Timo Klock

According to our database, Timo Klock authored at least 12 papers between 2017 and 2023.

Collaborative distances:

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

2023

Gradient is All You Need?

Konstantin Riedl, Timo Klock, Carina Geldhauser, Massimo Fornasier

CoRR, 2023

2022

Finite Sample Identification of Wide Shallow Neural Networks with Biases.

Massimo Fornasier, Timo Klock, Marco Mondelli, Michael Rauchensteiner

CoRR, 2022

Semi-Supervised Manifold Learning with Complexity Decoupled Chart Autoencoders.

Stefan C. Schonsheck, Scott Mahan, Timo Klock, Alexander Cloninger, Rongjie Lai

CoRR, 2022

Convergence of Anisotropic Consensus-Based Optimization in Mean-Field Law.

Massimo Fornasier, Timo Klock, Konstantin Riedl

Proceedings of the Applications of Evolutionary Computation - 25th European Conference, 2022

2021

A deep network construction that adapts to intrinsic dimensionality beyond the domain.

Alexander Cloninger, Timo Klock

Neural Networks, 2021

Consensus-based optimization methods converge globally in mean-field law.

Massimo Fornasier, Timo Klock, Konstantin Riedl

CoRR, 2021

Stable Recovery of Entangled Weights: Towards Robust Identification of Deep Neural Networks from Minimal Samples.

Christian Fiedler, Massimo Fornasier, Timo Klock, Michael Rauchensteiner

CoRR, 2021

Landscape analysis of an improved power method for tensor decomposition.

Joe Kileel, Timo Klock, João M. Pereira

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020

ReLU nets adapt to intrinsic dimensionality beyond the target domain.

Alexander Cloninger, Timo Klock

CoRR, 2020

2019

Robust and Resource Efficient Identification of Two Hidden Layer Neural Networks.

Massimo Fornasier, Timo Klock, Michael Rauchensteiner

CoRR, 2019

Nonlinear generalization of the single index model.

Zeljko Kereta, Timo Klock, Valeriya Naumova

CoRR, 2019

2017

Adaptive multi-penalty regularization based on a generalized Lasso path.

Markus Grasmair, Timo Klock, Valeriya Naumova

CoRR, 2017