Schuster, Tal

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

Yala, A., Lehman, C., Schuster, T., Portnoi, T., & Barzilay, R. (2019) · Radiology

Digital Radiography and Breast ImagingAI in cancer detection

Cited by 758

Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

Srivastava, A., Rastogi, A., Rao, A., Shoeb, A. A. M., Abid, A., Fisch, A., Brown, A. R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al. (2022) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 569

Mammographic Breast Density Assessment Using Deep Learning: Clinical Implementation

Lehman, C. D., Yala, A., Schuster, T., Dontchos, B., Bahl, M., Swanson, K., & Barzilay, R. (2019) · Radiology

Digital Radiography and Breast ImagingAI in cancer detection

Cited by 294

A Deep Learning Model to Triage Screening Mammograms: A Simulation Study

Yala, A., Schuster, T., Miles, R., Barzilay, R., & Lehman, C. (2019) · Radiology

AI in cancer detectionDigital Radiography and Breast Imaging

Cited by 210

Towards Debiasing Fact Verification Models

Schuster, T., Shah, D., Yeo, Y. J. S., Roberto Filizzola Ortiz, D., Santus, E., & Barzilay, R. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Misinformation and Its ImpactsTopic Modeling

Cited by 171

Transformer Memory as a Differentiable Search Index

Tay, Y., Tran, V., Dehghani, M., Ni, J., Bahri, D., Mehta, H., Qin, Z., Hui, K., Zhao, Z., Gupta, J., et al. (2022) · Advances in Neural Information Processing Systems 35

Topic ModelingInformation Retrieval and Search Behavior

Cited by 128

Get Your Vitamin C! Robust Fact Verification with Contrastive Evidence

Schuster, T., Fisch, A., & Barzilay, R. (2021) · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Misinformation and Its ImpactsTopic Modeling

Cited by 126

Gemma 4 Technical Report

Gemma Team, Abd, S. E., Aggarwal, V., Algayres, R., Andreev, A., Bachem, O., Ballantyne, I., Brick, C., Cărbune, V., Casbon, M., et al. (2026) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 112

The limitations of stylometry for detecting machine-generated fake news

Schuster, T., Schuster, R., Shah, D. J., & Barzilay, R. (2020) · Computational Linguistics

Misinformation and Its ImpactsAuthorship Attribution and Profiling

Cited by 109

Confident adaptive language modeling

Schuster, T., Fisch, A., Gupta, J., Dehghani, M., Bahri, D., Tran, V., Tay, Y., & Metzler, D. (2022) · Advances in Neural Information Processing Systems 35

Natural Language Processing TechniquesSpeech Recognition and Synthesis

Cited by 91

Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities

Comanici, G., Bieber, E., Schaekermann, M., Pasupat, I., Sachdeva, N., Dhillon, I., Blistein, M., Ram, O., Zhang, D., Rosen, E., et al. (2025) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 69

Tomayto, Tomahto. Beyond Token-level Answer Equivalence for Question Answering Evaluation

Bulian, J., Buck, C., Gajewski, W., Börschinger, B., & Schuster, T. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Topic ModelingNatural Language Processing Techniques

Cited by 44

Conformal Risk Control

Angelopoulos, A. N., Bates, S., Fisch, A., Lei, L., & Schuster, T. (2022) · arXiv

Machine Learning and AlgorithmsMachine Learning and Data Classification

Cited by 37

Attributed Question Answering: Evaluation and Modeling for Attributed Large Language Models

Bohnet, B., Tran, V. Q., Verga, P., Aharoni, R., Andor, D., Soares, L. B., Ciaramita, M., Eisenstein, J., Ganchev, K., Herzig, J., et al. (2022) · arXiv

Topic ModelingAuthorship Attribution and Profiling

Cited by 30

Stretching Sentence-pair NLI Models to Reason over Long Documents and Clusters

Schuster, T., Chen, S., Buthpitiya, S., Fabrikant, A., & Metzler, D. (2022) · Findings of the Association for Computational Linguistics: EMNLP 2022

Topic ModelingNatural Language Processing Techniques

Cited by 17

Attribute First, then Generate: Locally-attributable Grounded Text Generation

Slobodkin, A., Hirsch, E., Cattan, A., Schuster, T., & Dagan, I. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 17

Programming Puzzles

Schuster, T., Kalyan, A., Polozov, O., & Kalai, A. T. (2021) · arXiv

Teaching and Learning ProgrammingSoftware Engineering Research

Cited by 2