SivakumarĬontingency-Aware Exploration in Reinforcement Learning Weiwei Kong, Christopher Liaw, Aranyak Mehta, D.
Understanding and Improving Interpolation in Autoencoders via an Adversarial Regularizerĭavid Berthelot, Colin Raffel, Aurko Roy, Ian Goodfellow (no longer at Google)Ī new dog learns old tricks: RL finds classic optimization algorithms Ishaan Gulrajani, Colin Raffel, Luke Metz Towards GAN Benchmarks Which Require Generalization Ting Chen, Mario Lucic, Neil Houlsby, Sylvain Gelly On Self Modulation for Generative Adversarial Networks
Ian Goodfellow (no longer at Google), Jascha Sohl-Dickstein Gamaleldin Elsayed, Ian Goodfellow (no longer at Google), Jascha Sohl-Dickstein Zhenjia Xu, Zhijian Liu, Chen Sun, Kevin Murphy, William Freeman, Joshua B Tenenbaum, Jiajun WuĪdversarial Reprogramming of Neural Networks Unsupervised Discovery of Parts, Structure, and Dynamics The Singular Values of Convolutional Layers Yonglong Tian, Andrew Luo, Xingyuan Sun, Kevin Ellis, William Freeman, Joshua B Tenenbaum, Jiajun Wu Learning to Infer and Execute 3D Shape Programs Yunchao Liu, Zheng Wu, Daniel Ritchie, William Freeman, Joshua B Tenenbaum, Jiajun Wu Learning to Describe Scenes with Programs
Pramod Kaushik Mudrakarta, Mark Sandler, Andrey Zhmoginov, Andrew Howard K for the Price of 1: Parameter-efficient Multi-task and Transfer Learning Jesse Engel, Kumar Krishna Agrawal, Shuo Chen, Ishaan Gulrajani, Chris Donahue, Adam Roberts GANSynth: Adversarial Neural Audio Synthesis Yunbo Wang, Lu Jiang, Ming-Hsuan Yang, Li-Jia Li, Mingsheng Long, Li Fei-Fei Abolafia, Jeffrey Pennington, Jascha Sohl-Dicksteinĭiversity-Sensitive Conditional Generative Adversarial Networksĭingdong Yang, Seunghoon Hong, Yunseok Jang, Tianchen Zhao, Honglak Leeĭiversity and Depth in Per-Example Routing ModelsĮidetic 3D LSTM: A Model for Video Prediction and Beyond Roman Novak, Lechao Xiao, Yasaman Bahri, Jaehoon Lee, Greg Yang, Jiri Hron, Daniel A. Baraniukīayesian Deep Convolutional Networks with Many Channels are Gaussian Processes Luke Metz, Niru Maheswaranathan, Brian Cheung, Jascha Sohl-DicksteinĪ Data-Driven and Distributed Approach to Sparse Signal Representation and RecoveryĪli Mousavi, Gautam Dasarathy, Richard G.
Boards and beyond videos 2019 google drive update#
Meta-Learning Update Rules for Unsupervised Representation Learning Generating High Fidelity Images with Subscale Pixel Networks and Multidimensional UpscalingĮnabling Factorized Piano Music Modeling and Generation with the MAESTRO DatasetĬurtis Hawthorne, Andrew Stasyuk, Adam Roberts, Ian Simon, Anna Huang, Sander Dieleman, Erich Elsen, Jesse Engel, Douglas Eck
Hugo Larochelle, Samy Bengio, Tara SainathĬhelsea Finn, Dale Schuurmans, Dumitru Erhan, Katherine Heller, Lihong Li, Samy Bengio, Rohit Prabhavalkar, Alex Wiltschko, Slav Petrov, George Dahl You can also learn more about our research being presented at ICLR 2019 in the list below (Googlers highlighted in blue). If you are attending ICLR 2019, we hope you'll stop by our booth and chat with our researchers about the projects and opportunities at Google that go into solving interesting problems for billions of people. As Platinum Sponsor of ICLR 2019, Google will have a strong presence with over 200 researchers attending, contributing to and learning from the broader academic research community by presenting papers and posters, in addition to participating on organizing committees and in workshops. ICLR offers conference and workshop tracks, both of which include invited talks along with oral and poster presentations of some of the latest research on deep learning, metric learning, kernel learning, compositional models, non-linear structured prediction and issues regarding non-convex optimization.Īt the forefront of innovation in neural networks and deep learning, Google focuses on on both theory and application, developing learning approaches to understand and generalize. This week, New Orleans, LA hosts the 7th International Conference on Learning Representations ( ICLR 2019), a conference focused on how one can learn meaningful and useful representations of data for machine learning. Posted by Andrew Helton, Editor, Google AI Communications
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