activity
20232026
most citedAre Large Language Models Really Robust to Word-Level Perturbations?

4 citations · 4 across the 3 of their papers we have counts for

collaborators

5 papers

cs.LG2026

Stable Attention Response for Reliable Precipitation Nowcasting

Penghui Wen, Zexin Hu, Sen Zhang +6

Precipitation nowcasting remains challenging due to the highly localized, rapidly evolving, and heterogeneous nature of atmospheric dynamics. Although recent methods increasingly a…

cs.AI2025

Neuron-level Balance between Stability and Plasticity in Deep Reinforcement Learning

Jiahua Lan, Sen Zhang, Haixia Pan +3

In contrast to the human ability to continuously acquire knowledge, agents struggle with the stability-plasticity dilemma in deep reinforcement learning (DRL), which refers to the…

cs.LG2024

FreDF: Learning to Forecast in the Frequency Domain

Hao Wang, Licheng Pan, Zhichao Chen +6

Time series modeling presents unique challenges due to autocorrelation in both historical data and future sequences. While current research predominantly addresses autocorrelation…

cs.LG2023

Revisiting Plasticity in Visual Reinforcement Learning: Data, Modules and Training Stages

Guozheng Ma, Lu Li, Sen Zhang +6

Plasticity, the ability of a neural network to evolve with new data, is crucial for high-performance and sample-efficient visual reinforcement learning (VRL). Although methods like…

cs.CL20234 cited

Are Large Language Models Really Robust to Word-Level Perturbations?

Haoyu Wang, Guozheng Ma, Cong Yu +10

The swift advancement in the scales and capabilities of Large Language Models (LLMs) positions them as promising tools for a variety of downstream tasks. In addition to the pursuit…