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Siyu Zhu

Siyu Zhu has 3 papers in our library, in 2024–2026, most of them on Language models & pretraining; often with Hanchen Xia, Hao Chen, Jinhao Cui.

3 papers in our library · 2024–2026

OpenAlex · ORCID · h-index 22 · 68 works · 3,104 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 98 times in our library · 3 more in our library without a page yet · ⭐ Paper Stars ranking: #8184

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 2 more institutions on single bylines are not listed.

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

 Works by year

2020s

 Papers

The most cited first (OpenAlex and our library).

  1. Liger Kernel: Efficient Triton Kernels for LLM Training (2024 · 87 citations)
    Training large language models is costly. Running PyTorch step by step in eager mode adds overhead from dispatching and kernel launches, and storing every intermediate activation for the backward pass uses a lot of GPU memory. A LinkedIn team presents Liger-Kernel, an open-source library of GPU kernels written in…
  2. Modeling Multi-Dimensional Cognitive States in Large Language Models under Cognitive Crowding (2026)
    Most LLM work on human cognition treats emotion, stance or intent as separate tasks. Psychology, however, views these dimensions as one interacting system. The authors build CognitiveBench, an English Twitter (X) dataset with expert labels for four dimensions at once: emotion, thinking style, stance and intent. On…
  3. T: Progressive Block Scaling for Masked Diffusion Language Models Through Trajectory Aware Reinforcement Learning (2026)
    Masked diffusion language models (MDMs) usually decode in blocks. Larger blocks allow more tokens to be finalized in parallel, but math reasoning accuracy tends to drop as block size grows, especially for smaller models. The authors propose T⋆, a curriculum built on the trajectory-aware RL method TraceRL. It starts…

All works

6 works of theirs in our library, the most cited first; 3 with a page here, the others link their original until we summarise them.

  1. Neural Window Fully-connected CRFs for Monocular Depth Estimation ↗IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · no summary yetWeihao Yuan et al.2022 · 372 citations
  2. CondLaneNet: a Top-to-down Lane Detection Framework Based on Conditional Convolution ↗IEEE/CVF International Conference on Computer Vision (ICCV) · no summary yetLizhe Liu et al.2021 · 348 citations
  3. Batch DropBlock Network for Person Re-Identification and Beyond ↗IEEE/CVF International Conference on Computer Vision (ICCV) · no summary yetZuozhuo Dai et al.2019 · 261 citations
  4. Liger Kernel: Efficient Triton Kernels for LLM TrainingarXiv:2410.10989Pin-Lun Hsu, Yun Dai, Vignesh Kothapalli, et al.2024 · 87 citations

 Co-authors here

6 co-authors with a page, the most papers together first.

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

 Cited by

Papers of our library cite their works 98 times (of the 149,439 papers whose bibliographies we read); 12 citing papers with a page here →.

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