1 citations · 1 across the 4 of their papers we have counts for
4 papers
CoDeQ: End-to-End Joint Model Compression with Dead-Zone Quantizer for High-Sparsity and Low-Precision Networks
Jonathan Wenshøj, Tong Chen, Bob Pepin +1
While joint pruning--quantization is theoretically superior to sequential application, current joint methods rely on auxiliary procedures outside the training loop for finding comp…
Oscillations Make Neural Networks Robust to Quantization
Jonathan Wenshøj, Bob Pepin, Raghavendra Selvan
We challenge the prevailing view that weight oscillations observed during Quantization Aware Training (QAT) are merely undesirable side-effects and argue instead that they are an e…
When Can Memorization Improve Fairness?
Bob Pepin, Christian Igel, Raghavendra Selvan
We study to which extent additive fairness metrics (statistical parity, equal opportunity and equalized odds) can be influenced in a multi-class classification problem by memorizin…
PePR: Performance Per Resource Unit as a Metric to Promote Small-Scale Deep Learning in Medical Image Analysis
Raghavendra Selvan, Bob Pepin, Christian Igel +2
The recent advances in deep learning (DL) have been accelerated by access to large-scale data and compute. These large-scale resources have been used to train progressively larger…