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Neural networks and the bias/variance dilemma
S. Geman, E. Bienenstock, and R. Doursat · 1992
Earlier work this paper cites.
Forward models: Supervised learning with a distal teacher
M. I. Jordan and D. E. Rumelhart · 1992
Earlier work this paper cites.
Controlling the false discovery rate: A practical and powerful approach to multiple testing
Y. Benjamini and Y. Hochberg · 1995
Earlier work this paper cites.
Introduction to Reinforcement Learning
R. S. Sutton and A. G. Barto · 1998
Earlier work this paper cites.
Using confidence bounds for exploitation-exploration trade-offs
P. Auer · 2003
Earlier work this paper cites.
Apprenticeship learning via inverse reinforcement learning
P. Abbeel and A. Y. Ng · 2004
Earlier work this paper cites.
Reducing the dimensionality of data with neural networks
G. E. Hinton and R. R. Salakhutdinov · 2006
Earlier work this paper cites.
Springer Handbook of Robotics
B. Siciliano and O. Khatib · 2007
Earlier work this paper cites.
Core knowledge
E. S. Spelke and K. D. Kinzler · 2007
Earlier work this paper cites.
Introduction to Nonparametric Estimation
A. B. Tsybakov · 2008
Earlier work this paper cites.
Extracting and composing robust features with denoising autoencoders
P. Vincent, H. Larochelle, Y. Bengio, and P.-A. Manzagol · 2008
Earlier work this paper cites.
ImageNet: A Large-Scale Hierarchical Image Database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
Earlier work this paper cites.
Intrinsic shape signatures: A shape descriptor for 3d object recognition
Y. Zhong · 2009
Earlier work this paper cites.
A theory of learning from different domains
S. Ben-David, J. Blitzer, K. Crammer, A. Kulesza, F. Pereira, and J. Vaughan · 2010
Earlier work this paper cites.
Analysis of thompson sampling for the multi-armed bandit problem
S. Agrawal and N. Goyal · 2012
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
Earlier work this paper cites.
Indoor Segmentation and Support Inference from RGBD Images
N. Silberman, D. Hoiem, P. Kohli, and R. Fergus · 2012
Earlier work this paper cites.
Unsupervised discovery of mid-level discriminative patches
S. Singh, A. Gupta, and A. A. Efros · 2012
Earlier work this paper cites.
Representation learning: A review and new perspectives
Y. Bengio, A. Courville, and P. Vincent · 2013
Earlier work this paper cites.
Auto-encoding variational bayes
D. P. Kingma and M. Welling · 2013
Earlier work this paper cites.
Playing atari with deep reinforcement learning
V. Mnih, K. Kavukcuoglu, D. Silver, A. Graves, I. Antonoglou, D. Wierstra, and M. A. Riedmiller · 2013
Earlier work this paper cites.
Intriguing properties of neural networks
C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. J. Goodfellow, and R. Fergus · 2013
Earlier work this paper cites.
Depth map prediction from a single image using a multi-scale deep network
D. Eigen, C. Puhrsch, and R. Fergus · 2014
Earlier work this paper cites.
Deep domain confusion: Maximizing for domain invariance
E. Tzeng, J. Hoffman, N. Zhang, K. Saenko, and T. Darrell · 2014
Earlier work this paper cites.
Unsupervised visual representation learning by context prediction
C. Doersch, A. Gupta, and A. A. Efros · 2015
Earlier work this paper cites.
R. B. Girshick · 2015
Earlier work this paper cites.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2015
Earlier work this paper cites.
End-to-end training of deep visuomotor policies
S. Levine, C. Finn, T. Darrell, and P. Abbeel · 2015
Earlier work this paper cites.
End-to-end training of deep visuomotor policies
S. Levine, C. Finn, T. Darrell, and P. Abbeel · 2015
Earlier work this paper cites.
Learning transferable features with deep adaptation networks
M. Long and J. Wang · 2015
Earlier work this paper cites.
Human-level control through deep reinforcement learning
V. Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness, M. G. Bellemare, A. Graves, M. Riedmiller, A. K. Fidjeland, G. Ostrovski, S. Petersen, C. Beattie, A. Sadik, I. Antonoglou, H. King, D. Kumaran, D. Wierstra, S. Legg, and D. Hassabis · 2015
Earlier work this paper cites.
Understanding mid-level representations in visual processing
J. W. Peirce · 2015
Cited alongside, same era.
High-dimensional continuous control using generalized advantage estimation
J. Schulman, P. Moritz, S. Levine, M. I. Jordan, and P. Abbeel · 2015
Cited alongside, same era.
Simultaneous deep transfer across domains and tasks
E. Tzeng, J. Hoffman, T. Darrell, and K. Saenko · 2015
Cited alongside, same era.
Unsupervised learning of visual representations using videos
X. Wang and A. Gupta · 2015
Cited alongside, same era.
Learning to poke by poking: Experiential learning of intuitive physics
P. Agrawal, A. Nair, P. Abbeel, J. Malik, and S. Levine · 2016
Cited alongside, same era.
R. Rahmatizadeh, P. Abolghasemi, L. Bölöni, and S. Levine · 2017
Later among the works it cites.
Maximum classifier discrepancy for unsupervised domain adaptation
K. Saito, K. Watanabe, Y. Ushiku, and T. Harada · 2017
Later among the works it cites.
Proximal policy optimization algorithms
J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov · 2017
Later among the works it cites.
Adversarial discriminative domain adaptation
E. Tzeng, J. Hoffman, K. Saenko, and T. Darrell · 2017
Later among the works it cites.
Posecnn: A convolutional neural network for 6d object pose estimation in cluttered scenes
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J. Donahue, P. Krähenbühl, and T. Darrell · 2016
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Learning to act by predicting the future
A. Dosovitskiy and V. Koltun · 2016
Cited alongside, same era.
A machine learning approach to visual perception of forest trails for mobile robots
A. Giusti, J. Guzzi, D. C. Cireşan, F. He, J. P. Rodríguez, F. Fontana, M. Faessler, C. Forster, J. Schmidhuber, G. D. Caro, D. Scaramuzza, and L. M. Gambardella · 2016
Cited alongside, same era.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Cited alongside, same era.
Vizdoom: A doom-based AI research platform for visual reinforcement learning
M. Kempka, M. Wydmuch, G. Runc, J. Toczek, and W. Jaskowski · 2016
Cited alongside, same era.
Deeper depth prediction with fully convolutional residual networks
I. Laina, C. Rupprecht, V. Belagiannis, F. Tombari, and N. Navab · 2016
Cited alongside, same era.
Building machines that learn and think like people
B. M. Lake, T. D. Ullman, J. B. Tenenbaum, and S. J. Gershman · 2016
Cited alongside, same era.
Y. Xiang, T. Schmidt, V. Narayanan, and D. Fox · 2017
Later among the works it cites.
Places: A 10 million image database for scene recognition
B. Zhou, A. Lapedriza, A. Khosla, A. Oliva, and A. Torralba · 2017
Later among the works it cites.
Visual semantic planning using deep successor representations
Y. Zhu, D. Gordon, E. Kolve, D. Fox, L. Fei-Fei, A. Gupta, R. Mottaghi, and A. Farhadi · 2017
Later among the works it cites.
On evaluation of embodied navigation agents
P. Anderson, A. X. Chang, D. S. Chaplot, A. Dosovitskiy, S. Gupta, V. Koltun, J. Kosecka, J. Malik, R. Mottaghi, M. Savva, and A. R. Zamir · 2018
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Openpose: Realtime multi-person 2d pose estimation using part affinity fields
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On offline evaluation of vision-based driving models
F. Codevilla, A. López, V. Koltun, and A. Dosovitskiy · 2018
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Deep object-centric representations for generalizable robot learning
C. Devin, P. Abbeel, T. Darrell, and S. Levine · 2018
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Neural scene representation and rendering
S. M. A. Eslami, D. Jimenez Rezende, F. Besse, F. Viola, A. S. Morcos, M. Garnelo, A. Ruderman, A. A. Rusu, I. Danihelka, K. Gregor, D. P. Reichert, L. Buesing, T. Weber, O. Vinyals, D. Rosenbaum, N. Rabinowitz, H. King, C. Hillier, M. Botvinick, D. Wierstra, K. Kavukcuoglu, and D. Hassabis · 2018
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Probabilistic Model-Agnostic Meta-Learning
C. Finn, K. Xu, and S. Levine · 2018
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Recasting gradient-based meta-learning as hierarchical bayes
E. Grant, C. Finn, S. Levine, T. Darrell, and T. L. Griffiths · 2018
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Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor
T. Haarnoja, A. Zhou, P. Abbeel, and S. Levine · 2018
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Learning 3d human dynamics from video
A. Kanazawa, J. Zhang, P. Felsen, and J. Malik · 2018
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Bayesian Model-Agnostic Meta-Learning
T. Kim, J. Yoon, O. Dia, S. Kim, Y. Bengio, and S. Ahn · 2018
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Visual representations for semantic target driven navigation
A. Mousavian, A. Toshev, M. Fiser, J. Kosecka, and J. Davidson · 2018
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On first-order meta-learning algorithms
A. Nichol, J. Achiam, and J. Schulman · 2018
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Moment matching for multi-source domain adaptation
X. Peng, Q. Bai, X. Xia, Z. Huang, K. Saenko, and B. Wang · 2018
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S-RL toolbox: Environments, datasets and evaluation metrics for state representation learning
A. Raffin, A. Hill, R. Traoré, T. Lesort, N. D. Rodríguez, and D. Filliat · 2018
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A general reinforcement learning algorithm that masters chess, shogi, and go through self-play
D. Silver, T. Hubert, J. Schrittwieser, I. Antonoglou, M. Lai, A. Guez, M. Lanctot, L. Sifre, D. Kumaran, T. Graepel, T. Lillicrap, K. Simonyan, and D. Hassabis · 2018
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Universal planning networks: Learning generalizable representations for visuomotor control
A. Srinivas, A. Jabri, P. Abbeel, S. Levine, and C. Finn · 2018
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Representation learning with contrastive predictive coding
A. van den Oord, Y. Li, and O. Vinyals · 2018
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Gibson env: real-world perception for embodied agents
F. Xia, A. R. Zamir, Z.-Y. He, A. Sax, J. Malik, and S. Savarese · 2018
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Visual semantic navigation using scene priors
W. Yang, X. Wang, A. Farhadi, A. Gupta, and R. Mottaghi · 2018
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One-shot hierarchical imitation learning of compound visuomotor tasks
T. Yu, P. Abbeel, S. Levine, and C. Finn · 2018
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Taskonomy: Disentangling task transfer learning
A. R. Zamir, A. Sax, W. B. Shen, L. J. Guibas, J. Malik, and S. Savarese · 2018
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Learning Exploration Policies for Navigation
T. Chen, S. Gupta, and A. Gupta · 2019
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K. Kang, S. Belkhale, G. Kahn, P. Abbeel, and S. Levine · 2019
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