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12/02/2022
Transformer-Based Learned Optimization
In this paper, we propose a new approach to learned optimization. As com...
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11/17/2022
VeLO: Training Versatile Learned Optimizers by Scaling Up
While deep learning models have replaced hand-designed features across m...
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10/31/2022
Allowing Safe Contact in Robotic Goal-Reaching: Planning and Tracking in Operational and Null Spaces
In recent years, impressive results have been achieved in robotic manipu...
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03/22/2022
Practical tradeoffs between memory, compute, and performance in learned optimizers
Optimization plays a costly and crucial role in developing machine learn...
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11/10/2021
Gradients are Not All You Need
Differentiable programming techniques are widely used in the community a...
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06/24/2021
Brax – A Differentiable Physics Engine for Large Scale Rigid Body Simulation
We present Brax, an open source library for rigid body simulation with a...
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01/14/2021
Training Learned Optimizers with Randomly Initialized Learned Optimizers
Learned optimizers are increasingly effective, with performance exceedin...
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09/23/2020
Tasks, stability, architecture, and compute: Training more effective learned optimizers, and using them to train themselves
Much as replacing hand-designed features with learned functions has revo...
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02/27/2020
Using a thousand optimization tasks to learn hyperparameter search strategies
We present TaskSet, a dataset of tasks for use in training and evaluatin...
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10/29/2019
Learning to Predict Without Looking Ahead: World Models Without Forward Prediction
Much of model-based reinforcement learning involves learning a model of ...
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10/24/2018
Learned optimizers that outperform SGD on wall-clock and test loss
Deep learning has shown that learned functions can dramatically outperfo...
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10/24/2018
Learned optimizers that outperform SGD on wall-clock and validation loss
Deep learning has shown that learned functions can dramatically outperfo...
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11/04/2016