Search Results for author: C. Daniel Freeman

Found 17 papers, 10 papers with code

Improving Large Language Model Fine-tuning for Solving Math Problems

no code implementations16 Oct 2023 Yixin Liu, Avi Singh, C. Daniel Freeman, John D. Co-Reyes, Peter J. Liu

With these methods, we present a thorough empirical study on a series of PaLM 2 models and find: (1) The quality and style of the step-by-step solutions used for fine-tuning can make a significant impact on the model performance; (2) While solution re-ranking and majority voting are both effective for improving the model performance when used separately, they can also be used together for an even greater performance boost; (3) Multi-task fine-tuning that sequentially separates the solution generation and evaluation tasks can offer improved performance compared with the solution fine-tuning baseline.

Language Modelling Large Language Model +2

Transformer-Based Learned Optimization

no code implementations CVPR 2023 Erik Gärtner, Luke Metz, Mykhaylo Andriluka, C. Daniel Freeman, Cristian Sminchisescu

We propose a new approach to learned optimization where we represent the computation of an optimizer's update step using a neural network.

VeLO: Training Versatile Learned Optimizers by Scaling Up

1 code implementation17 Nov 2022 Luke Metz, James Harrison, C. Daniel Freeman, Amil Merchant, Lucas Beyer, James Bradbury, Naman Agrawal, Ben Poole, Igor Mordatch, Adam Roberts, Jascha Sohl-Dickstein

While deep learning models have replaced hand-designed features across many domains, these models are still trained with hand-designed optimizers.

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

1 code implementation22 Mar 2022 Luke Metz, C. Daniel Freeman, James Harrison, Niru Maheswaranathan, Jascha Sohl-Dickstein

We further leverage our analysis to construct a learned optimizer that is both faster and more memory efficient than previous work.

Gradients are Not All You Need

1 code implementation10 Nov 2021 Luke Metz, C. Daniel Freeman, Samuel S. Schoenholz, Tal Kachman

Differentiable programming techniques are widely used in the community and are responsible for the machine learning renaissance of the past several decades.

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 reinforcement-learning +1

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 reinforcement-learning +1

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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