Artificial Neural Networks and Machine Learning - ICANN 2021
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Artificial Neural Networks and Machine Learning - ICANN 2021

Artificial Neural Networks and Machine Learning - ICANN 2021


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About the Book

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


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Product Details
  • ISBN-13: 9783030863791
  • Publisher: Springer Nature Switzerland AG
  • Binding: Paperback
  • Language: English
  • Returnable: N
  • Sub Title: 30th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 14-17, 2021, Proceedings, Part IV
  • Width: 156 mm
  • ISBN-10: 3030863794
  • Publisher Date: 11 Sep 2021
  • Height: 234 mm
  • No of Pages: 732
  • Spine Width: 37 mm
  • Weight: 1056 gr


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