Jack W. Rae

4.6k total citations
10 papers, 184 citations indexed

About

Jack W. Rae is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Molecular Biology. According to data from OpenAlex, Jack W. Rae has authored 10 papers receiving a total of 184 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Artificial Intelligence, 6 papers in Computer Vision and Pattern Recognition and 1 paper in Molecular Biology. Recurrent topics in Jack W. Rae's work include Topic Modeling (4 papers), Advanced Neural Network Applications (4 papers) and Neural Networks and Applications (3 papers). Jack W. Rae is often cited by papers focused on Topic Modeling (4 papers), Advanced Neural Network Applications (4 papers) and Neural Networks and Applications (3 papers). Jack W. Rae collaborates with scholars based in United States and United Kingdom. Jack W. Rae's co-authors include Ali Razavi, Po-Sen Huang, Pushmeet Kohli, Robert Stanforth, Dani Yogatama, Huan Zhang, Johannes Welbl, Andrew Trask, Tim Harley and Chris Dyer and has published in prestigious journals such as arXiv (Cornell University), International Conference on Machine Learning and International Conference on Learning Representations.

In The Last Decade

Jack W. Rae

10 papers receiving 176 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Jack W. Rae United States	6	138	48	12	10	9	10	184
Christopher Akiki Germany	4	133 1.0×	21 0.4×	6 0.5×	14 1.4×	5 0.6×	10	201
Daniel Hesslow France	2	139 1.0×	24 0.5×	7 0.6×	13 1.3×	4 0.4×	4	194
Tuan Dung Nguyen Australia	7	71 0.5×	33 0.7×	12 1.0×	7 0.7×	3 0.3×	14	153
Yury Zemlyanskiy United States	5	127 0.9×	28 0.6×	11 0.9×	20 2.0×	2 0.2×	6	192
Christos Louizos Netherlands	5	119 0.9×	51 1.1×	3 0.3×	12 1.2×	2 0.2×	12	145
Giuseppe Marra Belgium	6	110 0.8×	26 0.5×	5 0.4×	12 1.2×		20	140
Ayush K Tarun India	4	108 0.8×	38 0.8×	12 1.0×	17 1.7×		6	178
Mikhail Yurochkin United States	6	138 1.0×	20 0.4×	10 0.8×	13 1.3×	3 0.3×	19	169
Lex Weaver Australia	5	164 1.2×	32 0.7×	11 0.9×	5 0.5×		6	197
Samuel Weinbach United States	2	176 1.3×	31 0.6×	5 0.4×	42 4.2×	2 0.2×	2	236

Countries citing papers authored by Jack W. Rae

Since Specialization

Citations

This map shows the geographic impact of Jack W. Rae's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Jack W. Rae with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jack W. Rae more than expected).

Fields of papers citing papers by Jack W. Rae

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jack W. Rae. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Jack W. Rae. The network helps show where Jack W. Rae may publish in the future.

Co-authorship network of co-authors of Jack W. Rae

This figure shows the co-authorship network connecting the top 25 collaborators of Jack W. Rae. A scholar is included among the top collaborators of Jack W. Rae based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Jack W. Rae. Jack W. Rae is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

10 of 10 papers shown

1.

Jayakumar, Siddhant M., Razvan Pascanu, Jack W. Rae, Simon Osindero, & Erich Elsen. (2021). Top-KAST: Top-K Always Sparse Training. arXiv (Cornell University). 33. 20744–20754. 2 indexed citations

2.

Song, Hao, Abbas Abdolmaleki, Jost Tobias Springenberg, et al.. (2020). V-MPO: On-Policy Maximum a Posteriori Policy Optimization for Discrete and Continuous Control. arXiv (Cornell University). 3 indexed citations

3.

Jayakumar, Siddhant M., Jacob Menick, Wojciech Marian Czarnecki, et al.. (2020). Multiplicative Interactions and Where to Find Them. International Conference on Learning Representations. 20 indexed citations

4.

Bartunov, Sergey, Jack W. Rae, Simon Osindero, & Timothy Lillicrap. (2020). Meta-Learning Deep Energy-Based Memory Models. International Conference on Learning Representations. 1 indexed citations

5.

Parisotto, Emilio, Francis Song, Jack W. Rae, et al.. (2020). Stabilizing Transformers for Reinforcement Learning. International Conference on Machine Learning. 1. 7487–7498. 9 indexed citations

6.

Huang, Po-Sen, Huan Zhang, Robert Stanforth, et al.. (2020). Reducing Sentiment Bias in Language Models via Counterfactual Evaluation. 65–83. 74 indexed citations

7.

Rae, Jack W. & Ali Razavi. (2020). Do Transformers Need Deep Long-Range Memory?. 7524–7529. 20 indexed citations

8.

Rae, Jack W., Sergey Bartunov, & Timothy Lillicrap. (2019). Meta-Learning Neural Bloom Filters. arXiv (Cornell University). 5271–5280. 3 indexed citations

9.

Trask, Andrew, Felix Hill, Scott Reed, et al.. (2018). Neural Arithmetic Logic Units. arXiv (Cornell University). 31. 8046–8055. 27 indexed citations

10.

Rae, Jack W., Jonathan J. Hunt, Tim Harley, et al.. (2016). Scaling Memory-Augmented Neural Networks with Sparse Reads and Writes. arXiv (Cornell University). 29. 3628–3636. 25 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

Explore authors with similar magnitude of impact