activity
20172023
most citedBatch Normalization is a Cause of Adversarial Vulnerability

54 citations · 99 across the 8 of their papers we have counts for

collaborators

9 papers

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.RO2021

Towards developing a realistic robotics simulation environment of an indoor vegetable greenhouse

Brent Van De Walker, Brendan Byrne, Joshua Near +5

This article presents a method for developing a realistic robotics simulation environment for application in vegetable greenhouses. The method pipeline starts with the construction…

cs.CV2020★ 12 cited

An Image Labeling Tool and Agricultural Dataset for Deep Learning

Patrick Wspanialy, Justin Brooks, Medhat Moussa

We introduce a labeling tool and dataset aimed to facilitate computer vision research in agriculture. The annotation tool introduces novel methods for labeling with a variety of ma…

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★ 4 cited

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…