40 citations · 246 across the 29 of their papers we have counts for
7 papers · 1 filter
Controlling Over-generalization and its Effect on Adversarial Examples Generation and Detection
Mahdieh Abbasi, Arezoo Rajabi, Azadeh Sadat Mozafari +2
Convolutional Neural Networks (CNNs) significantly improve the state-of-the-art for many applications, especially in computer vision. However, CNNs still suffer from a tendency to…
Evaluating and Characterizing Incremental Learning from Non-Stationary Data
Alejandro Cervantes, Christian Gagné, Pedro Isasi +1
Incremental learning from non-stationary data poses special challenges to the field of machine learning. Although new algorithms have been developed for this, assessment of results…
Towards Dependable Deep Convolutional Neural Networks (CNNs) with Out-distribution Learning
Mahdieh Abbasi, Arezoo Rajabi, Christian Gagné +1
Detection and rejection of adversarial examples in security sensitive and safety-critical systems using deep CNNs is essential. In this paper, we propose an approach to augment CNN…
Learning to Become an Expert: Deep Networks Applied To Super-Resolution Microscopy
Louis-Émile Robitaille, Audrey Durand, Marc-André Gardner +3
With super-resolution optical microscopy, it is now possible to observe molecular interactions in living cells. The obtained images have a very high spatial precision but their ove…
The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communities
Joel Lehman, Jeff Clune, Dusan Misevic +50
Biological evolution provides a creative fount of complex and subtle adaptations, often surprising the scientists who discover them. However, because evolution is an algorithmic pr…
Out-distribution training confers robustness to deep neural networks
Mahdieh Abbasi, Christian Gagné
The easiness at which adversarial instances can be generated in deep neural networks raises some fundamental questions on their functioning and concerns on their use in critical sy…