1 citations · 3 across the 7 of their papers we have counts for
7 papers
Archtree: on-the-fly tree-structured exploration for latency-aware pruning of deep neural networks
Rémi Ouazan Reboul, Edouard Yvinec, Arnaud Dapogny +1
Deep neural networks (DNNs) have become ubiquitous in addressing a number of problems, particularly in computer vision. However, DNN inference is computationally intensive, which c…
Network Memory Footprint Compression Through Jointly Learnable Codebooks and Mappings
Edouard Yvinec, Arnaud Dapogny, Kevin Bailly
The massive interest in deep neural networks (DNNs) for both computer vision and natural language processing has been sparked by the growth in computational power. However, this le…
Gradient-Based Post-Training Quantization: Challenging the Status Quo
Edouard Yvinec, Arnaud Dapogny, Kevin Bailly
Quantization has become a crucial step for the efficient deployment of deep neural networks, where floating point operations are converted to simpler fixed point operations. In its…
NUPES : Non-Uniform Post-Training Quantization via Power Exponent Search
Edouard Yvinec, Arnaud Dapogny, Kevin Bailly
Deep neural network (DNN) deployment has been confined to larger hardware devices due to their expensive computational requirements. This challenge has recently reached another sca…
Fighting over-fitting with quantization for learning deep neural networks on noisy labels
Gauthier Tallec, Edouard Yvinec, Arnaud Dapogny +1
The rising performance of deep neural networks is often empirically attributed to an increase in the available computational power, which allows complex models to be trained upon l…
PowerQuant: Automorphism Search for Non-Uniform Quantization
Edouard Yvinec, Arnaud Dapogny, Matthieu Cord +1
Deep neural networks (DNNs) are nowadays ubiquitous in many domains such as computer vision. However, due to their high latency, the deployment of DNNs hinges on the development of…