Humans excel at solving a wide variety of challenging problems, from low-level motor control through to high-level cognitive tasks. Our goal at DeepMind is to create artificial agents that can achieve a similar level of performance and generality. Like a human, our agents learn for themselves to achieve successful strategies that lead to the greatest long-term rewards.
Part I: Intuition (you are reading it now) Part II: How Capsules Work Part III: Dynamic Routing Between Capsules Part IV: CapsNet Architecture (coming soon) Quick announcement about our new…
Geoffrey Hinton has finally expressed what many have been uneasy about. In a recent AI conference, Hinton remarked that he was “deeply suspicious” of back-propagation, and said “My view is throw it…
Proceedings of the 1st Annual Conference on Robot Learning on 13-15 November 2017 Published as Volume 78 by the Proceedings of Machine Learning Research on 18 October 2017. Volume Edited by: Sergey Levine Vincent Vanhoucke Ken Goldberg Series Editors: Neil D. Lawrence Mark Reid
Marvin is a deep learning framework designed first and foremost to be hackable. It is naively simple for fast prototyping, uses only basic C/C++, and only calls CUDA and cuDNN as dependencies.
P. Huang, K. Matzen, J. Kopf, N. Ahuja, and J. Huang. (2018)cite arxiv:1804.00650Comment: CVPR 2018. Project page: https://phuang17.github.io/DeepMVS/ Code: https://github.com/phuang17/DeepMVS.
K. Zhang, M. Sun, T. Han, X. Yuan, L. Guo, and T. Liu. (2016)cite arxiv:1608.02908Comment: IEEE Transactions on Circuits and Systems for Video Technology 2017.
S. Levine, P. Pastor, A. Krizhevsky, and D. Quillen. (2016)cite arxiv:1603.02199Comment: This is an extended version of "Learning Hand-Eye Coordination for Robotic Grasping with Large-Scale Data Collection," ISER 2016. Draft modified to correct typo in Algorithm 1 and add a link to the publicly available dataset.
A. Zeng, S. Song, M. Nießner, M. Fisher, J. Xiao, and T. Funkhouser. (2016)cite arxiv:1603.08182Comment: To appear at the Conference on Computer Vision and Pattern Recognition (CVPR) 2017. Project webpage: http://3dmatch.cs.princeton.edu.
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A. Mousavian, D. Anguelov, J. Flynn, and J. Kosecka. (2016)cite arxiv:1612.00496Comment: To appear in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017.
H. Lin, M. Tegmark, and D. Rolnick. (2016)cite arxiv:1608.08225Comment: Replaced to match version published in Journal of Statistical Physics: https://link.springer.com/article/10.1007/s10955-017-1836-5 Improved refs & discussion, typos fixed. 16 pages, 3 figs.
V. Patraucean, A. Handa, and R. Cipolla. (2015)cite arxiv:1511.06309Comment: The experiments section has been extended and a direct application to weakly-supervised video segmentation through label propagation has been included.