3 citations · 17 across the 14 of their papers we have counts for
13 papers
Reasoning Like an Economist: Post-Training on Economic Problems Induces Strategic Generalization in LLMs
Yufa Zhou, Shaobo Wang, Xingyu Dong +7
Directly training Large Language Models (LLMs) for Multi-Agent Systems (MAS) remains challenging due to intricate reward modeling, dynamic agent interactions, and demanding general…
FastCar: Cache Attentive Replay for Fast Auto-Regressive Video Generation on the Edge
Xuan Shen, Weize Ma, Yufa Zhou +11
Auto-regressive (AR) models, initially successful in language generation, have recently shown promise in visual generation tasks due to their superior sampling efficiency. Unlike i…
DraftAttention: Fast Video Diffusion via Low-Resolution Attention Guidance
Xuan Shen, Chenxia Han, Yufa Zhou +7
Diffusion transformer-based video generation models (DiTs) have recently attracted widespread attention for their excellent generation quality. However, their computational cost re…
Numerical Pruning for Efficient Autoregressive Models
Xuan Shen, Zhao Song, Yufa Zhou +12
Transformers have emerged as the leading architecture in deep learning, proving to be versatile and highly effective across diverse domains beyond language and image processing. Ho…
LazyDiT: Lazy Learning for the Acceleration of Diffusion Transformers
Xuan Shen, Zhao Song, Yufa Zhou +12
Diffusion Transformers have emerged as the preeminent models for a wide array of generative tasks, demonstrating superior performance and efficacy across various applications. The…
Beyond Linear Approximations: A Novel Pruning Approach for Attention Matrix
Yingyu Liang, Jiangxuan Long, Zhenmei Shi +2
Large Language Models (LLMs) have shown immense potential in enhancing various aspects of our daily lives, from conversational AI to search and AI assistants. However, their growin…
On Fine-Grained I/O Complexity of Attention Backward Passes
Xiaoyu Li, Yingyu Liang, Zhenmei Shi +3
Large Language Models (LLMs) exhibit exceptional proficiency in handling extensive context windows in natural language. Nevertheless, the quadratic scaling of attention computation…
Looped ReLU MLPs May Be All You Need as Practical Programmable Computers
Yingyu Liang, Zhizhou Sha, Zhenmei Shi +2
Previous work has demonstrated that attention mechanisms are Turing complete. More recently, it has been shown that a looped 9-layer Transformer can function as a universal program…
Multi-Layer Transformers Gradient Can be Approximated in Almost Linear Time
Yingyu Liang, Zhizhou Sha, Zhenmei Shi +2
The computational complexity of the self-attention mechanism in popular transformer architectures poses significant challenges for training and inference, and becomes the bottlenec…
Provable Differentially Private Computation of the Cross-Attention Mechanism
Yekun Ke, Yingyu Liang, Zhenmei Shi +2
Cross-attention has emerged as a cornerstone module in modern artificial intelligence, underpinning critical applications such as retrieval-augmented generation (RAG), system promp…
Unraveling the Smoothness Properties of Diffusion Models: A Gaussian Mixture Perspective
Yingyu Liang, Zhenmei Shi, Zhao Song +1
Diffusion models have made rapid progress in generating high-quality samples across various domains. However, a theoretical understanding of the Lipschitz continuity and second mom…
Training Tensor Attention Efficiently: From Cubic to Almost Linear Time
Yang Cao, Yingyu Liang, Zhenmei Shi +1
Tensor Attention, a multi-view attention that is able to capture high-order correlations among multiple modalities, can overcome the representational limitations of classical matri…
Differentially Private Attention Computation
Yeqi Gao, Zhao Song, Xin Yang +1
Large language models (LLMs), especially those based on the Transformer architecture, have had a profound impact on various aspects of daily life, such as natural language processi…