15 citations · 32 across the 23 of their papers we have counts for
11 papers · 1 filter
Continuous and Differentiable Neural Network Representations of the Surfzone Sea-Surface from Lidar Point Clouds
David R. Culver, Matthias Morzfeld, Falk Feddersen
Ocean waves are often analyzed via sea-surface observations. Multi-beam scanning lidar enables two-dimensional measurements of the water surface via sequenced point clouds…
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.…
On the Stability and Convergence of Flow Map Training
Ishan Nath, Tom Stanic, Avishek Joey Bose +2
Flow maps are a recent method of probabilistic generation that amortize diffusion trajectories into one-step samplers, unlocking efficient generation. While progress has been made…
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…