activity
20182024
most citedSelf-supervised Contrastive Representation Learning for Semi-supervised Time-Series Classification

216 citations · 666 across the 28 of their papers we have counts for

collaborators

28 papers

cs.LG2024

Self-evolving Autoencoder Embedded Q-Network

J. Senthilnath, Bangjian Zhou, Zhen Wei Ng +7

In the realm of sequential decision-making tasks, the exploration capability of a reinforcement learning (RL) agent is paramount for achieving high rewards through interactions wit…

cs.LG2024

Evolving Restricted Boltzmann Machine-Kohonen Network for Online Clustering

J. Senthilnath, Adithya Bhattiprolu, Ankur Singh +4

A novel online clustering algorithm is presented where an Evolving Restricted Boltzmann Machine (ERBM) is embedded with a Kohonen Network called ERBM-KNet. The proposed ERBM-KNet e…

cs.LG2023★ 6 cited

Fully-Connected Spatial-Temporal Graph for Multivariate Time-Series Data

Yucheng Wang, Yuecong Xu, Jianfei Yang +4

Multivariate Time-Series (MTS) data is crucial in various application fields. With its sequential and multi-source (multiple sensors) properties, MTS data inherently exhibits Spati…

cs.LG2023

Graph-Aware Contrasting for Multivariate Time-Series Classification

Yucheng Wang, Yuecong Xu, Jianfei Yang +4

Contrastive learning, as a self-supervised learning paradigm, becomes popular for Multivariate Time-Series (MTS) classification. It ensures the consistency across different views o…

cs.CV2023★ 1 cited

Efficient Joint Optimization of Layer-Adaptive Weight Pruning in Deep Neural Networks

Kaixin Xu, Zhe Wang, Xue Geng +4

In this paper, we propose a novel layer-adaptive weight-pruning approach for Deep Neural Networks (DNNs) that addresses the challenge of optimizing the output distortion minimizati…

cs.CV2023★ 1 cited

Unlimited Knowledge Distillation for Action Recognition in the Dark

Ruibing Jin, Guosheng Lin, Min Wu +4

Dark videos often lose essential information, which causes the knowledge learned by networks is not enough to accurately recognize actions. Existing knowledge assembling methods re…