Search Results for author: Jonas Fischer

Found 11 papers, 3 papers with code

Finding Interpretable Class-Specific Patterns through Efficient Neural Search

no code implementations7 Dec 2023 Nils Philipp Walter, Jonas Fischer, Jilles Vreeken

Discovering patterns in data that best describe the differences between classes allows to hypothesize and reason about class-specific mechanisms.

Understanding and Mitigating Classification Errors Through Interpretable Token Patterns

no code implementations18 Nov 2023 Michael A. Hedderich, Jonas Fischer, Dietrich Klakow, Jilles Vreeken

Characterizing these errors in easily interpretable terms gives insight into whether a classifier is prone to making systematic errors, but also gives a way to act and improve the classifier.

Classification NER +1

Preserving local densities in low-dimensional embeddings

no code implementations31 Jan 2023 Jonas Fischer, Rebekka Burkholz, Jilles Vreeken

We show, however, that these methods fail to reconstruct local properties, such as relative differences in densities (Fig.

Plant 'n' Seek: Can You Find the Winning Ticket?

1 code implementation ICLR 2022 Jonas Fischer, Rebekka Burkholz

The lottery ticket hypothesis has sparked the rapid development of pruning algorithms that aim to reduce the computational costs associated with deep learning during training and model deployment.

Lottery Tickets with Nonzero Biases

no code implementations21 Oct 2021 Jonas Fischer, Advait Gadhikar, Rebekka Burkholz

The strong lottery ticket hypothesis holds the promise that pruning randomly initialized deep neural networks could offer a computationally efficient alternative to deep learning with stochastic gradient descent.

Label-Descriptive Patterns and Their Application to Characterizing Classification Errors

2 code implementations18 Oct 2021 Michael Hedderich, Jonas Fischer, Dietrich Klakow, Jilles Vreeken

Characterizing these errors in easily interpretable terms gives insight into whether a classifier is prone to making systematic errors, but also gives a way to act and improve the classifier.

Descriptive named-entity-recognition +4

Federated Learning from Small Datasets

1 code implementation7 Oct 2021 Michael Kamp, Jonas Fischer, Jilles Vreeken

Federated learning allows multiple parties to collaboratively train a joint model without sharing local data.

Federated Learning

Picking Daisies in Private: Federated Learning from Small Datasets

no code implementations29 Sep 2021 Michael Kamp, Jonas Fischer, Jilles Vreeken

Federated learning allows multiple parties to collaboratively train a joint model without sharing local data.

Federated Learning

Factoring out prior knowledge from low-dimensional embeddings

no code implementations2 Mar 2021 Edith Heiter, Jonas Fischer, Jilles Vreeken

Low-dimensional embedding techniques such as tSNE and UMAP allow visualizing high-dimensional data and therewith facilitate the discovery of interesting structure.

What's in the Box? Exploring the Inner Life of Neural Networks with Robust Rules

no code implementations1 Jan 2021 Jonas Fischer, Anna Oláh, Jilles Vreeken

In particular, we consider activation values of a network for given data, and propose to mine noise-robust rules of the form $X \rightarrow Y$ , where $X$ and $Y$ are sets of neurons in different layers.

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