154 citations · 374 across the 20 of their papers we have counts for
21 papers · 1 filter
TaskMet: Task-Driven Metric Learning for Model Learning
Dishank Bansal, Ricky T. Q. Chen, Mustafa Mukadam +1
Deep learning models are often deployed in downstream tasks that the training procedure may not be aware of. For example, models solely trained to achieve accurate predictions may…
On Kinetic Optimal Probability Paths for Generative Models
Neta Shaul, Ricky T. Q. Chen, Maximilian Nickel +2
Recent successful generative models are trained by fitting a neural network to an a-priori defined tractable probability density path taking noise to training examples. In this pap…
Multisample Flow Matching: Straightening Flows with Minibatch Couplings
Aram-Alexandre Pooladian, Heli Ben-Hamu, Carles Domingo-Enrich +3
Simulation-free methods for training continuous-time generative models construct probability paths that go between noise distributions and individual data samples. Recent works, su…
Distributional GFlowNets with Quantile Flows
Dinghuai Zhang, Ling Pan, Ricky T. Q. Chen +2
Generative Flow Networks (GFlowNets) are a new family of probabilistic samplers where an agent learns a stochastic policy for generating complex combinatorial structure through a s…
Flow Matching on General Geometries
Ricky T. Q. Chen, Yaron Lipman
We propose Riemannian Flow Matching (RFM), a simple yet powerful framework for training continuous normalizing flows on manifolds. Existing methods for generative modeling on manif…
Latent Discretization for Continuous-time Sequence Compression
Ricky T. Q. Chen, Matthew Le, Matthew Muckley +2
Neural compression offers a domain-agnostic approach to creating codecs for lossy or lossless compression via deep generative models. For sequence compression, however, most deep s…