828 citations · 5.6k across the 171 of their papers we have counts for
22 papers · 1 filter
A Cosmic-Scale Benchmark for Symmetry-Preserving Data Processing
Julia Balla, Siddharth Mishra-Sharma, Carolina Cuesta-Lazaro +2
Efficiently processing structured point cloud data while preserving multiscale information is a key challenge across domains, from graphics to atomistic modeling. Using a curated d…
Hamiltonian Score Matching and Generative Flows
Peter Holderrieth, Yilun Xu, Tommi Jaakkola
Classical Hamiltonian mechanics has been widely used in machine learning in the form of Hamiltonian Monte Carlo for applications with predetermined force fields. In this work, we e…
Fictitious Synthetic Data Can Improve LLM Factuality via Prerequisite Learning
Yujian Liu, Shiyu Chang, Tommi Jaakkola +1
Recent studies have identified one aggravating factor of LLM hallucinations as the knowledge inconsistency between pre-training and fine-tuning, where unfamiliar fine-tuning data m…
An Information Criterion for Controlled Disentanglement of Multimodal Data
Chenyu Wang, Sharut Gupta, Xinyi Zhang +4
Multimodal representation learning seeks to relate and decompose information inherent in multiple modalities. By disentangling modality-specific information from information that i…
Generator Matching: Generative modeling with arbitrary Markov processes
Peter Holderrieth, Marton Havasi, Jason Yim +6
We introduce Generator Matching, a modality-agnostic framework for generative modeling using arbitrary Markov processes. Generators characterize the infinitesimal evolution of a Ma…
Fine-Tuning Discrete Diffusion Models via Reward Optimization with Applications to DNA and Protein Design
Chenyu Wang, Masatoshi Uehara, Yichun He +7
Recent studies have demonstrated the strong empirical performance of diffusion models on discrete sequences across domains from natural language to biological sequence generation.…
Think While You Generate: Discrete Diffusion with Planned Denoising
Sulin Liu, Juno Nam, Andrew Campbell +4
Discrete diffusion has achieved state-of-the-art performance, outperforming or approaching autoregressive models on standard benchmarks. In this work, we introduce Discrete Diffusi…
Identifying biological perturbation targets through causal differential networks
Menghua Wu, Umesh Padia, Sean H. Murphy +2
Identifying variables responsible for changes to a biological system enables applications in drug target discovery and cell engineering. Given a pair of observational and intervent…
Generative Modeling of Molecular Dynamics Trajectories
Bowen Jing, Hannes Stärk, Tommi Jaakkola +1
Molecular dynamics (MD) is a powerful technique for studying microscopic phenomena, but its computational cost has driven significant interest in the development of deep learning-b…
Structural Constraint Integration in Generative Model for Discovery of Quantum Material Candidates
Ryotaro Okabe, Mouyang Cheng, Abhijatmedhi Chotrattanapituk +9
Billions of organic molecules are known, but only a tiny fraction of the functional inorganic materials have been discovered, a particularly relevant problem to the community searc…
DisCo-Diff: Enhancing Continuous Diffusion Models with Discrete Latents
Yilun Xu, Gabriele Corso, Tommi Jaakkola +2
Diffusion models (DMs) have revolutionized generative learning. They utilize a diffusion process to encode data into a simple Gaussian distribution. However, encoding a complex, po…
Revisiting Who's Harry Potter: Towards Targeted Unlearning from a Causal Intervention Perspective
Yujian Liu, Yang Zhang, Tommi Jaakkola +1
This paper investigates Who's Harry Potter (WHP), a pioneering yet insufficiently understood method for LLM unlearning. We explore it in two steps. First, we introduce a new task o…
Thermodynamically Informed Multimodal Learning of High-Dimensional Free Energy Models in Molecular Coarse Graining
Blake R. Duschatko, Xiang Fu, Cameron Owen +4
We present a differentiable formalism for learning free energies that is capable of capturing arbitrarily complex model dependencies on coarse-grained coordinates and finite-temper…
A Recipe for Charge Density Prediction
Xiang Fu, Andrew Rosen, Kyle Bystrom +5
In density functional theory, charge density is the core attribute of atomic systems from which all chemical properties can be derived. Machine learning methods are promising in si…
In-Context Symmetries: Self-Supervised Learning through Contextual World Models
Sharut Gupta, Chenyu Wang, Yifei Wang +2
At the core of self-supervised learning for vision is the idea of learning invariant or equivariant representations with respect to a set of data transformations. This approach, ho…
Verlet Flows: Exact-Likelihood Integrators for Flow-Based Generative Models
Ezra Erives, Bowen Jing, Tommi Jaakkola
Approximations in computing model likelihoods with continuous normalizing flows (CNFs) hinder the use of these models for importance sampling of Boltzmann distributions, where exac…
Deep Confident Steps to New Pockets: Strategies for Docking Generalization
Gabriele Corso, Arthur Deng, Benjamin Fry +3
Accurate blind docking has the potential to lead to new biological breakthroughs, but for this promise to be realized, docking methods must generalize well across the proteome. Exi…
Dirichlet Flow Matching with Applications to DNA Sequence Design
Hannes Stark, Bowen Jing, Chenyu Wang +4
Discrete diffusion or flow models could enable faster and more controllable sequence generation than autoregressive models. We show that naïve linear flow matching on the simplex i…
Generative Flows on Discrete State-Spaces: Enabling Multimodal Flows with Applications to Protein Co-Design
Andrew Campbell, Jason Yim, Regina Barzilay +2
Combining discrete and continuous data is an important capability for generative models. We present Discrete Flow Models (DFMs), a new flow-based model of discrete data that provid…
AlphaFold Meets Flow Matching for Generating Protein Ensembles
Bowen Jing, Bonnie Berger, Tommi Jaakkola
The biological functions of proteins often depend on dynamic structural ensembles. In this work, we develop a flow-based generative modeling approach for learning and sampling the…
Sample, estimate, aggregate: A recipe for causal discovery foundation models
Menghua Wu, Yujia Bao, Regina Barzilay +1
Causal discovery, the task of inferring causal structure from data, has the potential to uncover mechanistic insights from biological experiments, especially those involving pertur…
Improved motif-scaffolding with SE(3) flow matching
Jason Yim, Andrew Campbell, Emile Mathieu +9
Protein design often begins with the knowledge of a desired function from a motif which motif-scaffolding aims to construct a functional protein around. Recently, generative models…