C. Freeman

发表

Understanding and correcting pathologies in the training of learned optimizers

Jeremy Nixon, Jascha Sohl-Dickstein, Niru Maheswaranathan, 2018, ICML.

Stable quantum memories with limited measurement

K. B. Whaley, C. Herdman, C. Freeman, 2017, Physical Review A.

Using a thousand optimization tasks to learn hyperparameter search strategies

Ben Poole, J. Sohl-Dickstein, Luke Metz, 2020, ArXiv.

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

Luke Metz, Niru Maheswaranathan, J. Harrison, 2022, CoLLAs.

VeLO: Training Versatile Learned Optimizers by Scaling Up

Ben Poole, Igor Mordatch, James Bradbury, 2022, ArXiv.

Transformer-Based Learned Optimization

M. Andriluka, C. Sminchisescu, Luke Metz, 2022, ArXiv.

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

K. B. Whaley, C. Herdman, C. Freeman, 2014, 1405.2315.

Gradients are Not All You Need pdf

Samuel S. Schoenholz, Tal Kachman, Luke Metz, 2021, ArXiv.

Modern Approaches to Exact Diagonalization and Selected Configuration Interaction with the Adaptive Sampling CI Method.

Daniel S. Levine, K. Birgitta Whaley, C. Daniel Freeman, 2018, Journal of chemical theory and computation.

Topology and Geometry of Half-Rectified Network Optimization pdf

Joan Bruna, C. Daniel Freeman, Joan Bruna, 2016, ICLR.

Breakup measurements of particle unbound states in 10 B

C. Freeman, K. Jones, N. Orr, 2001 .

The Toric Code at Finite Temperature

Christian Daniel Freeman, C. Freeman, 2018 .

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

Olivier Bachem, Sertan Girgin, Igor Mordatch, 2021, NeurIPS Datasets and Benchmarks.

Training Learned Optimizers with Randomly Initialized Learned Optimizers pdf

Jascha Sohl-Dickstein, Niru Maheswaranathan, Luke Metz, 2021, ArXiv.

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

Jascha Sohl-Dickstein, Niru Maheswaranathan, Ben Poole, 2020, ArXiv.

Bi-Manual Manipulation and Attachment via Sim-to-Real Reinforcement Learning

Seyed Kamyar Seyed Ghasemipour, Igor Mordatch, C. Freeman, 2022, ArXiv.

Topology and Geometry of Deep Rectified Network Optimization Landscapes

Joan Bruna, C. Daniel Freeman, Joan Bruna, 2016 .

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

Luke Metz, C. Daniel Freeman, David Ha, 2019, NeurIPS.

Transformer-Based Learned Optimization

M. Andriluka, C. Sminchisescu, Luke Metz, 2022, 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Next Generation of Selected Configuration Interaction and Exact Diagonalization Techniques: The Adaptive Sampling Configuration Interaction (ASCI) Method

K. Birgitta Whaley, K. B. Whaley, Norm M. Tubman, 2018 .

Monte Carlo Tensor Network Renormalization

K. B. Whaley, N. Tubman, C. Freeman, 2017, 1710.03757.

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

Jeremy Nixon, Jascha Sohl-Dickstein, Niru Maheswaranathan, 2018, ArXiv.

Learned optimizers that outperform on wall-clock and validation loss

Jeremy Nixon, Jascha Sohl-Dickstein, Niru Maheswaranathan, 2018 .

Allowing Safe Contact in Robotic Goal-Reaching: Planning and Tracking in Operational and Null Spaces

M. Tomizuka, Wenzhao Lian, C. Freeman, 2022, 2023 IEEE International Conference on Robotics and Automation (ICRA).

Entanglement structure of non-equilibrium steady states

N. Tubman, C. Freeman, R. Mahajan, 2016, 1608.05074.