Talk:Timeline of OpenAI
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| Year | Month and date | Domain | Event type | Details |
|---|---|---|---|---|
| 2016 | May 25 | Publication | "Adversarial Training Methods for Semi-Supervised Text Classification" is submitted to the ArXiv. The paper proposes a method that achieves better results on multiple benchmark semi-supervised and purely supervised tasks.[1] | |
| 2016 | June 21 | Publication | "Concrete Problems in AI Safety" is submitted to the arXiv. The paper explores practical problems in machine learning systems.[2] | |
| 2016 | October 11 | Publication | "Transfer from Simulation to Real World through Learning Deep Inverse Dynamics Model", a paper on robotics, is submitted to the ArXiv. It investigates settings where the sequence of states traversed in simulation remains reasonable for the real world.[3] | |
| 2016 | October 18 | Publication | "Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data", a paper on safety, is submitted to the ArXiv. It shows an approach to providing strong privacy guarantees for training data: Private Aggregation of Teacher Ensembles (PATE).[4] | |
| 2016 | November 2 | Publication | "Extensions and Limitations of the Neural GPU" is first submitted to the ArXiv. The paper shows that there are two simple ways of improving the performance of the Neural GPU: by carefully designing a curriculum, and by increasing model size.[5] | |
| 2016 | November 8 | Publication | "Variational Lossy Autoencoder", a paper on generative models, is submitted to the ArXiv. It presents a method to learn global representations by combining Variational Autoencoder (VAE) with neural autoregressive models.[6] | |
| 2016 | November 9 | Publication | "RL2: Fast Reinforcement Learning via Slow Reinforcement Learning", a paper on reinforcement learning, is first submitted to the ArXiv. It seeks to bridge the gap in number of trials between the machine learning process which requires a huge number of trials, and animals which can learn new tasks in just a few trials, benefiting from their prior knowledge about the world.[7] | |
| 2016 | November 11 | Publication | "A Connection between Generative Adversarial Networks, Inverse Reinforcement Learning, and Energy-Based Models", a paper on generative models, is first submitted to the ArXiv.[8] | |
| 2016 | November 14 | Publication | "On the Quantitative Analysis of Decoder-Based Generative Models", a paper on generative models, is submitted to the ArXiv. It introduces a technique to analyze the performance of decoder-based models.[9] | |
| 2016 | November 15 | Publication | "#Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning", a paper on reinforcement learning, is first submitted to the ArXiv.[10] | |
| 2017 | January 19 | Publication | "PixelCNN++: Improving the PixelCNN with Discretized Logistic Mixture Likelihood and Other Modifications", a paper on generative models, is submitted to the ArXiv.[11] | |
| 2017 | February 8 | Publication | "Adversarial Attacks on Neural Network Policies" is submitted to the ArXiv. The paper shows that adversarial attacks are effective when targeting neural network policies in reinforcement learning.[12] | |
| 2017 | March 6 | Publication | "Third-Person Imitation Learning", a paper on robotics, is submitted to the ArXiv. It presents a method for unsupervised third-person imitation learning.[13] | |
| 2017 | March 10 | Publication | "Evolution Strategies as a Scalable Alternative to Reinforcement Learning" is submitted to the ArXiv. It explores the use of Evolution Strategies (ES), a class of black box optimization algorithms.[14] | |
| 2017 | March 12 | Publication | "Prediction and Control with Temporal Segment Models", a paper on generative models, is first submitted to the ArXiv. It introduces a method for learning the dynamics of complex nonlinear systems based on deep generative models over temporal segments of states and actions.[15] | |
| 2017 | March 15 | Publication | "Emergence of Grounded Compositional Language in Multi-Agent Populations" is first submitted to ArXiv. The paper proposes a multi-agent learning environment and learning methods that bring about emergence of a basic compositional language.[16] | |
| 2017 | March 20 | Publication | "Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World", a paper on robotics, is subitted to the ArXiv. It explores domain randomization, a simple technique for training models on simulated images that transfer to real images by randomizing rendering in the simulator.[17] | |
| 2017 | March 21 | Publication | "One-Shot Imitation Learning", a paper on robotics, is first submitted to the ArXiv. The paper proposes a meta-learning framework for optimizing imitation learning.[18] | |
| 2017 | June 7 | Publication | "Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments" is submitted to the ArXiv. The paper explores deep reinforcement learning methods for multi-agent domains.[19] | |
| 2017 | September 13 | Reinforcement learning | Publication | "Learning with Opponent-Learning Awareness" is first uploaded to the ArXiv. The paper presents Learning with Opponent-Learning Awareness (LOLA), a method in which each agent shapes the anticipated learning of the other agents in an environment.[20][21] |
| 2017 | October 17 | Robotics | Publication | "Domain Randomization and Generative Models for Robotic Grasping", a paper on robotics, is first submitted to the ArXiv. It explores a novel data generation pipeline for training a deep neural network to perform grasp planning that applies the idea of domain randomization to object synthesis.[22] |
| 2017 | October 18 | Publication | "Sim-to-Real Transfer of Robotic Control with Dynamics Randomization", a paper on robotics, is first submitted to ArXiv. It describes a solution for strategies that are successful in simulation but may not transfer to their real world counterparts due to modeling error.[23] | |
| 2017 | October 26 | Publication | "Meta Learning Shared Hierarchies", a paper on reinforcement learning, is submitted to the ArXiv. The paper describes the development of a metalearning approach for learning hierarchically structured policies, improving sample efficiency on unseen tasks through the use of shared primitives.[24] | |
| 2017 | October 31 | Publication | "Backpropagation through the Void: Optimizing control variates for black-box gradient estimation", a paper on reinforcement learning, is first submitted to the ArXiv. It introduces a general framework for learning low-variance, unbiased gradient estimators for black-box functions of random variables.[25] | |
| 2017 | November 2 | Publication | "Interpretable and Pedagogical Examples", a paper on language, is first submitted to the ArXiv. It shows that training the student and teacher iteratively, rather than jointly, can produce interpretable teaching strategies.[26] |
- ↑ Miyato, Takeru; Dai, Andrew M.; Goodfellow, Ian. "Adversarial Training Methods for Semi-Supervised Text Classification". arxiv.org. Retrieved 28 March 2020.
- ↑ "[1606.06565] Concrete Problems in AI Safety". June 21, 2016. Retrieved July 25, 2017.
- ↑ Christiano, Paul; Shah, Zain; Mordatch, Igor; Schneider, Jonas; Blackwell, Trevor; Tobin, Joshua; Abbeel, Pieter; Zaremba, Wojciech. "Transfer from Simulation to Real World through Learning Deep Inverse Dynamics Model". arxiv.org. Retrieved 28 March 2020.
- ↑ Papernot, Nicolas; Abadi, Martín; Erlingsson, Úlfar; Goodfellow, Ian; Talwar, Kunal. "Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data". arxiv.org. Retrieved 28 March 2020.
- ↑ Price, Eric; Zaremba, Wojciech; Sutskever, Ilya. "Extensions and Limitations of the Neural GPU". arxiv.org. Retrieved 28 March 2020.
- ↑ Chen, Xi; Kingma, Diederik P.; Salimans, Tim; Duan, Yan; Dhariwal, Prafulla; Schulman, John; Sutskever, Ilya; Abbeel, Pieter. "Variational Lossy Autoencoder". arxiv.org.
- ↑ Duan, Yan; Schulman, John; Chen, Xi; Bartlett, Peter L.; Sutskever, Ilya; Abbeel, Pieter. "RL2: Fast Reinforcement Learning via Slow Reinforcement Learning". arxiv.org. Retrieved 28 March 2020.
- ↑ Finn, Chelsea; Christiano, Paul; Abbeel, Pieter; Levine, Sergey. "A Connection between Generative Adversarial Networks, Inverse Reinforcement Learning, and Energy-Based Models". arxiv.org. Retrieved 28 March 2020.
- ↑ Wu, Yuhuai; Burda, Yuri; Salakhutdinov, Ruslan; Grosse, Roger. "On the Quantitative Analysis of Decoder-Based Generative Models". arxiv.org. Retrieved 28 March 2020.
- ↑ "#Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning". arxiv.org. Retrieved 28 March 2020.
- ↑ Salimans, Tim; Karpathy, Andrej; Chen, Xi; Kingma, Diederik P. "PixelCNN++: Improving the PixelCNN with Discretized Logistic Mixture Likelihood and Other Modifications". arxiv.org. Retrieved 28 March 2020.
- ↑ Huang, Sandy; Papernot, Nicolas; Goodfellow, Ian; Duan, Yan; Abbeel, Pieter. "Adversarial Attacks on Neural Network Policies". arxiv.org. Retrieved 28 March 2020.
- ↑ Stadie, Bradly C.; Abbeel, Pieter; Sutskever, Ilya. "arxiv.org". arxiv.org. Retrieved 28 March 2020.
- ↑ Salimans, Tim; Ho, Jonathan; Chen, Xi; Sidor, Szymon; Sutskever, Ilya. "Evolution Strategies as a Scalable Alternative to Reinforcement Learning". arxiv.org. Retrieved 28 March 2020.
- ↑ Mishra, Nikhil; Abbeel, Pieter; Mordatch, Igor. "Prediction and Control with Temporal Segment Models". arxiv.org. Retrieved 28 March 2020.
- ↑ Mordatch, Igor; Abbeel, Pieter. "Emergence of Grounded Compositional Language in Multi-Agent Populations". arxiv.org. Retrieved 26 March 2020.
- ↑ Tobin, Josh; Fong, Rachel; Ray, Alex; Schneider, Jonas; Zaremba, Wojciech; Abbeel, Pieter. "Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World". arxiv.org. Retrieved 28 March 2020.
- ↑ "One-Shot Imitation Learning". arxiv.org. Retrieved 28 March 2020.
- ↑ "Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments". arxiv.org.
- ↑ "[1709.04326] Learning with Opponent-Learning Awareness". Retrieved March 2, 2018.
- ↑ gwern (August 16, 2017). "September 2017 news - Gwern.net". Retrieved March 2, 2018.
- ↑ "Domain Randomization and Generative Models for Robotic Grasping". arxiv.org. Retrieved 27 March 2020.
- ↑ Bin Peng, Xue; Andrychowicz, Marcin; Zaremba, Wojciech; Abbeel, Pieter. "Sim-to-Real Transfer of Robotic Control with Dynamics Randomization". arxiv.org. Retrieved 26 March 2020.
- ↑ Frans, Kevin; Ho, Jonathan; Chen, Xi ChenXi; Abbeel, Pieter; Schulman, John. "Meta Learning Shared Hierarchies". arxiv.org. Retrieved 26 March 2020.
- ↑ Grathwohl, Will; Choi, Dami; Wu, Yuhuai; Roeder, Geoffrey; Duvenaud, David. "Backpropagation through the Void: Optimizing control variates for black-box gradient estimation". arxiv.org. Retrieved 26 March 2020.
- ↑ Milli, Smitha; Abbeel, Pieter; Mordatch, Igor. "Interpretable and Pedagogical Examples". arxiv.org. Retrieved 26 March 2020.
