15 citations · 32 across the 23 of their papers we have counts for
26 papers · 1 filter
Steered Not Stirred: Effective Reward Tilting using Surrogate Reward Mixing
Alec Sargood, Olivia Gallup, Georg Maierhofer +4
Continuous-time generative models, from diffusion models, flow matching, and now flow maps, owe their practical value to high-fidelity generation and steerability. In many applicat…
Normalizing Flow Maps
Francesco M. Ruscio, Timothy James Hitge, Fred Zhangzhi Peng +6
Scalable approaches to learning neural dynamical systems have placed diffusion, flow matching, and few-step generators in flow maps at the forefront of generative modeling. Such st…
Learning smooth dynamics via Kinematic Interpolants
Yiheng, Jiang, Youssef Marzouk +2
Inferring dynamics from real-world experimental data is inherently ambiguous, as many plausible dynamical systems can explain the same positional observations at measurement times.…
Autoregressive Boltzmann Generators
Danyal Rehman, Charlie B. Tan, Yoshua Bengio +2
Efficient sampling of molecular systems at thermodynamic equilibrium is a hallmark challenge in statistical physics. This challenge has driven the development of Boltzmann Generato…
Few-step Cofolding with All-Atom Flow Maps
Gianluca Scarpellini, Ron Shprints, Peter Holderrieth +7
All-atom generative modeling of 3D biomolecular complexes has emerged as the dominant paradigm for predicting the structure of proteins and protein-ligand systems. Generating struc…
Aligning Flow Map Policies with Optimal Q-Guidance
Christos Ziakas, Alessandra Russo, Avishek Joey Bose
Generative policies based on expressive model classes, such as diffusion and flow matching, are well-suited to complex control problems with highly multimodal action distributions.…
Coupling Models for One-Step Discrete Generation
Fred Zhangzhi Peng, Avishek Joey Bose, Anru R. Zhang +1
Generative modeling over discrete structures underpins applications across deep learning, from biological sequence design and code generation to large language models, yet generati…
Learned Relay Representations for Forward-Thinking Discrete Diffusion Models
Benjamin Rozonoyer, Jacopo Minniti, Dhruvesh Patel +4
When Masked Diffusion Models (MDMs) generate sequences through iterative refinement, the rich internal computation over masked positions is discarded, forcing every subsequent refi…
General Multimodal Protein Design Enables DNA-Encoding of Chemistry
Jarrid Rector-Brooks, Théophile Lambert, Marta Skreta +15
Evolution is an extraordinary engine for enzymatic diversity, yet the chemistry it has explored remains a narrow slice of what DNA can encode. Deep generative models can design new…
OXtal: An All-Atom Diffusion Model for Organic Crystal Structure Prediction
Emily Jin, Andrei Cristian Nica, Mikhail Galkin +8
Accurately predicting experimentally realizable 3D molecular crystal structures from their 2D chemical graphs is a long-standing open challenge in computational chemistry called cr…
Curly Flow Matching for Learning Non-gradient Field Dynamics
Katarina Petrović, Lazar Atanackovic, Viggo Moro +5
Modeling the transport dynamics of natural processes from population-level observations is a ubiquitous problem in the natural sciences. Such models rely on key assumptions about t…
Generalised Flow Maps for Few-Step Generative Modelling on Riemannian Manifolds
Oscar Davis, Michael S. Albergo, Nicholas M. Boffi +2
Geometric data and purpose-built generative models on them have become ubiquitous in high-impact deep learning application domains, ranging from protein backbone generation and com…
Planner Aware Path Learning in Diffusion Language Models Training
Fred Zhangzhi Peng, Zachary Bezemek, Jarrid Rector-Brooks +5
Diffusion language models have emerged as a powerful alternative to autoregressive models, enabling fast inference through more flexible and parallel generation paths. This flexibi…
Progressive Inference-Time Annealing of Diffusion Models for Sampling from Boltzmann Densities
Tara Akhound-Sadegh, Jungyoon Lee, Avishek Joey Bose +7
Sampling efficiently from a target unnormalized probability density remains a core challenge, with relevance across countless high-impact scientific applications. A promising appro…
RETRO SYNFLOW: Discrete Flow Matching for Accurate and Diverse Single-Step Retrosynthesis
Robin Yadav, Qi Yan, Guy Wolf +2
A fundamental problem in organic chemistry is identifying and predicting the series of reactions that synthesize a desired target product molecule. Due to the combinatorial nature…
Efficient Regression-Based Training of Normalizing Flows for Boltzmann Generators
Danyal Rehman, Oscar Davis, Jiarui Lu +5
Simulation-free training frameworks have been at the forefront of the generative modelling revolution in continuous spaces, leading to large-scale diffusion and flow matching model…
Scalable Equilibrium Sampling with Sequential Boltzmann Generators
Charlie B. Tan, Avishek Joey Bose, Chen Lin +3
Scalable sampling of molecular states in thermodynamic equilibrium is a long-standing challenge in statistical physics. Boltzmann generators tackle this problem by pairing normaliz…
The Superposition of Diffusion Models Using the Itô Density Estimator
Marta Skreta, Lazar Atanackovic, Avishek Joey Bose +2
The Cambrian explosion of easily accessible pre-trained diffusion models suggests a demand for methods that combine multiple different pre-trained diffusion models without incurrin…
Predicting Drug Effects from High-Dimensional, Asymmetric Drug Datasets by Using Graph Neural Networks: A Comprehensive Analysis of Multitarget Drug Effect Prediction
Avishek Bose, Guojing Cong
Graph neural networks (GNNs) have emerged as one of the most effective ML techniques for drug effect prediction from drug molecular graphs. Despite having immense potential, GNN mo…
Steering Masked Discrete Diffusion Models via Discrete Denoising Posterior Prediction
Jarrid Rector-Brooks, Mohsin Hasan, Zhangzhi Peng +9
Generative modeling of discrete data underlies important applications spanning text-based agents like ChatGPT to the design of the very building blocks of life in protein sequences…
Sequence-Augmented SE(3)-Flow Matching For Conditional Protein Backbone Generation
Guillaume Huguet, James Vuckovic, Kilian Fatras +9
Proteins are essential for almost all biological processes and derive their diverse functions from complex 3D structures, which are in turn determined by their amino acid sequences…
Metric Flow Matching for Smooth Interpolations on the Data Manifold
Kacper Kapuśniak, Peter Potaptchik, Teodora Reu +5
Matching objectives underpin the success of modern generative models and rely on constructing conditional paths that transform a source distribution into a target distribution. Des…
Fisher Flow Matching for Generative Modeling over Discrete Data
Oscar Davis, Samuel Kessler, Mircea Petrache +3
Generative modeling over discrete data has recently seen numerous success stories, with applications spanning language modeling, biological sequence design, and graph-structured mo…
Iterated Denoising Energy Matching for Sampling from Boltzmann Densities
Tara Akhound-Sadegh, Jarrid Rector-Brooks, Avishek Joey Bose +9
Efficiently generating statistically independent samples from an unnormalized probability distribution, such as equilibrium samples of many-body systems, is a foundational problem…