Anirudh Goyal
TitleCited byYear
An actor-critic algorithm for sequence prediction
D Bahdanau, P Brakel, K Xu, A Goyal, R Lowe, J Pineau, A Courville, ...
arXiv preprint arXiv:1607.07086, 2016
Professor forcing: A new algorithm for training recurrent networks
A Goyal, A Lamb, Y Zhang, S Zhang, AC Courville, Y Bengio
Advances In Neural Information Processing Systems, 4601-4609, 2016
Zoneout: Regularizing rnns by randomly preserving hidden activations
D Krueger, T Maharaj, J Kramár, M Pezeshki, N Ballas, NR Ke, A Goyal, ...
arXiv preprint arXiv:1606.01305, 2016
Z-forcing: Training stochastic recurrent networks
A Goyal, A Sordoni, MA Côté, NR Ke, Y Bengio
Advances in neural information processing systems, 6713-6723, 2017
Sparse Attentive Backtracking: Temporal credit assignment through reminding
NR Ke, A Goyal, O Bilaniuk, J Binas, MC Mozer, C Pal, Y Bengio
Advances in Neural Information Processing Systems, 7651-7662, 2018
InfoBot: Transfer and Exploration via the Information Bottleneck
A Goyal, R Islam, D Strouse, Z Ahmed, M Botvinick, H Larochelle, ...
ICLR'19, 2019
Recall traces: Backtracking models for efficient reinforcement learning
A Goyal, P Brakel, W Fedus, T Lillicrap, S Levine, H Larochelle, Y Bengio
ICLR'19, 2018
State-reification networks: Improving generalization by modeling the distribution of hidden representations
A Lamb, J Binas, A Goyal, S Subramanian, I Mitliagkas, D Kazakov, ...
arXiv preprint arXiv:1905.11382, 2019
A meta-transfer objective for learning to disentangle causal mechanisms
Y Bengio, T Deleu, N Rahaman, R Ke, S Lachapelle, O Bilaniuk, A Goyal, ...
arXiv preprint arXiv:1901.10912, 2019
Variational walkback: Learning a transition operator as a stochastic recurrent net
A Goyal, NR Ke, S Ganguli, Y Bengio
Advances in Neural Information Processing Systems, 4392-4402, 2017
Extending the Framework of Equilibrium Propagation to General Dynamics
B Scellier, A Goyal, J Binas, T Mesnard, Y Bengio
Actual: Actor-critic under adversarial learning
A Goyal, NR Ke, A Lamb, RD Hjelm, C Pal, J Pineau, Y Bengio
arXiv preprint arXiv:1711.04755, 2017
Learning Dynamics Model in Reinforcement Learning by Incorporating the Long Term Future
NR Ke, A Singh, A Touati, A Goyal, Y Bengio, D Parikh, D Batra
arXiv preprint arXiv:1903.01599, 2019
Maximum Entropy Generators for Energy-Based Models
R Kumar, A Goyal, A Courville, Y Bengio
arXiv preprint arXiv:1901.08508, 2019
Reinforcement Learning with Competitive Ensembles of Information-Constrained Primitives
A Goyal, S Sodhani, J Binas, XB Peng, S Levine, Y Bengio
arXiv preprint arXiv:1906.10667, 2019
Learning Powerful Policies by Using Consistent Dynamics Model
S Sodhani, A Goyal, T Deleu, Y Bengio, S Levine, J Tang
arXiv preprint arXiv:1906.04355, 2019
Learning Neural Causal Models from Unknown Interventions
NR Ke, O Bilaniuk, A Goyal, S Bauer, H Larochelle, C Pal, Y Bengio
arXiv preprint arXiv:1910.01075, 2019
Recurrent Independent Mechanisms
A Goyal, A Lamb, J Hoffmann, S Sodhani, S Levine, Y Bengio, ...
arXiv preprint arXiv:1909.10893, 2019
Improved training of generative models
A Goyal
Communication Topologies Between Learning Agents in Deep Reinforcement Learning
D Adjodah, D Calacci, A Dubey, A Goyal, P Krafft, E Moro, A Pentland
arXiv preprint arXiv:1902.06740, 2019
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