Artificial Neural Networks and Machine Learning – ICANN 2021
Springer International Publishing (Verlag)
978-3-030-86379-1 (ISBN)
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.
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 constantof 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 th
| Erscheinungsdatum | 14.09.2021 |
|---|---|
| Reihe/Serie | Lecture Notes in Computer Science | Theoretical Computer Science and General Issues |
| Zusatzinfo | XXIV, 703 p. 242 illus., 210 illus. in color. |
| Verlagsort | Cham |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Gewicht | 1098 g |
| Themenwelt | Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik |
| Schlagworte | Applications • Artificial Intelligence • Computer Hardware • Computer Networks • Computer Science • Computer systems • computer vision • conference proceedings • Databases • Education • Engineering • Image Analysis • Image Processing • Informatics • Internet • learning • machine learning • Mathematics • network architecture • Network Protocols • Neural networks • Reinforcement Learning • Research • Signal Processing |
| ISBN-10 | 3-030-86379-4 / 3030863794 |
| ISBN-13 | 978-3-030-86379-1 / 9783030863791 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
| Haben Sie eine Frage zum Produkt? |
aus dem Bereich
