Titlebook: Neural Information Processing; 30th International C Biao Luo,Long Cheng,Chaojie Li Conference proceedings 2024 The Editor(s) (if applicable

arousal

Accelerate Support Vector Clustering via?Spectral Data Compression while preserving the key cluster properties of the original data sets based on a novel spectral data compression approach. Then, the resultant spectrally-compressed data sets are leveraged for the development of fast and high quality algorithm for support vector clustering. We conducted extensive e

CON

A Novel Iterative Fusion Multi-task Learning Framework for?Solving Dense Predictiontimation, Edge Estimation, etc. With advanced deep learning, many dense prediction tasks have been greatly improved. Multi-task learning is one of the top research lines to boost task performance further. Properly designed multi-task model architectures have better performance and minor memory usage

Chivalrous

Anti-interference Zeroing Neural Network Model for?Time-Varying Tensor Square Root Findingut existing research mainly focuses on solving the time-invariant matrix square root problem. So far, few researchers have studied the time-varying tensor square root (TVTSR) problem. In this study, a novel anti-interference zeroing neural network (AIZNN) model is proposed to solve TVTSR problem onl

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Grievance

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Human-Guided Transfer Learning for Autonomous Robot sometimes unavoidable. While the long learning time can be tolerated for many problems, it is crucial for autonomous robots learning in physical environments. One way to alleviate this problem is through transfer learning, which applies knowledge from one domain to another. In this study, we propos

Aqueous-Humor

Leveraging Two-Scale Features to?Enhance Fine-Grained Object Retrievalion for fine-grained object retrieval (FGOR). However, existing methods construct the embedding based solely on features extracted by the last layer of CNN, neglecting the potential benefits of leveraging features from other layers. Based on the fact that features extracted by different layers of CN