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09/15/2023
Compositional Foundation Models for Hierarchical Planning
To make effective decisions in novel environments with long-horizon goal...
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07/02/2023
Optimizing protein fitness using Gibbs sampling with Graph-based Smoothing
The ability to design novel proteins with higher fitness on a given task...
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06/26/2023
Restart Sampling for Improving Generative Processes
Generative processes that involve solving differential equations, such a...
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04/05/2023
GenPhys: From Physical Processes to Generative Models
Since diffusion models (DM) and the more recent Poisson flow generative ...
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04/05/2023
EigenFold: Generative Protein Structure Prediction with Diffusion Models
Protein structure prediction has reached revolutionary levels of accurac...
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02/08/2023
PFGM++: Unlocking the Potential of Physics-Inspired Generative Models
We introduce a new family of physics-inspired generative models termed P...
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02/05/2023
SE(3) diffusion model with application to protein backbone generation
The design of novel protein structures remains a challenge in protein en...
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02/01/2023
Stable Target Field for Reduced Variance Score Estimation in Diffusion Models
Diffusion models generate samples by reversing a fixed forward diffusion...
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11/28/2022
Is Conditional Generative Modeling all you need for Decision-Making?
Recent improvements in conditional generative modeling have made it poss...
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10/14/2022
Efficiently Controlling Multiple Risks with Pareto Testing
Machine learning applications frequently come with multiple diverse obje...
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10/04/2022
DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking
Predicting the binding structure of a small molecule ligand to a protein...
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09/22/2022
Poisson Flow Generative Models
We propose a new "Poisson flow" generative model (PFGM) that maps a unif...
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08/25/2022
Calibrated Selective Classification
Selective classification allows models to abstain from making prediction...
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07/14/2022
Antibody-Antigen Docking and Design via Hierarchical Equivariant Refinement
Computational antibody design seeks to automatically create an antibody ...
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06/08/2022
Diffusion probabilistic modeling of protein backbones in 3D for the motif-scaffolding problem
Construction of a scaffold structure that supports a desired motif, conf...
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05/03/2022
Subspace Diffusion Generative Models
Score-based models generate samples by mapping noise to data (and vice v...
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01/27/2022
Controlling Directions Orthogonal to a Classifier
We propose to identify directions invariant to a given classifier so tha...
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11/15/2021
Independent SE(3)-Equivariant Models for End-to-End Rigid Protein Docking
Protein complex formation is a central problem in biology, being involve...
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10/19/2021
Learning Representations that Support Robust Transfer of Predictors
Ensuring generalization to unseen environments remains a challenge. Doma...
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10/12/2021
Crystal Diffusion Variational Autoencoder for Periodic Material Generation
Generating the periodic structure of stable materials is a long-standing...
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10/09/2021
Iterative Refinement Graph Neural Network for Antibody Sequence-Structure Co-design
Antibodies are versatile proteins that bind to pathogens like viruses an...
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02/17/2021
Few-shot Conformal Prediction with Auxiliary Tasks
We develop a novel approach to conformal prediction when the target task...
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11/09/2020
Modeling Drug Combinations based on Molecular Structures and Biological Targets
Drug combinations play an important role in therapeutics due to its bett...
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09/28/2020
Graph Adversarial Networks: Protecting Information against Adversarial Attacks
We study the problem of protecting information when learning with graph ...
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07/06/2020
Relaxed Conformal Prediction Cascades for Efficient Inference Over Many Labels
Providing a small set of promising candidates in place of a single predi...
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06/15/2020
Improved Conditional Flow Models for Molecule to Image Synthesis
In this paper, we aim to synthesize cell microscopy images under differe...
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06/08/2020
Optimal Transport Graph Neural Networks
Current graph neural network (GNN) architectures naively average or sum ...
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06/06/2020
Domain Extrapolation via Regret Minimization
Many real prediction tasks such as molecular property prediction require...
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05/05/2020
Adaptive Invariance for Molecule Property Prediction
Effective property prediction methods can help accelerate the search for...
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02/20/2020
The Benefits of Pairwise Discriminators for Adversarial Training
Adversarial training methods typically align distributions by solving tw...
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02/14/2020
Generalization and Representational Limits of Graph Neural Networks
We address two fundamental questions about graph neural networks (GNNs)....
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02/11/2020
Improving Molecular Design by Stochastic Iterative Target Augmentation
Generative models in molecular design tend to be richly parameterized, d...
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02/08/2020
Composing Molecules with Multiple Property Constraints
Drug discovery aims to find novel compounds with specified chemical prop...
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02/08/2020
Hierarchical Generation of Molecular Graphs using Structural Motifs
Graph generation techniques are increasingly being adopted for drug disc...
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02/08/2020
Blank Language Models
We propose Blank Language Model (BLM), a model that generates sequences ...
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05/29/2019
Latent Space Secrets of Denoising Text-Autoencoders
While neural language models have recently demonstrated impressive perfo...
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05/29/2019
Path-Augmented Graph Transformer Network
Much of the recent work on learning molecular representations has been b...
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05/29/2019
Strategic Prediction with Latent Aggregative Games
We introduce a new class of context dependent, incomplete information ga...
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05/29/2019
Solving graph compression via optimal transport
We propose a new approach to graph compression by appeal to optimal tran...
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04/02/2019
Are Learned Molecular Representations Ready For Prime Time?
Advancements in neural machinery have led to a wide range of algorithmic...
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03/11/2019
Alignment Based Matching Networks for One-Shot Classification and Open-Set Recognition
Deep learning for object classification relies heavily on convolutional ...
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12/03/2018
Learning Multimodal Graph-to-Graph Translation for Molecular Optimization
We view molecular optimization as a graph-to-graph translation problem. ...
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07/24/2018
The Variational Homoencoder: Learning to learn high capacity generative models from few examples
Hierarchical Bayesian methods can unify many related tasks (e.g. k-shot ...
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06/07/2018
Direct Optimization through for Discrete Variational Auto-Encoder
Reparameterization of variational auto-encoders with continuous latent s...
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02/12/2018
Junction Tree Variational Autoencoder for Molecular Graph Generation
We seek to automate the design of molecules based on specific chemical p...
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09/13/2017
Predicting Organic Reaction Outcomes with Weisfeiler-Lehman Network
The prediction of organic reaction outcomes is a fundamental problem in ...
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08/01/2017
Deep Transfer in Reinforcement Learning by Language Grounding
In this paper, we explore the utilization of natural language to drive t...
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05/26/2017
Style Transfer from Non-Parallel Text by Cross-Alignment
This paper focuses on style transfer on the basis of non-parallel text. ...
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05/25/2017
Deriving Neural Architectures from Sequence and Graph Kernels
The design of neural architectures for structured objects is typically g...
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01/01/2017