289 citations · 1.3k across the 22 of their papers we have counts for
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The Riemannian Geometry of Deep Generative Models
Hang Shao, Abhishek Kumar, P. Thomas Fletcher
Deep generative models learn a mapping from a low dimensional latent space to a high-dimensional data space. Under certain regularity conditions, these models parameterize nonlinea…
BlockDrop: Dynamic Inference Paths in Residual Networks
Zuxuan Wu, Tushar Nagarajan, Abhishek Kumar +4
Very deep convolutional neural networks offer excellent recognition results, yet their computational expense limits their impact for many real-world applications. We introduce Bloc…
Variational Inference of Disentangled Latent Concepts from Unlabeled Observations
Abhishek Kumar, Prasanna Sattigeri, Avinash Balakrishnan
Disentangled representations, where the higher level data generative factors are reflected in disjoint latent dimensions, offer several benefits such as ease of deriving invariant…
SenGen: Sentence Generating Neural Variational Topic Model
Ramesh Nallapati, Igor Melnyk, Abhishek Kumar +1
We present a new topic model that generates documents by sampling a topic for one whole sentence at a time, and generating the words in the sentence using an RNN decoder that is co…
Semi-supervised Learning with GANs: Manifold Invariance with Improved Inference
Abhishek Kumar, Prasanna Sattigeri, P. Thomas Fletcher
Semi-supervised learning methods using Generative Adversarial Networks (GANs) have shown promising empirical success recently. Most of these methods use a shared discriminator/clas…