2 citations · 6 across the 11 of their papers we have counts for
7 papers · 1 filter
Finding Interpretable Class-Specific Patterns through Efficient Neural Search
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. In molecular biology, for example,…
Preserving local densities in low-dimensional embeddings
Jonas Fischer, Rebekka Burkholz, Jilles Vreeken
Low-dimensional embeddings and visualizations are an indispensable tool for analysis of high-dimensional data. State-of-the-art methods, such as tSNE and UMAP, excel in unveiling l…
Plant 'n' Seek: Can You Find the Winning Ticket?
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 m…
Lottery Tickets with Nonzero Biases
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 w…
Label-Descriptive Patterns and Their Application to Characterizing Classification Errors
Michael Hedderich, Jonas Fischer, Dietrich Klakow +1
State-of-the-art deep learning methods achieve human-like performance on many tasks, but make errors nevertheless. Characterizing these errors in easily interpretable terms gives i…
Federated Learning from Small Datasets
Michael Kamp, Jonas Fischer, Jilles Vreeken
Federated learning allows multiple parties to collaboratively train a joint model without sharing local data. This enables applications of machine learning in settings of inherentl…
Factoring out prior knowledge from low-dimensional embeddings
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. Although they ar…