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Himanshu Asnani

7 accepted papers

2021

FlexAE: flexibly learning latent priors for wasserstein auto-encoders

UAI 2021poster

Auto-Encoder (AE) based neural generative frameworks model the joint-distribution between the data and the latent space using an Encoder-Decoder pair, with regularization imposed in terms of a prior over the latent space. Despite their advantages, such as stability in training, efficient inference,…

Cited by 10SourcePDFScholar
2020

C-MI-GAN : Estimation of Conditional Mutual Information using MinMax formulation

UAI 2020poster

Estimation of information theoretic quantities such as mutual information and its conditional variant has drawn interest in recent times owing to their multifaceted applications. Newly proposed neural estimators for these quantities have overcome severe drawbacks of classical $k$NN-based estimators…

Cited by 17SourcePDFScholar
2020

MaskAAE: Latent space optimization for Adversarial Auto-Encoders

UAI 2020poster

The field of neural generative models is dominated by the highly successful Generative Adversarial Networks (GANs) despite their challenges, such as training instability and mode collapse. Auto-Encoders (AE) with regularized latent space provide an alternative framework for generative models, albeit…

Cited by 18SourcePDFScholar
2019

CCMI : Classifier based Conditional Mutual Information Estimation

UAI 2019poster

Conditional Mutual Information (CMI) is a measure of conditional dependence between random variables X and Y, given another random variable Z. It can be used to quantify conditional dependence among variables in many data-driven inference problems such as graphical models, causal learning, feature s…

2019

Turbo Autoencoder: Deep learning based channel codes for point-to-point communication channels

NeurIPS 2019poster

Designing codes that combat the noise in a communication medium has remained a significant area of research in information theory as well as wireless communications. Asymptotically optimal channel codes have been developed by mathematicians for communicating under canonical models after over 60 year…

2018

Estimators for Multivariate Information Measures in General Probability Spaces

NeurIPS 2018poster

Information theoretic quantities play an important role in various settings in machine learning, including causality testing, structure inference in graphical models, time-series problems, feature selection as well as in providing privacy guarantees. A key quantity of interest is the mutual informat…

Cited by 19SourcePDFScholar