Scott E. Reed

According to our database, Scott E. Reed authored at least 32 papers between 2014 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

RoboCat: A Self-Improving Generalist Agent for Robotic Manipulation.

Konstantinos Bousmalis, Giulia Vezzani, Dushyant Rao, Coline Manon Devin, Alex X. Lee, Maria Bauzá Villalonga, Todor Davchev, Yuxiang Zhou, Agrim Gupta, Akhil Raju, Antoine Laurens, Claudio Fantacci, Valentin Dalibard, Martina Zambelli, Murilo Fernandes Martins, Rugile Pevceviciute, Michiel Blokzijl, Misha Denil, Nathan Batchelor, Thomas Lampe, Emilio Parisotto, Konrad Zolna, Scott E. Reed, Sergio Gómez Colmenarejo, Jon Scholz, Abbas Abdolmaleki, Oliver Groth, Jean-Baptiste Regli, Oleg Sushkov, Thomas Rothörl, José Enrique Chen, Yusuf Aytar, Dave Barker, Joy Ortiz, Martin A. Riedmiller, Jost Tobias Springenberg, Raia Hadsell, Francesco Nori, Nicolas Heess

Trans. Mach. Learn. Res., 2024

Genie: Generative Interactive Environments.

Jake Bruce, Michael D. Dennis, Ashley Edwards, Jack Parker-Holder, Yuge Shi, Edward Hughes, Matthew Lai, Aditi Mavalankar, Richie Steigerwald, Chris Apps, Yusuf Aytar, Sarah Bechtle, Feryal M. P. Behbahani, Stephanie C. Y. Chan, Nicolas Heess, Lucy Gonzalez, Simon Osindero, Sherjil Ozair, Scott E. Reed, Jingwei Zhang, Konrad Zolna, Jeff Clune, Nando de Freitas, Satinder Singh, Tim Rocktäschel

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

RoboCat: A Self-Improving Foundation Agent for Robotic Manipulation.

Konstantinos Bousmalis, Giulia Vezzani, Dushyant Rao, Coline Devin, Alex X. Lee, Maria Bauzá, Todor Davchev, Yuxiang Zhou, Agrim Gupta, Akhil Raju, Antoine Laurens, Claudio Fantacci, Valentin Dalibard, Martina Zambelli, Murilo F. Martins, Rugile Pevceviciute, Michiel Blokzijl, Misha Denil, Nathan Batchelor, Thomas Lampe, Emilio Parisotto, Konrad Zolna, Scott E. Reed, Sergio Gómez Colmenarejo, Jon Scholz, Abbas Abdolmaleki, Oliver Groth, Jean-Baptiste Regli, Oleg Sushkov, Thomas Rothörl, José Enrique Chen, Yusuf Aytar, Dave Barker, Joy Ortiz, Martin A. Riedmiller, Jost Tobias Springenberg, Raia Hadsell, Francesco Nori, Nicolas Heess

CoRR, 2023

2022

A Generalist Agent.

Scott E. Reed, Konrad Zolna, Emilio Parisotto, Sergio Gómez Colmenarejo, Alexander Novikov, Gabriel Barth-Maron, Mai Gimenez, Yury Sulsky, Jackie Kay, Jost Tobias Springenberg, Tom Eccles, Jake Bruce, Ali Razavi, Ashley Edwards, Nicolas Heess, Yutian Chen, Raia Hadsell, Oriol Vinyals, Mahyar Bordbar, Nando de Freitas

Trans. Mach. Learn. Res., 2022

2021

Shaking the foundations: delusions in sequence models for interaction and control.

Pedro A. Ortega, Markus Kunesch, Grégoire Delétang, Tim Genewein, Jordi Grau-Moya, Joel Veness, Jonas Buchli, Jonas Degrave, Bilal Piot, Julien Pérolat, Tom Everitt, Corentin Tallec, Emilio Parisotto, Tom Erez, Yutian Chen, Scott E. Reed, Marcus Hutter, Nando de Freitas, Shane Legg

CoRR, 2021

2020

Semi-supervised reward learning for offline reinforcement learning.

Ksenia Konyushkova, Konrad Zolna, Yusuf Aytar, Alexander Novikov, Scott E. Reed, Serkan Cabi, Nando de Freitas

CoRR, 2020

Offline Learning from Demonstrations and Unlabeled Experience.

Konrad Zolna, Alexander Novikov, Ksenia Konyushkova, Çaglar Gülçehre, Ziyu Wang, Yusuf Aytar, Misha Denil, Nando de Freitas, Scott E. Reed

CoRR, 2020

Scaling data-driven robotics with reward sketching and batch reinforcement learning.

Serkan Cabi, Sergio Gómez Colmenarejo, Alexander Novikov, Ksenia Konyushkova, Scott E. Reed, Rae Jeong, Konrad Zolna, Yusuf Aytar, David Budden, Mel Vecerík, Oleg Sushkov, David Barker, Jonathan Scholz, Misha Denil, Nando de Freitas, Ziyu Wang

Proceedings of the Robotics: Science and Systems XVI, 2020

Critic Regularized Regression.

Ziyu Wang, Alexander Novikov, Konrad Zolna, Josh Merel, Jost Tobias Springenberg, Scott E. Reed, Bobak Shahriari, Noah Y. Siegel, Çaglar Gülçehre, Nicolas Heess, Nando de Freitas

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Task-Relevant Adversarial Imitation Learning.

Konrad Zolna, Scott E. Reed, Alexander Novikov, Sergio Gómez Colmenarejo, David Budden, Serkan Cabi, Misha Denil, Nando de Freitas, Ziyu Wang

Proceedings of the 4th Conference on Robot Learning, 2020

2019

A Framework for Data-Driven Robotics.

Serkan Cabi, Sergio Gómez Colmenarejo, Alexander Novikov, Ksenia Konyushkova, Scott E. Reed, Rae Jeong, Konrad Zolna, Yusuf Aytar, David Budden, Mel Vecerík, Oleg Sushkov, David Barker, Jonathan Scholz, Misha Denil, Nando de Freitas, Ziyu Wang

CoRR, 2019

Learning Compositional Neural Programs with Recursive Tree Search and Planning.

Thomas Pierrot, Guillaume Ligner, Scott E. Reed, Olivier Sigaud, Nicolas Perrin, Alexandre Laterre, David Kas, Karim Beguir, Nando de Freitas

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Sample Efficient Adaptive Text-to-Speech.

Yutian Chen, Yannis M. Assael, Brendan Shillingford, David Budden, Scott E. Reed, Heiga Zen, Quan Wang, Luis C. Cobo, Andrew Trask, Ben Laurie, Çaglar Gülçehre, Aäron van den Oord, Oriol Vinyals, Nando de Freitas

Proceedings of the 7th International Conference on Learning Representations, 2019

2018

One-Shot High-Fidelity Imitation: Training Large-Scale Deep Nets with RL.

Tom Le Paine, Sergio Gomez Colmenarejo, Ziyu Wang, Scott E. Reed, Yusuf Aytar, Tobias Pfaff, Matthew W. Hoffman, Gabriel Barth-Maron, Serkan Cabi, David Budden, Nando de Freitas

CoRR, 2018

Neural Arithmetic Logic Units.

Andrew Trask, Felix Hill, Scott E. Reed, Jack W. Rae, Chris Dyer, Phil Blunsom

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Few-shot Autoregressive Density Estimation: Towards Learning to Learn Distributions.

Scott E. Reed, Yutian Chen, Thomas Paine, Aäron van den Oord, S. M. Ali Eslami, Danilo J. Rezende, Oriol Vinyals, Nando de Freitas

Proceedings of the 6th International Conference on Learning Representations, 2018

2017

Parallel Multiscale Autoregressive Density Estimation.

Scott E. Reed, Aäron van den Oord, Nal Kalchbrenner, Sergio Gomez Colmenarejo, Ziyu Wang, Dan Belov, Nando de Freitas

CoRR, 2017

Robust Imitation of Diverse Behaviors.

Ziyu Wang, Josh Merel, Scott E. Reed, Nando de Freitas, Gregory Wayne, Nicolas Heess

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Parallel Multiscale Autoregressive Density Estimation.

Scott E. Reed, Aäron van den Oord, Nal Kalchbrenner, Sergio Gomez Colmenarejo, Ziyu Wang, Yutian Chen, Dan Belov, Nando de Freitas

Proceedings of the 34th International Conference on Machine Learning, 2017

2016

Deep Neural Networks for Visual Reasoning, Program Induction, and Text-to-Image Synthesis.

Scott E. Reed

PhD thesis, 2016

Neural Programmer-Interpreters.

Scott E. Reed, Nando de Freitas

Proceedings of the 4th International Conference on Learning Representations, 2016

Learning What and Where to Draw.

Scott E. Reed, Zeynep Akata, Santosh Mohan, Samuel Tenka, Bernt Schiele, Honglak Lee

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Generative Adversarial Text to Image Synthesis.

Scott E. Reed, Zeynep Akata, Xinchen Yan, Lajanugen Logeswaran, Bernt Schiele, Honglak Lee

Proceedings of the 33nd International Conference on Machine Learning, 2016

SSD: Single Shot MultiBox Detector.

Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott E. Reed, Cheng-Yang Fu, Alexander C. Berg

Proceedings of the Computer Vision - ECCV 2016, 2016

Learning Deep Representations of Fine-Grained Visual Descriptions.

Scott E. Reed, Zeynep Akata, Honglak Lee, Bernt Schiele

Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, 2016

2015

Training Deep Neural Networks on Noisy Labels with Bootstrapping.

Scott E. Reed, Honglak Lee, Dragomir Anguelov, Christian Szegedy, Dumitru Erhan, Andrew Rabinovich

Proceedings of the 3rd International Conference on Learning Representations, 2015

Weakly-supervised Disentangling with Recurrent Transformations for 3D View Synthesis.

Jimei Yang, Scott E. Reed, Ming-Hsuan Yang, Honglak Lee

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Deep Visual Analogy-Making.

Scott E. Reed, Yi Zhang, Yuting Zhang, Honglak Lee

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Going deeper with convolutions.

Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott E. Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015

Evaluation of output embeddings for fine-grained image classification.

Zeynep Akata, Scott E. Reed, Daniel Walter, Honglak Lee, Bernt Schiele

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015

2014

Scalable, High-Quality Object Detection.

Christian Szegedy, Scott E. Reed, Dumitru Erhan, Dragomir Anguelov

CoRR, 2014

Learning to Disentangle Factors of Variation with Manifold Interaction.

Scott E. Reed, Kihyuk Sohn, Yuting Zhang, Honglak Lee

Proceedings of the 31th International Conference on Machine Learning, 2014