Tal Schuster

2.5k total citations · 1 hit paper
17 papers, 982 citations indexed

About

Tal Schuster is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Oncology. According to data from OpenAlex, Tal Schuster has authored 17 papers receiving a total of 982 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Artificial Intelligence, 3 papers in Computer Vision and Pattern Recognition and 3 papers in Oncology. Recurrent topics in Tal Schuster's work include Topic Modeling (10 papers), Natural Language Processing Techniques (7 papers) and AI in cancer detection (4 papers). Tal Schuster is often cited by papers focused on Topic Modeling (10 papers), Natural Language Processing Techniques (7 papers) and AI in cancer detection (4 papers). Tal Schuster collaborates with scholars based in United States, Israel and Netherlands. Tal Schuster's co-authors include Regina Barzilay, Adam Yala, Constance D. Lehman, Brian N. Dontchos, Randy C. Miles, Manisha Bahl, Kyle Swanson, Ori Ram, Amir Globerson and Adam Fisch and has published in prestigious journals such as Radiology, American Journal of Roentgenology and JCO Clinical Cancer Informatics.

In The Last Decade

Tal Schuster

16 papers receiving 943 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.

A Deep Learning Mammography-based Model for Improved Breast Cancer Risk Prediction

2019 442 citations Adam Yala, Constance D. Lehman et al. Radiology profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Tal Schuster United States	9	732	493	225	174	172	17	982
Lily H. Peng United States	6	446 0.6×	376 0.8×	134 0.6×	129 0.7×	93 0.5×	6	689
Niels Olson United States	6	467 0.6×	370 0.8×	132 0.6×	137 0.8×	83 0.5×	9	734
Thomas de Bel Netherlands	10	641 0.9×	459 0.9×	245 1.1×	103 0.6×	159 0.9×	16	1.0k
Adam Yala United States	13	920 1.3×	746 1.5×	332 1.5×	256 1.5×	272 1.6×	22	1.4k
Krzysztof J. Geras United States	11	531 0.7×	569 1.2×	153 0.7×	116 0.7×	76 0.4×	28	907
Jonas Teuwen Netherlands	15	671 0.9×	962 2.0×	268 1.2×	180 1.0×	120 0.7×	62	1.4k
Vera Sorin Israel	17	462 0.6×	564 1.1×	219 1.0×	544 3.1×	85 0.5×	59	1.2k
Arash Mohtashamian United States	4	386 0.5×	312 0.6×	120 0.5×	118 0.7×	63 0.4×	11	567
Kunal Nagpal United States	5	345 0.5×	258 0.5×	108 0.5×	113 0.6×	97 0.6×	10	592
Ellery Wulczyn United States	9	746 1.0×	357 0.7×	122 0.5×	74 0.4×	89 0.5×	16	986

Countries citing papers authored by Tal Schuster

Since Specialization

Citations

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

Fields of papers citing papers by Tal Schuster

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Tal Schuster

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

17 of 17 papers shown

1.

Chen, Sihao, Chaitanya Malaviya, Alex Fabrikant, et al.. (2025). On Reference (In-)Determinacy in Natural Language Inference. 8066–8078.

2.

Schuster, Tal, Haitian Sun, Jai Prakash Gupta, et al.. (2024). SEMQA: Semi-Extractive Multi-Source Question Answering. 1363–1381. 1 indexed citations

3.

Louis, Annie, et al.. (2023). LAIT: Efficient Multi-Segment Encoding in Transformers with Layer-Adjustable Interaction. 10251–10269. 1 indexed citations

4.

Schuster, Tal, et al.. (2023). SDOH-NLI: a Dataset for Inferring Social Determinants of Health from Clinical Notes. 4789–4798. 1 indexed citations

5.

Chen, Sihao, Senaka Buthpitiya, Alex Fabrikant, Dan Roth, & Tal Schuster. (2023). PropSegmEnt: A Large-Scale Corpus for Proposition-Level Segmentation and Entailment Recognition. 8874–8893. 3 indexed citations

6.

Börschinger, Benjamin, et al.. (2022). Tomayto, Tomahto. Beyond Token-level Answer Equivalence for Question Answering Evaluation. 291–305. 23 indexed citations

7.

Schuster, Tal, Sihao Chen, Senaka Buthpitiya, Alex Fabrikant, & Donald Metzler. (2022). Stretching Sentence-pair NLI Models to Reason over Long Documents and Clusters. 394–412. 13 indexed citations

8.

Schuster, Tal, Adam Fisch, Tommi Jaakkola, & Regina Barzilay. (2021). Consistent Accelerated Inference via Confident Adaptive Transformers. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. 4962–4979. 22 indexed citations

9.

Schuster, Tal, et al.. (2020). Distilling the Evidence to Augment Fact Verification Models. 47–51. 10 indexed citations

10.

Santus, Enrico, Tal Schuster, Amir Tahmasebi, et al.. (2020). Exploiting Rules to Enhance Machine Learning in Extracting Information From Multi-Institutional Prostate Pathology Reports. JCO Clinical Cancer Informatics. 4(4). 865–874. 4 indexed citations

11.

Schuster, Tal, Roei Schuster, Darsh Shah, & Regina Barzilay. (2019). Are We Safe Yet? The Limitations of Distributional Features for Fake News Detection.. arXiv (Cornell University). 6 indexed citations

12.

Schuster, Tal, Ori Ram, Regina Barzilay, & Amir Globerson. (2019). Cross-Lingual Alignment of Contextual Word Embeddings, with Applications to Zero-shot Dependency Parsing. 1599–1613. 91 indexed citations

13.

Yala, Adam, Tal Schuster, Regina Barzilay, et al.. (2019). Deep Learning Model to Assess Cancer Risk on the Basis of a Breast MR Image Alone. American Journal of Roentgenology. 213(1). 227–233. 27 indexed citations

14.

Yala, Adam, et al.. (2019). A Deep Learning Mammography-based Model for Improved Breast Cancer Risk Prediction. Radiology. 292(1). 60–66. 442 indexed citations breakdown →

15.

Yala, Adam, Tal Schuster, Randy C. Miles, Regina Barzilay, & Constance D. Lehman. (2019). A Deep Learning Model to Triage Screening Mammograms: A Simulation Study. Radiology. 293(1). 38–46. 142 indexed citations

16.

Lehman, Constance D., Adam Yala, Tal Schuster, et al.. (2018). Mammographic Breast Density Assessment Using Deep Learning: Clinical Implementation. Radiology. 290(1). 52–58. 188 indexed citations

17.

Schuster, Tal, et al.. (2017). Optical Flow Requires Multiple Strategies (but Only One Network). 6921–6930. 8 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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