1 citations · 1 across the 8 of their papers we have counts for
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GIST: Generator-state-Informed Supervision for Tabular Foundation Models
Zhangyang Peng, Jingze Shi, Tongyi Zhang +1
Tabular foundation models (TFMs) based on prior-data fitted networks typically use sampled hard query labels, leaving avoidable sampling variability in pretraining. A key observati…
MassAlloc Attention: Let Attention Allocate Its Own Compute
Jingze Shi, Zhangyang Peng, Xianduo Li +6
FullAttn often assigns negligible normalized mass to much of the causal score space, yet dense kernels execute the complete post-score path after forming each QK tile. We introduce…
CoWindow Attention: Full Causal Coverage Is a Collective Property
Jingze Shi, Zhangyang Peng, Xianduo Li +6
FullAttn repeatedly exposes the complete causal history to every attention head, creating substantial redundant computation and memory traffic even with IO-efficient dense kernels.…
OmniMoE: An Efficient MoE by Orchestrating Atomic Experts at Scale
Jingze Shi, Zhangyang Peng, Yizhang Zhu +3
Mixture-of-Experts (MoE) architectures are evolving towards finer granularity to improve parameter efficiency. However, existing MoE designs face an inherent trade-off between the…
Towards Automated Kernel Generation in the Era of LLMs
Yang Yu, Peiyu Zang, Chi Hsu Tsai +11
The performance of modern AI systems is fundamentally constrained by the quality of their underlying GPU kernels, which translate high-level algorithmic semantics into low-level ha…