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Learning a Compressed Sensing Measurement Matrix via Gradient Unrolling
Shanshan Wu, Alexandros G. Dimakis, Sujay Sanghavi +5
Linear encoding of sparse vectors is widely popular, but is commonly data-independent -- missing any possible extra (but a priori unknown) structure beyond sparsity. In this paper…
cpSGD: Communication-efficient and differentially-private distributed SGD
Naman Agarwal, Ananda Theertha Suresh, Felix Yu +2
Distributed stochastic gradient descent is an important subroutine in distributed learning. A setting of particular interest is when the clients are mobile devices, where two impor…
Lattice Rescoring Strategies for Long Short Term Memory Language Models in Speech Recognition
Shankar Kumar, Michael Nirschl, Daniel Holtmann-Rice +3
Recurrent neural network (RNN) language models (LMs) and Long Short Term Memory (LSTM) LMs, a variant of RNN LMs, have been shown to outperform traditional N-gram LMs on speech rec…
Compact Nonlinear Maps and Circulant Extensions
Felix X. Yu, Sanjiv Kumar, Henry Rowley +1
Kernel approximation via nonlinear random feature maps is widely used in speeding up kernel machines. There are two main challenges for the conventional kernel approximation method…
Circulant Binary Embedding
Felix X. Yu, Sanjiv Kumar, Yunchao Gong +1
Binary embedding of high-dimensional data requires long codes to preserve the discriminative power of the input space. Traditional binary coding methods often suffer from very high…
On Learning from Label Proportions
Felix X. Yu, Krzysztof Choromanski, Sanjiv Kumar +2
Learning from Label Proportions (LLP) is a learning setting, where the training data is provided in groups, or "bags", and only the proportion of each class in each bag is known. T…