1 citations · 1 across the 6 of their papers we have counts for
6 papers
PIPE : Parallelized Inference Through Post-Training Quantization Ensembling of Residual Expansions
Edouard Yvinec, Arnaud Dapogny, Kevin Bailly
Deep neural networks (DNNs) are ubiquitous in computer vision and natural language processing, but suffer from high inference cost. This problem can be addressed by quantization, w…
To Fold or Not to Fold: a Necessary and Sufficient Condition on Batch-Normalization Layers Folding
Edouard Yvinec, Arnaud Dapogny, Kevin Bailly
Batch-Normalization (BN) layers have become fundamental components in the evermore complex deep neural network architectures. Such models require acceleration processes for deploym…
SPIQ: Data-Free Per-Channel Static Input Quantization
Edouard Yvinec, Arnaud Dapogny, Matthieu Cord +1
Computationally expensive neural networks are ubiquitous in computer vision and solutions for efficient inference have drawn a growing attention in the machine learning community.…
RED++ : Data-Free Pruning of Deep Neural Networks via Input Splitting and Output Merging
Edouard Yvinec, Arnaud Dapogny, Matthieu Cord +1
Pruning Deep Neural Networks (DNNs) is a prominent field of study in the goal of inference runtime acceleration. In this paper, we introduce a novel data-free pruning protocol RED+…
RED : Looking for Redundancies for Data-Free Structured Compression of Deep Neural Networks
Edouard Yvinec, Arnaud Dapogny, Matthieu Cord +1
Deep Neural Networks (DNNs) are ubiquitous in today's computer vision land-scape, despite involving considerable computational costs. The mainstream approaches for runtime accelera…
DeeSCo: Deep heterogeneous ensemble with Stochastic Combinatory loss for gaze estimation
Edouard Yvinec, Arnaud Dapogny, Kévin Bailly
From medical research to gaming applications, gaze estimation is becoming a valuable tool. While there exists a number of hardware-based solutions, recent deep learning-based appro…