1.3k citations · 4.9k across the 150 of their papers we have counts for
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Semi-Supervised Learning with the Deep Rendering Mixture Model
Tan Nguyen, Wanjia Liu, Ethan Perez +2
Semi-supervised learning algorithms reduce the high cost of acquiring labeled training data by using both labeled and unlabeled data during learning. Deep Convolutional Networks (D…
A Probabilistic Framework for Deep Learning
Ankit B. Patel, Tan Nguyen, Richard G. Baraniuk
We develop a probabilistic framework for deep learning based on the Deep Rendering Mixture Model (DRMM), a new generative probabilistic model that explicitly capture variations in…
One-Bit Compressive Sensing of Dictionary-Sparse Signals
Rich Baraniuk, Simon Foucart, Deanna Needell +2
One-bit compressive sensing has extended the scope of sparse recovery by showing that sparse signals can be accurately reconstructed even when their linear measurements are subject…
Near-Isometric Binary Hashing for Large-scale Datasets
Amirali Aghazadeh, Andrew Lan, Anshumali Shrivastava +1
We develop a scalable algorithm to learn binary hash codes for indexing large-scale datasets. Near-isometric binary hashing (NIBH) is a data-dependent hashing scheme that quantizes…