3 citations · 3 across the 4 of their papers we have counts for
6 papers
Barron Optimal Transport I: Generative Modeling
Evan Dogariu, Joan Bruna
Motivated by recent applications in generative modeling and sampling, we introduce a framework for optimal measure transport where cost captures the notion of neural network comple…
Generative Modeling from Black-box Corruptions via Self-Consistent Stochastic Interpolants
Chirag Modi, Jiequn Han, Eric Vanden-Eijnden +1
Transport-based methods have emerged as a leading paradigm for building generative models from large, clean datasets. However, in many scientific and engineering domains, clean dat…
Compositional Reasoning with Transformers, RNNs, and Chain of Thought
Gilad Yehudai, Noah Amsel, Joan Bruna
It is well understood that different neural network architectures are suited to different tasks, but is there always a single best architecture for a given task? We compare the exp…
On the Benefits of Rank in Attention Layers
Noah Amsel, Gilad Yehudai, Joan Bruna
Attention-based mechanisms are widely used in machine learning, most prominently in transformers. However, hyperparameters such as the rank of the attention matrices and the number…
Posterior Sampling with Denoising Oracles via Tilted Transport
Joan Bruna, Jiequn Han
Score-based diffusion models have significantly advanced high-dimensional data generation across various domains, by learning a denoising oracle (or score) from datasets. From a Ba…
Stochastic Optimal Control Matching
Carles Domingo-Enrich, Jiequn Han, Brandon Amos +2
Stochastic optimal control, which has the goal of driving the behavior of noisy systems, is broadly applicable in science, engineering and artificial intelligence. Our work introdu…