114 citations · 151 across the 6 of their papers we have counts for
7 papers
Communication-Efficient Federated Distillation
Felix Sattler, Arturo Marban, Roman Rischke +1
Communication constraints are one of the major challenges preventing the wide-spread adoption of Federated Learning systems. Recently, Federated Distillation (FD), a new algorithmi…
Learning Sparse & Ternary Neural Networks with Entropy-Constrained Trained Ternarization (EC2T)
Arturo Marban, Daniel Becking, Simon Wiedemann +1
Deep neural networks (DNN) have shown remarkable success in a variety of machine learning applications. The capacity of these models (i.e., number of parameters), endows them with…
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…
Entropy-Constrained Training of Deep Neural Networks
Simon Wiedemann, Arturo Marban, Klaus-Robert Müller +1
We propose a general framework for neural network compression that is motivated by the Minimum Description Length (MDL) principle. For that we first derive an expression for the en…
A Recurrent Convolutional Neural Network Approach for Sensorless Force Estimation in Robotic Surgery
Arturo Marban, Vignesh Srinivasan, Wojciech Samek +2
Providing force feedback as relevant information in current Robot-Assisted Minimally Invasive Surgery systems constitutes a technological challenge due to the constraints imposed b…
Robustifying Models Against Adversarial Attacks by Langevin Dynamics
Vignesh Srinivasan, Arturo Marban, Klaus-Robert Müller +2
Adversarial attacks on deep learning models have compromised their performance considerably. As remedies, a lot of defense methods were proposed, which however, have been circumven…