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Neural Information Processing: 29th International Conference, ICONIP 2022, Virtual Event, November 22–26, 2022, Proceedings, Part I de Mohammad Tanveer

Neural Information Processing: 29th International Conference, ICONIP 2022, Virtual Event, November 22–26, 2022, Proceedings, Part I: Lecture Notes in Computer Science, cartea 13623

Editat de Mohammad Tanveer, Sonali Agarwal, Seiichi Ozawa, Asif Ekbal, Adam Jatowt
en Limba Engleză Paperback – 13 apr 2023
The three-volume set LNCS 13623, 13624, and 13625 constitutes the refereed proceedings of the 29th International Conference on Neural Information Processing, ICONIP 2022, held as a virtual event, November 22–26, 2022.

The 146 papers presented in the proceedings set were carefully reviewed and selected from 810 submissions. They were organized in topical sections as follows: Theory and Algorithms; Cognitive Neurosciences; Human Centered Computing; and Applications.

The ICONIP conference aims to provide a leading international forum for researchers, scientists, and industry professionals who are working in neuroscience, neural networks, deep learning, and related fields to share their new ideas, progress, and achievements.



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Specificații

ISBN-13: 9783031301049
ISBN-10: 3031301048
Pagini: 627
Ilustrații: XXXV, 627 p. 172 illus., 162 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.92 kg
Ediția:1st ed. 2023
Editura: Springer International Publishing
Colecția Springer
Seria Lecture Notes in Computer Science

Locul publicării:Cham, Switzerland

Cuprins

Theory and Algorithms.- Solving Partial Differential Equations using Point-based Neural Networks.- Patch Mix Augmentation with Dual Encoders for Meta-Learning.- Tacit Commitments Emergence in Multi-agent Reinforcement Learning.- Saccade Direction Information Channel.- Shared-Attribute Multi-Graph Clustering with Global Self-Attention.- Mutual Diverse-Label Adversarial Training.- Multi-Agent Hyper-Attention Policy Optimization.- Filter Pruning via Similarity Clustering for Deep Convolutional Neural Networks.- FPD: Feature Pyramid Knowledge Distillation.- An effective ensemble model related to incremental learning in neural machine translation.- Local-Global Semantic Fusion Single-shot Classification Method.- Self-Reinforcing Feedback Domain Adaptation Channel.- General Algorithm for Learning from Grouped Uncoupled Data and Pairwise Comparison Data.- Additional Learning for Joint Probability Distribution Matching in BiGAN.- Multi-View Self-Attention for Regression Domain Adaptation with Feature Selection.- EigenGRF: Layer-Wise Eigen-Learning for Controllable Generative Radiance Fields.- Partial Label learning with Gradually Induced Error-Correction Output Codes.- HMC-PSO: A Hamiltonian Monte Carlo and Particle Swarm Optimization-based optimizer.- Heterogeneous Graph Representation for Knowledge Tracing.- Intuitionistic fuzzy universum support vector machine.- Support vector machine based models with sparse auto-encoder based features for classification problem.- Selectively increasing the diversity of GAN-generated samples.- Cooperation and Competition: Flocking with Evolutionary Multi-Agent Reinforcement Learning.- Differentiable Causal Discovery Under Heteroscedastic Noise.- IDPL: Intra-subdomain adaptation adversarial learning segmentation method based on Dynamic Pseudo Labels.- Adaptive Scaling for U-Net in Time Series Classification.- Permutation Elementary Cellular Automata: Analysis and Application of Simple Examples.- SSPR: A Skyline-Based Semantic Place Retrieval Method.- Double Regularization-based RVFL and edRVFL Networks for Sparse-Dataset Classification.- Adaptive Tabu Dropout for Regularization of Deep Neural Networks.- Class-Incremental Learning with Multiscale Distillation for Weakly Supervised Temporal Action Localization.- Nearest Neighbor Classifier with Margin Penalty for Active Learning.- Factual Error Correction in Summarization with Retriever-Reader Pipeline.- Context-adapted Multi-policy Ensemble Method for Generalization in Reinforcement Learning.- Self-attention based multi-scale graph convolutional networks.- Synesthesia Transformer with Contrastive Multimodal Learning.- Context-based Point Generation Network for Point Cloud Completion.- Temporal Neighborhood Change Centrality for Important Node Identification in Temporal Networks.- DOM2R-Graph: A Web Attribute Extraction Architecture with Relation-aware Heterogeneous Graph Transformer.- Sparse Linear Capsules for Matrix Factorization-based Collaborative Filtering.- PromptFusion: a Low-cost Prompt-based Task Composition for Multi-task Learning.- A fast and efficient algorithm for filtering the training dataset.- Entropy-minimization Mean Teacher for Source-Free Domain Adaptive Object Detection.- IA-CL: A Deep Bidirectional Competitive Learning Method for Traveling Salesman Problem.- Boosting Graph Convolutional Networks With Semi-Supervised Training.- Auxiliary Network: Scalable and agile online learning for dynamic system with inconsistently available inputs.- VAAC: V-value Attention Actor-Critic for Cooperative Multi-agent Reinforcement Learning.- An Analytical Estimation of Spiking Neural Networks Energy Efficiency.- Correlation Based Semantic Transfer with Application to Domain Adaptation.- Minimum Variance Embedded Intuitionistic Fuzzy Weighted Random Vector Functional Link Network.- Neural Network Compression by Joint Sparsity Promotion and Redundancy Reduction.