4 citations · 6 across the 7 of their papers we have counts for
4 papers · 1 filter
Provable benefits of score matching
Chirag Pabbaraju, Dhruv Rohatgi, Anish Sevekari +3
Score matching is an alternative to maximum likelihood (ML) for estimating a probability distribution parametrized up to a constant of proportionality. By fitting the ''score'' of…
Progressive Ensemble Distillation: Building Ensembles for Efficient Inference
Don Kurian Dennis, Abhishek Shetty, Anish Sevekari +2
We study the problem of progressive ensemble distillation: Given a large, pretrained teacher model , we seek to decompose the model into smaller, low-inference cost student mode…
Pitfalls of Gaussians as a noise distribution in NCE
Holden Lee, Chirag Pabbaraju, Anish Sevekari +1
Noise Contrastive Estimation (NCE) is a popular approach for learning probability density functions parameterized up to a constant of proportionality. The main idea is to design a…
Universal Approximation for Log-concave Distributions using Well-conditioned Normalizing Flows
Holden Lee, Chirag Pabbaraju, Anish Sevekari +1
Normalizing flows are a widely used class of latent-variable generative models with a tractable likelihood. Affine-coupling (Dinh et al, 2014-16) models are a particularly common t…