Marks, Debora

12 publications

NeurIPS 2024 Multi-Scale Representation Learning for Protein Fitness Prediction Zuobai Zhang, Pascal Notin, Yining Huang, Aurélie Lozano, Vijil Chenthamarakshan, Debora Marks, Payel Das, Jian Tang

NeurIPSW 2023 An Energy Based Model for Incorporating Sequence Priors for Target-Specific Antibody Design Yining Huang, Steffanie Paul, Debora Marks

NeurIPSW 2023 Combining Structure and Sequence for Superior Fitness Prediction Steffanie Paul, Aaron Kollasch, Pascal Notin, Debora Marks

NeurIPS 2023 ProteinGym: Large-Scale Benchmarks for Protein Fitness Prediction and Design Pascal Notin, Aaron Kollasch, Daniel Ritter, Lood van Niekerk, Steffanie Paul, Han Spinner, Nathan Rollins, Ada Shaw, Rose Orenbuch, Ruben Weitzman, Jonathan Frazer, Mafalda Dias, Dinko Franceschi, Yarin Gal, Debora Marks

NeurIPS 2023 ProteinNPT: Improving Protein Property Prediction and Design with Non-Parametric Transformers Pascal Notin, Ruben Weitzman, Debora Marks, Yarin Gal

AISTATS 2022 Optimal Design of Stochastic DNA Synthesis Protocols Based on Generative Sequence Models Eli N. Weinstein, Alan N. Amin, Will S. Grathwohl, Daniel Kassler, Jean Disset, Debora Marks

NeurIPS 2022 Non-Identifiability and the Blessings of Misspecification in Models of Molecular Fitness Eli Weinstein, Alan Amin, Jonathan Frazer, Debora Marks

ICML 2022 Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-Time Retrieval Pascal Notin, Mafalda Dias, Jonathan Frazer, Javier Marchena-Hurtado, Aidan N Gomez, Debora Marks, Yarin Gal

NeurIPS 2021 A Generative Nonparametric Bayesian Model for Whole Genomes Alan Amin, Eli N Weinstein, Debora Marks

ICML 2021 A Structured Observation Distribution for Generative Biological Sequence Prediction and Forecasting Eli N Weinstein, Debora Marks

ICLR 2019 Learning Protein Structure with a Differentiable Simulator John Ingraham, Adam Riesselman, Chris Sander, Debora Marks

ICML 2017 Variational Inference for Sparse and Undirected Models John Ingraham, Debora Marks