2 citations · 4 across the 16 of their papers we have counts for
34 papers
Provable Failure of Language Models in Learning Majority Boolean Logic via Gradient Descent
Bo Chen, Zhenmei Shi, Zhao Song +1
Recent advancements in Transformer-based architectures have led to impressive breakthroughs in natural language processing tasks, with models such as GPT-4, Claude, and Gemini demo…
Can You Count to Nine? A Human Evaluation Benchmark for Counting Limits in Modern Text-to-Video Models
Xuyang Guo, Zekai Huang, Jiayan Huo +4
Generative models have driven significant progress in a variety of AI tasks, including text-to-video generation, where models like Video LDM and Stable Video Diffusion can produce…
Time and Memory Trade-off of KV-Cache Compression in Tensor Transformer Decoding
Yifang Chen, Xiaoyu Li, Yingyu Liang +3
The key-value (KV) cache in the tensor version of transformers presents a significant bottleneck during inference. While previous work analyzes the fundamental space complexity bar…
Theoretical Guarantees for High Order Trajectory Refinement in Generative Flows
Chengyue Gong, Xiaoyu Li, Yingyu Liang +4
Flow matching has emerged as a powerful framework for generative modeling, offering computational advantages over diffusion models by leveraging deterministic Ordinary Differential…
HOFAR: High-Order Augmentation of Flow Autoregressive Transformers
Yingyu Liang, Zhizhou Sha, Zhenmei Shi +2
Flow Matching and Transformer architectures have demonstrated remarkable performance in image generation tasks, with recent work FlowAR [Ren et al., 2024] synergistically integrati…
Scaling Law Phenomena Across Regression Paradigms: Multiple and Kernel Approaches
Yifang Chen, Xuyang Guo, Xiaoyu Li +3
Recently, Large Language Models (LLMs) have achieved remarkable success. A key factor behind this success is the scaling law observed by OpenAI. Specifically, for models with Trans…