438 citations · 979 across the 11 of their papers we have counts for
7 papers · 1 filter
Real-Time Adaptive Image Compression
Oren Rippel, Lubomir Bourdev
We present a machine learning-based approach to lossy image compression which outperforms all existing codecs, while running in real-time. Our algorithm typically produces files 2.…
Metric Learning with Adaptive Density Discrimination
Oren Rippel, Manohar Paluri, Piotr Dollar +1
Distance metric learning (DML) approaches learn a transformation to a representation space where distance is in correspondence with a predefined notion of similarity. While such mo…
Spectral Representations for Convolutional Neural Networks
Oren Rippel, Jasper Snoek, Ryan P. Adams
Discrete Fourier transforms provide a significant speedup in the computation of convolutions in deep learning. In this work, we demonstrate that, beyond its advantages for efficien…
Scalable Bayesian Optimization Using Deep Neural Networks
Jasper Snoek, Oren Rippel, Kevin Swersky +6
Bayesian optimization is an effective methodology for the global optimization of functions with expensive evaluations. It relies on querying a distribution over functions defined b…
Learning Ordered Representations with Nested Dropout
Oren Rippel, Michael A. Gelbart, Ryan P. Adams
In this paper, we study ordered representations of data in which different dimensions have different degrees of importance. To learn these representations we introduce nested dropo…
Avoiding pathologies in very deep networks
David Duvenaud, Oren Rippel, Ryan P. Adams +1
Choosing appropriate architectures and regularization strategies for deep networks is crucial to good predictive performance. To shed light on this problem, we analyze the analogou…