C. Daniel Freeman | DeepAI

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Featured Co-authors

Masayoshi Tomizuka
149 publications
Joan Bruna
96 publications
Jascha Sohl-Dickstein
68 publications
Igor Mordatch
55 publications
Cristian Sminchisescu
48 publications
Olivier Bachem
36 publications
Ben Poole
34 publications
Adam Roberts
32 publications
Lucas Beyer
32 publications
Luke Metz
30 publications
Samuel S. Schoenholz
26 publications

∙ 12/02/2022

Transformer-Based Learned Optimization

In this paper, we propose a new approach to learned optimization. As com...

0 Erik Gärtner, et al. ∙

∙ 11/17/2022

VeLO: Training Versatile Learned Optimizers by Scaling Up

While deep learning models have replaced hand-designed features across m...

0 Luke Metz, et al. ∙

∙ 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...

0 Xinghao Zhu, et al. ∙

∙ 03/22/2022

Practical tradeoffs between memory, compute, and performance in learned optimizers

Optimization plays a costly and crucial role in developing machine learn...

10 Luke Metz, et al. ∙

∙ 11/10/2021

Gradients are Not All You Need

Differentiable programming techniques are widely used in the community a...

14 Luke Metz, et al. ∙

∙ 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...

0 C. Daniel Freeman, et al. ∙

∙ 01/14/2021

Training Learned Optimizers with Randomly Initialized Learned Optimizers

Learned optimizers are increasingly effective, with performance exceedin...

8 Luke Metz, et al. ∙

∙ 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...

12 Luke Metz, et al. ∙

∙ 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...

71 Luke Metz, et al. ∙

∙ 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 ...

30 C. Daniel Freeman, et al. ∙

∙ 10/24/2018

Learned optimizers that outperform SGD on wall-clock and test loss

Deep learning has shown that learned functions can dramatically outperfo...

8 Luke Metz, et al. ∙

∙ 10/24/2018

Learned optimizers that outperform SGD on wall-clock and validation loss

Deep learning has shown that learned functions can dramatically outperfo...

2 Luke Metz, et al. ∙

∙ 11/04/2016

Topology and Geometry of Half-Rectified Network Optimization

The loss surface of deep neural networks has recently attracted interest...

0 C. Daniel Freeman, et al. ∙

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