12 citations · 25 across the 6 of their papers we have counts for
4 papers · 1 filter
A Survey of Deep Learning: From Activations to Transformers
Johannes Schneider, Michalis Vlachos
Deep learning has made tremendous progress in the last decade. A key success factor is the large amount of architectures, layers, objectives, and optimization techniques. They incl…
Reflective-Net: Learning from Explanations
Johannes Schneider, Michalis Vlachos
We examine whether data generated by explanation techniques, which promote a process of self-reflection, can improve classifier performance. Our work is based on the idea that huma…
Explaining Neural Networks by Decoding Layer Activations
Johannes Schneider, Michalis Vlachos
We present a `CLAssifier-DECoder' architecture (\emph{ClaDec}) which facilitates the comprehension of the output of an arbitrary layer in a neural network (NN). It uses a decoder t…
Deceptive AI Explanations: Creation and Detection
Johannes Schneider, Christian Meske, Michalis Vlachos
Artificial intelligence (AI) comes with great opportunities but can also pose significant risks. Automatically generated explanations for decisions can increase transparency and fo…