C. Daniel Freeman

26 papers A* 4Journal 18Unranked 4

Year	Rank	Type	Title / Venue / Authors
2024	—	`conf`	Learned Neural Physics Simulation for Articulated 3D Human Pose Reconstruction. ECCV (84) Mykhaylo Andriluka, Baruch Tabanpour, C. Daniel Freeman, Cristian Sminchisescu
2024	J	`jnl`	Learned Neural Physics Simulation for Articulated 3D Human Pose Reconstruction. CoRR Mykhaylo Andriluka, Baruch Tabanpour, C. Daniel Freeman, Cristian Sminchisescu
2024	J	`jnl`	Training Language Models on the Knowledge Graph: Insights on Hallucinations and Their Detectability. CoRR Jiri Hron, Laura Culp, Gamaleldin F. Elsayed, Rosanne Liu, Ben Adlam, Maxwell L. Bileschi, Bernd Bohnet, JD Co-Reyes, Noah Fiedel, C. Daniel Freeman, Izzeddin Gur, Kathleen Kenealy, Jaehoon Lee, Peter J. Liu, Gaurav Mishra, Igor Mordatch, Azade Nova, Roman Novak, Aaron Parisi, Jeffrey Pennington, Alex Rizkowsky, Isabelle Simpson, Hanie Sedghi, Jascha Sohl-Dickstein, Kevin Swersky, Sharad Vikram, Tris Warkentin, Lechao Xiao, Kelvin Xu, Jasper Snoek, Simon Kornblith
2023	A*	`conf`	Allowing Safe Contact in Robotic Goal-Reaching: Planning and Tracking in Operational and Null Spaces. ICRA Xinghao Zhu, Wenzhao Lian, Bodi Yuan, C. Daniel Freeman, Masayoshi Tomizuka
2023	J	`jnl`	Frontier Language Models are not Robust to Adversarial Arithmetic, or "What do I need to say so you agree 2+2=5? CoRR C. Daniel Freeman, Laura Culp, Aaron Parisi, Maxwell L. Bileschi, Gamaleldin F. Elsayed, Alex Rizkowsky, Isabelle Simpson, Alex Alemi, Azade Nova, Ben Adlam, Bernd Bohnet, Gaurav Mishra, Hanie Sedghi, Igor Mordatch, Izzeddin Gur, Jaehoon Lee, John D. Co-Reyes, Jeffrey Pennington, Kelvin Xu, Kevin Swersky, Kshiteej Mahajan, Lechao Xiao, Rosanne Liu, Simon Kornblith, Noah Constant, Peter J. Liu, Roman Novak, Yundi Qian, Noah Fiedel, Jascha Sohl-Dickstein
2023	J	`jnl`	Improving Large Language Model Fine-tuning for Solving Math Problems. CoRR Yixin Liu, Avi Singh, C. Daniel Freeman, John D. Co-Reyes, Peter J. Liu
2023	A*	`conf`	Transformer-Based Learned Optimization. CVPR Erik Gärtner, Luke Metz, Mykhaylo Andriluka, C. Daniel Freeman, Cristian Sminchisescu
2022	J	`jnl`	Allowing Safe Contact in Robotic Goal-Reaching: Planning and Tracking in Operational and Null Spaces. CoRR Xinghao Zhu, Wenzhao Lian, Bodi Yuan, C. Daniel Freeman, Masayoshi Tomizuka
2022	J	`jnl`	Bi-Manual Manipulation and Attachment via Sim-to-Real Reinforcement Learning. CoRR Satoshi Kataoka, Seyed Kamyar Seyed Ghasemipour, C. Daniel Freeman, Igor Mordatch
2022	—	`conf`	Practical Tradeoffs between Memory, Compute, and Performance in Learned Optimizers. CoLLAs Luke Metz, C. Daniel Freeman, James Harrison, Niru Maheswaranathan, Jascha Sohl-Dickstein
2022	J	`jnl`	Practical tradeoffs between memory, compute, and performance in learned optimizers. CoRR Luke Metz, C. Daniel Freeman, James Harrison, Niru Maheswaranathan, Jascha Sohl-Dickstein
2022	J	`jnl`	Transformer-Based Learned Optimization. CoRR Erik Gärtner, Luke Metz, Mykhaylo Andriluka, C. Daniel Freeman, Cristian Sminchisescu
2022	J	`jnl`	VeLO: Training Versatile Learned Optimizers by Scaling Up. CoRR Luke Metz, James Harrison, C. Daniel Freeman, Amil Merchant, Lucas Beyer, James Bradbury, Naman Agrawal, Ben Poole, Igor Mordatch, Adam Roberts, Jascha Sohl-Dickstein
2021	—	`conf`	Brax - A Differentiable Physics Engine for Large Scale Rigid Body Simulation. NeurIPS Datasets and Benchmarks C. Daniel Freeman, Erik Frey, Anton Raichuk, Sertan Girgin, Igor Mordatch, Olivier Bachem
2021	J	`jnl`	Brax - A Differentiable Physics Engine for Large Scale Rigid Body Simulation. CoRR C. Daniel Freeman, Erik Frey, Anton Raichuk, Sertan Girgin, Igor Mordatch, Olivier Bachem
2021	J	`jnl`	Braxlines: Fast and Interactive Toolkit for RL-driven Behavior Engineering beyond Reward Maximization. CoRR Shixiang Shane Gu, Manfred Diaz, C. Daniel Freeman, Hiroki Furuta, Seyed Kamyar Seyed Ghasemipour, Anton Raichuk, Byron David, Erik Frey, Erwin Coumans, Olivier Bachem
2021	J	`jnl`	Gradients are Not All You Need. CoRR Luke Metz, C. Daniel Freeman, Samuel S. Schoenholz, Tal Kachman
2021	J	`jnl`	Training Learned Optimizers with Randomly Initialized Learned Optimizers. CoRR Luke Metz, C. Daniel Freeman, Niru Maheswaranathan, Jascha Sohl-Dickstein
2020	J	`jnl`	Tasks, stability, architecture, and compute: Training more effective learned optimizers, and using them to train themselves. CoRR Luke Metz, Niru Maheswaranathan, C. Daniel Freeman, Ben Poole, Jascha Sohl-Dickstein
2020	J	`jnl`	Using a thousand optimization tasks to learn hyperparameter search strategies. CoRR Luke Metz, Niru Maheswaranathan, Ruoxi Sun, C. Daniel Freeman, Ben Poole, Jascha Sohl-Dickstein
2019	A*	`conf`	Learning to Predict Without Looking Ahead: World Models Without Forward Prediction. NeurIPS C. Daniel Freeman, David Ha, Luke Metz
2019	J	`jnl`	Learning to Predict Without Looking Ahead: World Models Without Forward Prediction. CoRR C. Daniel Freeman, Luke Metz, David Ha
2019	A*	`conf`	Understanding and correcting pathologies in the training of learned optimizers. ICML Luke Metz, Niru Maheswaranathan, Jeremy Nixon, C. Daniel Freeman, Jascha Sohl-Dickstein
2018	J	`jnl`	Learned optimizers that outperform SGD on wall-clock and test loss. CoRR Luke Metz, Niru Maheswaranathan, Jeremy Nixon, C. Daniel Freeman, Jascha Sohl-Dickstein
2017	—	`conf`	Topology and Geometry of Half-Rectified Network Optimization. ICLR (Poster) C. Daniel Freeman, Joan Bruna
2016	J	`jnl`	Topology and Geometry of Half-Rectified Network Optimization. CoRR C. Daniel Freeman, Joan Bruna