The four-volume set CCIS 1791, 1792, 1793 and 1794 constitutes the refereed proceedings of the 29th International Conference on Neural Information Processing, ICONIP 2022, held as a virtual event, November 2226, 2022.
The 213 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.
Theory and Algorithms.- Knowledge Transfer from Situation Evaluation to
Multi-agent Reinforcement Learning.- Sequential three-way rules class-overlap
under-sampling based on fuzzy hierarchical subspace for imbalanced data.-
Two-stage Multilayer Perceptron Hawkes Process.- The Context Hierarchical
Contrastive Learning for Time Series in Frequency Domain.- Hawkes Process via
Graph Contrastive Discriminant representation Learning and Transformer
capturing long-term dependencies.- A Temporal Consistency Enhancement
Algorithm Based On Pixel Flicker Correction.- Data representation and
clustering with double low-rank constraints.- RoMA: a Method for Neural
Network Robustness Measurement and Assessment.- Independent Relationship
Detection for Real-Time Scene Graph Generation.- A multi-label feature
selection method based on feature graph with ridge regression and eigenvector
centrality.- O3GPT: A Guidance-Oriented Periodic Testing Framework with
Online Learning, Online Testing,and Online Feedback.- AFFSRN: Attention-Based
Feature Fusion Super-Resolution Network.- Temporal-Sequential Learning with
Columnar-Structured Spiking Neural Networks.- Graph Attention Transformer
Network for Robust Visual Tracking.- GCL-KGE:Graph Contrastive Learning for
Knowledge Graph Embedding.- Towards a Unified Benchmark for Reinforcement
Learning in Sparse Reward Environments.- Effect of Logistic Activation
Function and Multiplicative Input Noise on DNN-kWTA model.- A High-Speed
SSVEP-Based Speller Using Continuous Spelling Method.- AAT: Non-Local
Networks for Sim-to-Real Adversarial Augmentation Transfer.- Aggregating
Intra-class and Inter-class information for Multi-label Text Classification.-
Fast estimation of multidimensional regression functions by the Parzen
kernel-based method.- ReGAE: Graph autoencoder based on recursive neural
networks.- Efficient Uncertainty Quantification for Under-constraint
Prediction following Learning using MCMC.- SMART: A Robustness Evaluation
Framework for Neural Networks.- Time-aware Quaternion Convolutional Network
for Temporal Knowledge Graph Reasoning.- SumBART - An improved BART model for
abstractive text summarization.- Saliency-Guided Learned Image Compression
for Object Detection.- Multi-Label Learning with Data Self-Augmentation.-
MnRec: A News Recommendation Fusion Model Combining Multi-granularity
Information.- Infinite Label Selection Method for Mutil-label
Classification.- Simultaneous Perturbation Method for Multi-Task Weight
Optimization in One-Shot Meta-Learning.- Searching for Textual Adversarial
Examples with Learned Strategy.- Multivariate Time Series Retrieval with
Binary Coding from Transformer. -Learning TSP Combinatorial Search and
Optimization with Heuristic Search.- A Joint Learning Model for Open Set
Recognition with Post-processing.- Cross-Layer Fusion for Feature
Distillation.- MCHPT: A Weakly Supervise Based Merchant Pre-trained Model.-
Progressive Latent Replay for efficient Generative Rehearsal.- Generalization
Bounds for Set-to-Set Matching with Negative Sampling.- ADA: An
Attention-Based Data Augmentation Approach to Handle Imbalanced Textual
Datasets.- Countering the Anti-detection Adversarial Attacks.- Evolving
Temporal Knowledge Graphs by Iterative Spatio-Temporal Walks.- Improving
Knowledge Graph Embedding Using Dynamic Aggregation of Neighbor Information.-
Generative Generalized Zero-Shot Learning based on Auxiliary-Features.-
Learning Stable Representations with Progressive Autoencoder (PAE).- Effect
of Image Down-sampling on Detection of Adversarial Examples .- Boosting the
Robustness of Neural Networks with M-PGD.- StatMix: Data augmentation method
that relies on image statistics in federated learning.- Classification by
Components Including Chow's Reject Option. -Community discovery algorithm
based on improved deep sparse autoencoder.- Fairly Constricted
Multi-Objective Particle Swarm Optimization.- Argument Classification with
BERT plus Contextual, Structural andSyntactic Features as Text.- Variance
Reduction for Deep Q-Learning using Stochastic Recursive Gradient.-
Optimizing Knowledge Distillation Via Shallow Texture Knowledge Transfer.-
Unsupervised Domain Adaptation Supplemented with Generated Images.- MAR2MIX:
A Novel Model for Dynamic Problem in Multi-Agent Reinforcement Learning.-
Adversarial Training with Knowledge Distillation Considering Intermediate
Representations in CNNs.- Deep Contrastive Multi-view Subspace Clustering.
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