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Found 10 papers, 7 papers with code
PURE: Turning Polysemantic Neurons Into Pure Features by Identifying Relevant Circuits
The field of mechanistic interpretability aims to study the role of individual neurons in Deep Neural Networks.
Decoupling Pixel Flipping and Occlusion Strategy for Consistent XAI Benchmarks
The R-OMS score enables a systematic comparison of occlusion strategies and resolves the disagreement problem by grouping consistent PF rankings.
XAI-based Comparison of Input Representations for Audio Event Classification
Deep neural networks are a promising tool for Audio Event Classification.
Explainable AI for Time Series via Virtual Inspection Layers
In this way, we extend the applicability of a family of XAI methods to domains (e. g. speech) where the input is only interpretable after a transformation.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
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Multi-dimensional concept discovery (MCD): A unifying framework with completeness guarantees
For the trustworthy application of XAI, in particular for high-stake decisions, a more global model understanding is required.
Sparse Subspace Clustering for Concept Discovery (SSCCD)
We empirically demonstrate the soundness of the proposed Sparse Subspace Clustering for Concept Discovery (SSCCD) method for a variety of different image classification tasks.
Predicting the Binding of SARS-CoV-2 Peptides to the Major Histocompatibility Complex with Recurrent Neural Networks
Predicting the binding of viral peptides to the major histocompatibility complex with machine learning can potentially extend the computational immunology toolkit for vaccine development, and serve as a key component in the fight against a pandemic.
PredDiff: Explanations and Interactions from Conditional Expectations
PredDiff is a model-agnostic, local attribution method that is firmly rooted in probability theory.
Generative Neural Samplers for the Quantum Heisenberg Chain
Generative neural samplers offer a complementary approach to Monte Carlo methods for problems in statistical physics and quantum field theory.
AudioMNIST: Exploring Explainable Artificial Intelligence for Audio Analysis on a Simple Benchmark
Explainable Artificial Intelligence (XAI) is targeted at understanding how models perform feature selection and derive their classification decisions.
