暂无分享,去创建一个
Razvan Pascanu | Wojciech Czarnecki | Balaji Lakshminarayanan | Siddhant M. Jayakumar | Yunshu Du | Wojciech M. Czarnecki | Razvan Pascanu | Balaji Lakshminarayanan | Yunshu Du
[1] A. Goldstein. Cauchy's method of minimization , 1962 .
[2] Ben J. A. Kröse,et al. Learning from delayed rewards , 1995, Robotics Auton. Syst..
[3] Rich Caruana,et al. Multitask Learning , 1998, Encyclopedia of Machine Learning and Data Mining.
[4] Ronald J. Williams,et al. Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning , 2004, Machine Learning.
[5] J.L. Carroll,et al. Task similarity measures for transfer in reinforcement learning task libraries , 2005, Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005..
[6] Peter Stone,et al. Transfer Learning for Reinforcement Learning Domains: A Survey , 2009, J. Mach. Learn. Res..
[7] Alexander J. Smola,et al. Multitask Learning without Label Correspondences , 2010, NIPS.
[8] Qiang Yang,et al. A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.
[9] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[10] Yoshua Bengio,et al. How transferable are features in deep neural networks? , 2014, NIPS.
[11] Eric Eaton,et al. An automated measure of MDP similarity for transfer in reinforcement learning , 2014, AAAI 2014.
[12] Geoffrey E. Hinton,et al. Distilling the Knowledge in a Neural Network , 2015, ArXiv.
[13] Shane Legg,et al. Human-level control through deep reinforcement learning , 2015, Nature.
[14] Michael S. Bernstein,et al. ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.
[15] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.
[16] Marc G. Bellemare,et al. The Arcade Learning Environment: An Evaluation Platform for General Agents , 2012, J. Artif. Intell. Res..
[17] Razvan Pascanu,et al. Policy Distillation , 2015, ICLR.
[18] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[19] John W. Fisher,et al. Dreaming More Data: Class-dependent Distributions over Diffeomorphisms for Learned Data Augmentation , 2015, AISTATS.
[20] Alex Graves,et al. Asynchronous Methods for Deep Reinforcement Learning , 2016, ICML.
[21] Jian Sun,et al. Identity Mappings in Deep Residual Networks , 2016, ECCV.
[22] Demis Hassabis,et al. Mastering the game of Go with deep neural networks and tree search , 2016, Nature.
[23] Kibok Lee,et al. Augmenting Supervised Neural Networks with Unsupervised Objectives for Large-scale Image Classification , 2016, ICML.
[24] Lukasz Kaiser,et al. Attention is All you Need , 2017, NIPS.
[25] Sepp Hochreiter,et al. GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium , 2017, NIPS.
[26] Naftali Tishby,et al. Opening the Black Box of Deep Neural Networks via Information , 2017, ArXiv.
[27] Geoffrey E. Hinton,et al. Regularizing Neural Networks by Penalizing Confident Output Distributions , 2017, ICLR.
[28] Christopher Burgess,et al. beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework , 2016, ICLR 2016.
[29] Yee Whye Teh,et al. Distral: Robust multitask reinforcement learning , 2017, NIPS.
[30] Razvan Pascanu,et al. Learning to Navigate in Complex Environments , 2016, ICLR.
[31] Tom Schaul,et al. Reinforcement Learning with Unsupervised Auxiliary Tasks , 2016, ICLR.
[32] Alec Radford,et al. Proximal Policy Optimization Algorithms , 2017, ArXiv.
[33] Sepp Hochreiter,et al. GANs Trained by a Two Time-Scale Update Rule Converge to a Nash Equilibrium , 2017, ArXiv.
[34] Zhao Chen,et al. GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networks , 2017, ICML.
[35] Shane Legg,et al. IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures , 2018, ICML.
[36] Pericles A. Mitkas,et al. Deep Reinforcement Learning for Doom using Unsupervised Auxiliary Tasks , 2018, ArXiv.
[37] Andrew Zisserman,et al. Kickstarting Deep Reinforcement Learning , 2018, ArXiv.
[38] Roberto Cipolla,et al. Multi-task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[39] Wojciech Czarnecki,et al. Multi-task Deep Reinforcement Learning with PopArt , 2018, AAAI.