2 citations · 2 across the 6 of their papers we have counts for
9 papers
CMuon: Accelerating and Stabilizing Diffusion Transformer Training via Chunked Momentum Orthogonalization
Chuyan Chen, Peng Sun, Kun Yuan
Diffusion Transformers (DiTs) have achieved state-of-the-art (SOTA) performance in visual generative modeling, yet their training remains computationally prohibitive. While the rec…
Mixture-of-Channels: Exploiting Sparse FFNs for Efficient LLMs Pre-Training and Inference
Tong Wu, Yutong He, Bin Wang +1
Large language models (LLMs) have demonstrated remarkable success across diverse artificial intelligence tasks, driven by scaling laws that correlate model size and training data w…
An All-Reduce Compatible Top-K Compressor for Communication-Efficient Distributed Learning
Chuyan Chen, Chenyang Ma, Zhangxin Li +3
Communication remains a central bottleneck in large-scale distributed machine learning, and gradient sparsification has emerged as a promising strategy to alleviate this challenge.…
MeCeFO: Enhancing LLM Training Robustness via Fault-Tolerant Optimization
Rizhen Hu, Yutong He, Ran Yan +3
As distributed optimization scales to meet the demands of Large Language Model (LLM) training, hardware failures become increasingly non-negligible. Existing fault-tolerant trainin…
From PowerSGD to PowerSGD+: Low-Rank Gradient Compression for Distributed Optimization with Convergence Guarantees
Shengping Xie, Chuyan Chen, Kun Yuan
Low-rank gradient compression methods, such as PowerSGD, have gained attention in communication-efficient distributed optimization. However, the convergence guarantees of PowerSGD…
Greedy Low-Rank Gradient Compression for Distributed Learning with Convergence Guarantees
Chuyan Chen, Yutong He, Pengrui Li +2
Distributed optimization is pivotal for large-scale signal processing and machine learning, yet communication overhead remains a major bottleneck. Low-rank gradient compression, in…
TAH-QUANT: Effective Activation Quantization in Pipeline Parallelism over Slow Network
Guangxin He, Yuan Cao, Yutong He +4
Decentralized training of large language models offers the opportunity to pool computational resources across geographically distributed participants, but is often bottlenecked by…
CE-LoRA: Computation-Efficient LoRA Fine-Tuning for Language Models
Guanduo Chen, Yutong He, Yipeng Hu +2
Large Language Models (LLMs) demonstrate exceptional performance across various tasks but demand substantial computational resources even for fine-tuning computation. Although Low-…
Subspace Optimization for Large Language Models with Convergence Guarantees
Yutong He, Pengrui Li, Yipeng Hu +2
Subspace optimization algorithms, such as GaLore (Zhao et al., 2024), have gained attention for pre-training and fine-tuning large language models (LLMs) due to their memory effici…