no code implementations • 16 Dec 2019 • Charlie Frogner, Sebastian Claici, Edward Chien, Justin Solomon
We examine the performance of this new formulation on 14 real datasets and find that it often yields effective classifiers with nontrivial performance guarantees in situations where conventional DRL produces neither.
2 code implementations • 8 May 2019 • Charlie Frogner, Farzaneh Mirzazadeh, Justin Solomon
Euclidean embeddings of data are fundamentally limited in their ability to capture latent semantic structures, which need not conform to Euclidean spatial assumptions.
no code implementations • ICLR 2019 • Charlie Frogner, Farzaneh Mirzazadeh, Justin Solomon
Despite their prevalence, Euclidean embeddings of data are fundamentally limited in their ability to capture latent semantic structures, which need not conform to Euclidean spatial assumptions.
no code implementations • 12 Jun 2018 • Charlie Frogner, Tomaso Poggio
We present a novel approximate inference method for diffusion processes, based on the Wasserstein gradient flow formulation of the diffusion.
no code implementations • NeurIPS 2015 • Charlie Frogner, Chiyuan Zhang, Hossein Mobahi, Mauricio Araya-Polo, Tomaso Poggio
In this paper we develop a loss function for multi-label learning, based on the Wasserstein distance.