Difference between revisions of "Talk:Timeline of OpenAI"

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| 2017 || {{dts|February 8}} || || Publication || "Adversarial Attacks on Neural Network Policies" is submitted to the {{w|ArXiv}}. The paper shows that adversarial attacks are effective when targeting neural network policies in reinforcement learning.<ref>{{cite web |last1=Huang |first1=Sandy |last2=Papernot |first2=Nicolas |last3=Goodfellow |first3=Ian |last4=Duan |first4=Yan |last5=Abbeel |first5=Pieter |title=Adversarial Attacks on Neural Network Policies |url=https://arxiv.org/abs/1702.02284 |website=arxiv.org |accessdate=28 March 2020}}</ref>
 
| 2017 || {{dts|February 8}} || || Publication || "Adversarial Attacks on Neural Network Policies" is submitted to the {{w|ArXiv}}. The paper shows that adversarial attacks are effective when targeting neural network policies in reinforcement learning.<ref>{{cite web |last1=Huang |first1=Sandy |last2=Papernot |first2=Nicolas |last3=Goodfellow |first3=Ian |last4=Duan |first4=Yan |last5=Abbeel |first5=Pieter |title=Adversarial Attacks on Neural Network Policies |url=https://arxiv.org/abs/1702.02284 |website=arxiv.org |accessdate=28 March 2020}}</ref>
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| 2017 || {{dts|March 6}} || || Publication || "Third-Person Imitation Learning", a paper on {{w|robotics}}, is submitted to the {{w|ArXiv}}. It presents a method for unsupervised third-person imitation learning.<ref>{{cite web |last1=Stadie |first1=Bradly C. |last2=Abbeel |first2=Pieter |last3=Sutskever |first3=Ilya |title=arxiv.org |url=https://arxiv.org/abs/1703.01703|website=arxiv.org |accessdate=28 March 2020}}</ref>
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| 2017 || {{dts|March 10}} || || Publication || "Evolution Strategies as a Scalable Alternative to Reinforcement Learning" is submitted to the {{w|ArXiv}}. It explores the use of Evolution Strategies (ES), a class of black box optimization algorithms.<ref>{{cite web |last1=Salimans |first1=Tim |last2=Ho |first2=Jonathan |last3=Chen |first3=Xi |last4=Sidor |first4=Szymon |last5=Sutskever |first5=Ilya |title=Evolution Strategies as a Scalable Alternative to Reinforcement Learning |url=https://arxiv.org/abs/1703.03864 |website=arxiv.org |accessdate=28 March 2020}}</ref>
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| 2017 || {{dts|March 12}} || || Publication || "Prediction and Control with Temporal Segment Models", a paper on generative models, is first submitted to the {{w|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.<ref>{{cite web |last1=Mishra |first1=Nikhil |last2=Abbeel |first2=Pieter |last3=Mordatch |first3=Igor |title=Prediction and Control with Temporal Segment Models |url=https://arxiv.org/abs/1703.04070 |website=arxiv.org |accessdate=28 March 2020}}</ref>
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| 2017 || {{dts|March 15}} || || Publication || "Emergence of Grounded Compositional Language in Multi-Agent Populations" is first submitted to {{w|ArXiv}}. The paper proposes a multi-agent learning environment and learning methods that bring about emergence of a basic compositional language.<ref>{{cite web |last1=Mordatch |first1=Igor |last2=Abbeel |first2=Pieter |title=Emergence of Grounded Compositional Language in Multi-Agent Populations |url=https://arxiv.org/abs/1703.04908 |website=arxiv.org |accessdate=26 March 2020}}</ref>
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| 2017 || {{dts|March 20}} || || Publication || "Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World", a paper on {{w|robotics}}, is subitted to the {{w|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.<ref>{{cite web |last1=Tobin |first1=Josh |last2=Fong |first2=Rachel |last3=Ray |first3=Alex |last4=Schneider |first4=Jonas |last5=Zaremba |first5=Wojciech |last6=Abbeel |first6=Pieter |title=Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World |url=https://arxiv.org/abs/1703.06907 |website=arxiv.org |accessdate=28 March 2020}}</ref>
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| 2017 || {{dts|March 21}} || || Publication || "One-Shot Imitation Learning", a paper on {{w|robotics}}, is first submitted to the {{w|ArXiv}}. The paper proposes a meta-learning framework for optimizing imitation learning.<ref>{{cite web |title=One-Shot Imitation Learning |url=https://arxiv.org/abs/1703.07326 |website=arxiv.org |accessdate=28 March 2020}}</ref>
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| 2017 || {{dts|June 7}} || || Publication || "Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments" is submitted to the {{w|ArXiv}}. The paper explores deep {{w|reinforcement learning}} methods for multi-agent domains.<ref>{{cite web |title=Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments |website=arxiv.org |accessdate=5 April 2020}}</ref>
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| 2017 || {{Dts|September 13}} || {{w|Reinforcement learning}} || Publication || "Learning with Opponent-Learning Awareness" is first uploaded to the {{w|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.<ref>{{cite web |url=https://arxiv.org/abs/1709.04326 |title=[1709.04326] Learning with Opponent-Learning Awareness |accessdate=March 2, 2018}}</ref><ref>{{cite web |url=https://www.gwern.net/newsletter/2017/09 |author=gwern |date=August 16, 2017 |title=September 2017 news - Gwern.net |accessdate=March 2, 2018}}</ref>
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| 2017 || {{dts|October 17}} || {{w|Robotics}} || Publication || "Domain Randomization and Generative Models for Robotic Grasping", a paper on {{w|robotics}}, is first submitted to the {{w|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.<ref>{{cite web |title=Domain Randomization and Generative Models for Robotic Grasping |url=https://arxiv.org/abs/1710.06425 |website=arxiv.org |accessdate=27 March 2020}}</ref>
 
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Revision as of 20:17, 5 May 2020

Removed Rows

In case any of these events turn our to be relevant, please place them back on the timeline or let me know and I'll do it.

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]
  1. ↑ Miyato, Takeru; Dai, Andrew M.; Goodfellow, Ian. "Adversarial Training Methods for Semi-Supervised Text Classification". arxiv.org. Retrieved 28 March 2020. 
  2. ↑ "[1606.06565] Concrete Problems in AI Safety". June 21, 2016. Retrieved July 25, 2017. 
  3. ↑ 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. 
  4. ↑ 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. 
  5. ↑ Price, Eric; Zaremba, Wojciech; Sutskever, Ilya. "Extensions and Limitations of the Neural GPU". arxiv.org. Retrieved 28 March 2020. 
  6. ↑ Chen, Xi; Kingma, Diederik P.; Salimans, Tim; Duan, Yan; Dhariwal, Prafulla; Schulman, John; Sutskever, Ilya; Abbeel, Pieter. "Variational Lossy Autoencoder". arxiv.org. 
  7. ↑ 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. 
  8. ↑ 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. 
  9. ↑ Wu, Yuhuai; Burda, Yuri; Salakhutdinov, Ruslan; Grosse, Roger. "On the Quantitative Analysis of Decoder-Based Generative Models". arxiv.org. Retrieved 28 March 2020. 
  10. ↑ "#Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning". arxiv.org. Retrieved 28 March 2020. 
  11. ↑ 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. 
  12. ↑ Huang, Sandy; Papernot, Nicolas; Goodfellow, Ian; Duan, Yan; Abbeel, Pieter. "Adversarial Attacks on Neural Network Policies". arxiv.org. Retrieved 28 March 2020. 
  13. ↑ Stadie, Bradly C.; Abbeel, Pieter; Sutskever, Ilya. "arxiv.org". arxiv.org. Retrieved 28 March 2020. 
  14. ↑ 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. 
  15. ↑ Mishra, Nikhil; Abbeel, Pieter; Mordatch, Igor. "Prediction and Control with Temporal Segment Models". arxiv.org. Retrieved 28 March 2020. 
  16. ↑ Mordatch, Igor; Abbeel, Pieter. "Emergence of Grounded Compositional Language in Multi-Agent Populations". arxiv.org. Retrieved 26 March 2020. 
  17. ↑ 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. 
  18. ↑ "One-Shot Imitation Learning". arxiv.org. Retrieved 28 March 2020. 
  19. ↑ "Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments". arxiv.org. 
  20. ↑ "[1709.04326] Learning with Opponent-Learning Awareness". Retrieved March 2, 2018. 
  21. ↑ gwern (August 16, 2017). "September 2017 news - Gwern.net". Retrieved March 2, 2018. 
  22. ↑ "Domain Randomization and Generative Models for Robotic Grasping". arxiv.org. Retrieved 27 March 2020. 
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