173 citations · 876 across the 56 of their papers we have counts for
6 papers · 1 filter
Structured Transforms for Small-Footprint Deep Learning
Vikas Sindhwani, Tara N. Sainath, Sanjiv Kumar
We consider the task of building compact deep learning pipelines suitable for deployment on storage and power constrained mobile devices. We propose a unified framework to learn a…
Quasi-Monte Carlo Feature Maps for Shift-Invariant Kernels
Haim Avron, Vikas Sindhwani, Jiyan Yang +1
We consider the problem of improving the efficiency of randomized Fourier feature maps to accelerate training and testing speed of kernel methods on large datasets. These approxima…
High-performance Kernel Machines with Implicit Distributed Optimization and Randomization
Vikas Sindhwani, Haim Avron
In order to fully utilize "big data", it is often required to use "big models". Such models tend to grow with the complexity and size of the training data, and do not make strong p…
Near-separable Non-negative Matrix Factorization with - and Bregman Loss Functions
Abhishek Kumar, Vikas Sindhwani
Recently, a family of tractable NMF algorithms have been proposed under the assumption that the data matrix satisfies a separability condition Donoho & Stodden (2003); Arora et al.…
Fast Conical Hull Algorithms for Near-separable Non-negative Matrix Factorization
Abhishek Kumar, Vikas Sindhwani, Prabhanjan Kambadur
The separability assumption (Donoho & Stodden, 2003; Arora et al., 2012) turns non-negative matrix factorization (NMF) into a tractable problem. Recently, a new class of provably-c…
Scalable Matrix-valued Kernel Learning for High-dimensional Nonlinear Multivariate Regression and Granger Causality
Vikas Sindhwani, Minh Ha Quang, Aurelie C. Lozano
We propose a general matrix-valued multiple kernel learning framework for high-dimensional nonlinear multivariate regression problems. This framework allows a broad class of mixed…