Tommi Jaakkola

research

∙ 09/15/2023

Compositional Foundation Models for Hierarchical Planning

To make effective decisions in novel environments with long-horizon goal...

0 Anurag Ajay, et al. ∙

research

∙ 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...

0 Andrew Kirjner, et al. ∙

research

∙ 06/26/2023

Restart Sampling for Improving Generative Processes

Generative processes that involve solving differential equations, such a...

0 Yilun Xu, et al. ∙

research

∙ 04/05/2023

GenPhys: From Physical Processes to Generative Models

Since diffusion models (DM) and the more recent Poisson flow generative ...

0 Ziming Liu, et al. ∙

research

∙ 04/05/2023

EigenFold: Generative Protein Structure Prediction with Diffusion Models

Protein structure prediction has reached revolutionary levels of accurac...

0 Bowen Jing, et al. ∙

research

∙ 02/08/2023

PFGM++: Unlocking the Potential of Physics-Inspired Generative Models

We introduce a new family of physics-inspired generative models termed P...

0 Yilun Xu, et al. ∙

research

∙ 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...

0 Jason Yim, et al. ∙

research

∙ 02/01/2023

Stable Target Field for Reduced Variance Score Estimation in Diffusion Models

Diffusion models generate samples by reversing a fixed forward diffusion...

0 Yilun Xu, et al. ∙

research

∙ 11/28/2022

Is Conditional Generative Modeling all you need for Decision-Making?

Recent improvements in conditional generative modeling have made it poss...

0 Anurag Ajay, et al. ∙

research

∙ 10/14/2022

Efficiently Controlling Multiple Risks with Pareto Testing

Machine learning applications frequently come with multiple diverse obje...

0 Bracha Laufer-Goldshtein, et al. ∙

research

∙ 10/04/2022

DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking

Predicting the binding structure of a small molecule ligand to a protein...

21 Gabriele Corso, et al. ∙

research

∙ 09/22/2022

Poisson Flow Generative Models

We propose a new "Poisson flow" generative model (PFGM) that maps a unif...

7 Yilun Xu, et al. ∙

research

∙ 08/25/2022

Calibrated Selective Classification

Selective classification allows models to abstain from making prediction...

3 Adam Fisch, et al. ∙

research

∙ 07/14/2022

Antibody-Antigen Docking and Design via Hierarchical Equivariant Refinement

Computational antibody design seeks to automatically create an antibody ...

12 Wengong Jin, et al. ∙

research

∙ 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...

15 Brian L Trippe, et al. ∙

research

∙ 05/03/2022

Subspace Diffusion Generative Models

Score-based models generate samples by mapping noise to data (and vice v...

0 Bowen Jing, et al. ∙

research

∙ 01/27/2022

Controlling Directions Orthogonal to a Classifier

We propose to identify directions invariant to a given classifier so tha...

0 Yilun Xu, et al. ∙

research

∙ 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...

21 Octavian-Eugen Ganea, et al. ∙

research

∙ 10/19/2021

Learning Representations that Support Robust Transfer of Predictors

Ensuring generalization to unseen environments remains a challenge. Doma...

0 Yilun Xu, et al. ∙

research

∙ 10/12/2021

Crystal Diffusion Variational Autoencoder for Periodic Material Generation

Generating the periodic structure of stable materials is a long-standing...

18 Tian Xie, et al. ∙

research

∙ 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...

0 Wengong Jin, et al. ∙

research

∙ 02/17/2021

Few-shot Conformal Prediction with Auxiliary Tasks

We develop a novel approach to conformal prediction when the target task...

6 Adam Fisch, et al. ∙

research

∙ 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...

9 Wengong Jin, et al. ∙

research

∙ 09/28/2020

Graph Adversarial Networks: Protecting Information against Adversarial Attacks

We study the problem of protecting information when learning with graph ...

20 Peiyuan Liao, et al. ∙

research

∙ 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...

8 Adam Fisch, et al. ∙

research

∙ 06/15/2020

Improved Conditional Flow Models for Molecule to Image Synthesis

In this paper, we aim to synthesize cell microscopy images under differe...

14 Karren Yang, et al. ∙

research

∙ 06/08/2020

Optimal Transport Graph Neural Networks

Current graph neural network (GNN) architectures naively average or sum ...

0 Gary Becigneul, et al. ∙

research

∙ 06/06/2020

Domain Extrapolation via Regret Minimization

Many real prediction tasks such as molecular property prediction require...

0 Wengong Jin, et al. ∙

research

∙ 05/05/2020

Adaptive Invariance for Molecule Property Prediction

Effective property prediction methods can help accelerate the search for...

0 Wengong Jin, et al. ∙

research

∙ 02/20/2020

The Benefits of Pairwise Discriminators for Adversarial Training

Adversarial training methods typically align distributions by solving tw...

0 Shangyuan Tong, et al. ∙

research

∙ 02/14/2020

Generalization and Representational Limits of Graph Neural Networks

We address two fundamental questions about graph neural networks (GNNs)....

0 Vikas K. Garg, et al. ∙

research

∙ 02/11/2020

Improving Molecular Design by Stochastic Iterative Target Augmentation

Generative models in molecular design tend to be richly parameterized, d...

13 Kevin Yang, et al. ∙

research

∙ 02/08/2020

Composing Molecules with Multiple Property Constraints

Drug discovery aims to find novel compounds with specified chemical prop...

7 Wengong Jin, et al. ∙

research

∙ 02/08/2020

Hierarchical Generation of Molecular Graphs using Structural Motifs

Graph generation techniques are increasingly being adopted for drug disc...

14 Wengong Jin, et al. ∙

research

∙ 02/08/2020

Blank Language Models

We propose Blank Language Model (BLM), a model that generates sequences ...

0 Tianxiao Shen, et al. ∙

research

∙ 05/29/2019

Latent Space Secrets of Denoising Text-Autoencoders

While neural language models have recently demonstrated impressive perfo...

0 Tianxiao Shen, et al. ∙

research

∙ 05/29/2019

Path-Augmented Graph Transformer Network

Much of the recent work on learning molecular representations has been b...

0 Benson Chen, et al. ∙

research

∙ 05/29/2019

Strategic Prediction with Latent Aggregative Games

We introduce a new class of context dependent, incomplete information ga...

0 Vikas K. Garg, et al. ∙

research

∙ 05/29/2019

Solving graph compression via optimal transport

We propose a new approach to graph compression by appeal to optimal tran...

1 Vikas K. Garg, et al. ∙

research

∙ 04/02/2019

Are Learned Molecular Representations Ready For Prime Time?

Advancements in neural machinery have led to a wide range of algorithmic...

0 Kevin Yang, et al. ∙

research

∙ 03/11/2019

Alignment Based Matching Networks for One-Shot Classification and Open-Set Recognition

Deep learning for object classification relies heavily on convolutional ...

0 Paresh Malalur, et al. ∙

research

∙ 12/03/2018

Learning Multimodal Graph-to-Graph Translation for Molecular Optimization

We view molecular optimization as a graph-to-graph translation problem. ...

0 Wengong Jin, et al. ∙

research

∙ 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 ...

0 Luke B. Hewitt, et al. ∙

research

∙ 06/07/2018

Direct Optimization through for Discrete Variational Auto-Encoder

Reparameterization of variational auto-encoders with continuous latent s...

0 Guy Lorberbom, et al. ∙

research

∙ 02/12/2018

Junction Tree Variational Autoencoder for Molecular Graph Generation

We seek to automate the design of molecules based on specific chemical p...

0 Wengong Jin, et al. ∙

research

∙ 09/13/2017

Predicting Organic Reaction Outcomes with Weisfeiler-Lehman Network

The prediction of organic reaction outcomes is a fundamental problem in ...

0 Wengong Jin, et al. ∙

research

∙ 08/01/2017

Deep Transfer in Reinforcement Learning by Language Grounding

In this paper, we explore the utilization of natural language to drive t...

0 Karthik Narasimhan, et al. ∙

research

∙ 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. ...

0 Tianxiao Shen, et al. ∙

research

∙ 05/25/2017

Deriving Neural Architectures from Sequence and Graph Kernels

The design of neural architectures for structured objects is typically g...

0 Tao Lei, et al. ∙

research

∙ 01/01/2017

Aspect-augmented Adversarial Networks for Domain Adaptation

We introduce a neural method for transfer learning between two (source a...

0 Yuan Zhang, et al. ∙

Tommi Jaakkola

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