynamical Models and Time Series -- Neuroscience -- Cortical Models -- Line Attractors and Neural Fields -- Spiking and Single Cell Models -- Applications -- Users and Social Technologies -- Demonstrations. |
| Record Nr. | UNISA-996202529403316 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2014 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Artificial Neural Networks and Machine Learning -- ICANN 2014 : 24th International Conference on Artificial Neural Networks, Hamburg, Germany, September 15-19, 2014, Proceedings / / edited by Stefan Wermter, Cornelius Weber, Wlodzislaw Duch, Timo Honkela, Petia Koprinkova-Hristova, Sven Magg, Günther Palm, Allessandro E.P. Villa
| Artificial Neural Networks and Machine Learning -- ICANN 2014 : 24th International Conference on Artificial Neural Networks, Hamburg, Germany, September 15-19, 2014, Proceedings / / edited by Stefan Wermter, Cornelius Weber, Wlodzislaw Duch, Timo Honkela, Petia Koprinkova-Hristova, Sven Magg, Günther Palm, Allessandro E.P. Villa |
| Edizione | [1st ed. 2014.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2014 |
| Descrizione fisica | 1 online resource (XXV, 852 p. 338 illus.) |
| Disciplina | 006.3 |
| Collana | Theoretical Computer Science and General Issues |
| Soggetto topico |
Artificial intelligence
Computer science Algorithms Pattern recognition systems Application software Computer vision Artificial Intelligence Theory of Computation Automated Pattern Recognition Computer and Information Systems Applications Computer Vision |
| ISBN | 3-319-11179-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Recurrent Networks -- Sequence Learning -- Echo State Networks -- Recurrent Network Theory -- Competitive Learning and Self-Organisation.- Clustering and Classification -- Trees and Graphs -- Human-Machine Interaction -- Deep Networks.- Theory -- Optimization -- Layered Networks -- Reinforcement Learning and Action -- Vision -- Detection and Recognition -- Invariances and Shape Recovery -- Attention and Pose Estimation -- Supervised Learning -- Ensembles -- Regression -- Classification -- Dynamical Models and Time Series -- Neuroscience -- Cortical Models -- Line Attractors and Neural Fields -- Spiking and Single Cell Models -- Applications -- Users and Social Technologies -- Demonstrations. |
| Record Nr. | UNINA-9910484763103321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2014 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Artificial Neural Networks and Machine Learning – ICANN 2020 [[electronic resource] ] : 29th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 15–18, 2020, Proceedings, Part I / / edited by Igor Farkaš, Paolo Masulli, Stefan Wermter
| Artificial Neural Networks and Machine Learning – ICANN 2020 [[electronic resource] ] : 29th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 15–18, 2020, Proceedings, Part I / / edited by Igor Farkaš, Paolo Masulli, Stefan Wermter |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
| Descrizione fisica | 1 online resource (XXVII, 891 p. 348 illus., 260 illus. in color.) |
| Disciplina | 006.3 |
| Collana | Theoretical Computer Science and General Issues |
| Soggetto topico |
Artificial intelligence
Computer networks Image processing—Digital techniques Computer vision Computers Application software Computer engineering Artificial Intelligence Computer Communication Networks Computer Imaging, Vision, Pattern Recognition and Graphics Computing Milieux Computer and Information Systems Applications Computer Engineering and Networks |
| ISBN | 3-030-61609-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Adversarial Machine Learning -- On the security relevance of initial weights in deep neural networks -- Fractal Residual Network for Face Image Super-Resolution -- From Imbalanced Classification to Supervised Outlier Detection Problems: Adversarially Trained Auto Encoders -- Generating Adversarial Texts for Recurrent Neural Networks -- Enforcing Linearity in DNN succours Robustness and Adversarial Image Generation -- Computational Analysis of Robustness in Neural Network Classifiers -- Bioinformatics and Biosignal Analysis -- Convolutional neural networks with reusable full-dimension-long layers for feature selection and classification of motor imagery in EEG signals -- Compressing Genomic Sequences by Using Deep Learning -- Learning Tn5 sequence bias from ATAC-seq on naked chromatin -- Tucker tensor decomposition of multi-session EEG data -- Reactive Hand Movements from Arm Kinematics and EMG Signals Based on Hierarchical Gaussian Process Dynamical Models -- Cognitive Models -- Investigating Efficient Learning and Compositionality in Generative LSTM Networks -- Fostering Event Compression using Gated Surprise -- Physiologically-inspired Neural Circuits for the Recognition of Dynamic Faces -- Hierarchical Modeling with Neurodynamical Agglomerative Analysis -- Convolutional Neural Networks and Kernel Methods -- Deep and Wide Neural Networks Covariance Estimation -- Monotone deep Spectrum Kernels -- Permutation Learning in Convolutional Neural Networks for Time Series Analysis -- Deep Learning Applications I -- GTFNet: Ground Truth Fitting Network for Crowd Counting -- Evaluation of Deep Learning Methods for Bone Suppression from Dual Energy Chest Radiography -- Multi-Person Absolute 3D Human Pose Estimation with Weak Depth Supervision -- Solar Power Forecasting Based on Pattern Sequence Similarity and Meta-Learning -- Analysis and Prediction of Deforming 3D Shapes using Oriented Bounding Boxes and LSTM Autoencoders -- Deep Learning Applications II -- Novel Sketch-based 3D Model Retrieval via Cross-domain Feature Clustering and Matching -- Multi-objective Cuckoo Algorithm for Mobile Devices Network Architecture Search -- DeepED: a Deep Learning Framework for Estimating Evolutionary Distances -- Interpretable Machine Learning Structure for an Early Prediction of Lane Changes -- Explainable Methods -- Convex Density Constraints for Computing Plausible Counterfactual Explanations -- Identifying Critical States by the Action-Based Variance of Expected Return -- Explaining Concept Drift by Means of Direction -- Few-shot Learning -- Context Adaptive Metric Model for Meta-Learning -- Ensemble-Based Deep Metric Learning for Few-Shot Learning -- More Attentional Local Descriptors for Few-shot Learning -- Implementation of Siamese-based Few-shot Learning Algorithms for the Distinction of COPD and Asthma Subjects -- Few-Shot Learning for Medical Image Classification -- Generative Adversarial Network -- Adversarial Defense via Attention-based Randomized Smoothing -- Learning to Learn from Mistakes: Robust Optimization for Adversarial Noise -- Unsupervised Anomaly Detection with a GAN Augmented Autoencoder -- An Efficient Blurring-Reconstruction Model to Defend against Adversarial Attacks -- EdgeAugment: Data Augmentation by Fusing and Filling Edge Map -- Face Anti-spoofing with a Noise-Attention Network Using Color-Channel Difference Images -- Generative and Graph Models -- Variational Autoencoder with Global- and Medium Timescale Auxiliaries for Emotion Recognition from Speech -- Improved Classification Based on Deep Belief Networks -- Temporal Anomaly Detection by Deep Generative Models with Applications to Biological Data -- Inferring, Predicting, and Denoising Causal Wave Dynamics -- PART-GAN: Privacy-Preserving Time-Series Sharing -- EvoNet: A Neural Network for Predicting the Evolution of Dynamic Graphs -- Hybrid Neural-symbolic Architectures -- Facial Expression Recognition Method based on a Part-based Temporal Convolutional Network with a Graph-Structured Representation -- Generating Facial Expressions Associated with Text -- Image Processing -- Bilinear Fusion of Commonsense Knowledge with Attention-Based NLI Models -- Neural-Symbolic Relational Reasoning on Graph Models: Effective Link Inference and Computation from Knowledge Bases -- Tell Me Why You Feel That Way: Processing Compositional Dependency for Tree-LSTM Aspect Sentiment Triplet Extraction (TASTE) -- SOM-based System for Sequence Chunking and Planning -- Bilinear Models for Machine Learning -- Enriched Feature Representation and Combination for Deep Saliency Detection -- Spectral Graph Reasoning Network for Hyperspectral Image Classification -- Salient Object Detection with Edge Recalibration -- Multi-Scale Cross-Modal Spatial Attention Fusion for Multi-label Image Recognition -- A New Efficient Finger-Vein Verification Based on Lightweight Neural Network Using Multiple Schemes -- Medical Image Processing -- SU-Net: An Efficient Encoder-Decoder Model of Federated Learning for Brain Tumor Segmentation -- Synthesis of Registered Multimodal Medical Images with Lesions -- ACE-Net: Adaptive Context Extraction Network for Medical Image Segmentation -- Wavelet U-Net for Medical Image Segmentation -- Recurrent Neural Networks -- Character-based LSTM-CRF with semantic features for Chinese Event Element Recognition -- Sequence Prediction using Spectral RNNs -- Attention Based Mechanism for Energy Load Time Series Forecasting: AN-LSTM -- DartsReNet: Exploring new RNN cells in ReNet architectures -- On Multi-modal Fusion for Freehand Gesture Recognition -- Recurrent Neural Network Learning of Performance and Intrinsic Population Dynamics from Sparse Neural Data. |
| Record Nr. | UNISA-996418311103316 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Artificial Neural Networks and Machine Learning – ICANN 2020 [[electronic resource] ] : 29th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 15–18, 2020, Proceedings, Part II / / edited by Igor Farkaš, Paolo Masulli, Stefan Wermter
| Artificial Neural Networks and Machine Learning – ICANN 2020 [[electronic resource] ] : 29th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 15–18, 2020, Proceedings, Part II / / edited by Igor Farkaš, Paolo Masulli, Stefan Wermter |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
| Descrizione fisica | 1 online resource (XXVII, 891 p. 402 illus., 247 illus. in color.) |
| Disciplina | 006.32 |
| Collana | Theoretical Computer Science and General Issues |
| Soggetto topico |
Artificial intelligence
Computer engineering Computer networks Application software Computers Image processing—Digital techniques Computer vision Artificial Intelligence Computer Engineering and Networks Computer and Information Systems Applications Computing Milieux Computer Imaging, Vision, Pattern Recognition and Graphics |
| ISBN | 3-030-61616-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Model Compression I -- Fine-grained Channel Pruning for Deep Residual Neural Networks -- A Lightweight Fully Convolutional Neural Network of High Accuracy Surface Defect Detection -- Detecting Uncertain BNN Outputs on FPGA Using Monte Carlo Dropout Sampling -- Neural network compression via learnable wavelet transforms -- Fast and Robust Compression of Deep Convolutional Neural Networks -- Model Compression II -- Pruning artificial neural networks: a way to find well-generalizing, high-entropy sharp minima -- Log-Nets: Logarithmic Feature-Product Layers Yield More Compact Networks -- Tuning Deep Neural Network's hyperparameters constrained to deployability on tiny systems -- Obstacles to Depth Compression of Neural Networks -- Multi-task and Multi-label Learning -- Multi-Label Quadruplet Dictionary Learning -- Pareto Multi-Task Deep Learning -- Convex Graph Laplacian Multi-Task Learning SVM -- Neural Network Theory and Information Theoretic Learning -- Prediction Stability as a Criterion in Active Learning -- Neural Spectrum Alignment: Empirical Study -- Nonlinear, Nonequilibrium Landscape Approach to Neural Network Dynamics -- Hopfield Networks for Vector Quantization -- Prototype-Based Online Learning on Homogeneously Labeled Streaming Data -- Normalization and Regularization Methods -- Neural Network Training with Safe Regularization in the Null Space of Batch Activations -- The Effect of Batch Normalization in the Symmetric Phase -- Regularized Pooling -- Reinforcement Learning I -- Deep Recurrent Deterministic Policy Gradient for Physical Control -- Exploration via Progress-Driven Intrinsic Rewards -- An improved reinforcement learning based heuristic dynamic programming algorithm for model-free optimal control -- PBCS: Efficient Exploration and Exploitation Using a Synergy between Reinforcement Learning and Motion Planning -- Understanding failures of deterministic actor-critic with continuous action spaces and sparse rewards -- Reinforcement Learning II -- GAN-based Planning Model in Deep Reinforcement Learning -- Guided Reinforcement Learning via Sequence Learning -- Neural Machine Translation based on Improved Actor-Critic Method -- Neural Machine Translation based on Prioritized Experience Replay -- Improving Multi-Agent Reinforcement Learning with Imperfect Human Knowledge -- Reinforcement Learning III -- Adaptive Skill Acquisition in Hierarchical Reinforcement Learning -- Social Navigation with Human Empowerment driven Deep Reinforcement Learning -- Curious Hierarchical Actor-Critic Reinforcement Learning -- Policy Entropy for Out-of-Distribution Classification -- Reservoir Computing -- Analysis of reservoir structure contributing to robustness against structural failure of Liquid State Machine -- Quantifying robustness and capacity of reservoir computers with consistency profiles -- Two-Step FORCE Learning Algorithm for Fast Convergence in Reservoir Computing -- Morphological Computation of Skin Focusing on Fingerprint Structure -- Time Series Clustering with Deep Reservoir Computing -- ReservoirPy: an Efficient and User-Friendly Library to Design Echo State Networks -- Robotics and Neural Models of Perception and Action -- Adaptive, Neural Robot Control – Path Planning on 3D Spiking Neural Networks -- CABIN: A Novel Cooperative Attention Based Location Prediction Network Using Internal-External Trajectory Dependencies -- Neuro-Genetic Visuomotor Architecture for Robotic Grasping -- From Geometries to Contact Graphs -- Sentiment Classification -- Structural Position Network for Aspect-based Sentiment Classification -- Cross-Domain Sentiment Classification using Topic Attention and Dual-Task Adversarial Training -- Data Augmentation for Sentiment Analysis in English – the Online Approach -- Spiking Neural Networks I -- Dendritic computation in a point neuron model. -- Benchmarking Deep Spiking Neural Networks on Neuromorphic Hardware -- Unsupervised Learning of Spatio-Temporal Receptive Fields from an Event-Based Vision Sensor -- Spike-Train Level Unsupervised Learning Algorithm for Deep Spiking Belief Networks -- Spiking Neural Networks II -- Modelling Neuromodulated Information Flow and Energetic Consumption at Thalamic Relay Synapses -- Learning Precise Spike Timings with Eligibility Traces -- Meta-STDP rule stabilizes synaptic weights under in vivo-like ongoing spontaneous activity in a computational model of CA1 pyramidal cell -- Adaptive Chemotaxis for improved Contour Tracking using Spiking Neural Networks -- Text Understanding I -- Mental Imagery-Driven Neural Network to Enhance Representation for Implicit Discourse Relation Recognition -- Adaptive Convolution Kernel for Text Classification via Multi-Channel Representations -- Text generation in discrete space -- Short text processing for analyzing user portraits: A dynamic combination -- Text Understanding II -- A Hierarchical Fine-Tuning Approach Based on Joint Embedding of Words and Parent Categories for Hierarchical Multi-label Text Classification -- Boosting Tricks for Word Mover’s Distance -- Embedding Compression with Right Triangle Similarity Transformations -- Neural Networks for Detecting Irrelevant Questions during Visual Question Answering -- F-Measure Optimisation and Label Regularisation for Energy-based Neural Dialogue State Tracking Models -- Unsupervised Learning -- Unsupervised Change Detection using Joint Autoencoders for Age-Related Macular Degeneration Progression -- A fast algorithm to find Best Matching Units in Self-Organizing Maps -- Tumor Characterization using Unsupervised Learning of Mathematical Relations within Breast Cancer Data -- Balanced SAM-kNN: Online Learning with Heterogeneous Drift and Imbalanced Data -- A Rigorous Link Between Self-Organizing Maps and Gaussian Mixture Models -- Collaborative Clustering through Optimal Transport. |
| Record Nr. | UNISA-996418295303316 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Artificial Neural Networks and Machine Learning – ICANN 2020 : 29th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 15–18, 2020, Proceedings, Part I / / edited by Igor Farkaš, Paolo Masulli, Stefan Wermter
| Artificial Neural Networks and Machine Learning – ICANN 2020 : 29th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 15–18, 2020, Proceedings, Part I / / edited by Igor Farkaš, Paolo Masulli, Stefan Wermter |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
| Descrizione fisica | 1 online resource (XXVII, 891 p. 348 illus., 260 illus. in color.) |
| Disciplina | 006.3 |
| Collana | Theoretical Computer Science and General Issues |
| Soggetto topico |
Artificial intelligence
Computer networks Image processing—Digital techniques Computer vision Computers Application software Computer engineering Artificial Intelligence Computer Communication Networks Computer Imaging, Vision, Pattern Recognition and Graphics Computing Milieux Computer and Information Systems Applications Computer Engineering and Networks |
| ISBN | 3-030-61609-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Adversarial Machine Learning -- On the security relevance of initial weights in deep neural networks -- Fractal Residual Network for Face Image Super-Resolution -- From Imbalanced Classification to Supervised Outlier Detection Problems: Adversarially Trained Auto Encoders -- Generating Adversarial Texts for Recurrent Neural Networks -- Enforcing Linearity in DNN succours Robustness and Adversarial Image Generation -- Computational Analysis of Robustness in Neural Network Classifiers -- Bioinformatics and Biosignal Analysis -- Convolutional neural networks with reusable full-dimension-long layers for feature selection and classification of motor imagery in EEG signals -- Compressing Genomic Sequences by Using Deep Learning -- Learning Tn5 sequence bias from ATAC-seq on naked chromatin -- Tucker tensor decomposition of multi-session EEG data -- Reactive Hand Movements from Arm Kinematics and EMG Signals Based on Hierarchical Gaussian Process Dynamical Models -- Cognitive Models -- Investigating Efficient Learning and Compositionality in Generative LSTM Networks -- Fostering Event Compression using Gated Surprise -- Physiologically-inspired Neural Circuits for the Recognition of Dynamic Faces -- Hierarchical Modeling with Neurodynamical Agglomerative Analysis -- Convolutional Neural Networks and Kernel Methods -- Deep and Wide Neural Networks Covariance Estimation -- Monotone deep Spectrum Kernels -- Permutation Learning in Convolutional Neural Networks for Time Series Analysis -- Deep Learning Applications I -- GTFNet: Ground Truth Fitting Network for Crowd Counting -- Evaluation of Deep Learning Methods for Bone Suppression from Dual Energy Chest Radiography -- Multi-Person Absolute 3D Human Pose Estimation with Weak Depth Supervision -- Solar Power Forecasting Based on Pattern Sequence Similarity and Meta-Learning -- Analysis and Prediction of Deforming 3D Shapes using Oriented Bounding Boxes and LSTM Autoencoders -- Deep Learning Applications II -- Novel Sketch-based 3D Model Retrieval via Cross-domain Feature Clustering and Matching -- Multi-objective Cuckoo Algorithm for Mobile Devices Network Architecture Search -- DeepED: a Deep Learning Framework for Estimating Evolutionary Distances -- Interpretable Machine Learning Structure for an Early Prediction of Lane Changes -- Explainable Methods -- Convex Density Constraints for Computing Plausible Counterfactual Explanations -- Identifying Critical States by the Action-Based Variance of Expected Return -- Explaining Concept Drift by Means of Direction -- Few-shot Learning -- Context Adaptive Metric Model for Meta-Learning -- Ensemble-Based Deep Metric Learning for Few-Shot Learning -- More Attentional Local Descriptors for Few-shot Learning -- Implementation of Siamese-based Few-shot Learning Algorithms for the Distinction of COPD and Asthma Subjects -- Few-Shot Learning for Medical Image Classification -- Generative Adversarial Network -- Adversarial Defense via Attention-based Randomized Smoothing -- Learning to Learn from Mistakes: Robust Optimization for Adversarial Noise -- Unsupervised Anomaly Detection with a GAN Augmented Autoencoder -- An Efficient Blurring-Reconstruction Model to Defend against Adversarial Attacks -- EdgeAugment: Data Augmentation by Fusing and Filling Edge Map -- Face Anti-spoofing with a Noise-Attention Network Using Color-Channel Difference Images -- Generative and Graph Models -- Variational Autoencoder with Global- and Medium Timescale Auxiliaries for Emotion Recognition from Speech -- Improved Classification Based on Deep Belief Networks -- Temporal Anomaly Detection by Deep Generative Models with Applications to Biological Data -- Inferring, Predicting, and Denoising Causal Wave Dynamics -- PART-GAN: Privacy-Preserving Time-Series Sharing -- EvoNet: A Neural Network for Predicting the Evolution of Dynamic Graphs -- Hybrid Neural-symbolic Architectures -- Facial Expression Recognition Method based on a Part-based Temporal Convolutional Network with a Graph-Structured Representation -- Generating Facial Expressions Associated with Text -- Image Processing -- Bilinear Fusion of Commonsense Knowledge with Attention-Based NLI Models -- Neural-Symbolic Relational Reasoning on Graph Models: Effective Link Inference and Computation from Knowledge Bases -- Tell Me Why You Feel That Way: Processing Compositional Dependency for Tree-LSTM Aspect Sentiment Triplet Extraction (TASTE) -- SOM-based System for Sequence Chunking and Planning -- Bilinear Models for Machine Learning -- Enriched Feature Representation and Combination for Deep Saliency Detection -- Spectral Graph Reasoning Network for Hyperspectral Image Classification -- Salient Object Detection with Edge Recalibration -- Multi-Scale Cross-Modal Spatial Attention Fusion for Multi-label Image Recognition -- A New Efficient Finger-Vein Verification Based on Lightweight Neural Network Using Multiple Schemes -- Medical Image Processing -- SU-Net: An Efficient Encoder-Decoder Model of Federated Learning for Brain Tumor Segmentation -- Synthesis of Registered Multimodal Medical Images with Lesions -- ACE-Net: Adaptive Context Extraction Network for Medical Image Segmentation -- Wavelet U-Net for Medical Image Segmentation -- Recurrent Neural Networks -- Character-based LSTM-CRF with semantic features for Chinese Event Element Recognition -- Sequence Prediction using Spectral RNNs -- Attention Based Mechanism for Energy Load Time Series Forecasting: AN-LSTM -- DartsReNet: Exploring new RNN cells in ReNet architectures -- On Multi-modal Fusion for Freehand Gesture Recognition -- Recurrent Neural Network Learning of Performance and Intrinsic Population Dynamics from Sparse Neural Data. |
| Record Nr. | UNINA-9910427704003321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Artificial Neural Networks and Machine Learning – ICANN 2020 : 29th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 15–18, 2020, Proceedings, Part II / / edited by Igor Farkaš, Paolo Masulli, Stefan Wermter
| Artificial Neural Networks and Machine Learning – ICANN 2020 : 29th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 15–18, 2020, Proceedings, Part II / / edited by Igor Farkaš, Paolo Masulli, Stefan Wermter |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
| Descrizione fisica | 1 online resource (XXVII, 891 p. 402 illus., 247 illus. in color.) |
| Disciplina | 006.32 |
| Collana | Theoretical Computer Science and General Issues |
| Soggetto topico |
Artificial intelligence
Computer engineering Computer networks Application software Computers Image processing—Digital techniques Computer vision Artificial Intelligence Computer Engineering and Networks Computer and Information Systems Applications Computing Milieux Computer Imaging, Vision, Pattern Recognition and Graphics |
| ISBN | 3-030-61616-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Model Compression I -- Fine-grained Channel Pruning for Deep Residual Neural Networks -- A Lightweight Fully Convolutional Neural Network of High Accuracy Surface Defect Detection -- Detecting Uncertain BNN Outputs on FPGA Using Monte Carlo Dropout Sampling -- Neural network compression via learnable wavelet transforms -- Fast and Robust Compression of Deep Convolutional Neural Networks -- Model Compression II -- Pruning artificial neural networks: a way to find well-generalizing, high-entropy sharp minima -- Log-Nets: Logarithmic Feature-Product Layers Yield More Compact Networks -- Tuning Deep Neural Network's hyperparameters constrained to deployability on tiny systems -- Obstacles to Depth Compression of Neural Networks -- Multi-task and Multi-label Learning -- Multi-Label Quadruplet Dictionary Learning -- Pareto Multi-Task Deep Learning -- Convex Graph Laplacian Multi-Task Learning SVM -- Neural Network Theory and Information Theoretic Learning -- Prediction Stability as a Criterion in Active Learning -- Neural Spectrum Alignment: Empirical Study -- Nonlinear, Nonequilibrium Landscape Approach to Neural Network Dynamics -- Hopfield Networks for Vector Quantization -- Prototype-Based Online Learning on Homogeneously Labeled Streaming Data -- Normalization and Regularization Methods -- Neural Network Training with Safe Regularization in the Null Space of Batch Activations -- The Effect of Batch Normalization in the Symmetric Phase -- Regularized Pooling -- Reinforcement Learning I -- Deep Recurrent Deterministic Policy Gradient for Physical Control -- Exploration via Progress-Driven Intrinsic Rewards -- An improved reinforcement learning based heuristic dynamic programming algorithm for model-free optimal control -- PBCS: Efficient Exploration and Exploitation Using a Synergy between Reinforcement Learning and Motion Planning -- Understanding failures of deterministic actor-critic with continuous action spaces and sparse rewards -- Reinforcement Learning II -- GAN-based Planning Model in Deep Reinforcement Learning -- Guided Reinforcement Learning via Sequence Learning -- Neural Machine Translation based on Improved Actor-Critic Method -- Neural Machine Translation based on Prioritized Experience Replay -- Improving Multi-Agent Reinforcement Learning with Imperfect Human Knowledge -- Reinforcement Learning III -- Adaptive Skill Acquisition in Hierarchical Reinforcement Learning -- Social Navigation with Human Empowerment driven Deep Reinforcement Learning -- Curious Hierarchical Actor-Critic Reinforcement Learning -- Policy Entropy for Out-of-Distribution Classification -- Reservoir Computing -- Analysis of reservoir structure contributing to robustness against structural failure of Liquid State Machine -- Quantifying robustness and capacity of reservoir computers with consistency profiles -- Two-Step FORCE Learning Algorithm for Fast Convergence in Reservoir Computing -- Morphological Computation of Skin Focusing on Fingerprint Structure -- Time Series Clustering with Deep Reservoir Computing -- ReservoirPy: an Efficient and User-Friendly Library to Design Echo State Networks -- Robotics and Neural Models of Perception and Action -- Adaptive, Neural Robot Control – Path Planning on 3D Spiking Neural Networks -- CABIN: A Novel Cooperative Attention Based Location Prediction Network Using Internal-External Trajectory Dependencies -- Neuro-Genetic Visuomotor Architecture for Robotic Grasping -- From Geometries to Contact Graphs -- Sentiment Classification -- Structural Position Network for Aspect-based Sentiment Classification -- Cross-Domain Sentiment Classification using Topic Attention and Dual-Task Adversarial Training -- Data Augmentation for Sentiment Analysis in English – the Online Approach -- Spiking Neural Networks I -- Dendritic computation in a point neuron model. -- Benchmarking Deep Spiking Neural Networks on Neuromorphic Hardware -- Unsupervised Learning of Spatio-Temporal Receptive Fields from an Event-Based Vision Sensor -- Spike-Train Level Unsupervised Learning Algorithm for Deep Spiking Belief Networks -- Spiking Neural Networks II -- Modelling Neuromodulated Information Flow and Energetic Consumption at Thalamic Relay Synapses -- Learning Precise Spike Timings with Eligibility Traces -- Meta-STDP rule stabilizes synaptic weights under in vivo-like ongoing spontaneous activity in a computational model of CA1 pyramidal cell -- Adaptive Chemotaxis for improved Contour Tracking using Spiking Neural Networks -- Text Understanding I -- Mental Imagery-Driven Neural Network to Enhance Representation for Implicit Discourse Relation Recognition -- Adaptive Convolution Kernel for Text Classification via Multi-Channel Representations -- Text generation in discrete space -- Short text processing for analyzing user portraits: A dynamic combination -- Text Understanding II -- A Hierarchical Fine-Tuning Approach Based on Joint Embedding of Words and Parent Categories for Hierarchical Multi-label Text Classification -- Boosting Tricks for Word Mover’s Distance -- Embedding Compression with Right Triangle Similarity Transformations -- Neural Networks for Detecting Irrelevant Questions during Visual Question Answering -- F-Measure Optimisation and Label Regularisation for Energy-based Neural Dialogue State Tracking Models -- Unsupervised Learning -- Unsupervised Change Detection using Joint Autoencoders for Age-Related Macular Degeneration Progression -- A fast algorithm to find Best Matching Units in Self-Organizing Maps -- Tumor Characterization using Unsupervised Learning of Mathematical Relations within Breast Cancer Data -- Balanced SAM-kNN: Online Learning with Heterogeneous Drift and Imbalanced Data -- A Rigorous Link Between Self-Organizing Maps and Gaussian Mixture Models -- Collaborative Clustering through Optimal Transport. |
| Record Nr. | UNINA-9910427681203321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Biomimetic Neural Learning for Intelligent Robots [[electronic resource] ] : Intelligent Systems, Cognitive Robotics, and Neuroscience / / edited by Stefan Wermter, Günther Palm, Mark Elshaw
| Biomimetic Neural Learning for Intelligent Robots [[electronic resource] ] : Intelligent Systems, Cognitive Robotics, and Neuroscience / / edited by Stefan Wermter, Günther Palm, Mark Elshaw |
| Edizione | [1st ed. 2005.] |
| Pubbl/distr/stampa | Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2005 |
| Descrizione fisica | 1 online resource (IX, 383 p.) |
| Disciplina | 629.8/92632 |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Robotics
Automation Cognitive psychology Neurosciences Artificial intelligence Computer science Special purpose computers Robotics and Automation Cognitive Psychology Artificial Intelligence Computer Science, general Special Purpose and Application-Based Systems |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Towards Biomimetic Neural Learning for Intelligent Robots -- Towards Biomimetic Neural Learning for Intelligent Robots -- I: Biomimetic Multimodal Learning in Neuron-Based Robots -- The Intentional Attunement Hypothesis The Mirror Neuron System and Its Role in Interpersonal Relations -- Sequence Detector Networks and Associative Learning of Grammatical Categories -- A Distributed Model of Spatial Visual Attention -- A Hybrid Architecture Using Cross-Correlation and Recurrent Neural Networks for Acoustic Tracking in Robots -- Image Invariant Robot Navigation Based on Self Organising Neural Place Codes -- Detecting Sequences and Understanding Language with Neural Associative Memories and Cell Assemblies -- Combining Visual Attention, Object Recognition and Associative Information Processing in a NeuroBotic System -- Towards Word Semantics from Multi-modal Acoustico-Motor Integration: Application of the Bijama Model to the Setting of Action-Dependant Phonetic Representations -- Grounding Neural Robot Language in Action -- A Spiking Neural Network Model of Multi-modal Language Processing of Robot Instructions -- II: Biomimetic Cognitive Behaviour in Robots -- A Virtual Reality Platform for Modeling Cognitive Development -- Learning to Interpret Pointing Gestures: Experiments with Four-Legged Autonomous Robots -- Reinforcement Learning Using a Grid Based Function Approximator -- Spatial Representation and Navigation in a Bio-inspired Robot -- Representations for a Complex World: Combining Distributed and Localist Representations for Learning and Planning -- MaximumOne: An Anthropomorphic Arm with Bio-inspired Control System -- LARP, Biped Robotics Conceived as Human Modelling -- Novelty and Habituation: The Driving Forces in Early Stage Learning for Developmental Robotics -- Modular Learning Schemes for Visual Robot Control -- Neural Robot Detection in RoboCup -- A Scale Invariant Local Image Descriptor for Visual Homing. |
| Record Nr. | UNISA-996465830703316 |
| Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2005 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Biomimetic neural learning for intelligent robots : intelligent systems, cognitive robotics, and neuroscience / / Stefan Wermter, Gunther Palm, Mark Elshaw (eds.)
| Biomimetic neural learning for intelligent robots : intelligent systems, cognitive robotics, and neuroscience / / Stefan Wermter, Gunther Palm, Mark Elshaw (eds.) |
| Edizione | [1st ed. 2005.] |
| Pubbl/distr/stampa | Berlin ; ; New York, : Springer, c2005 |
| Descrizione fisica | 1 online resource (IX, 383 p.) |
| Disciplina | 629.8/92632 |
| Altri autori (Persone) |
WermterStefan
PalmGunther ElshawMark |
| Collana |
Lecture notes in computer scienceLecture notes in artificial intelligence
State-of-the-art survey |
| Soggetto topico |
Robots - Control systems
Neural networks (Computer science) Machine learning Artificial intelligence Soft computing Robotics Cognitive neuroscience |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | pt. 1. Biomimetic multimodal learning in neuron-based robots -- pt. 2. Biomimetic cognitive behaviour in robots. |
| Record Nr. | UNINA-9910484148903321 |
| Berlin ; ; New York, : Springer, c2005 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Cognitive systems and information processing : 6th International Conference, ICCSIP 2021, Suzhou, China, November 20-21, 2021, Revised selected papers / / Fuchun Sun [and five others], editors
| Cognitive systems and information processing : 6th International Conference, ICCSIP 2021, Suzhou, China, November 20-21, 2021, Revised selected papers / / Fuchun Sun [and five others], editors |
| Pubbl/distr/stampa | Singapore : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (555 pages) |
| Disciplina | 006.3 |
| Collana | Communications in Computer and Information Science |
| Soggetto topico | Computational intelligence |
| Soggetto non controllato |
Mechanical Engineering
Technology & Engineering |
| ISBN |
981-16-9246-7
981-16-9247-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996549371703316 |
| Singapore : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Cognitive systems and information processing : 6th International Conference, ICCSIP 2021, Suzhou, China, November 20-21, 2021, Revised selected papers / / Fuchun Sun [and five others], editors
| Cognitive systems and information processing : 6th International Conference, ICCSIP 2021, Suzhou, China, November 20-21, 2021, Revised selected papers / / Fuchun Sun [and five others], editors |
| Pubbl/distr/stampa | Singapore : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (555 pages) |
| Disciplina | 006.3 |
| Collana | Communications in Computer and Information Science |
| Soggetto topico | Computational intelligence |
| Soggetto non controllato |
Mechanical Engineering
Technology & Engineering |
| ISBN |
981-16-9246-7
981-16-9247-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910743389303321 |
| Singapore : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
