11 citations · 47 across the 48 of their papers we have counts for
12 papers · 1 filter
Opportunities and limitations of explaining quantum machine learning
Elies Gil-Fuster, Jonas R. Naujoks, Grégoire Montavon +3
A common trait of many machine learning models is that it is often difficult to understand and explain what caused the model to produce the given output. While the explainability o…
Beyond Scalars: Concept-Based Alignment Analysis in Vision Transformers
Johanna Vielhaben, Dilyara Bareeva, Jim Berend +2
Vision transformers (ViTs) can be trained using various learning paradigms, from fully supervised to self-supervised. Diverse training protocols often result in significantly diffe…
Quanda: An Interpretability Toolkit for Training Data Attribution Evaluation and Beyond
Dilyara Bareeva, Galip Ümit Yolcu, Anna Hedström +4
In recent years, training data attribution (TDA) methods have emerged as a promising direction for the interpretability of neural networks. While research around TDA is thriving, l…
Synthetic Generation of Dermatoscopic Images with GAN and Closed-Form Factorization
Rohan Reddy Mekala, Frederik Pahde, Simon Baur +11
In the realm of dermatological diagnoses, where the analysis of dermatoscopic and microscopic skin lesion images is pivotal for the accurate and early detection of various medical…
PINNfluence: Interpreting PINNs through Influence Functions
Aleksander Krasowski, Jonas R. Naujoks, Moritz Weckbecker +5
Physics-informed neural networks (PINNs) have emerged as a powerful deep learning approach for solving partial differential equations (PDEs) in the physical sciences, yet their beh…
Pruning By Explaining Revisited: Optimizing Attribution Methods to Prune CNNs and Transformers
Sayed Mohammad Vakilzadeh Hatefi, Maximilian Dreyer, Reduan Achtibat +3
To solve ever more complex problems, Deep Neural Networks are scaled to billions of parameters, leading to huge computational costs. An effective approach to reduce computational r…