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• Adversarial machine learning.- An Improved (Adversarial) Reprogramming Technique for Neural Networks.- Adversarial Robustness in Deep Learning: Attacks on Fragile Neurons.- How to compare adversarial robustness of classifiers from a global perspective.- Multiple-Model based Defense for Deep Reinforcement Learning against Adversarial Attack.- Neural Paraphrase Generation with Multi-Domain Corpus.- Leveraging Adversarial Training to Facilitate Grammatical Error Correction.- Statistical Certification of Acceptable Robustness for Neural Networks.- Model Extraction and Adversarial Attacks on Neural Networks using Switching Power Information.- Anomaly detection.- o 0097 - CmaGraph: A TriBlocks Anomaly Detection Method in Dynamic Graph Using Evolutionary Community Representation Learning.- Falcon: Malware Detection and Categorization with Network Traffic Images.- Attention-based Bi-LSTM for Anomaly Detection on Time-Series Data.- Semi-supervised Graph Edge Convolutional Network for Anomaly Detection.- Feature Creation Towards the Detection of Non-Control-Flow Hijacking Attacks.- Attention and transformers I.- An Attention Module for Convolutional Neural Networks.- Attention-based 3D neural architectures for predicting cracks in designs.- Entity-aware Biaffine Attention for Constituent Parsing.- Attention-based Multi-View Feature Fusion for Cross-Domain Recommendation.- Say in Human-like Way: Hierarchical Cross-modal Information Abstraction and Summarization for Controllable Captioning.- DAEMA: Denoising Autoencoder with Mask Attention.- Spatial-Temporal Traffic Data Imputation via Graph Attention Convolutional Network.- EGAT: Edge-Featured Graph Attention Network.- Attention and transformers II.- Knowledge Graph Enhanced Transformer for Generative Question Answering Tasks.- GAttANet: Global attention agreement for convolutional neural networks.- Classification Models for Partially Ordered Sequences.- TINet: Multi-dimensional Traffic Data Imputation via Transformer Network.- Sequential Self-Attentive model for Knowledge Tracing.- Multi-Object Tracking based on Nearest Optimal Template Library.- TSTNet: A Sequence to Sequence Transformer Network for Spatial-temporal Traffic Prediction.- Audio and multimodal applications.- A multimode two-stream network for egocentric action recognition.- Behavior of Keyword Spotting Networks Under Noisy Conditions.- Robust Stroke Recognition via Vision and IMU in Robotic Table Tennis.- AMVAE: Asymmetric Multimodal Variational Autoencoder for Multi-view Representation.- Enhancing Separate Encoding with Multi-layer Feature Alignment for Image-Text Matching.- Bird Audio Diarization with Faster R-CNN.- Multi-Modal Chorus Recognition for Improving Song Search.- FaVoA: Face-Voice Association Favours Ambiguous Speaker Detection.- Bioinformatics and biosignal analysis.- Identification of Incorrect Karyotypes Using Deep Learning.- A Metagraph-Based Model for Predicting Drug-Target Interaction on Heterogeneous Network.- Evaluating Multiple-Concept Biomedical Hypotheses Based on Deep Sets.- A Network Embedding Based Approach to Drug-Target Interaction Prediction Using Additional Implicit Networks.- Capsule networks.- CNNapsule: A Lightweight Network with Fusion Features for Monocular Depth Estimation.- Learning Optimal Primary Capsules by Information Bottleneck.- Capsule Networks with Routing Annealing.- Training Deep Capsule Networks with Residual Connections.- Cognitive models.- Interpretable Visual Understanding with Cognitive Attention Network.- A Bio-Inspired Mechanism Based on Neural Threshold Regulation to Compensate Variability in Network Connectivity.- A Predictive Coding Account for Chaotic Itinerancy.- A Computational Model of the Effect of Short-Term Monocular Deprivation on Binocular Rivalry in the Context of Amblyopia.- Transitions among metastable states underlie context-dependent working memories in a multiple timescale network.
• (source: Nielsen Book Data)

The proceedings set LNCS 12891, LNCS 12892, LNCS 12893, LNCS 12894 and LNCS 12895 constitute the proceedings of the 30th International Conference on Artificial Neural Networks, ICANN 2021, held in Bratislava, Slovakia, in September 2021.* The total of 265 full papers presented in these proceedings was carefully reviewed and selected from 496 submissions, and organized in 5 volumes. In this volume, the papers focus on topics such as adversarial machine learning, anomaly detection, attention and transformers, audio and multimodal applications, bioinformatics and biosignal analysis, capsule networks and cognitive models. *The conference was held online 2021 due to the COVID-19 pandemic.
(source: Nielsen Book Data)

• Computer vision and object detection.- Selective Multi-Scale Learning for Object Detection.- DRENet: Giving Full Scope to Detection and Regression-based Estimation for Video Crowd Counting.- Sisfrutos Papaya: a Dataset for Detection and Classification of Diseases in Papaya.- Faster-LTN: a neuro-symbolic, end-to-end object detection architecture.- GC-MRNet: Gated Cascade Multi-stage Regression Network for Crowd Counting.- Latent Feature-Aware and Local Structure-Preserving Network for 3D Completion from a single depth view.- Facial Expression Recognition by Expression-Specific Representation Swapping.- Iterative Error Removal for Time-of-Flight Depth Imaging.- Blurred Image Recognition: A Joint Motion Deblurring and Classification Loss-Aware Approach.- Learning How to Zoom in: Weakly Supervised ROI-based-DAM for Fine-Grained Visual Classification.- Convolutional neural networks and kernel methods.- (Input) Size Matters for CNN Classifiers.- Accelerating Depthwise Separable Convolutions with Vector Processor.- KCNet: Kernel-based Canonicalization Network for entities in Recruitment Domain.- Deep Unitary Convolutional Neural Networks.- Deep learning and optimization I.- DPWTE: A Deep Learning Approach to Survival Analysis using a Parsimonious Mixture of Weibull Distributions.- First-order and second-order variants of the gradient descent in a unified framework.- Bayesian optimization for backpropagation in Monte-Carlo tree search.- Growing Neural Networks Achieve Flatter Minima.- Dynamic Neural Diversification: Path to Computationally Sustainable Neural Networks.- Curved SDE-Net Leads to Better Generalization for Uncertainty Estimates of DNNs.- EIS - Efficient and Trainable Activation Functions for Better Accuracy and Performance.- Deep learning and optimization II.- Why Mixup Improves the Model Performance.- Mixup gamblers: Learning to abstain with auto-calibrated reward for mixed samples.- Non-Iterative Phase Retrieval With Cascaded Neural Networks.- Incorporating Discrete Wavelet Transformation Decomposition Convolution into Deep Network to Achieve Light Training.- MMF: A loss extension for feature learning in open set recognition.- On the selection of loss functions under known weak label models.- Distributed and continual learning.- Bilevel Online Deep Learning in Non-stationary Environment.- A Blockchain Based Decentralized Gradient Aggregation Design for Federated Learning.- Continual Learning for Fake News Detection from Social Media.- Balanced Softmax Cross-Entropy for Incremental Learning.- Generalised Controller Design using Continual Learning.- DRILL: Dynamic Representations for Imbalanced Lifelong Learning.- Principal Gradient Direction and Confidence Reservoir Sampling for Continual Learning.- Explainable methods.- Spontaneous Symmetry Breaking in Data Visualization.- Deep NLP Explainer: Using Prediction Slope To Explain NLP Models.- Empirically explaining SGD from a line search perspective.- Towards Ontologically Explainable Classifiers.- Few-shot learning.- Leveraging the Feature Distribution in Transfer-based Few-Shot Learning.- One-Shot Meta-Learning for Radar-Based Gesture Sequences Recognition.- Few-Shot Learning With Random Erasing and Task-Relevant Feature Transforming.- Fostering Compositionality in Latent, Generative Encodings to Solve the Omniglot Challenge.- Better Few-shot Text Classification with Pre-trained Language Model.- Generative adversarial networks.- Leveraging GANs via Non-local Features.- On Mode Collapse in Generative Adversarial Networks.- Image Inpainting Using Wasserstein Generative Adversarial Imputation Network.- COViT-GAN: Vision Transformer for COVID-19 Detection in CT Scan Images with Self-Attention GAN for Data Augmentation.- PhonicsGAN: Synthesizing Graphical Videos from Phonics Songs.- A Progressive Image Inpainting Algorithm with a Mask Auto-update Branch.- Hybrid Generative Models for Two-Dimensional Datasets.- Towards Compressing Efficient Generative Adversarial Networks for Image Translation via Pruning and Distilling.- .
• (source: Nielsen Book Data)

The proceedings set LNCS 12891, LNCS 12892, LNCS 12893, LNCS 12894 and LNCS 12895 constitute the proceedings of the 30th International Conference on Artificial Neural Networks, ICANN 2021, held in Bratislava, Slovakia, in September 2021.* The total of 265 full papers presented in these proceedings was carefully reviewed and selected from 496 submissions, and organized in 5 volumes. In this volume, the papers focus on topics such as computer vision and object detection, convolutional neural networks and kernel methods, deep learning and optimization, distributed and continual learning, explainable methods, few-shot learning and generative adversarial networks. *The conference was held online 2021 due to the COVID-19 pandemic.
(source: Nielsen Book Data)

• Generative neural networks.- Binding and Perspective Taking as Inference in a Generative Neural Network Model.- Advances in Password Recovery using Generative Deep Learning Techniques.- o 0886 - Dilated Residual Aggregation Network for Text-guided Image Manipulation.- Denoising AutoEncoder based Delete and Generate Approach for Text Style Transfer.- GUIS2Code: A Computer Vision Tool to Generate Code Automatically from Graphical User Interface Sketches.- Generating Math Word Problems from Equations with Topic Consistency Maintaining and Commonsense Enforcement.- Generative properties of Universal Bidirectional Activation-based Learning.- Graph neural networks I.- Joint Graph Contextualized Network for Sequential Recommendation.- Relevance-Aware Q-matrix Calibration for Knowledge Tracing.- LGACN: A Light Graph Adaptive Convolution Network for Collaborative Filtering.- HawkEye: Cross-Platform Malware Detection with Representation Learning on Graphs.- An Empirical Study of the Expressiveness of Graph Kernels and Graph Neural Networks.- Multi-resolution Graph Neural Networks for PDE approximation.- Link Prediction on Knowledge Graph by Rotation Embedding on the Hyperplane in the Complex Vector Space.- Graph neural networks II.- Contextualise Entities and Relations: An Interaction Method for Knowledge Graph Completion.- Civil Unrest Event Forecasting Using Graphical and Sequential Neural Networks.- Parameterized Hypercomplex Graph Neural Networks for Graph Classification.- Feature Interaction Based Graph Convolutional Networks For Image-text Retrieval.- Generalizing Message Passing Neural Networks to Heterophily using Position Information.- Local and Non-local Context Graph Convolutional Networks for Skeleton-based Action Recognition.-STGATP: A Spatio-temporal Graph Attention Network for Long-term Traffic Prediction.- Hierarchical and ensemble models.- Integrating N-Gram Features into Pre-Trained Model: A Novel Ensemble Model for Multi-Target Stance Detection.- Hierarchical Ensemble for Multi-view Clustering.- Structure-Aware Multi-Scale Hierarchical Graph Convolutional Network for Skeleton Action Recognition.- Learning Hierarchical Reasoning for Text-based Visual Question Answering.- Hierarchical Deep Gaussian Processes Latent Variable Model via Expectation Propagation.- Adaptive Consensus-Based Ensemble for Improved Deep Learning Inference Cost.- Human pose estimation.- Multi-Branch Network for Small Human Pose Estimation.- PNO: Personalized Network Optimization for Human Pose and Shape Reconstruction.- JointPose: Jointly Optimizing Evolutionary Data Augmentation and Prediction Neural Network for 3D Human Pose Estimation.- DeepRehab: Real Time Pose Estimation on the Edge for Knee Injury Rehabilitation.- Image processing.- Subspace constraint for Single Image Super-Resolution.- Towards Fine-Grained Control over Latent Space for Unpaired Image-to-Image Translation.- FMSNet: Underwater Image Restoration by Learning from a Synthesized Dataset.- Towards Measuring Bias in Image Classification.- Towards Image Retrieval with Noisy Labels via Non-deterministic Features.- Image segmentation.- Improving Visual Question Answering by Semantic Segmentation.- Weakly Supervised Semantic Segmentation with Patch-Based Metric Learning Enhancement.- ComBiNet: Compact Convolutional Bayesian Neural Network for Image Segmentation.- Depth Mapping Hybrid Deep Learning Method for Optic Disc and Cup Segmentation on Stereoscopic Ocular Fundus.- RATS: Robust Automated Tracking and Segmentation of Similar Instances.- Knowledge distillation.- Data Diversification Revisited: Why Does It Work?.- A Generalized Meta-Loss Function for Distillation Based Learning Using Privileged Information for Classification and Regression.- Empirical Study of Data-Free Iterative Knowledge Distillation.- Adversarial Variational Knowledge Distillation.- Extract then Distill: Efficient and Effective Task-Agnostic BERT Distillation.- Medical image processing.- Semi-supervised Learning based Right Ventricle Segmentation Using Deep Convolutional Boltzmann Machine Shape Model.- Improved U-Net for Plaque Segmentation of Intracoronary Optical Coherence Tomography Images.- Approximated Masked Global Context Network for Skin Lesion Segmentation.- DSNet: Dynamic Selection Network for Biomedical Image Segmentation.- Computational Approach to Identifying Contrast-Driven Retinal Ganglion Cells.- Radiological Identification of Hip Joint Centers from X-ray Images Using Fast Deep Stacked Network and Dynamic Registration Graph.- A Two-Branch Neural Network for Non-Small-Cell Lung Cancer Classification and Segmentation.- Uncertainty Quantification and Estimation in Medical Image Classification.- Labeling Chest X-Ray Reports Using Deep Learning.
• (source: Nielsen Book Data)

The proceedings set LNCS 12891, LNCS 12892, LNCS 12893, LNCS 12894 and LNCS 12895 constitute the proceedings of the 30th International Conference on Artificial Neural Networks, ICANN 2021, held in Bratislava, Slovakia, in September 2021.* The total of 265 full papers presented in these proceedings was carefully reviewed and selected from 496 submissions, and organized in 5 volumes. In this volume, the papers focus on topics such as generative neural networks, graph neural networks, hierarchical and ensemble models, human pose estimation, image processing, image segmentation, knowledge distillation, and medical image processing. *The conference was held online 2021 due to the COVID-19 pandemic.
(source: Nielsen Book Data)

• Model compression.- Blending Pruning Criteria for Convolutional Neural Networks.- BFRIFP: Brain Functional Reorganization Inspired Filter Pruning.- CupNet - Pruning a network for geometric data.- Pruned-YOLO: Learning Efficient Object Detector Using Model Pruning.- Gator: Customizable Channel Pruning of Neural Networks with Gating.- Multi-task and multi-label learning.- MMF: Multi-Task Multi-Structure Fusion for Hierarchical Image Classification.- GLUNet: Global-Local Fusion U-Net for 2D Medical Image Segmentation.- Textbook Question Answering with Multi-type Question Learning and Contextualized Diagram Representation.- A Multi-Task MRC Framework for Chinese Emotion Cause and Experiencer Extraction.- Fairer Machine Learning Through Multi-objective Evolutionary Learning.- Neural network theory.- Single neurons with delay-based learning can generalise between time-warped patterns.- Estimating Expected Calibration Errors.- LipBAB: Computing exact Lipschitz constant of ReLU networks.- Nonlinear Lagrangean Neural Networks.- Normalization and Regularization Methods.- Energy Conservation in Infinitely Wide Neural-Networks.- Class-Similarity Based Label Smoothing for Confidence Calibration.- Jacobian Regularization for Mitigating Universal Adversarial Perturbations.- Layer-wise Activation Cluster Analysis of CNNs to Detect Out-of-Distribution Samples.- Weight and Gradient Centralization in Deep Neural Networks.- LocalNorm: Robust Image Classification through Dynamically Regularized Normalization.- Channel Capacity of Neural Networks.- RIAP: A method for Effective Receptive Field Rectification.- Curriculum Learning Revisited: Incremental Batch Learning with Instance Typicality Ranking.- Person re-identification.- Interesting Receptive Region and Feature Excitation for Partial Person Re-Identification.- Improved Occluded Person Re-Identification with Multi-feature Fusion.- Joint Weights-averaged and Feature-separated Learning for Person Re-identification.- Semi-Hard Margin Support Vector Machines for Personal Authentication with an Aerial Signature Motion.-Recurrent neural networks.- Dynamic identification of stop locations from GPS trajectories based on their temporal and spatial characteristics.- Separation of Memory and Processing in Dual Recurrent Neural Networks.- Predicting Landfall's Location and Time of a Tropical Cyclone Using Reanalysis Data.- Latent State Inference in a Spatiotemporal Generative Model.- Deep learning models and interpretations for multivariate discrete-valued event sequence prediction.- End-to-End On-Line Multi-Object Tracking on Sparse Point Clouds Using Recurrent Convolutional Networks.- M-ary Hopfield Neural Network based Associative Memory Formulation: Limit-cycle based Sequence Storage and Retrieval.- Training Many-to-Many Recurrent Neural Networks with Target Propagation.- Early Recognition of Ball Catching Success in Clinical Trials with RNN-Based Predictive Classification.- Precise temporal P300 detection in Brain Computer Interface EEG signals using a Long-Short Term Memory.- Noise Quality and Super-Turing Computation in Recurrent Neural Networks.- Reinforcement learning I.- Learning to Plan via a Multi-Step Policy Regression Method.- Behaviour-conditioned policies for cooperative reinforcement learning tasks.- Integrated Actor-Critic for Deep Reinforcement Learning.- Learning to Assist Agents by Observing Them.- Reinforcement Syntactic Dependency Tree Reasoning for Target-Oriented Opinion Word Extraction.- Learning distinct strategies for heterogeneous cooperative multi-agent reinforcement learning.- MAT-DQN: Toward Interpretable Multi-Agent Deep Reinforcement Learning for Coordinated Activities.- Selection-Expansion: a unifying framework for motion-planning and diversity search algorithms.- A Hand Gesture Recognition System using EMG and Reinforcement Learning: a Q-Learning Approach.- Reinforcement learning II.- Reinforcement learning for the privacy preservation and manipulation of eye tracking data.- Reinforcement Symbolic Learning.- Deep Reinforcement Learning for Job Scheduling on Cluster.- Independent Deep Deterministic Policy Gradient Reinforcement Learning in Cooperative Multiagent Pursuit Games.- Avoid Overfitting in Deep Reinforcement Learning: Increasing Robustness through Decentralized Control.- Advances in Adaptive Skill Acquisition.- Aspect-Based Sentiment Classification with Reinforcement Learning and Local Understanding.- Latent dynamics for artefact-free character animation via data-driven reinforcement learning.- Intrinsic Motivation Model Based on Reward Gating.
• (source: Nielsen Book Data)

The proceedings set LNCS 12891, LNCS 12892, LNCS 12893, LNCS 12894 and LNCS 12895 constitute the proceedings of the 30th International Conference on Artificial Neural Networks, ICANN 2021, held in Bratislava, Slovakia, in September 2021.* The total of 265 full papers presented in these proceedings was carefully reviewed and selected from 496 submissions, and organized in 5 volumes. In this volume, the papers focus on topics such as model compression, multi-task and multi-label learning, neural network theory, normalization and regularization methods, person re-identification, recurrent neural networks, and reinforcement learning. *The conference was held online 2021 due to the COVID-19 pandemic.
(source: Nielsen Book Data)

• Representation learning.- SageDy: A Novel Sampling and Aggregating based Representation Learning Approach for Dynamic Networks.- CuRL: Coupled Representation Learning of cards and merchants to detect transaction frauds.- Revisiting Loss Functions for Person Re-Identification.- Statistical Characteristics of Deep Representations: An Empirical Investigation.- Reservoir computing.- Unsupervised Pretraining of Echo State Networks for Onset Detection.- Canary Song Decoder: Transduction and Implicit Segmentation with ESNs and LTSMs.- Which Hype for my New Task? Hints and Random Search for Echo State Networks Hyperparameters.- Semi- and Unsupervised learning.- A new Nearest Neighbor Median Shift Clustering for Binary Data.- Self-supervised Multi-view Clustering for Unsupervised Image Segmentation.- Evaluate Pseudo Labeling and CNN for multi-variate time series classification in low-data regimes.- Deep Variational Autoencoder with Shallow Parallel Path for Top-N Recommendation (VASP).- Short Text Clustering with A Deep Multi-Embedded Self-Supervised Model.- Brain-like approaches to unsupervised learning of hidden representations - a comparative study.- Spiking neural networks.- A Subthreshold Spiking Neuron Circuit Based on the Izhikevich Model.- SiamSNN: Siamese Spiking Neural Networks for Energy-Efficient Object Tracking.- The principle of weight divergence facilitation for unsupervised pattern recognition in spiking neural networks.- Algorithm For 3D-Chemotaxis Using Spiking Neural Network.- Signal Denoising with Recurrent Spiking Neural Networks and Active Tuning.- Dynamic Action Inference with Recurrent Spiking Neural Networks.- End-to-end Spiking Neural Network for Speech Recognition Using Resonating Input Neurons.- Text understanding I.- Visual-Textual Semantic Alignment Network for Visual Question Answering.- Which and Where to Focus: A Simple yet Accurate Framework for Arbitrary-Shaped Nearby Text Detection in Scene Images.- STCP: An Efficient Model Combing Subject Triples and Constituency Parsing for Recognizing Textual Entailment.- A Latent Variable Model with Hierarchical structure and GPT-2 for long text generation.- A Scoring Model Assisted by Frequency for Multi-Document Summarization.- A Strategy for Referential Problem in Low-Resource Neural Machine Translation.- A Unified Summarization Model with Semantic Guide and Keyword Coverage Mechanism.- Hierarchical Lexicon Embedding Architecture for Chinese Named Entity Recognition.- Evidence Augment for Multiple-Choice Machine Reading Comprehension by Weak Supervision.- Resolving Ambiguity in Hedge Detection by Automatic Generation of Linguistic Rules.- Text understanding II.- Detecting Scarce Emotions Using BERT and Hyperparameter Optimization.- Design and Evaluation of Deep Learning Models for Real-Time Credibility Assessment in Twitter.- T-Bert: A Spam Review Detection Model Combining Group Intelligence and Personalized Sentiment Information.- Graph Enhanced BERT for Stance-aware Rumor Verification on Social Media.- Deep Learning for Suicide and Depression Identification with Unsupervised Label Correction.- Learning to Remove: Towards Isotropic Pre-trained BERT Embedding.- ExBERT: An External Knowledge Enhanced BERT for Natural Language Inference.- Multi-Features-Based Automatic Clinical Coding for Chinese ICD-9-CM-3.- Style as Sentiment versus Style as Formality: the same or different?.- Transfer and meta learning.- Low-resource Neural Machine Translation Using XLNet Pre-training Model.- Self-Learning for Received Signal Strength MapReconstruction with Neural Architecture Search.- Propagation-aware Social Recommendation by Transfer Learning.- Evaluation of Transfer Learning for Visual Road Condition Assessment.- EPE-NAS: Efficient Performance Estimation Without Training for Neural Architecture Search.- DVAMN: Dual Visual Attention Matching Network for Zero-Shot Action Recognition.- Dynamic Tuning and Weighting of Meta-Learning for NMT Domain Adaptation.- Improving Transfer Learning in Unsupervised Language Adaptation.- Sample-Label View Transfer Active Learning for Time Series Classification.- Video processing.- Learning Traffic as Videos: A Spatio-Temporal VAE Approach for Traffic Data Imputation.- Traffic Camera Calibration via Vehicle Vanishing Point Detection.- Efficient Spatio-Temporal Network with Gated Fusion for Video Super-Resolution.- Adaptive Correlation Filters Feature Fusion Learning for Visual Tracking.- Dense video captioning for incomplete videos.- Modeling Context-guided Visual and Linguistic Semantic Feature for Video Captioning.
• (source: Nielsen Book Data)

The proceedings set LNCS 12891, LNCS 12892, LNCS 12893, LNCS 12894 and LNCS 12895 constitute the proceedings of the 30th International Conference on Artificial Neural Networks, ICANN 2021, held in Bratislava, Slovakia, in September 2021.* The total of 265 full papers presented in these proceedings was carefully reviewed and selected from 496 submissions, and organized in 5 volumes. In this volume, the papers focus on topics such as representation learning, reservoir computing, semi- and unsupervised learning, spiking neural networks, text understanding, transfers and meta learning, and video processing. *The conference was held online 2021 due to the COVID-19 pandemic.
(source: Nielsen Book Data)