Jonas Kohler

Affiliations:
  • ETH Zurich, Zurich, Switzerland


According to our database1, Jonas Kohler authored at least 20 papers between 2017 and 2023.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Dataset
Other 

Links

On csauthors.net:

Bibliography

2023
fMPI: Fast Novel View Synthesis in the Wild with Layered Scene Representations.
CoRR, 2023

Adaptive Guidance: Training-free Acceleration of Conditional Diffusion Models.
CoRR, 2023

Cache Me if You Can: Accelerating Diffusion Models through Block Caching.
CoRR, 2023

2022
Insights on the interplay of network architectures and optimization algorithms in deep learning.
PhD thesis, 2022

Vanishing Curvature in Randomly Initialized Deep ReLU Networks.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021
Synthesizing Speech from Intracranial Depth Electrodes using an Encoder-Decoder Framework.
CoRR, 2021

Safe Deep Reinforcement Learning for Multi-Agent Systems with Continuous Action Spaces.
CoRR, 2021

Vanishing Curvature and the Power of Adaptive Methods in Randomly Initialized Deep Networks.
CoRR, 2021

This Looks Like That... Does it? Shortcomings of Latent Space Prototype Interpretability in Deep Networks.
CoRR, 2021

Learning Generative Models of Textured 3D Meshes from Real-World Images.
Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision, 2021

2020
Two-Level K-FAC Preconditioning for Deep Learning.
CoRR, 2020

Theoretical Understanding of Batch-normalization: A Markov Chain Perspective.
CoRR, 2020

Batch normalization provably avoids ranks collapse for randomly initialised deep networks.
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.
CoRR, 2019

Ellipsoidal Trust Region Methods and the Marginal Value of Hessian Information for Neural Network Training.
CoRR, 2019

The Role of Memory in Stochastic Optimization.
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.
Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018
Towards a Theoretical Understanding of Batch Normalization.
CoRR, 2018

Escaping Saddles with Stochastic Gradients.
Proceedings of the 35th International Conference on Machine Learning, 2018

2017
Sub-sampled Cubic Regularization for Non-convex Optimization.
Proceedings of the 34th International Conference on Machine Learning, 2017


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