Search Results for author: C. Daniel Freeman

Found 11 papers, 6 papers with code

Brax -- A Differentiable Physics Engine for Large Scale Rigid Body Simulation

1 code implementation24 Jun 2021 C. Daniel Freeman, Erik Frey, Anton Raichuk, Sertan Girgin, Igor Mordatch, Olivier Bachem

We present Brax, an open source library for rigid body simulation with a focus on performance and parallelism on accelerators, written in JAX.

OpenAI Gym

Training Learned Optimizers with Randomly Initialized Learned Optimizers

no code implementations14 Jan 2021 Luke Metz, C. Daniel Freeman, Niru Maheswaranathan, Jascha Sohl-Dickstein

We show that a population of randomly initialized learned optimizers can be used to train themselves from scratch in an online fashion, without resorting to a hand designed optimizer in any part of the process.

Overcoming barriers to the training of effective learned optimizers

no code implementations1 Jan 2021 Luke Metz, Niru Maheswaranathan, C. Daniel Freeman, Ben Poole, Jascha Sohl-Dickstein

In this work we focus on general-purpose learned optimizers capable of training a wide variety of problems with no user-specified hyperparameters.

Tasks, stability, architecture, and compute: Training more effective learned optimizers, and using them to train themselves

no code implementations23 Sep 2020 Luke Metz, Niru Maheswaranathan, C. Daniel Freeman, Ben Poole, Jascha Sohl-Dickstein

In this work we focus on general-purpose learned optimizers capable of training a wide variety of problems with no user-specified hyperparameters.

Learning to Predict Without Looking Ahead: World Models Without Forward Prediction

2 code implementations NeurIPS 2019 C. Daniel Freeman, Luke Metz, David Ha

That useful models can arise out of the messy and slow optimization process of evolution suggests that forward-predictive modeling can arise as a side-effect of optimization under the right circumstances.

Model-based Reinforcement Learning

Understanding and correcting pathologies in the training of learned optimizers

1 code implementation24 Oct 2018 Luke Metz, Niru Maheswaranathan, Jeremy Nixon, C. Daniel Freeman, Jascha Sohl-Dickstein

Deep learning has shown that learned functions can dramatically outperform hand-designed functions on perceptual tasks.

Topology and Geometry of Half-Rectified Network Optimization

1 code implementation4 Nov 2016 C. Daniel Freeman, Joan Bruna

Our theoretical work quantifies and formalizes two important \emph{folklore} facts: (i) the landscape of deep linear networks has a radically different topology from that of deep half-rectified ones, and (ii) that the energy landscape in the non-linear case is fundamentally controlled by the interplay between the smoothness of the data distribution and model over-parametrization.

Engineering autonomous error correction in stabilizer codes at finite temperature

1 code implementation16 Mar 2016 C. Daniel Freeman, C. M. Herdman, K. B. Whaley

We present an error correcting protocol that enhances the lifetime of stabilizer code based qubits which are susceptible to the creation of pairs of localized defects (due to string-like error operators) at finite temperature, such as the toric code.

Quantum Physics Statistical Mechanics

Relaxation dynamics of the toric code in contact with a thermal reservoir: Finite-size scaling in a low temperature regime

1 code implementation9 May 2014 C. Daniel Freeman, C. M. Herdman, Dylan J Gorman, K. B. Whaley

In contrast to the size-independent bound predicted for the toric code in the thermodynamic limit, we identify a low-temperature regime on finite lattices below a size-dependent crossover temperature with nontrivial finite-size and temperature scaling of the relaxation time.

Statistical Mechanics Quantum Physics

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