Oren Rippel

Suggest Changes
Yoshua Bengio15
Geoffrey E. Hinton12
Li Fei-Fei11
Ryan P. Adams10
Sean Ma10
10Ryan P. Adams
7Sandeep Robert Datta
6Zoubin Ghahramani
5Seungryul Baek
5Kwang In Kim

Filter Results:

  • Full text PDF available (7)

Publication Year

2013
2017
  • This year (1)
  • Last five years (7)

Publication Type

Co-author

Publication Venue

Data Set Used

Key Phrases

Learn More
Metric Learning with Adaptive Density Discrimination
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
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
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)
High-Dimensional Probability Estimation with Deep Density Models
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
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)
Avoiding pathologies in very deep networks
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)
  • ‹
  • 1
  • ›