290 citations · 889 across the 57 of their papers we have counts for
58 papers
Visual Representation Matters: Exploiting Temporal Differences in Video-to-Audio Generation
Zehua Chen, Junyou Wang, Yuxuan Jiang +5
Video-to-audio (V2A) generation extends image-to-audio generation (I2A) by introducing consecutive frames that provide essential temporal cues for audio synthesis. However, existin…
KernelBenchX: A Comprehensive Benchmark for Evaluating LLM-Generated GPU Kernels
Han Wang, Jintao Zhang, Kai Jiang +3
LLM-based Triton kernel generation has attracted significant interest, yet a fundamental empirical question remains unanswered: where does this capability break down, and why? We p…
SageBwd: A Trainable Low-bit Attention
Jintao Zhang, Marco Chen, Haoxu Wang +5
Low-bit attention, such as SageAttention, has emerged as an effective approach for accelerating model inference, but its applicability to training remains poorly understood. In pri…
Deterministic Differentiable Structured Pruning for Large Language Models
Weiyu Huang, Pengle Zhang, Xiaolu Zhang +3
Structured pruning reduces LLM inference cost by removing low-importance architectural components. This can be viewed as learning a multiplicative gate for each component under an…
TetraJet-v2: Accurate NVFP4 Training for Large Language Models with Oscillation Suppression and Outlier Control
Yuxiang Chen, Yifan Liu, Xiaoming Xu +5
Large Language Models (LLMs) training is prohibitively expensive, driving interest in low-precision fully-quantized training (FQT). While novel 4-bit formats like NVFP4 offer subst…
CAST: Continuous and Differentiable Semi-Structured Sparsity-Aware Training for Large Language Models
Weiyu Huang, Yuezhou Hu, Jun Zhu +1
Sparsity-aware training is an effective approach for transforming large language models (LLMs) into hardware-friendly sparse patterns, thereby reducing latency and memory consumpti…