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M. Moussa

6 papers hereh-index 191.5k citations74 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author1
  • last author5

Across the 6 of 6 papers where every author was matched, so the position is known.

fields
  • cs.LG3
  • cs.CV2
  • cs.RO1
same name
  • M. Moussa — 17 papers, h 19
  • M. Moussa — 4 papers, h 3
  • M. Moussa — 2 papers, h 6
  • M. Moussa — 2 papers, h 13
  • M. Moussa — 1 paper, h 9
  • M. Moussa — 1 paper, h 10

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20172021
most citedBatch Normalization is a Cause of Adversarial Vulnerability

54 citations · 88 across the 4 of their papers we have counts for

collaborators
Showing cs.LGShow all

4 papers · 1 filter

cs.LG2023

Implicit Sensing in Traffic Optimization: Advanced Deep Reinforcement Learning Techniques

Emanuel Figetakis, Yahuza Bello, Ahmed Refaey +2

A sudden roadblock on highways due to many reasons such as road maintenance, accidents, and car repair is a common situation we encounter almost daily. Autonomous Vehicles (AVs) eq…

cs.LG2019★ 54 cited

Batch Normalization is a Cause of Adversarial Vulnerability

Angus Galloway, Anna Golubeva, Thomas Tanay +2

Batch normalization (batch norm) is often used in an attempt to stabilize and accelerate training in deep neural networks. In many cases it indeed decreases the number of parameter…

cs.LG2018

Predicting Adversarial Examples with High Confidence

Angus Galloway, Graham W. Taylor, Medhat Moussa

It has been suggested that adversarial examples cause deep learning models to make incorrect predictions with high confidence. In this work, we take the opposite stance: an overly…

cs.LG2017★ 22 cited

Attacking Binarized Neural Networks

Angus Galloway, Graham W. Taylor, Medhat Moussa

Neural networks with low-precision weights and activations offer compelling efficiency advantages over their full-precision equivalents. The two most frequently discussed benefits…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.