2 citations · 4 across the 11 of their papers we have counts for
5 papers · 1 filter
VPFlow: Value-Predictive Planning with Imperfect World Models for Offline Goal Reaching
Yunzhe Hu, Dong Xu
Offline goal-conditioned reinforcement learning enables skill acquisition by reaching any goal from any state without dense rewards. Model-based methods allow for planning by scali…
SAFEx: Analyzing Vulnerabilities of MoE-Based LLMs via Stable Safety-critical Expert Identification
Zhenglin Lai, Mengyao Liao, Bingzhe Wu +5
Large language models with Mixture-of-Experts (MoE) architectures achieve efficiency and scalability, yet their routing mechanisms introduce safety alignment challenges insufficien…
Hyper-SET: Designing Transformers via Hyperspherical Energy Minimization
Yunzhe Hu, Difan Zou, Dong Xu
Transformer-based models have achieved remarkable success, but their core components, Transformer layers, are largely heuristics-driven and engineered from the bottom up, calling f…
An In-depth Investigation of Sparse Rate Reduction in Transformer-like Models
Yunzhe Hu, Difan Zou, Dong Xu
Deep neural networks have long been criticized for being black-box. To unveil the inner workings of modern neural architectures, a recent work \cite{yu2024white} proposed an inform…
Gear Training: A new way to implement high-performance model-parallel training
Hao Dong, Shuai Li, Dongchang Xu +2
The training of Deep Neural Networks usually needs tremendous computing resources. Therefore many deep models are trained in large cluster instead of single machine or GPU. Though…