---
title: "James Lee-Thorp — author profile and papers"
description: "James Lee-Thorp: 2 papers on Elamur Research (2022–2023) — affiliations, topics, co-authors and who cites them in our library."
canonical: "https://research.elamur.ai/free/en/authors/james-lee-thorp/"
---
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# James Lee-Thorp

2 papers on this site · 2022–2023

James Lee-Thorp has 2 papers on this site, in 2022–2023; often with [Joshua Ainslie](https://md.research.elamur.ai/free/en/authors/joshua-ainslie/), [Santiago Ontañón](https://md.research.elamur.ai/free/en/authors/santiago-ontanon/), [Sumit Sanghai](https://md.research.elamur.ai/free/en/authors/sumit-sanghai/).

No OpenAlex profile matched yet.

[2 works on this site](https://md.research.elamur.ai/free/en/authors/james-lee-thorp/#papers) · [658 citations in our library](https://md.research.elamur.ai/free/en/authors/james-lee-thorp/cited-by/)

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

Elsewhere: [Google Scholar](https://scholar.google.com/citations?view_op=search_authors&mauthors=James+Lee-Thorp) · [Semantic Scholar](https://www.semanticscholar.org/search?q=James+Lee-Thorp) · [dblp](https://dblp.org/search?q=James+Lee-Thorp) · [ACL Anthology](https://aclanthology.org/search/?q=James+Lee-Thorp)

## Works by year

### 2020s

[**2023** 1 paper](https://md.research.elamur.ai/free/en/papers/ainslie-2023-gqa-training-generalized-multi-query/) · [**2022** 1 paper](https://md.research.elamur.ai/free/en/papers/thorp-2022-fnet-mixing-tokens-fourier-transforms/)

## Papers

The most cited first (OpenAlex and our library).

1. **[GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints](https://md.research.elamur.ai/free/en/papers/ainslie-2023-gqa-training-generalized-multi-query/)** *(2023 · 1 012 citations)* During autoregressive decoding, a Transformer model loads the attention keys and values from memory at every step.
2. **[FNet: Mixing Tokens with Fourier Transforms](https://md.research.elamur.ai/free/en/papers/thorp-2022-fnet-mixing-tokens-fourier-transforms/)** *(2022 · 593 citations)* The authors ask whether the self-attention sublayers of a Transformer encoder are truly necessary, or whether cheaper token-mixing operations can replace them.

## Co-authors here

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

- [Joshua Ainslie](https://md.research.elamur.ai/free/en/authors/joshua-ainslie/) ×2
- [Santiago Ontañón](https://md.research.elamur.ai/free/en/authors/santiago-ontanon/)
- [Sumit Sanghai](https://md.research.elamur.ai/free/en/authors/sumit-sanghai/)

## Cited by

Papers of our library cite their works 658 times (of the 140 884 papers whose bibliographies we read); [29 citing papers with a page here →](https://md.research.elamur.ai/free/en/authors/james-lee-thorp/cited-by/).