Wojciech Samek

TL;DR: Two approaches to explaining predictions of deep learning models are presented, one method which computes the sensitivity of the prediction with respect to changes in the input and one approach which meaningfully decomposes the decision in terms of the input variables.

TL;DR: In this paper, the authors propose sparse ternary compression (STC), a new compression framework that is specifically designed to meet the requirements of the federated learning environment, which extends the existing compression technique of top- $k$ gradient sparsification with a novel mechanism to enable downstream compression as well as ternarization and optimal Golomb encoding of the weight updates.

TL;DR: The authors investigate how these methods approach learning in order to assess the dependability of their decision making and propose a semi-automated Spectral Relevance Analysis that provides a practically effective way of characterizing and validating the behavior of nonlinear learning machines.

TL;DR: Sparse ternary compression (STC) is proposed, a new compression framework that is specifically designed to meet the requirements of the federated learning environment and advocate for a paradigm shift in federated optimization toward high-frequency low-bitwidth communication, in particular in the bandwidth-constrained learning environments.