no code implementations • 2 Jul 2023 • Benoit Dherin, Huiyi Hu, Jie Ren, Michael W. Dusenberry, Balaji Lakshminarayanan
We introduce a new deep generative model useful for uncertainty quantification: the Morse neural network, which generalizes the unnormalized Gaussian densities to have modes of high-dimensional submanifolds instead of just discrete points.
1 code implementation • 15 Jul 2022 • Dustin Tran, Jeremiah Liu, Michael W. Dusenberry, Du Phan, Mark Collier, Jie Ren, Kehang Han, Zi Wang, Zelda Mariet, Huiyi Hu, Neil Band, Tim G. J. Rudner, Karan Singhal, Zachary Nado, Joost van Amersfoort, Andreas Kirsch, Rodolphe Jenatton, Nithum Thain, Honglin Yuan, Kelly Buchanan, Kevin Murphy, D. Sculley, Yarin Gal, Zoubin Ghahramani, Jasper Snoek, Balaji Lakshminarayanan
A recent trend in artificial intelligence is the use of pretrained models for language and vision tasks, which have achieved extraordinary performance but also puzzling failures.
no code implementations • ICLR 2022 • Shengyang Sun, Daniele Calandriello, Huiyi Hu, Ang Li, Michalis Titsias
A challenging problem in task-free continual learning is the online selection of a representative replay memory from data streams.
no code implementations • NeurIPS Workshop ImageNet_PPF 2021 • Huiyi Hu, Ang Li, Daniele Calandriello, Dilan Gorur
We present the One Pass ImageNet (OPIN) problem, which aims to study the effectiveness of deep learning in a streaming setting.
no code implementations • 21 Oct 2021 • Seyed Iman Mirzadeh, Arslan Chaudhry, Dong Yin, Huiyi Hu, Razvan Pascanu, Dilan Gorur, Mehrdad Farajtabar
A primary focus area in continual learning research is alleviating the "catastrophic forgetting" problem in neural networks by designing new algorithms that are more robust to the distribution shifts.
no code implementations • ICML Workshop INNF 2021 • Polina Kirichenko, Mehrdad Farajtabar, Dushyant Rao, Balaji Lakshminarayanan, Nir Levine, Ang Li, Huiyi Hu, Andrew Gordon Wilson, Razvan Pascanu
Learning new tasks continuously without forgetting on a constantly changing data distribution is essential for real-world problems but extremely challenging for modern deep learning.
1 code implementation • 5 Dec 2019 • Stanislav Fort, Huiyi Hu, Balaji Lakshminarayanan
One possible explanation for this gap between theory and practice is that popular scalable variational Bayesian methods tend to focus on a single mode, whereas deep ensembles tend to explore diverse modes in function space.
1 code implementation • ICCV 2019 • Ang Li, Huiyi Hu, Piotr Mirowski, Mehrdad Farajtabar
The ability to navigate from visual observations in unfamiliar environments is a core component of intelligent agents and an ongoing challenge for Deep Reinforcement Learning (RL).
no code implementations • 24 Jul 2018 • Timothy A. Mann, Sven Gowal, András György, Ray Jiang, Huiyi Hu, Balaji Lakshminarayanan, Prav Srinivasan
Predicting delayed outcomes is an important problem in recommender systems (e. g., if customers will finish reading an ebook).
4 code implementations • 13 Feb 2017 • Raymond Smith, Chunhui Gu, Dar-Shyang Lee, Huiyi Hu, Ranjith Unnikrishnan, Julian Ibarz, Sacha Arnoud, Sophia Lin
We introduce the French Street Name Signs (FSNS) Dataset consisting of more than a million images of street name signs cropped from Google Street View images of France.
Ranked #3 on Optical Character Recognition (OCR) on FSNS - Test
no code implementations • 15 Jun 2014 • Xavier Bresson, Huiyi Hu, Thomas Laurent, Arthur Szlam, James Von Brecht
In this work we propose a simple and easily parallelizable algorithm for multiway graph partitioning.