14 citations · 21 across the 20 of their papers we have counts for
10 papers · 1 filter
AIR: Analytic Imbalance Rectifier for Continual Learning
Di Fang, Yinan Zhu, Runze Fang +4
Continual learning (CL) agents incrementally learn from sequentially arriving data and adapt to the dynamic, ever-changing nature of real-world environments. However, many existing…
GenderAlign: An Alignment Dataset for Mitigating Gender Bias in Large Language Models
Tao Zhang, Ziqian Zeng, Yuxiang Xiao +4
Large Language Models (LLMs) are prone to generating content that exhibits gender biases, raising significant ethical concerns. Alignment, the process of fine-tuning LLMs to better…
PrivacyRestore: Privacy-Preserving Inference in Large Language Models via Privacy Removal and Restoration
Ziqian Zeng, Jianwei Wang, Junyao Yang +4
The widespread usage of online Large Language Models (LLMs) inference services has raised significant privacy concerns about the potential exposure of private information in user i…
Online Analytic Exemplar-Free Continual Learning with Large Models for Imbalanced Autonomous Driving Task
Huiping Zhuang, Di Fang, Kai Tong +4
In autonomous driving, even a meticulously trained model can encounter failures when facing unfamiliar scenarios. One of these scenarios can be formulated as an online continual le…
Eraser: Jailbreaking Defense in Large Language Models via Unlearning Harmful Knowledge
Weikai Lu, Ziqian Zeng, Jianwei Wang +5
Jailbreaking attacks can enable Large Language Models (LLMs) to bypass the safeguard and generate harmful content. Existing jailbreaking defense methods have failed to address the…
DS-AL: A Dual-Stream Analytic Learning for Exemplar-Free Class-Incremental Learning
Huiping Zhuang, Run He, Kai Tong +3
Class-incremental learning (CIL) under an exemplar-free constraint has presented a significant challenge. Existing methods adhering to this constraint are prone to catastrophic for…
F-OAL: Forward-only Online Analytic Learning with Fast Training and Low Memory Footprint in Class Incremental Learning
Huiping Zhuang, Yuchen Liu, Run He +5
Online Class Incremental Learning (OCIL) aims to train models incrementally, where data arrive in mini-batches, and previous data are not accessible. A major challenge in OCIL is C…
GACL: Exemplar-Free Generalized Analytic Continual Learning
Huiping Zhuang, Yizhu Chen, Di Fang +5
Class incremental learning (CIL) trains a network on sequential tasks with separated categories in each task but suffers from catastrophic forgetting, where models quickly lose pre…
REAL: Representation Enhanced Analytic Learning for Exemplar-free Class-incremental Learning
Run He, Di Fang, Yizhu Chen +5
Exemplar-free class-incremental learning (EFCIL) aims to mitigate catastrophic forgetting in class-incremental learning (CIL) without available historical training samples as exemp…
Chimera: A Lossless Decoding Method for Accelerating Large Language Models Inference by Fusing all Tokens
Ziqian Zeng, Jiahong Yu, Qianshi Pang +4
Large language models (LLMs) have demonstrated remarkable capabilities across various tasks. However, their widespread application is hindered by the resource-intensive decoding pr…