Scott E. Reed

Affiliations:
  • University of Michigan, Ann Arbor, Department of Electrical Engineering and Computer Science


According to our database1, Scott E. Reed authored at least 32 papers between 2014 and 2024.

Collaborative distances:

Timeline

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Bibliography

2024
RoboCat: A Self-Improving Generalist Agent for Robotic Manipulation.
Trans. Mach. Learn. Res., 2024


2023
RoboCat: A Self-Improving Foundation Agent for Robotic Manipulation.
CoRR, 2023

2022
A Generalist Agent.
Trans. Mach. Learn. Res., 2022

2021
Shaking the foundations: delusions in sequence models for interaction and control.
CoRR, 2021

2020
Semi-supervised reward learning for offline reinforcement learning.
CoRR, 2020

Offline Learning from Demonstrations and Unlabeled Experience.
CoRR, 2020

Scaling data-driven robotics with reward sketching and batch reinforcement learning.
Proceedings of the Robotics: Science and Systems XVI, 2020

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

Task-Relevant Adversarial Imitation Learning.
Proceedings of the 4th Conference on Robot Learning, 2020

2019
A Framework for Data-Driven Robotics.
CoRR, 2019

Learning Compositional Neural Programs with Recursive Tree Search and Planning.
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.
Proceedings of the 7th International Conference on Learning Representations, 2019

2018
One-Shot High-Fidelity Imitation: Training Large-Scale Deep Nets with RL.
CoRR, 2018

Neural Arithmetic Logic Units.
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.
Proceedings of the 6th International Conference on Learning Representations, 2018

2017
Parallel Multiscale Autoregressive Density Estimation.
CoRR, 2017

Robust Imitation of Diverse Behaviors.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Parallel Multiscale Autoregressive Density Estimation.
Proceedings of the 34th International Conference on Machine Learning, 2017

2016
Deep Neural Networks for Visual Reasoning, Program Induction, and Text-to-Image Synthesis.
PhD thesis, 2016

Neural Programmer-Interpreters.
Proceedings of the 4th International Conference on Learning Representations, 2016

Learning What and Where to Draw.
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.
Proceedings of the 33nd International Conference on Machine Learning, 2016

SSD: Single Shot MultiBox Detector.
Proceedings of the Computer Vision - ECCV 2016, 2016

Learning Deep Representations of Fine-Grained Visual Descriptions.
Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, 2016

2015
Training Deep Neural Networks on Noisy Labels with Bootstrapping.
Proceedings of the 3rd International Conference on Learning Representations, 2015

Weakly-supervised Disentangling with Recurrent Transformations for 3D View Synthesis.
Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Deep Visual Analogy-Making.
Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Going deeper with convolutions.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015

Evaluation of output embeddings for fine-grained image classification.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015

2014
Scalable, High-Quality Object Detection.
CoRR, 2014

Learning to Disentangle Factors of Variation with Manifold Interaction.
Proceedings of the 31th International Conference on Machine Learning, 2014


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