Jonas Kohler

According to our database, Jonas Kohler authored at least 23 papers between 2017 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Deep learning applied to the segmentation of rodent brain MRI data outperforms noisy ground truth on full-fledged brain atlases.

Jonas Kohler, Thomas Bielser, Stanislaw Adaszewski, Basil Künnecke, Andreas Bruns

NeuroImage, 2024

Movie Gen: A Cast of Media Foundation Models.

Adam Polyak, Amit Zohar, Andrew Brown, Andros Tjandra, Animesh Sinha, Ann Lee, Apoorv Vyas, Bowen Shi, Chih-Yao Ma, Ching-Yao Chuang, David Yan, Dhruv Choudhary, Dingkang Wang, Geet Sethi, Guan Pang, Haoyu Ma, Ishan Misra, Ji Hou, Jialiang Wang, Kiran Jagadeesh, Kunpeng Li, Luxin Zhang, Mannat Singh, Mary Williamson, Matt Le, Matthew Yu, Mitesh Kumar Singh, Peizhao Zhang, Peter Vajda, Quentin Duval, Rohit Girdhar, Roshan Sumbaly, Sai Saketh Rambhatla, Sam S. Tsai, Samaneh Azadi, Samyak Datta, Sanyuan Chen, Sean Bell, Sharadh Ramaswamy, Shelly Sheynin, Siddharth Bhattacharya, Simran Motwani, Tao Xu, Tianhe Li, Tingbo Hou, Wei-Ning Hsu, Xi Yin, Xiaoliang Dai, Yaniv Taigman, Yaqiao Luo, Yen-Cheng Liu, Yi-Chiao Wu, Yue Zhao, Yuval Kirstain, Zecheng He, Zijian He, Albert Pumarola, Ali K. Thabet, Artsiom Sanakoyeu, Arun Mallya, Baishan Guo, Boris Araya, Breena Kerr, Carleigh Wood, Ce Liu, Cen Peng, Dmitry Vengertsev, Edgar Schönfeld, Elliot Blanchard, Felix Juefei-Xu, Fraylie Nord, Jeff Liang, John Hoffman, Jonas Kohler, Kaolin Fire, Karthik Sivakumar, Lawrence Chen, Licheng Yu, Luya Gao, Markos Georgopoulos, Rashel Moritz, Sara K. Sampson, Shikai Li, Simone Parmeggiani, Steve Fine, Tara Fowler, Vladan Petrovic, Yuming Du

CoRR, 2024

Imagine Flash: Accelerating Emu Diffusion Models with Backward Distillation.

Jonas Kohler, Albert Pumarola, Edgar Schönfeld, Artsiom Sanakoyeu, Roshan Sumbaly, Peter Vajda, Ali K. Thabet

CoRR, 2024

Cache Me if You Can: Accelerating Diffusion Models through Block Caching.

Felix Wimbauer, Bichen Wu, Edgar Schönfeld, Xiaoliang Dai, Ji Hou, Zijian He, Artsiom Sanakoyeu, Peizhao Zhang, Sam S. Tsai, Jonas Kohler, Christian Rupprecht, Daniel Cremers, Peter Vajda, Jialiang Wang

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024

2023

fMPI: Fast Novel View Synthesis in the Wild with Layered Scene Representations.

Jonas Kohler, Nicolas Griffiths Sanchez, Luca Cavalli, Catherine Herold, Albert Pumarola, Alberto Garcia Garcia, Ali K. Thabet

CoRR, 2023

Adaptive Guidance: Training-free Acceleration of Conditional Diffusion Models.

Angela Castillo, Jonas Kohler, Juan C. Pérez, Juan Pablo Pérez, Albert Pumarola, Bernard Ghanem, Pablo Arbeláez, Ali K. Thabet

CoRR, 2023

2022

Insights on the interplay of network architectures and optimization algorithms in deep learning.

Jonas Kohler

PhD thesis, 2022

Vanishing Curvature in Randomly Initialized Deep ReLU Networks.

Antonio Orvieto, Jonas Kohler, Dario Pavllo, Thomas Hofmann, Aurélien Lucchi

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

Synthesizing Speech from Intracranial Depth Electrodes using an Encoder-Decoder Framework.

Jonas Kohler, Maarten C. Ottenhoff, Sophocles Goulis, Miguel Angrick, Albert J. Colon, G. Louis Wagner, Simon Tousseyn, Pieter Leonard Kubben, Christian Herff

CoRR, 2021

Safe Deep Reinforcement Learning for Multi-Agent Systems with Continuous Action Spaces.

Ziyad Sheebaelhamd, Konstantinos Zisis, Athina Nisioti, Dimitris Gkouletsos, Dario Pavllo, Jonas Kohler

CoRR, 2021

Vanishing Curvature and the Power of Adaptive Methods in Randomly Initialized Deep Networks.

Antonio Orvieto, Jonas Kohler, Dario Pavllo, Thomas Hofmann, Aurélien Lucchi

CoRR, 2021

This Looks Like That... Does it? Shortcomings of Latent Space Prototype Interpretability in Deep Networks.

Adrian Hoffmann, Claudio Fanconi, Rahul Rade, Jonas Kohler

CoRR, 2021

Learning Generative Models of Textured 3D Meshes from Real-World Images.

Dario Pavllo, Jonas Kohler, Thomas Hofmann, Aurélien Lucchi

Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision, 2021

2020

Two-Level K-FAC Preconditioning for Deep Learning.

Nikolaos Tselepidis, Jonas Kohler, Antonio Orvieto

CoRR, 2020

Theoretical Understanding of Batch-normalization: A Markov Chain Perspective.

Hadi Daneshmand, Jonas Moritz Kohler, Francis R. Bach, Thomas Hofmann, Aurélien Lucchi

CoRR, 2020

Batch normalization provably avoids ranks collapse for randomly initialised deep networks.

Hadi Daneshmand, Jonas Moritz Kohler, Francis R. Bach, Thomas Hofmann, Aurélien Lucchi

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2019

A Stochastic Tensor Method for Non-convex Optimization.

Aurélien Lucchi, Jonas Kohler

CoRR, 2019

Ellipsoidal Trust Region Methods and the Marginal Value of Hessian Information for Neural Network Training.

Leonard Adolphs, Jonas Kohler, Aurélien Lucchi

CoRR, 2019

The Role of Memory in Stochastic Optimization.

Antonio Orvieto, Jonas Kohler, Aurélien Lucchi

Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, 2019

Exponential convergence rates for Batch Normalization: The power of length-direction decoupling in non-convex optimization.

Jonas Moritz Kohler, Hadi Daneshmand, Aurélien Lucchi, Thomas Hofmann, Ming Zhou, Klaus Neymeyr

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018

Towards a Theoretical Understanding of Batch Normalization.

Jonas Moritz Kohler, Hadi Daneshmand, Aurélien Lucchi, Ming Zhou, Klaus Neymeyr, Thomas Hofmann

CoRR, 2018

Escaping Saddles with Stochastic Gradients.

Hadi Daneshmand, Jonas Moritz Kohler, Aurélien Lucchi, Thomas Hofmann

Proceedings of the 35th International Conference on Machine Learning, 2018

2017

Sub-sampled Cubic Regularization for Non-convex Optimization.

Jonas Moritz Kohler, Aurélien Lucchi

Proceedings of the 34th International Conference on Machine Learning, 2017