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Metric Learning with Adaptive Density Discrimination

• Oren Rippel, Manohar Paluri, Piotr Dollár, Lubomir D. Bourdev
• ArXiv
• 2015

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 models offer a number of compelling benefits, it has been difficult for these to compete with modern classification algorithms in performance and even in feature extraction. In this… (More)

Spectral Representations for Convolutional Neural Networks

• Oren Rippel, Jasper Snoek, Ryan P. Adams
• NIPS
• 2015

Discrete Fourier transforms provide a significant speedup in the computation of con-volutions in deep learning. In this work, we demonstrate that, beyond its advantages for efficient computation, the spectral domain also provides a powerful representation in which to model and train convolutional neural networks (CNNs). We employ spectral representations to… (More)

Learning Ordered Representations with Nested Dropout

• Oren Rippel, Michael A. Gelbart, Ryan P. Adams
• ICML
• 2014

In this paper, we present results on ordered representations of data in which different dimensions have different degrees of importance. To learn these representations we introduce nested dropout, a procedure for stochastically removing coherent nested sets of hidden units in a neural network. We first present a sequence of theoretical results for the… (More)

Avoiding pathologies in very deep networks

• David K. Duvenaud, Oren Rippel, Ryan P. Adams, Zoubin Ghahramani
• AISTATS
• 2014

Choosing appropriate architectures and regular-ization strategies for deep networks is crucial to good predictive performance. To shed light on this problem, we analyze the analogous problem of constructing useful priors on compositions of functions. Specifically, we study the deep Gaussian process, a type of infinitely-wide, deep neural network. We show… (More)

High-Dimensional Probability Estimation with Deep Density Models

• Oren Rippel, Ryan P. Adams
• ArXiv
• 2013

One of the fundamental problems in machine learning is the estimation of a probability distribution from data. Many techniques have been proposed to study the structure of data, most often building around the assumption that observations lie on a lower-dimensional manifold of high probability. It has been more difficult, however, to exploit this insight to… (More)

Scalable Bayesian Optimization Using Deep Neural Networks

• Jasper Snoek, Oren Rippel, +6 authors Ryan P. Adams
• ICML
• 2015

Bayesian optimization is an effective methodology for the global optimization of functions with expensive evaluations. It relies on querying a distribution over functions defined by a relatively cheap surrogate model. An accurate model for this distribution over functions is critical to the effectiveness of the approach, and is typically fit using Gaussian… (More)

Real-Time Adaptive Image Compression

• Oren Rippel, Lubomir D. Bourdev
• ICML
• 2017

Streaming of digital media makes 70% of internet traffic, and is projected to reach 80% by 2020 (CIS, 2015). However, it has been challenging for existing commercial compression algorithms to adapt to the growing demand and the changing landscape of requirements and applications. While digital media are transmitted in a wide variety of settings, the… (More)

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