Timo Schick

2.4k total citations
13 papers, 466 citations indexed

About

Timo Schick is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Information Systems. According to data from OpenAlex, Timo Schick has authored 13 papers receiving a total of 466 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Artificial Intelligence, 5 papers in Computer Vision and Pattern Recognition and 2 papers in Information Systems. Recurrent topics in Timo Schick's work include Topic Modeling (10 papers), Natural Language Processing Techniques (6 papers) and Multimodal Machine Learning Applications (4 papers). Timo Schick is often cited by papers focused on Topic Modeling (10 papers), Natural Language Processing Techniques (6 papers) and Multimodal Machine Learning Applications (4 papers). Timo Schick collaborates with scholars based in Germany, United Kingdom and United States. Timo Schick's co-authors include Hinrich Schütze, Sahana Udupa, Helmut Schmid, Or Honovich, Thomas Scialom, Omer Levy, Νικόλαος Αλέτρας, Xilun Chen, Sebastian Riedel and Hannaneh Hajishirzi and has published in prestigious journals such as Nature Machine Intelligence, Transactions of the Association for Computational Linguistics and Open access LMU (Ludwid Maxmilian's Universitat Munchen).

In The Last Decade

Timo Schick

13 papers receiving 443 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Timo Schick Germany	9	412	95	50	16	15	13	466
Albert Webson United States	5	342 0.8×	71 0.7×	46 0.9×	14 0.9×	9 0.6×	6	434
Niklas Muennighoff United States	6	302 0.7×	55 0.6×	37 0.7×	10 0.6×	5 0.3×	8	379
Mor Geva United States	11	329 0.8×	85 0.9×	41 0.8×	5 0.3×	10 0.7×	29	383
Katherine Lee United States	7	265 0.6×	42 0.4×	55 1.1×	20 1.3×	35 2.3×	13	375
Gaole He Netherlands	10	308 0.7×	45 0.5×	131 2.6×	6 0.4×	28 1.9×	15	360
Piero Molino Italy	7	225 0.5×	111 1.2×	135 2.7×	17 1.1×	7 0.5×	15	333
Kelvin Guu United States	8	422 1.0×	153 1.6×	56 1.1×	6 0.4×	5 0.3×	9	481
Yeganeh Kordi United States	1	191 0.5×	43 0.5×	39 0.8×	6 0.4×	3 0.2×	2	269
Pigi Kouki United States	8	199 0.5×	51 0.5×	164 3.3×	19 1.2×	20 1.3×	9	287
Alham Fikri Aji United Kingdom	10	388 0.9×	79 0.8×	46 0.9×	13 0.8×	2 0.1×	36	451

Countries citing papers authored by Timo Schick

Since Specialization

Citations

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

Fields of papers citing papers by Timo Schick

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Timo Schick

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

13 of 13 papers shown

1.

Schick, Timo, et al.. (2024). LongForm: Effective Instruction Tuning with Reverse Instructions. 7056–7078. 1 indexed citations

2.

Schick, Timo, et al.. (2023). Semantic-Oriented Unlabeled Priming for Large-Scale Language Models. 32–38. 4 indexed citations

3.

Schick, Timo, et al.. (2023). Active Learning Principles for In-Context Learning with Large Language Models. 5011–5034. 16 indexed citations

4.

Honovich, Or, Thomas Scialom, Omer Levy, & Timo Schick. (2023). Unnatural Instructions: Tuning Language Models with (Almost) No Human Labor. 14409–14428. 50 indexed citations

5.

Asai, Akari, Timo Schick, Patrick Lewis, et al.. (2023). Task-aware Retrieval with Instructions. 3650–3675. 21 indexed citations

6.

Schick, Timo, et al.. (2023). MEAL: Stable and Active Learning for Few-Shot Prompting. 506–517. 4 indexed citations

7.

Broscheit, Samuel, Aleksandra Piktus, Patrick A. Lewis, et al.. (2023). Improving Wikipedia verifiability with AI. Nature Machine Intelligence. 5(10). 1142–1148. 12 indexed citations

8.

Vu, Tu, et al.. (2022). Leveraging QA Datasets to Improve Generative Data Augmentation. 9737–9750. 6 indexed citations

9.

Schick, Timo & Hinrich Schütze. (2022). True Few-Shot Learning with Prompts—A Real-World Perspective. Transactions of the Association for Computational Linguistics. 10. 716–731. 34 indexed citations

10.

Schick, Timo & Hinrich Schütze. (2021). Few-Shot Text Generation with Natural Language Instructions. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. 390–402. 75 indexed citations

11.

Schick, Timo, Sahana Udupa, & Hinrich Schütze. (2021). Self-Diagnosis and Self-Debiasing: A Proposal for Reducing Corpus-Based Bias in NLP. Transactions of the Association for Computational Linguistics. 9. 1408–1424. 137 indexed citations

12.

Schick, Timo, Helmut Schmid, & Hinrich Schütze. (2020). Automatically Identifying Words That Can Serve as Labels for Few-Shot Text Classification. 5569–5578. 92 indexed citations

13.

Schick, Timo, Helmut Schmid, & Hinrich Schütze. (2020). Automatically Identifying Words That Can Serve as Labels for Few-Shot Text Classification. Open access LMU (Ludwid Maxmilian's Universitat Munchen). 14 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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