12 citations · 22 across the 16 of their papers we have counts for
17 papers
On the Plasticity and Stability for Post-Training Large Language Models
Wenwen Qiang, Ziyin Gu, Jiahuan Zhou +4
Training stability remains a critical bottleneck for Group Relative Policy Optimization (GRPO), often manifesting as a trade-off between reasoning plasticity and general capability…
Causal Front-Door Adjustment for Robust Jailbreak Attacks on LLMs
Yao Zhou, Zeen Song, Wenwen Qiang +4
Jailbreak attacks on safety-aligned LLMs are increasingly effective, yet attack success alone does not reveal whether compliance is driven by preserved task intent, suppressed safe…
Towards Generalizable Reasoning: Group Causal Counterfactual Policy Optimization for LLM Reasoning
Jingyao Wang, Peizheng Guo, Wenwen Qiang +4
Large language models (LLMs) excel at complex tasks with advances in reasoning capabilities. However, existing reward mechanisms remain tightly coupled to final correctness and pay…
On the Transferability and Discriminability of Repersentation Learning in Unsupervised Domain Adaptation
Wenwen Qiang, Ziyin Gu, Lingyu Si +4
In this paper, we addressed the limitation of relying solely on distribution alignment and source-domain empirical risk minimization in Unsupervised Domain Adaptation (UDA). Our in…
Learning to Think: Information-Theoretic Reinforcement Fine-Tuning for LLMs
Jingyao Wang, Wenwen Qiang, Zeen Song +2
Large language models (LLMs) excel at complex tasks thanks to advances in their reasoning abilities. However, existing methods overlook the trade-off between reasoning effectivenes…
Spatio-Temporal Fuzzy-oriented Multi-Modal Meta-Learning for Fine-grained Emotion Recognition
Jingyao Wang, Wenwen Qiang, Changwen Zheng +1
Fine-grained emotion recognition (FER) plays a vital role in various fields, such as disease diagnosis, personalized recommendations, and multimedia mining. However, existing FER m…
Rethinking Generalizability and Discriminability of Self-Supervised Learning from Evolutionary Game Theory Perspective
Jiangmeng Li, Zehua Zang, Qirui Ji +6
Representations learned by self-supervised approaches are generally considered to possess sufficient generalizability and discriminability. However, we disclose a nontrivial mutual…
Neuromodulated Meta-Learning
Jingyao Wang, Huijie Guo, Wenwen Qiang +4
Humans excel at adapting perceptions and actions to diverse environments, enabling efficient interaction with the external world. This adaptive capability relies on the biological…
On the Generalization and Causal Explanation in Self-Supervised Learning
Wenwen Qiang, Zeen Song, Ziyin Gu +4
Self-supervised learning (SSL) methods learn from unlabeled data and achieve high generalization performance on downstream tasks. However, they may also suffer from overfitting to…
Self-Supervised Video Representation Learning in a Heuristic Decoupled Perspective
Zeen Song, Wenwen Qiang, Changwen Zheng +2
Video contrastive learning (V-CL) has emerged as a popular framework for unsupervised video representation learning, demonstrating strong results in tasks such as action classifica…
On the Discriminability of Self-Supervised Representation Learning
Zeen Song, Wenwen Qiang, Changwen Zheng +2
Self-supervised learning (SSL) has recently shown notable success in various visual tasks. However, in terms of discriminability, SSL is still not on par with supervised learning (…
Understanding Token-level Topological Structures in Transformer-based Time Series Forecasting
Jianqi Zhang, Wenwen Qiang, Jingyao Wang +3
Transformer-based methods have achieved state-of-the-art performance in time series forecasting (TSF) by capturing positional and semantic topological relationships among input tok…
BayesPrompt: Prompting Large-Scale Pre-Trained Language Models on Few-shot Inference via Debiased Domain Abstraction
Jiangmeng Li, Fei Song, Yifan Jin +4
As a novel and effective fine-tuning paradigm based on large-scale pre-trained language models (PLMs), prompt-tuning aims to reduce the gap between downstream tasks and pre-trainin…
Towards Task Sampler Learning for Meta-Learning
Jingyao Wang, Wenwen Qiang, Xingzhe Su +3
Meta-learning aims to learn general knowledge with diverse training tasks conducted from limited data, and then transfer it to new tasks. It is commonly believed that increasing ta…
Manifold Constraint Regularization for Remote Sensing Image Generation
Xingzhe Su, Changwen Zheng, Wenwen Qiang +4
Generative Adversarial Networks (GANs) have shown notable accomplishments in remote sensing domain. However, this paper reveals that their performance on remote sensing images fall…
MetaMask: Revisiting Dimensional Confounder for Self-Supervised Learning
Jiangmeng Li, Wenwen Qiang, Yanan Zhang +4
As a successful approach to self-supervised learning, contrastive learning aims to learn invariant information shared among distortions of the input sample. While contrastive learn…
Supporting Vision-Language Model Inference with Confounder-pruning Knowledge Prompt
Jiangmeng Li, Wenyi Mo, Wenwen Qiang +4
Vision-language models are pre-trained by aligning image-text pairs in a common space to deal with open-set visual concepts. To boost the transferability of the pre-trained models,…