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ORCIDJonas Kohler
Person information
- affiliation: Meta GenAI
- affiliation (PhD 2022): ETH Zurich, Switzerland
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2020 – today
- 2025
[c13]Angela Castillo, Jonas Kohler, Juan C. Pérez, Juan Pablo Pérez, Albert Pumarola, Bernard Ghanem
, Pablo Arbeláez, Ali K. Thabet:
Adaptive Guidance: Training-free Acceleration of Conditional Diffusion Models. AAAI 2025: 1962-1970- [c12]Yeongmin Kim, Sotiris Anagnostidis, Yuming Du, Edgar Schönfeld, Jonas Kohler, Markos Georgopoulos, Albert Pumarola, Ali K. Thabet, Artsiom Sanakoyeu:
Autoregressive Distillation of Diffusion Transformers. CVPR 2025: 15745-15756 - [c11]Sotiris Anagnostidis, Gregor Bachmann, Yeongmin Kim, Jonas Kohler, Markos Georgopoulos, Artsiom Sanakoyeu, Yuming Du, Albert Pumarola, Ali K. Thabet, Edgar Schönfeld:
FlexiDiT: Your Diffusion Transformer Can Easily Generate High-Quality Samples with Less Compute. CVPR 2025: 28316-28326 - [c10]Gregor Bachmann, Sotiris Anagnostidis, Albert Pumarola, Markos Georgopoulos, Artsiom Sanakoyeu, Yuming Du, Edgar Schönfeld, Ali K. Thabet, Jonas Kohler:
Judge Decoding: Faster Speculative Sampling Requires Going Beyond Model Alignment. ICLR 2025 - [c9]Jaskirat Singh, Junshen K. Chen, Jonas Kohler, Michael F. Cohen:
Storybooth: Training-Free Multi-Subject Consistency for Improved Visual Storytelling. ICLR 2025 - [i22]Gregor Bachmann, Sotiris Anagnostidis, Albert Pumarola, Markos Georgopoulos, Artsiom Sanakoyeu, Yuming Du, Edgar Schönfeld, Ali K. Thabet, Jonas Kohler:
Judge Decoding: Faster Speculative Sampling Requires Going Beyond Model Alignment. CoRR abs/2501.19309 (2025) - [i21]Sotiris Anagnostidis, Gregor Bachmann, Yeongmin Kim, Jonas Kohler, Markos Georgopoulos, Artsiom Sanakoyeu, Yuming Du, Albert Pumarola, Ali K. Thabet, Edgar Schönfeld:
FlexiDiT: Your Diffusion Transformer Can Easily Generate High-Quality Samples with Less Compute. CoRR abs/2502.20126 (2025) - [i20]Jaskirat Singh, Junshen Kevin Chen, Jonas Kohler, Michael F. Cohen:
Storybooth: Training-free Multi-Subject Consistency for Improved Visual Storytelling. CoRR abs/2504.05800 (2025) - [i19]Yeongmin Kim, Sotiris Anagnostidis, Yuming Du, Edgar Schönfeld, Jonas Kohler, Markos Georgopoulos, Albert Pumarola, Ali K. Thabet, Artsiom Sanakoyeu:
Autoregressive Distillation of Diffusion Transformers. CoRR abs/2504.11295 (2025) - 2024
- [j1]Jonas Kohler, Thomas Bielser, Stanislaw Adaszewski, Basil Künnecke, Andreas Bruns:
Deep learning applied to the segmentation of rodent brain MRI data outperforms noisy ground truth on full-fledged brain atlases. NeuroImage 304: 120934 (2024) - [c8]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:
Cache Me if You Can: Accelerating Diffusion Models through Block Caching. CVPR 2024: 6211-6220 - [i18]Jonas Kohler, Albert Pumarola, Edgar Schönfeld, Artsiom Sanakoyeu, Roshan Sumbaly, Peter Vajda, Ali K. Thabet:
Imagine Flash: Accelerating Emu Diffusion Models with Backward Distillation. CoRR abs/2405.05224 (2024) - [i17]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:
Movie Gen: A Cast of Media Foundation Models. CoRR abs/2410.13720 (2024) - 2023
- [i16]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:
Cache Me if You Can: Accelerating Diffusion Models through Block Caching. CoRR abs/2312.03209 (2023) - [i15]Angela Castillo, Jonas Kohler, Juan C. Pérez, Juan Pablo Pérez, Albert Pumarola, Bernard Ghanem, Pablo Arbeláez, Ali K. Thabet:
Adaptive Guidance: Training-free Acceleration of Conditional Diffusion Models. CoRR abs/2312.12487 (2023) - [i14]Jonas Kohler, Nicolas Griffiths Sanchez, Luca Cavalli, Catherine Herold, Albert Pumarola, Alberto Garcia Garcia, Ali K. Thabet:
fMPI: Fast Novel View Synthesis in the Wild with Layered Scene Representations. CoRR abs/2312.16109 (2023) - 2022
- [b1]Jonas Kohler:
Insights on the interplay of network architectures and optimization algorithms in deep learning. ETH Zurich, Zürich, Switzerland, 2022 - [c7]Antonio Orvieto, Jonas Kohler, Dario Pavllo, Thomas Hofmann, Aurélien Lucchi:
Vanishing Curvature in Randomly Initialized Deep ReLU Networks. AISTATS 2022: 7942-7975 - 2021
- [c6]Dario Pavllo, Jonas Kohler, Thomas Hofmann, Aurélien Lucchi:
Learning Generative Models of Textured 3D Meshes from Real-World Images. ICCV 2021: 13859-13869 - [i13]Dario Pavllo, Jonas Kohler, Thomas Hofmann, Aurélien Lucchi:
Learning Generative Models of Textured 3D Meshes from Real-World Images. CoRR abs/2103.15627 (2021) - [i12]Adrian Hoffmann, Claudio Fanconi, Rahul Rade, Jonas Kohler:
This Looks Like That... Does it? Shortcomings of Latent Space Prototype Interpretability in Deep Networks. CoRR abs/2105.02968 (2021) - [i11]Antonio Orvieto, Jonas Kohler, Dario Pavllo, Thomas Hofmann, Aurélien Lucchi:
Vanishing Curvature and the Power of Adaptive Methods in Randomly Initialized Deep Networks. CoRR abs/2106.03763 (2021) - [i10]Ziyad Sheebaelhamd, Konstantinos Zisis, Athina Nisioti, Dimitris Gkouletsos, Dario Pavllo, Jonas Kohler:
Safe Deep Reinforcement Learning for Multi-Agent Systems with Continuous Action Spaces. CoRR abs/2108.03952 (2021) - [i9]Jonas Kohler, Maarten C. Ottenhoff, Sophocles Goulis, Miguel Angrick, Albert J. Colon, G. Louis Wagner, Simon Tousseyn, Pieter Leonard Kubben, Christian Herff:
Synthesizing Speech from Intracranial Depth Electrodes using an Encoder-Decoder Framework. CoRR abs/2111.01457 (2021) - 2020
- [c5]Hadi Daneshmand, Jonas Moritz Kohler, Francis R. Bach, Thomas Hofmann, Aurélien Lucchi:
Batch normalization provably avoids ranks collapse for randomly initialised deep networks. NeurIPS 2020 - [i8]Hadi Daneshmand, Jonas Moritz Kohler, Francis R. Bach, Thomas Hofmann, Aurélien Lucchi:
Theoretical Understanding of Batch-normalization: A Markov Chain Perspective. CoRR abs/2003.01652 (2020) - [i7]Nikolaos Tselepidis, Jonas Kohler, Antonio Orvieto:
Two-Level K-FAC Preconditioning for Deep Learning. CoRR abs/2011.00573 (2020)
2010 – 2019
- 2019
- [c4]Jonas Moritz Kohler, Hadi Daneshmand, Aurélien Lucchi, Thomas Hofmann, Ming Zhou, Klaus Neymeyr:
Exponential convergence rates for Batch Normalization: The power of length-direction decoupling in non-convex optimization. AISTATS 2019: 806-815 - [c3]Antonio Orvieto, Jonas Kohler, Aurélien Lucchi:
The Role of Memory in Stochastic Optimization. UAI 2019: 356-366 - [i6]Leonard Adolphs, Jonas Kohler, Aurélien Lucchi:
Ellipsoidal Trust Region Methods and the Marginal Value of Hessian Information for Neural Network Training. CoRR abs/1905.09201 (2019) - [i5]Antonio Orvieto, Jonas Kohler, Aurélien Lucchi:
The Role of Memory in Stochastic Optimization. CoRR abs/1907.01678 (2019) - [i4]Aurélien Lucchi, Jonas Kohler:
A Stochastic Tensor Method for Non-convex Optimization. CoRR abs/1911.10367 (2019) - 2018
- [c2]Hadi Daneshmand, Jonas Moritz Kohler, Aurélien Lucchi, Thomas Hofmann:
Escaping Saddles with Stochastic Gradients. ICML 2018: 1163-1172 - [i3]Hadi Daneshmand, Jonas Moritz Kohler, Aurélien Lucchi, Thomas Hofmann:
Escaping Saddles with Stochastic Gradients. CoRR abs/1803.05999 (2018) - [i2]Jonas Moritz Kohler, Hadi Daneshmand, Aurélien Lucchi, Ming Zhou, Klaus Neymeyr, Thomas Hofmann:
Towards a Theoretical Understanding of Batch Normalization. CoRR abs/1805.10694 (2018) - 2017
- [c1]Jonas Moritz Kohler, Aurélien Lucchi:
Sub-sampled Cubic Regularization for Non-convex Optimization. ICML 2017: 1895-1904 - [i1]Jonas Moritz Kohler, Aurélien Lucchi:
Sub-sampled Cubic Regularization for Non-convex Optimization. CoRR abs/1705.05933 (2017)
Coauthor Index
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last updated on 2025-11-22 05:10 CET by the dblp team
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