Jonathan Lorraine

32 papers A* 4A 2Journal 22Unranked 3
YearRankTypeTitle / Venue / Authors
2026 J jnl
Motion Attribution for Video Generation.
CoRR
Xindi Wu, Despoina Paschalidou, Jun Gao, Antonio Torralba, Laura Leal-Taixé, Olga Russakovsky, Sanja Fidler, Jonathan Lorraine
2025 J jnl
Score Distillation Sampling for Audio: Source Separation, Synthesis, and Beyond.
CoRR
Jessie Richter-Powell, Antonio Torralba, Jonathan Lorraine
2024 A* conf
Graph Metanetworks for Processing Diverse Neural Architectures.
ICLR
Derek Lim, Haggai Maron, Marc T. Law, Jonathan Lorraine, James Lucas
2024 — conf
Improving Hyperparameter Optimization with Checkpointed Model Weights.
ECCV Workshops (11)
Nikhil Mehta, Jonathan Lorraine, Steve Masson, Ramanathan Arunachalam, Zaid Pervaiz Bhat, James Lucas, Arun George Zachariah
2024 J jnl
Improving Hyperparameter Optimization with Checkpointed Model Weights.
CoRR
Nikhil Mehta, Jonathan Lorraine, Steve Masson, Ramanathan Arunachalam, Zaid Pervaiz Bhat, James Lucas, Arun George Zachariah
2024 J jnl
JacNet: Learning Functions with Structured Jacobians.
CoRR
Jonathan Lorraine, Safwan Hossain
2024 — conf
LATTE3D: Large-scale Amortized Text-To-Enhanced3D Synthesis.
ECCV (77)
Kevin Xie, Jonathan Lorraine, Tianshi Cao, Jun Gao, James Lucas, Antonio Torralba, Sanja Fidler, Xiaohui Zeng
2024 J jnl
LATTE3D: Large-scale Amortized Text-To-Enhanced3D Synthesis.
CoRR
Kevin Xie, Jonathan Lorraine, Tianshi Cao, Jun Gao, James Lucas, Antonio Torralba, Sanja Fidler, Xiaohui Zeng
2024 J jnl
LLaMA-Mesh: Unifying 3D Mesh Generation with Language Models.
CoRR
Zhengyi Wang, Jonathan Lorraine, Yikai Wang, Hang Su, Jun Zhu, Sanja Fidler, Xiaohui Zeng
2024 J jnl
Multi-student Diffusion Distillation for Better One-step Generators.
CoRR
Yanke Song, Jonathan Lorraine, Weili Nie, Karsten Kreis, James Lucas
2024 — —
Scalable Nested Optimization For Deep Learning
Jonathan Lorraine
2024 J jnl
Scalable Nested Optimization for Deep Learning.
CoRR
Jonathan Lorraine
2024 J jnl
Training Data Attribution via Approximate Unrolled Differentiation.
CoRR
Juhan Bae, Wu Lin, Jonathan Lorraine, Roger B. Grosse
2024 A* conf
Training Data Attribution via Approximate Unrolling.
NeurIPS
Juhan Bae, Wu Lin, Jonathan Lorraine, Roger B. Grosse
2023 A* conf
ATT3D: Amortized Text-to-3D Object Synthesis.
ICCV
Jonathan Lorraine, Kevin Xie, Xiaohui Zeng, Chen-Hsuan Lin, Towaki Takikawa, Nicholas Sharp, Tsung-Yi Lin, Ming-Yu Liu, Sanja Fidler, James Lucas
2023 J jnl
ATT3D: Amortized Text-to-3D Object Synthesis.
CoRR
Jonathan Lorraine, Kevin Xie, Xiaohui Zeng, Chen-Hsuan Lin, Towaki Takikawa, Nicholas Sharp, Tsung-Yi Lin, Ming-Yu Liu, Sanja Fidler, James Lucas
2023 J jnl
Graph Metanetworks for Processing Diverse Neural Architectures.
CoRR
Derek Lim, Haggai Maron, Marc T. Law, Jonathan Lorraine, James Lucas
2023 J jnl
Using Large Language Models for Hyperparameter Optimization.
CoRR
Michael R. Zhang, Nishkrit Desai, Juhan Bae, Jonathan Lorraine, Jimmy Ba
2022 A conf
Lyapunov Exponents for Diversity in Differentiable Games.
AAMAS
Jonathan Lorraine, Paul Vicol, Jack Parker-Holder, Tal Kachman, Luke Metz, Jakob N. Foerster
2022 J jnl
On Implicit Bias in Overparameterized Bilevel Optimization.
CoRR
Paul Vicol, Jonathan Lorraine, Fabian Pedregosa, David Duvenaud, Roger B. Grosse
2022 J jnl
Task Selection for AutoML System Evaluation.
CoRR
Jonathan Lorraine, Nihesh Anderson, Chansoo Lee, Quentin de Laroussilhe, Mehadi Hassen
2021 J jnl
Complex Momentum for Learning in Games.
CoRR
Jonathan Lorraine, David Acuna, Paul Vicol, David Duvenaud
2021 J jnl
Input Convex Gradient Networks.
CoRR
Jack Richter-Powell, Jonathan Lorraine, Brandon Amos
2021 J jnl
Lyapunov Exponents for Diversity in Differentiable Games.
CoRR
Jonathan Lorraine, Paul Vicol, Jack Parker-Holder, Tal Kachman, Luke Metz, Jakob N. Foerster
2021 J jnl
Meta-Learning to Improve Pre-Training.
CoRR
Aniruddh Raghu, Jonathan Lorraine, Simon Kornblith, Matthew McDermott, David Duvenaud
2021 A* conf
Meta-learning to Improve Pre-training.
NeurIPS
Aniruddh Raghu, Jonathan Lorraine, Simon Kornblith, Matthew McDermott, David Duvenaud
2020 A conf
Optimizing Millions of Hyperparameters by Implicit Differentiation.
AISTATS
Jonathan Lorraine, Paul Vicol, David Duvenaud
2019 J jnl
Optimizing Millions of Hyperparameters by Implicit Differentiation.
CoRR
Jonathan Lorraine, Paul Vicol, David Duvenaud
2019 — conf
Self-Tuning Networks: Bilevel Optimization of Hyperparameters using Structured Best-Response Functions.
ICLR (Poster)
Matthew MacKay, Paul Vicol, Jonathan Lorraine, David Duvenaud, Roger B. Grosse
2019 J jnl
Self-Tuning Networks: Bilevel Optimization of Hyperparameters using Structured Best-Response Functions.
CoRR
Matthew MacKay, Paul Vicol, Jonathan Lorraine, David Duvenaud, Roger B. Grosse
2019 J jnl
Understanding Neural Architecture Search Techniques.
CoRR
George Adam, Jonathan Lorraine
2018 J jnl
Stochastic Hyperparameter Optimization through Hypernetworks.
CoRR
Jonathan Lorraine, David Duvenaud