Tim Dettmers

4.6k total citations · 2 hit papers
8 papers, 1.8k citations indexed

About

Tim Dettmers is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Management Science and Operations Research. According to data from OpenAlex, Tim Dettmers has authored 8 papers receiving a total of 1.8k indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Artificial Intelligence, 2 papers in Computer Vision and Pattern Recognition and 2 papers in Management Science and Operations Research. Recurrent topics in Tim Dettmers's work include Topic Modeling (4 papers), Natural Language Processing Techniques (3 papers) and Data Quality and Management (2 papers). Tim Dettmers is often cited by papers focused on Topic Modeling (4 papers), Natural Language Processing Techniques (3 papers) and Data Quality and Management (2 papers). Tim Dettmers collaborates with scholars based in Switzerland, United States and United Kingdom. Tim Dettmers's co-authors include Pasquale Minervini, Sebastian Riedel, Pontus Stenetorp, Colin Raffel, Ali Farhadi, Sewon Min, Akari Asai, Kaifeng Chen, Inderjit S. Dhillon and Aditya Kusupati and has published in prestigious journals such as arXiv (Cornell University), Proceedings of the AAAI Conference on Artificial Intelligence and Zenodo (CERN European Organization for Nuclear Research).

In The Last Decade

Tim Dettmers

7 papers receiving 1.7k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Convolutional 2D Knowledge Graph Embeddings

2018 1.4k citations Tim Dettmers, Pasquale Minervini et al. Proceedings of the AAAI Conference on Artificial Intelligence profile →
Convolutional 2D Knowledge Graph Embeddings

2017 325 citations Tim Dettmers, Pasquale Minervini et al. Zenodo (CERN European Organization for Nuclear Research) profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Tim Dettmers Switzerland	4	1.7k	338	262	214	177	8	1.8k
Justin Betteridge United States	7	1.4k 0.9×	233 0.7×	205 0.8×	58 0.3×	312 1.8×	11	1.6k
Bryan Kisiel United States	7	1.2k 0.7×	199 0.6×	207 0.8×	60 0.3×	309 1.7×	12	1.4k
Quanyu Dai China	16	622 0.4×	111 0.3×	163 0.6×	141 0.7×	296 1.7×	44	829
Alex Thomo Canada	14	424 0.3×	50 0.1×	178 0.7×	159 0.7×	167 0.9×	88	729
Mehrnoush Shamsfard Iran	15	765 0.5×	63 0.2×	103 0.4×	34 0.2×	353 2.0×	77	927
Shaohua Fan China	8	571 0.3×	26 0.1×	138 0.5×	178 0.8×	247 1.4×	14	714
Mauro Sozio France	14	499 0.3×	68 0.2×	158 0.6×	289 1.4×	204 1.2×	33	928
Srikanta Bedathur India	17	456 0.3×	51 0.2×	186 0.7×	181 0.8×	279 1.6×	77	896

Countries citing papers authored by Tim Dettmers

Since Specialization

Citations

This map shows the geographic impact of Tim Dettmers'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 Tim Dettmers with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tim Dettmers more than expected).

Fields of papers citing papers by Tim Dettmers

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Tim Dettmers. 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 Tim Dettmers. The network helps show where Tim Dettmers may publish in the future.

Co-authorship network of co-authors of Tim Dettmers

This figure shows the co-authorship network connecting the top 25 collaborators of Tim Dettmers. A scholar is included among the top collaborators of Tim Dettmers 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 Tim Dettmers. Tim Dettmers 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:

8 of 8 papers shown

1.

Asai, Akari, Tim Dettmers, Pang Wei Koh, et al.. (2024). Scaling Retrieval-Based Language Models with a Trillion-Token Datastore. 91260–91299.

2.

Chen, Kaifeng, Tim Dettmers, Inderjit S. Dhillon, et al.. (2024). MatFormer: Nested Transformer for Elastic Inference. 140535–140564. 1 indexed citations

3.

Liu, Zeyu, Tim Dettmers, Xi Lin, Veselin Stoyanov, & Xian Li. (2023). Towards A Unified View of Sparse Feed-Forward Network in Pretraining Large Language Model. 2 indexed citations

4.

Dettmers, Tim, et al.. (2023). Petals: Collaborative Inference and Fine-tuning of Large Models. 558–568. 15 indexed citations

5.

Ilharco, Gabriel, et al.. (2020). High Performance Natural Language Processing. 24–27. 2 indexed citations

6.

Dettmers, Tim, Pasquale Minervini, Pontus Stenetorp, & Sebastian Riedel. (2018). Convolutional 2D Knowledge Graph Embeddings. Proceedings of the AAAI Conference on Artificial Intelligence. 32(1). 1400 indexed citations breakdown →

7.

Dettmers, Tim, Pasquale Minervini, Pontus Stenetorp, & Sebastian Riedel. (2017). Convolutional 2D Knowledge Graph Embeddings. Zenodo (CERN European Organization for Nuclear Research). 325 indexed citations breakdown →

8.

Dettmers, Tim. (2016). 8-Bit Approximations for Parallelism in Deep Learning. arXiv (Cornell University). 17 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.

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