Search Results for author: Arturo Marban

Found 7 papers, 2 papers with code

Communication-Efficient Federated Distillation

no code implementations1 Dec 2020 Felix Sattler, Arturo Marban, Roman Rischke, Wojciech Samek

Communication constraints are one of the major challenges preventing the wide-spread adoption of Federated Learning systems.

Federated Learning Image Classification +2

Learning Sparse & Ternary Neural Networks with Entropy-Constrained Trained Ternarization (EC2T)

2 code implementations2 Apr 2020 Arturo Marban, Daniel Becking, Simon Wiedemann, Wojciech Samek

To address this problem, we propose Entropy-Constrained Trained Ternarization (EC2T), a general framework to create sparse and ternary neural networks which are efficient in terms of storage (e. g., at most two binary-masks and two full-precision values are required to save a weight matrix) and computation (e. g., MAC operations are reduced to a few accumulations plus two multiplications).

Image Classification

DeepCABAC: A Universal Compression Algorithm for Deep Neural Networks

1 code implementation27 Jul 2019 Simon Wiedemann, Heiner Kirchoffer, Stefan Matlage, Paul Haase, Arturo Marban, Talmaj Marinc, David Neumann, Tung Nguyen, Ahmed Osman, Detlev Marpe, Heiko Schwarz, Thomas Wiegand, Wojciech Samek

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 little loss of information.

Neural Network Compression Quantization +1

Entropy-Constrained Training of Deep Neural Networks

no code implementations18 Dec 2018 Simon Wiedemann, Arturo Marban, Klaus-Robert Müller, Wojciech Samek

We propose a general framework for neural network compression that is motivated by the Minimum Description Length (MDL) principle.

Neural Network Compression

A Recurrent Convolutional Neural Network Approach for Sensorless Force Estimation in Robotic Surgery

no code implementations22 May 2018 Arturo Marban, Vignesh Srinivasan, Wojciech Samek, Josep Fernández, Alicia Casals

The results suggest that the force estimation quality is better when both, the tool data and video sequences, are processed by the neural network model.

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