Here you can find a consolidated (a.k.a. slowly updated) list of my publications. A frequently updated (and possibly noisy) list of works is available on my Google Scholar profile.
Please find below a short list of highlight publications for my recent activity.
Corti, Francesco; Entezari, Rahim; Hooker, Sara; Bacciu, Davide; Saukh, Olga
Studying the impact of magnitude pruning on contrastive learning methods Workshop
ICML 2022 workshop on Hardware Aware Efficient Training (HAET 2022), 2022.
@workshop{nokey,
title = {Studying the impact of magnitude pruning on contrastive learning methods},
author = {Francesco Corti and Rahim Entezari and Sara Hooker and Davide Bacciu and Olga Saukh},
year = {2022},
date = {2022-07-23},
urldate = {2022-07-23},
booktitle = {ICML 2022 workshop on Hardware Aware Efficient Training (HAET 2022)},
abstract = {We study the impact of different pruning techniques on the representation learned by deep neural networks trained with contrastive loss functions. Our work finds that at high sparsity levels, contrastive learning results in a higher number of misclassified examples relative to models trained with traditional cross-entropy loss. To understand this pronounced difference, we use metrics such as the number of PIEs, qscore and pdepth to measure the impact of pruning on the learned representation quality. Our analysis suggests the schedule of the pruning method implementation matters. We find that the negative impact of sparsity on the quality of the learned representation is the highest when pruning is introduced early-on in training phase.},
keywords = {},
pubstate = {published},
tppubtype = {workshop}
}
We study the impact of different pruning techniques on the representation learned by deep neural networks trained with contrastive loss functions. Our work finds that at high sparsity levels, contrastive learning results in a higher number of misclassified examples relative to models trained with traditional cross-entropy loss. To understand this pronounced difference, we use metrics such as the number of PIEs, qscore and pdepth to measure the impact of pruning on the learned representation quality. Our analysis suggests the schedule of the pruning method implementation matters. We find that the negative impact of sparsity on the quality of the learned representation is the highest when pruning is introduced early-on in training phase.