2 citations · 2 across the 29 of their papers we have counts for
8 papers · 1 filter
Spectral Gating Networks
Jusheng Zhang, Yijia Fan, Kaitong Cai +5
Gating mechanisms are ubiquitous, yet a complementary question in feed-forward networks remains under-explored: how to introduce frequency-rich expressivity without sacrificing sta…
RevFFN: Memory-Efficient Full-Parameter Fine-Tuning of Mixture-of-Experts LLMs with Reversible Blocks
Ningyuan Liu, Jing Yang, Kaitong Cai +1
Full parameter fine tuning is a key technique for adapting large language models (LLMs) to downstream tasks, but it incurs substantial memory overhead due to the need to cache exte…
Causal Invariance and Counterfactual Learning Driven Cooperative Game for Multi-Label Classification
Yijia Fan, Jusheng Zhang, Kaitong Cai +2
Multi-label classification (MLC) remains vulnerable to label imbalance, spurious correlations, and distribution shifts, challenges that are particularly detrimental to rare label p…
Guardian: Decoupling Exploration from Safety in Reinforcement Learning
Kaitong Cai, Jusheng Zhang, Jing Yang +1
Hybrid offline--online reinforcement learning (O2O RL) promises both sample efficiency and robust exploration, but suffers from instability due to distribution shift between offlin…
Backward-Friendly Optimization: Training Large Language Models with Approximate Gradients under Memory Constraints
Jing Yang, Kaitong Cai, Yijia Fan +2
Full fine-tuning of Large Language Models (LLMs) is notoriously memory-intensive, primarily because conventional optimizers such as SGD or Adam assume access to exact gradients der…
Learning Dynamics of VLM Finetuning
Jusheng Zhang, Kaitong Cai, Jing Yang +1
Preference-based finetuning of vision--language models (VLMs) is brittle: trivially wrong negatives inject uninformative gradients that destabilize training. We recast alignment as…
CF-VLM:CounterFactual Vision-Language Fine-tuning
Jusheng Zhang, Kaitong Cai, Yijia Fan +2
Recent advances in vision-language models (VLMs) have greatly improved cross-modal semantic understanding, yet significant limitations remain in fine-grained discrimination and dee…
Kolmogorov-Arnold Fourier Networks
Jusheng Zhang, Yijia Fan, Kaitong Cai +2
Although Kolmogorov-Arnold-based interpretable networks (KANs) possess strong theoretical expressiveness, they suffer from severe parameter explosion and limited ability to capture…