Talk:Timeline of OpenAI
From Timelines
Removed Rows
In case any of these events turns out to be relevant, please place it 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 | 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.^{[2]} | |
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).^{[3]} | |
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.^{[4]} | |
2016 | November 8 | Generative models | 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.^{[5]} |
2016 | November 9 | Reinforcement learning | Publication | "RL^{2}: 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.^{[6]} |
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.^{[7]} | |
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.^{[8]} | |
2016 | November 15 | Reinforcement learning | Publication | "#Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning", a paper on reinforcement learning, is first submitted to the ArXiv.^{[9]} |
2016 | December 21 | Reinforcement learning | Publication | "Faulty Reward Functions in the Wild" is published. The post explores a failed reinforcement learning algorithm, which leads to misspecifying the reward function.^{[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 | 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.^{[20]} |
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.^{[21]} | |
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.^{[22]} | |
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.^{[23]} | |
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.^{[24]} | |
2017 | December 4 | Publication | "Learning Sparse Neural Networks through L_{0} Regularization", a paper on reinforcement learning, is submitted to the ArXiv. It describes a method which allows for straightforward and efficient learning of model structures with stochastic gradient descent.^{[25]} | |
2018 | February 3 | Publication | "DeepType: Multilingual Entity Linking by Neural Type System Evolution" a paper on reinforcement learning, is submitted to the ArXiv.^{[26]} | |
2018 | February 13 | Publication | "Evolved Policy Gradients", a reinforcement learning paper, is first submitted to the ArXiv. It proposes a metalearning approach for learning gradient-based reinforcement learning (RL) algorithms.^{[27]} | |
2018 | February 26 | Publication | "Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Research" is first submitted to the ArXiv. The paper introduces a suite of challenging continuous control tasks based on currently existing robotics hardware, and presents a set of concrete research ideas for improving reinforcement learning algorithms.^{[28]} | |
2018 | March 3 | Publication | "Some Considerations on Learning to Explore via Meta-Reinforcement Learning", a paper on reinforcement learning, is first submitted to ArXiv. It considers the problem of exploration in meta reinforcement learning.^{[29]} | |
2018 | March 8 | Publication | "On First-Order Meta-Learning Algorithms", a paper on reinforcement learning, is submitted to ArXiv. It analyzes meta-learning problems, where there is a distribution of tasks.^{[30]} | |
2018 | March 15 | Publication | "Improving GANs Using Optimal Transport", a paper on generative models, is first submitted to the ArXiv. It presents Optimal Transport GAN (OT-GAN), a variant of generative adversarial nets minimizing a new metric measuring the distance between the generator distribution and the data distribution.^{[31]} | |
2018 | March 20 | Publication | "Variance Reduction for Policy Gradient with Action-Dependent Factorized Baselines", a paper on reinforcement learning, is submitted to the ArXiv. The paper shows that the general idea of including additional information in baselines for improved variance reduction can be extended to partially observed and multi-agent tasks.^{[32]} | |
2018 | April 10 | Publication | "Gotta Learn Fast: A New Benchmark for Generalization in RL", a paper on reinforcement learning, is first submitted to the ArXiv. The report presents a new reinforcement learning benchmark intended to measure the performance of transfer learning and few-shot learning algorithms in the reinforcement learning domain.^{[33]} | |
2018 | June 2 | Publication | OpenAI publishes "GamePad: A Learning Environment for Theorem Proving" in arXiv. The paper introduces a system called GamePad that can be used to explore the application of machine learning methods to theorem proving in the Coq proof assistant.^{[34]} | |
2018 | June 17 | Reinforcement learning | Publication | OpenAI publishes paper on learning policy representations in multiagent systems. The paper proposes a general learning framework for modeling agent behavior in any multiagent system using only a handful of interaction data.^{[35]} |
2018 | July 9 | Generative models | Publication | "Glow: Generative Flow with Invertible 1x1 Convolutions" is first submitted to the ArXiv. The paper proposes a method for obtaining a significant improvement in log-likelihood on standard benchmarks.^{[36]} |
2018 | July 26 | Reinforcement learning} | Publication | OpenAI publishes paper on variational option discovery algorithms. The paper highlights a tight connection between variational option discovery methods and variational autoencoders, and introduces Variational Autoencoding Learning of Options by Reinforcement (VALOR), a new method derived from the connection.^{[37]} |
2018 | August 1 | Robotics | Publication | OpenAI publishes paper describing the use of reinforcement learning to learn dexterous in-hand manipulation policies which can perform vision-based object reorientation on a physical Shadow Dexterous Hand.^{[38]} |
2018 | October 2 | Generative models | Publication | OpenAI publishes paper on FFJORD (free-form continuous dynamics for scalable reversible generative models), aiming to demonstrate their approach on high-dimensional density estimation, image generation, and variational inference.^{[39]} |
2018 | October 19 | Reinforcement learning | Publication | OpenAI publishes paper proposing Iterated Amplification, an alternative training strategy which progressively builds up a training signal for difficult problems by combining solutions to easier subproblems.^{[40]} |
2018 | November 1 | Publication | OpenAI publishes research paper detailing AI able to defeat humans at the retro platformer Montezuma’s Revenge. The top-performing iteration found 22 of the 24 rooms in the first level, and occasionally discovered all 24.^{[41]}^{[42]} | |
2018 | November 5 | Reinforcement learning | Publication | OpenAI publishes paper proposing a plan online and learn offline (POLO) framework for the setting where an agent, with an internal model, needs to continually act and learn in the world.^{[43]} |
2018 | December 14 | Reinforcement learning | Publication | OpenAI publishes paper demonstrating that a simple and easy-to-measure statistic called the gradient noise scale predicts the largest useful batch size across many domains and applications, including a number of supervised learning datasets, reinforcement learning domains, and even generative model training.^{[44]} |
2019 | February 4 | Publication | OpenAI publishes paper showing computational limitations in robust classification and win-win results.^{[45]} | |
2019 | March 2 | Publication | OpenAi publishes paper presenting an artificial intelligence research environment that aims to simulate the natural environment setting in microcosm.^{[46]} | |
2019 | March 20 | Publication | OpenAI publishes paper presenting techniques to scale MCMC based energy base models training on continuous neural networks.^{[47]} | |
2019 | May 3 | Publication | OpenAI publishes study on the transfer of adversarial robustness of deep neural networks between different perturbation types.^{[48]} | |
2019 | May 28 | Publication | OpenAI publishes study on the dynamics of Stochastic Gradient Descent (SGD) in learning deep neural networks for several real and synthetic classification tasks.^{[49]} | |
2019 | July 10 | Publication | OpenAI publishes paper arguing that competitive pressures could incentivize AI companies to underinvest in ensuring their systems are safe, secure, and have a positive social impact.^{[50]} | |
2020 | January 23 | Publication | OpenAI publishes study on empirical scaling laws for language model performance on the cross-entropy loss.^{[51]} |
- ↑ Miyato, Takeru; Dai, Andrew M.; Goodfellow, Ian. "Adversarial Training Methods for Semi-Supervised Text Classification". arxiv.org. Retrieved 28 March 2020.
- ↑ 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.
- ↑ "Faulty Reward Functions in the Wild". openai.com. Retrieved 5 April 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.
- ↑ "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.
- ↑ Louizos, Christos; Welling, Max; Kingma, Diederik P. "Learning Sparse Neural Networks through L0 Regularization". arxiv.org. Retrieved 26 March 2020.
- ↑ Raiman, Jonathan; Raiman, Olivier. "DeepType: Multilingual Entity Linking by Neural Type System Evolution". arxiv.org. Retrieved 26 March 2020.
- ↑ Houthooft, Rein; Chen, Richard Y.; Isola, Phillip; Stadie, Bradly C.; Wolski, Filip; Ho, Jonathan; Abbeel, Pieter. "Evolved Policy Gradients". arxiv.org. Retrieved 26 March 2020.
- ↑ "Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Research". arxiv.org. Retrieved 26 March 2020.
- ↑ Stadie, Bradly C.; Yang, Ge; Houthooft, Rein; Chen, Xi; Duan, Yan; Wu, Yuhuai; Abbeel, Pieter; Sutskever, Ilya. "Some Considerations on Learning to Explore via Meta-Reinforcement Learning". arxiv.org. Retrieved 26 March 2020.
- ↑ Nichol, Alex; Achiam, Joshua; Schulman, John. "On First-Order Meta-Learning Algorithms". arxiv.org. Retrieved 26 March 2020.
- ↑ Salimans, Tim; Zhang, Han; Radford, Alec; Metaxas, Dimitris. "Improving GANs Using Optimal Transport". arxiv.org. Retrieved 26 March 2020.
- ↑ Wu, Cathy; Rajeswaran, Aravind; Duan, Yan; KumarVikash Kumar, Vikash; Bayen, Alexandre M; Kakade, Sham; Mordatch, Igor; Abbeel, Pieter. "Variance Reduction for Policy Gradient with Action-Dependent Factorized Baselines". arxiv.org. Retrieved 26 March 2020.
- ↑ Nichol, Alex; Pfau, Vicki; Hesse, Christopher; Klimov, Oleg; Schulman, John. "Gotta Learn Fast: A New Benchmark for Generalization in RL". arxiv.org. Retrieved 26 March 2020.
- ↑ Huang, Daniel; Dhariwal, Prafulla; Song, Dawn; Sutskever, Ilya. "GamePad: A Learning Environment for Theorem Proving". arxiv.org. Retrieved 26 March 2020.
- ↑ "Learning Policy Representations in Multiagent Systems". arxiv.org. Retrieved 26 March 2020.
- ↑ Kingma, Diederik P.; Dhariwal, Prafulla. "Glow: Generative Flow with Invertible 1x1 Convolutions". arxiv.org. Retrieved 26 March 2020.
- ↑ Achiam, Joshua; Edwards, Harrison; Amodei, Dario; Abbeel, Pieter. "Variational Option Discovery Algorithms". arxiv.org.
- ↑ "Learning Dexterous In-Hand Manipulation". arxiv.org. Retrieved 26 March 2020.
- ↑ Grathwohl, Will; Chen, Ricky T. Q.; Bettencourt, Jesse; Sutskever, Ilya; Duvenaud, David. "FFJORD: Free-form Continuous Dynamics for Scalable Reversible Generative Models". arxiv.org. Retrieved 26 March 2020.
- ↑ Christiano, Paul; Shlegeris, Buck; Amodei, Dario. "Supervising strong learners by amplifying weak experts". arxiv.org. Retrieved 26 March 2020.
- ↑ Wiggers, Kyle. "OpenAI made a system that's better at Montezuma's Revenge than humans". venturebeat.com. Retrieved 15 June 2019.
- ↑ Vincent, James. "New research from OpenAI uses curious AI to beat video games". theverge.com. Retrieved 15 June 2019.
- ↑ Lowrey, Kendall; Rajeswaran, Aravind; Kakade, Sham; Todorov, Emanuel; Mordatch, Igor. "Plan Online, Learn Offline: Efficient Learning and Exploration via Model-Based Control". arxiv.org.
- ↑ McCandlish, Sam; Kaplan, Jared; Amodei, Dario; OpenAI Dota Team. "An Empirical Model of Large-Batch Training". arxiv.org. Retrieved 25 March 2020.
- ↑ Degwekar, Akshay; Nakkiran, Preetum; Vaikuntanathan, Vinod. "Computational Limitations in Robust Classification and Win-Win Results". arxiv.org. Retrieved 25 March 2020.
- ↑ Suarez, Joseph; Du, Yilun; Isola, Phillip; Mordatch, Igor. "Neural MMO: A Massively Multiagent Game Environment for Training and Evaluating Intelligent Agents". arxiv.org. Retrieved 25 March 2020.
- ↑ Du, Yilun; Mordatch, Igor. "Implicit Generation and Generalization in Energy-Based Models". arxiv.org. Retrieved 25 March 2020.
- ↑ Kang, Daniel; Sun, Yi; Brown, Tom; Hendrycks, Dan; Steinhardt, Jacob. "Transfer of Adversarial Robustness Between Perturbation Types". arxiv.org. Retrieved 25 March 2020.
- ↑ Nakkiran, Preetum; Kaplun, Gal; Kalimeris, Dimitris; Yang, Tristan; Edelman, Benjamin L.; Zhang, Fred; Barak, Boaz. "SGD on Neural Networks Learns Functions of Increasing Complexity". arxiv.org. Retrieved 25 March 2020.
- ↑ Askell, Amanda; Brundage, Miles; Hadfield, Gillian. "The Role of Cooperation in Responsible AI Development". arxiv.org. Retrieved 25 March 2020.
- ↑ Kaplan, Jared; McCandlish, Sam; Henighan, Tom; Brown, Tom B.; Chess, Benjamin; Child, Rewon; Gray, Scott; Radford, Alec; Wu, Jeffrey; Amodei, Dario. "Scaling Laws for Neural Language Models". arxiv.org. Retrieved 25 March 2020.