73 citations · 142 across the 31 of their papers we have counts for
7 papers · 1 filter
BSFA: Leveraging the Subspace Dichotomy to Accelerate Neural Network Training
Wenjie Zhou, Bohan Wang, Wei Chen +1
Recent studies \citep{gur2018gradient,song2024does, wen2024understanding} highlight a fundamental dichotomy in deep learning optimization: Although parameter updates along the top…
Solving Formal Math Problems by Decomposition and Iterative Reflection
Yichi Zhou, Jianqiu Zhao, Yongxin Zhang +14
General-purpose Large Language Models (LLMs) have achieved remarkable success in intelligence, performing comparably to human experts on complex reasoning tasks such as coding and…
Selftok: Discrete Visual Tokens of Autoregression, by Diffusion, and for Reasoning
Bohan Wang, Zhongqi Yue, Fengda Zhang +15
We completely discard the conventional spatial prior in image representation and introduce a novel discrete visual tokenizer: Self-consistency Tokenizer (Selftok). At its design co…
AdamS: Momentum Itself Can Be A Normalizer for LLM Pretraining and Post-training
Huishuai Zhang, Bohan Wang, Luoxin Chen
We introduce AdamS, a simple yet effective alternative to Adam for large language model (LLM) pretraining and post-training. By leveraging a novel denominator, i.e., the root of we…
Synthetic Video Enhances Physical Fidelity in Video Synthesis
Qi Zhao, Xingyu Ni, Ziyu Wang +4
We investigate how to enhance the physical fidelity of video generation models by leveraging synthetic videos derived from computer graphics pipelines. These rendered videos respec…
Graph ODEs and Beyond: A Comprehensive Survey on Integrating Differential Equations with Graph Neural Networks
Zewen Liu, Xiaoda Wang, Bohan Wang +3
Graph Neural Networks (GNNs) and differential equations (DEs) are two rapidly advancing areas of research that have shown remarkable synergy in recent years. GNNs have emerged as p…