Search Results for author: Daniel Holtmann-Rice

Found 4 papers, 1 papers with code

Loss Decomposition for Fast Learning in Large Output Spaces

no code implementations ICML 2018 Ian En-Hsu Yen, Satyen Kale, Felix Yu, Daniel Holtmann-Rice, Sanjiv Kumar, Pradeep Ravikumar

For problems with large output spaces, evaluation of the loss function and its gradient are expensive, typically taking linear time in the size of the output space.

Word Embeddings

Learning a Compressed Sensing Measurement Matrix via Gradient Unrolling

1 code implementation26 Jun 2018 Shanshan Wu, Alexandros G. Dimakis, Sujay Sanghavi, Felix X. Yu, Daniel Holtmann-Rice, Dmitry Storcheus, Afshin Rostamizadeh, Sanjiv Kumar

Our experiments show that there is indeed additional structure beyond sparsity in the real datasets; our method is able to discover it and exploit it to create excellent reconstructions with fewer measurements (by a factor of 1. 1-3x) compared to the previous state-of-the-art methods.

Extreme Multi-Label Classification Multi-Label Classification +1

Lattice Rescoring Strategies for Long Short Term Memory Language Models in Speech Recognition

no code implementations15 Nov 2017 Shankar Kumar, Michael Nirschl, Daniel Holtmann-Rice, Hank Liao, Ananda Theertha Suresh, Felix Yu

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 recognition tasks.

speech-recognition Speech Recognition

Orthogonal Random Features

no code implementations NeurIPS 2016 Felix X. Yu, Ananda Theertha Suresh, Krzysztof Choromanski, Daniel Holtmann-Rice, Sanjiv Kumar

We present an intriguing discovery related to Random Fourier Features: in Gaussian kernel approximation, replacing the random Gaussian matrix by a properly scaled random orthogonal matrix significantly decreases kernel approximation error.

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