Talk:Timeline of OpenAI

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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]
  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.