Ré, Christopher

On the Opportunities and Risks of Foundation Models

Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., Bernstein, M. S., Bohg, J., Bosselut, A., Brunskill, E., et al. (2021) · arXiv

Ethics and Social Impacts of AIArtificial Intelligence Applications

Cited by 2,676

FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness

Dao, T., Fu, D. Y., Ermon, S., Rudra, A., & Ré, C. (2022) · arXiv

Parallel Computing and Optimization TechniquesAdvanced Neural Network Applications

Cited by 1,214

Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features

Yu, K.-H., Zhang, C., Berry, G. J., Altman, R. B., Ré, C., Rubin, D. L., & Snyder, M. (2016) · Nature Communications

AI in cancer detectionRadiomics and Machine Learning in Medical Imaging

Cited by 972

Efficiently Modeling Long Sequences with Structured State Spaces

Gu, A., Goel, K., & Ré, C. (2021) · arXiv

Topic ModelingAdvanced Neural Network Applications

Cited by 805

Sequence modeling and design from molecular to genome scale with Evo

Nguyen, E., Poli, M., Durrant, M. G., Kang, B., Katrekar, D., Li, D. B., Bartie, L. J., Thomas, A. W., King, S. H., Brixi, G., et al. (2024) · Science

Genomics and Phylogenetic StudiesMachine Learning in Bioinformatics

Cited by 580

HoloClean: holistic data repairs with probabilistic inference

Rekatsinas, T., Chu, X., Ilyas, I. F., & Ré, C. (2017) · Proceedings of the VLDB Endowment

Data Quality and ManagementAdvanced Database Systems and Queries

Cited by 480

Low-Dimensional Hyperbolic Knowledge Graph Embeddings

Chami, I., Wolf, A., Juan, D.-C., Sala, F., Ravi, S., & Ré, C. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Advanced Graph Neural NetworksTopic Modeling

Cited by 384

Hyperbolic Graph Convolutional Neural Networks

Chami, I., Ying, R., Ré, C., & Leskovec, J. (2019) · arXiv

Advanced Graph Neural NetworksNeural Networks and Applications

Cited by 344

Holistic Evaluation of Language Models

Liang, P., Bommasani, R., Lee, T., Tsipras, D., Soylu, D., Yasunaga, M., Zhang, Y., Narayanan, D., Wu, Y., Kumar, A., et al. (2022) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 255

Assessment of Convolutional Neural Networks for Automated Classification of Chest Radiographs

Dunnmon, J. A., Yi, D., Langlotz, C. P., Ré, C., Rubin, D. L., & Lungren, M. P. (2019) · Radiology

COVID-19 diagnosis using AIArtificial Intelligence in Healthcare and Education

Cited by 206

Legalbench: A Collaboratively Built Benchmark for Measuring Legal Reasoning in Large Language Models

Guha, N., Nyarko, J., Ho, D. E., Ré, C., Chilton, A., Narayana, A., Chohlas-Wood, A., Peters, A., Waldon, B., Rockmore, D., et al. (2023) · SSRN Electronic Journal

Artificial Intelligence in LawTopic Modeling

Cited by 154

Hungry Hungry Hippos: Towards Language Modeling with State Space Models

Fu, D. Y., Dao, T., Saab, K. K., Thomas, A. W., Rudra, A., & Ré, C. (2022) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 123

AMELIE speeds Mendelian diagnosis by matching patient phenotype and genotype to primary literature

Birgmeier, J., Haeussler, M., Deisseroth, C. A., Steinberg, E. H., Jagadeesh, K. A., Ratner, A. J., Guturu, H., Wenger, A. M., Diekhans, M. E., Stenson, P. D., et al. (2020) · Science Translational Medicine

Genomics and Rare DiseasesBiomedical Text Mining and Ontologies

Cited by 118

Hyena Hierarchy: Towards Larger Convolutional Language Models

Poli, M., Massaroli, S., Nguyen, E., Fu, D. Y., Dao, T., Baccus, S., Bengio, Y., Ermon, S., & Ré, C. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 96

FlexGen: High-Throughput Generative Inference of Large Language Models with a Single GPU

Sheng, Y., Zheng, L., Yuan, B., Li, Z., Ryabinin, M., Fu, D. Y., Xie, Z., Chen, B., Barrett, C., Gonzalez, J. E., et al. (2023) · arXiv

Parallel Computing and Optimization TechniquesNatural Language Processing Techniques

Cited by 86

HyenaDNA: Long-Range Genomic Sequence Modeling at Single Nucleotide Resolution

Nguyen, E., Poli, M., Faizi, M., Thomas, A., Wornow, M., Birch-Sykes, C., Massaroli, S., Patel, A., Rabideau, C., Bengio, Y., et al. (2023) · Advances in Neural Information Processing Systems 36

Genomics and Phylogenetic StudiesGenomic variations and chromosomal abnormalities

Cited by 66

Snorkel MeTaL: Weak Supervision for Multi-Task Learning

Ratner, A., Hancock, B., Dunnmon, J., Goldman, R., & Ré, C. (2018) · Proceedings of the Second Workshop on Data Management for End-To-End Machine Learning

Machine Learning and Data ClassificationMachine Learning and Algorithms

Cited by 43

Learning Fast Algorithms for Linear Transforms Using Butterfly Factorizations

Dao, T., Gu, A., Eichhorn, M., Rudra, A., & Ré, C. (2019) · arXiv

Advanced Neural Network ApplicationsNeural Networks and Applications

Cited by 43

LegalBench: A Collaboratively Built Benchmark for Measuring Legal Reasoning in Large Language Models

Guha, N., Nyarko, J., Ho, D. E., Ré, C., Chilton, A., Narayana, A., Chohlas-Wood, A., Peters, A., Waldon, B., Rockmore, D. N., et al. (2023) · arXiv

Artificial Intelligence in LawTopic Modeling

Cited by 30

Kaleidoscope: An Efficient, Learnable Representation For All Structured Linear Maps

Dao, T., Sohoni, N. S., Gu, A., Eichhorn, M., Blonder, A., Leszczynski, M., Rudra, A., & Ré, C. (2020) · arXiv

Advanced Neural Network ApplicationsNeural Networks and Applications

Cited by 21

Automating the Enterprise with Foundation Models

Wornow, M., Narayan, A., Opsahl-Ong, K., McIntyre, Q., Shah, N., & Ré, C. (2024) · Proceedings of the VLDB Endowment

Robotic Process Automation ApplicationsBusiness Process Modeling and Analysis

Cited by 15

Large Language Monkeys: Scaling Inference Compute with Repeated Sampling

Brown, B., Juravsky, J., Ehrlich, R., Clark, R., Le, Q. V., Ré, C., & Mirhoseini, A. (2024) · arXiv

Topic ModelingSoftware Engineering Research

Cited by 14

Pixelated Butterfly: Simple and Efficient Sparse training for Neural Network Models

Dao, T., Chen, B., Liang, K., Yang, J., Song, Z., Rudra, A., & Ré, C. (2021) · arXiv

Advanced Neural Network ApplicationsStochastic Gradient Optimization Techniques

Cited by 10

Simple linear attention language models balance the recall-throughput tradeoff

Arora, S., Eyuboglu, S., Zhang, M., Timalsina, A., Alberti, S., Zinsley, D., Zou, J., Rudra, A., & Ré, C. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 5