5 citations · 10 across the 7 of their papers we have counts for
4 papers · 1 filter
How (Implicit) Regularization of ReLU Neural Networks Characterizes the Learned Function -- Part II: the Multi-D Case of Two Layers with Random First Layer
Jakob Heiss, Josef Teichmann, Hanna Wutte
Randomized neural networks (randomized NNs), where only the terminal layer's weights are optimized constitute a powerful model class to reduce computational time in training the ne…
How Infinitely Wide Neural Networks Can Benefit from Multi-task Learning -- an Exact Macroscopic Characterization
Jakob Heiss, Josef Teichmann, Hanna Wutte
In practice, multi-task learning (through learning features shared among tasks) is an essential property of deep neural networks (NNs). While infinite-width limits of NNs can provi…
NOMU: Neural Optimization-based Model Uncertainty
Jakob Heiss, Jakob Weissteiner, Hanna Wutte +2
We study methods for estimating model uncertainty for neural networks (NNs) in regression. To isolate the effect of model uncertainty, we focus on a noiseless setting with scarce t…
How Implicit Regularization of ReLU Neural Networks Characterizes the Learned Function -- Part I: the 1-D Case of Two Layers with Random First Layer
Jakob Heiss, Josef Teichmann, Hanna Wutte
In this paper, we consider one dimensional (shallow) ReLU neural networks in which weights are chosen randomly and only the terminal layer is trained. First, we mathematically show…