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Large and diverse datasets have been the cornerstones of many impressive advancements in artificial intelligence.
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Slow feature analysis: Unsupervised learning of invariances
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Nearest neighbor estimates of entropy
H. Singh, N. Misra, V. Hnizdo, A. Fedorowicz, and E. Demchuk · 2003
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Reinforcement learming with augmented data
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Training generative adversarial networks with limited data
T. Karras, M. Aittala, J. Hellsten, S. Laine, J. Lehtinen, and T. Aila · 2006
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Speakers optimize information density through syntactic reduction
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Core knowledge
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Learning multiple layers of features from tiny images
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Deep learning from temporal coherence in video
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Reading digits in natural images with unsupervised feature learning
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The ecological approach to visual perception: classic edition
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Generative adversarial nets
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Unsupervised learning of spatiotemporally coherent metrics
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The psychology and neuroscience of curiosity
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Deep learning face attributes in the wild
Z. Liu, P. Luo, X. Wang, and X. Tang · 2015
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Human-level control through deep reinforcement learning
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Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop
F. Yu, A. Seff, Y. Zhang, S. Song, T. Funkhouser, and J. Xiao · 2015
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Learning to poke by poking: Experiential learning of intuitive physics
P. Agrawal, A. Nair, P. Abbeel, J. Malik, and S. Levine · 2016
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Sgdr: Stochastic gradient descent with warm restarts
I. Loshchilov and F. Hutter · 2016
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Supersizing self-supervision: Learning to grasp from 50k tries and 700 robot hours
L. Pinto and A. Gupta · 2016
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Deep reinforcement learning with double q-learning
H. van Hasselt, A. Guez, and D. Silver · 2016
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A baseline for detecting misclassified and out-of-distribution examples in neural networks
D. Hendrycks and K. Gimpel · 2017
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Ai2-thor: An interactive 3d environment for visual ai
E. Kolve, R. Mottaghi, W. Han, E. VanderBilt, L. Weihs, A. Herrasti, D. Gordon, Y. Zhu, A. Gupta, and A. Farhadi · 2017
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Pre-training without natural images
H. Kataoka, K. Okayasu, A. Matsumoto, E. Yamagata, R. Yamada, N. Inoue, A. Nakamura, and Y. Satoh · 2020
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Supervised contrastive learning
P. Khosla, P. Teterwak, C. Wang, A. Sarna, Y. Tian, P. Isola, A. Maschinot, C. Liu, and D. Krishnan · 2020
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Image augmentation is all you need: Regularizing deep reinforcement learning from pixels
I. Kostrikov, D. Yarats, and R. Fergus · 2020
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CURL: contrastive unsupervised representations for reinforcement learning
M. Laskin, A. Srinivas, and P. Abbeel · 2020
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Self-supervised learning through the eyes of a child
A. E. Orhan, V. V. Gupta, and B. M. Lake · 2020
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Curiosity-driven exploration by self-supervised prediction
D. Pathak, P. Agrawal, A. A. Efros, and T. Darrell · 2017
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Domain randomization and generative models for robotic grasping
J. Tobin, W. Zaremba, and P. Abbeel · 2017
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Representation learning with contrastive predictive coding
A. v. d. Oord, Y. Li, and O. Vinyals · 2018
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Time-contrastive networks: Self-supervised learning from video
P. Sermanet, C. Lynch, Y. Chebotar, J. Hsu, E. Jang, S. Schaal, S. Levine, and G. Brain · 2018
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Reinforcement learning: An introduction
R. S. Sutton and A. G. Barto · 2018
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Building generalizable agents with a realistic and rich 3d environment
Y. Wu, Y. Wu, G. Gkioxari, and Y. Tian · 2018
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Large-scale study of curiosity-driven learning
Y. Burda, H. Edwards, D. Pathak, A. J. Storkey, T. Darrell, and A. A. Efros · 2019
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Improved protein structure prediction using potentials from deep learning
A. W. Senior, R. Evans, J. Jumper, J. Kirkpatrick, L. Sifre, T. Green, C. Qin, A. Žídek, A. W. Nelson, A. Bridgland, et al · 2020
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Csi: Novelty detection via contrastive learning on distributionally shifted instances
J. Tack, S. Mo, J. Jeong, and J. Shin · 2020
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Learning to see by looking at noise
M. Baradad, J. Wulff, T. Wang, P. Isola, and A. Torralba · 2021
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Coverage as a principle for discovering transferable behavior in reinforcement learning, 2021
V. Campos, P. Sprechmann, S. S. Hansen, A. Barreto, C. Blundell, A. Vitvitskyi, S. Kapturowski, and A. P. Badia · 2021
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Seal: Self-supervised embodied active learning using exploration and 3d consistency
D. S. Chaplot, M. Dalal, S. Gupta, J. Malik, and R. R. Salakhutdinov · 2021
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Exploring simple siamese representation learning
X. Chen and K. He · 2021
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A large-scale study on unsupervised spatiotemporal representation learning
C. Feichtenhofer, H. Fan, B. Xiong, R. Girshick, and K. He · 2021
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Exploring the limits of out-of-distribution detection
S. Fort, J. Ren, and B. Lakshminarayanan · 2021
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Partial success in closing the gap between human and machine vision
R. Geirhos, K. Narayanappa, B. Mitzkus, T. Thieringer, M. Bethge, F. A. Wichmann, and W. Brendel · 2021
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Improving robustness using generated data
S. Gowal, S.-A. Rebuffi, O. Wiles, F. Stimberg, D. A. Calian, and T. A. Mann · 2021
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Generative models as a data source for multiview representation learning
A. Jahanian, X. Puig, Y. Tian, and P. Isola · 2021
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Highly accurate protein structure prediction with alphafold
J. Jumper, R. Evans, A. Pritzel, T. Green, M. Figurnov, O. Ronneberger, K. Tunyasuvunakool, R. Bates, A. Žídek, A. Potapenko, et al · 2021
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Urlb: Unsupervised reinforcement learning benchmark
M. Laskin, D. Yarats, H. Liu, K. Lee, A. Zhan, K. Lu, C. Cang, L. Pinto, and P. Abbeel · 2021
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Learning transferable visual models from natural language supervision
A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark, G. Krueger, and I. Sutskever · 2021
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Decoupling representation learning from reinforcement learning
A. Stooke, K. Lee, P. Abbeel, and M. Laskin · 2021
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Habitat 2.0: Training home assistants to rearrange their habitat
A. Szot, A. Clegg, E. Undersander, E. Wijmans, Y. Zhao, J. Turner, N. Maestre, M. Mukadam, D. S. Chaplot, O. Maksymets, et al · 2021
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Reinforcement learning with prototypical representations
D. Yarats, R. Fergus, A. Lazaric, and L. Pinto · 2021
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Mastering atari games with limited data
W. Ye, S. Liu, T. Kurutach, P. Abbeel, and Y. Gao · 2021
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Hierarchical text-conditional image generation with clip latents
A. Ramesh, P. Dhariwal, A. Nichol, C. Chu, and M. Chen · 2022
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