Jakob Heiss

发表

How Infinitely Wide Neural Networks Benefit from Multi-task Learning -- an Exact Macroscopic Characterization

J. Teichmann, Jakob Heiss, Hanna Wutte, 2021, 2112.15577.

Infinite wide (finite depth) Neural Networks benefit from multi-task learning unlike shallow Gaussian Processes - an exact quantitative macroscopic characterization

J. Teichmann, Jakob Heiss, Hanna Wutte, 2021, ArXiv.

NOMU: Neural Optimization-based Model Uncertainty

Sven Seuken, J. Teichmann, Jakob Weissteiner, 2021, ICML.

Bayesian Optimization-based Combinatorial Assignment

Julien N. Siems, Sven Seuken, Jakob Weissteiner, 2022, 2208.14698.

Monotone-Value Neural Networks: Exploiting Preference Monotonicity in Combinatorial Assignment

Julien N. Siems, Sven Seuken, Jakob Weissteiner, 2021, IJCAI.

How Infinitely Wide Neural Networks Can Benefit from Multi-task Learning -- an Exact Macroscopic Characterization

J. Teichmann, Jakob Heiss, Hanna Wutte, 2021, 2112.15577.

Bayesian Optimization-based Combinatorial Assignment

Julien N. Siems, Sven Seuken, Jakob Weissteiner, 2022, AAAI.

How implicit regularization of Neural Networks affects the learned function - Part I pdf

Josef Teichmann, Jakob Heiss, Hanna Wutte, 2019, ArXiv.

How Implicit Regularization of ReLU Neural Networks Characterizes the Learned Function -- Part I: the 1-D Case of Two Layers with Random First Layer

J. Teichmann, Jakob Heiss, Hanna Wutte, 2019, 1911.02903.

How (Implicit) Regularization of ReLU Neural Networks Characterizes the Learned Function - Part II: the Multi-D Case of Two Layers with Random First Layer

J. Teichmann, Jakob Heiss, Hanna Wutte, 2023, ArXiv.

NOMU: Neural Optimization-based Model Uncertainty

Sven Seuken, J. Teichmann, Jakob Weissteiner, 2021, ICML.

How Implicit Regularization of ReLU Neural Networks Characterizes the Learned Function -- Part I: the 1-D Case of Two Layers with Random First Layer

J. Teichmann, Jakob Heiss, Hanna Wutte, 2019, 1911.02903.