828 citations · 5.6k across the 171 of their papers we have counts for
16 papers · 1 filter
Is Conditional Generative Modeling all you need for Decision-Making?
Anurag Ajay, Yilun Du, Abhi Gupta +3
Recent improvements in conditional generative modeling have made it possible to generate high-quality images from language descriptions alone. We investigate whether these methods…
Poisson Flow Generative Models
Yilun Xu, Ziming Liu, Max Tegmark +1
We propose a new "Poisson flow" generative model (PFGM) that maps a uniform distribution on a high-dimensional hemisphere into any data distribution. We interpret the data points a…
Efficiently Controlling Multiple Risks with Pareto Testing
Bracha Laufer-Goldshtein, Adam Fisch, Regina Barzilay +1
Machine learning applications frequently come with multiple diverse objectives and constraints that can change over time. Accordingly, trained models can be tuned with sets of hype…
Forces are not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulations
Xiang Fu, Zhenghao Wu, Wujie Wang +4
Molecular dynamics (MD) simulation techniques are widely used for various natural science applications. Increasingly, machine learning (ML) force field (FF) models begin to replace…
DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking
Gabriele Corso, Hannes Stärk, Bowen Jing +2
Predicting the binding structure of a small molecule ligand to a protein -- a task known as molecular docking -- is critical to drug design. Recent deep learning methods that treat…
Calibrated Selective Classification
Adam Fisch, Tommi Jaakkola, Regina Barzilay
Selective classification allows models to abstain from making predictions (e.g., say "I don't know") when in doubt in order to obtain better effective accuracy. While typical selec…
Antibody-Antigen Docking and Design via Hierarchical Equivariant Refinement
Wengong Jin, Regina Barzilay, Tommi Jaakkola
Computational antibody design seeks to automatically create an antibody that binds to an antigen. The binding affinity is governed by the 3D binding interface where antibody residu…
Diffusion probabilistic modeling of protein backbones in 3D for the motif-scaffolding problem
Brian L. Trippe, Jason Yim, Doug Tischer +4
Construction of a scaffold structure that supports a desired motif, conferring protein function, shows promise for the design of vaccines and enzymes. But a general solution to thi…
Torsional Diffusion for Molecular Conformer Generation
Bowen Jing, Gabriele Corso, Jeffrey Chang +2
Molecular conformer generation is a fundamental task in computational chemistry. Several machine learning approaches have been developed, but none have outperformed state-of-the-ar…
Subspace Diffusion Generative Models
Bowen Jing, Gabriele Corso, Renato Berlinghieri +1
Score-based models generate samples by mapping noise to data (and vice versa) via a high-dimensional diffusion process. We question whether it is necessary to run this entire proce…
Simulate Time-integrated Coarse-grained Molecular Dynamics with Multi-Scale Graph Networks
Xiang Fu, Tian Xie, Nathan J. Rebello +2
Molecular dynamics (MD) simulation is essential for various scientific domains but computationally expensive. Learning-based force fields have made significant progress in accelera…
Adversarial Support Alignment
Shangyuan Tong, Timur Garipov, Yang Zhang +2
We study the problem of aligning the supports of distributions. Compared to the existing work on distribution alignment, support alignment does not require the densities to be matc…
Conformal Prediction Sets with Limited False Positives
Adam Fisch, Tal Schuster, Tommi Jaakkola +1
We develop a new approach to multi-label conformal prediction in which we aim to output a precise set of promising prediction candidates with a bounded number of incorrect answers.…
EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction
Hannes Stärk, Octavian-Eugen Ganea, Lagnajit Pattanaik +2
Predicting how a drug-like molecule binds to a specific protein target is a core problem in drug discovery. An extremely fast computational binding method would enable key applicat…
Syfer: Neural Obfuscation for Private Data Release
Adam Yala, Victor Quach, Homa Esfahanizadeh +5
Balancing privacy and predictive utility remains a central challenge for machine learning in healthcare. In this paper, we develop Syfer, a neural obfuscation method to protect aga…
Controlling Directions Orthogonal to a Classifier
Yilun Xu, Hao He, Tianxiao Shen +1
We propose to identify directions invariant to a given classifier so that these directions can be controlled in tasks such as style transfer. While orthogonal decomposition is dire…