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Mayank Mishra

Mayank Mishra has 3 papers in our library, in 2023–2024, most of them on Language models & pretraining, Code & programming languages, Efficient models & compression; often with Alex Gu, Aniruddha Nrusimha, Carolyn Jane Anderson.

3 papers in our library · 2023–2024

OpenAlex · ORCID · h-index 6 · 15 works · 1,005 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 760 times in our library · 4 more in our library without a page yet · ⭐ Paper Stars ranking: #5920

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

🏢IBMArmonk · United States2023–2024 · 6 papers

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.

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 3 worksComputer Science · 3 worksArtificial Intelligence · 3 worksLanguage and Linguistics · 3 worksInformation Systems · 2 worksComputer Vision and Pattern Recognition · 1 work

🧭 Topics 3Research subjects and languages the work is about.Efficient models & compression · 1 workCode & programming languages · 1 workLanguage models & pretraining · 1 work

 Works by year

2020s

20242 papers20231 paper

 Papers

The most cited first (OpenAlex and our library).

  1. SantaCoder: don't reach for the stars! (2023 · 167 citations)
    Most code LLMs are built with little transparency: training data, preprocessing, and design choices are rarely disclosed.
  2. Reducing Transformer Key-Value Cache Size with Cross-Layer Attention (2024 · 57 citations)
    When large language models generate text autoregressively, they store keys and values (the KV cache) for every token and layer.
  3. Granite Code Models: A Family of Open Foundation Models for Code Intelligence (2024 · 54 citations)
    IBM Research addresses a gap in open code LLMs for enterprise use. Large general-purpose models are costly to run. Smaller code models tend to do well at generation but weaker at repair and explanation. Many open models also have unclear data provenance or restrictive licenses. The authors built Granite Code, a…

All works

7 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. StarCoder: may the source be with you! ↗arXiv:2305.06161 · no summary yetRaymond Li et al.2023 · 732 citations
  2. SantaCoder: don't reach for the stars!arXiv:2301.03988Loubna Ben Allal, Raymond Li, Denis Kocetkov, et al.2023 · 167 citations
  3. Reducing Transformer Key-Value Cache Size with Cross-Layer AttentionConference on Neural Information Processing Systems (NeurIPS)William Brandon, Mayank Mishra, Aniruddha Nrusimha, et al.2024 · 57 citations
  4. Granite Code Models: A Family of Open Foundation Models for Code IntelligencearXiv:2405.04324Mayank Mishra, Matt Stallone, Gaoyuan Zhang, et al.2024 · 54 citations
  5. Dense Training, Sparse Inference: Rethinking Training of Mixture-of-Experts Language Models ↗arXiv:2404.05567 · no summary yetBowen Pan et al.2024 · 14 citations
  6. Mitigating the Impact of Outlier Channels for Language Model Quantization with Activation Regularization ↗arXiv:2404.03605 · no summary yetAniruddha Nrusimha et al.2024 · 6 citations
  7. FlashFormer: Whole-Model Kernels for Efficient Low-Batch Inference ↗arXiv (Cornell University) · no summary yetAniruddha Nrusimha et al.2025 · 3 citations

 Co-authors here

12 of 16 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 760 times (of the 149,439 papers whose bibliographies we read); 187 citing papers with a page here →.

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