171 citations · 299 across the 4 of their papers we have counts for
5 papers
Ground Truth Evaluation of Neural Network Explanations with CLEVR-XAI
Leila Arras, Ahmed Osman, Wojciech Samek
The rise of deep learning in today's applications entailed an increasing need in explaining the model's decisions beyond prediction performances in order to foster trust and accoun…
DeepCABAC: A Universal Compression Algorithm for Deep Neural Networks
Simon Wiedemann, Heiner Kirchoffer, Stefan Matlage +10
The field of video compression has developed some of the most sophisticated and efficient compression algorithms known in the literature, enabling very high compressibility for lit…
DeepCABAC: Context-adaptive binary arithmetic coding for deep neural network compression
Simon Wiedemann, Heiner Kirchhoffer, Stefan Matlage +9
We present DeepCABAC, a novel context-adaptive binary arithmetic coder for compressing deep neural networks. It quantizes each weight parameter by minimizing a weighted rate-distor…
Evaluating Recurrent Neural Network Explanations
Leila Arras, Ahmed Osman, Klaus-Robert Müller +1
Recently, several methods have been proposed to explain the predictions of recurrent neural networks (RNNs), in particular of LSTMs. The goal of these methods is to understand the…
Dual Recurrent Attention Units for Visual Question Answering
Ahmed Osman, Wojciech Samek
Visual Question Answering (VQA) requires AI models to comprehend data in two domains, vision and text. Current state-of-the-art models use learned attention mechanisms to extract r…