High-Dimensional Probability Estimation with Deep Density Models - Oren Rippel; Ryan Prescott Adams (CoRR, 2013)

Avoiding pathologies in very deep networks. - David Duvenaud; Oren Rippel; Ryan P. Adams; Zoubin Ghahramani (AISTATS, 2014)

Learning Ordered Representations with Nested Dropout. - Oren Rippel; Michael A. Gelbart; Ryan P. Adams (ICML, 2014)

Scalable Bayesian Optimization Using Deep Neural Networks. - Jasper Snoek; Oren Rippel; Kevin Swersky; Ryan Kiros; Nadathur Satish; Narayanan Sundaram; Md. Mostofa Ali Patwary; Prabhat; Ryan P. Adams (ICML, 2015)

Spectral Representations for Convolutional Neural Networks. - Oren Rippel; Jasper Snoek; Ryan P. Adams (NIPS, 2015)

Metric Learning with Adaptive Density Discrimination. - Oren Rippel; Manohar Paluri; Piotr Dollár; Lubomir D. Bourdev (ICLR (Poster), 2016)

Real-Time Adaptive Image Compression. - Oren Rippel; Lubomir D. Bourdev (ICML, 2017)

Learned Video Compression. - Oren Rippel; Sanjay Nair; Carissa Lew; Steve Branson; Alexander G. Anderson; Lubomir D. Bourdev (ICCV, 2019)

ELF-VC: Efficient Learned Flexible-Rate Video Coding. - Oren Rippel; Alexander G. Anderson; Kedar Tatwawadi; Sanjay Nair; Craig Lytle; Lubomir D. Bourdev (ICCV, 2021)

An Interactive Annotation Tool for Perceptual Video Compression. - Evgenya Pergament; Pulkit Tandon; Kedar Tatwawadi; Oren Rippel; Lubomir D. Bourdev; Bruno A. Olshausen; Tsachy Weissman; Sachin Katti; Alexander G. Anderson (QoMEX, 2022)