2.7k citations · 4k across the 68 of their papers we have counts for
15 papers · 1 filter
Adversarial Examples as an Input-Fault Tolerance Problem
Angus Galloway, Anna Golubeva, Graham W. Taylor
We analyze the adversarial examples problem in terms of a model's fault tolerance with respect to its input. Whereas previous work focuses on arbitrarily strict threat models, i.e.…
Spectral Multigraph Networks for Discovering and Fusing Relationships in Molecules
Boris Knyazev, Xiao Lin, Mohamed R. Amer +1
Spectral Graph Convolutional Networks (GCNs) are a generalization of convolutional networks to learning on graph-structured data. Applications of spectral GCNs have been successful…
Past, Present, and Future Approaches Using Computer Vision for Animal Re-Identification from Camera Trap Data
Stefan Schneider, Graham W. Taylor, Stefan S. Linquist +1
The ability of a researcher to re-identify (re-ID) an individual animal upon re-encounter is fundamental for addressing a broad range of questions in the study of ecosystem functio…
Self-Paced Learning with Adaptive Deep Visual Embeddings
Vithursan Thangarasa, Graham W. Taylor
Selecting the most appropriate data examples to present a deep neural network (DNN) at different stages of training is an unsolved challenge. Though practitioners typically ignore…
Stochastic Layer-Wise Precision in Deep Neural Networks
Griffin Lacey, Graham W. Taylor, Shawki Areibi
Low precision weights, activations, and gradients have been proposed as a way to improve the computational efficiency and memory footprint of deep neural networks. Recently, low pr…
Leveraging Uncertainty Estimates for Predicting Segmentation Quality
Terrance DeVries, Graham W. Taylor
The use of deep learning for medical imaging has seen tremendous growth in the research community. One reason for the slow uptake of these systems in the clinical setting is that t…
Quantitatively Evaluating GANs With Divergences Proposed for Training
Daniel Jiwoong Im, He Ma, Graham Taylor +1
Generative adversarial networks (GANs) have been extremely effective in approximating complex distributions of high-dimensional, input data samples, and substantial progress has be…
Generalized Hadamard-Product Fusion Operators for Visual Question Answering
Brendan Duke, Graham W. Taylor
We propose a generalized class of multimodal fusion operators for the task of visual question answering (VQA). We identify generalizations of existing multimodal fusion operators b…
Convolutional Neural Networks Regularized by Correlated Noise
Shamak Dutta, Bryan Tripp, Graham Taylor
Neurons in the visual cortex are correlated in their variability. The presence of correlation impacts cortical processing because noise cannot be averaged out over many neurons. In…
Adversarial Training Versus Weight Decay
Angus Galloway, Thomas Tanay, Graham W. Taylor
Performance-critical machine learning models should be robust to input perturbations not seen during training. Adversarial training is a method for improving a model's robustness t…
Deep Learning Object Detection Methods for Ecological Camera Trap Data
Stefan Schneider, Graham W. Taylor, Stefan C. Kremer
Deep learning methods for computer vision tasks show promise for automating the data analysis of camera trap images. Ecological camera traps are a common approach for monitoring an…
Real-Time End-to-End Action Detection with Two-Stream Networks
Alaaeldin El-Nouby, Graham W. Taylor
Two-stream networks have been very successful for solving the problem of action detection. However, prior work using two-stream networks train both streams separately, which preven…
Learning Confidence for Out-of-Distribution Detection in Neural Networks
Terrance DeVries, Graham W. Taylor
Modern neural networks are very powerful predictive models, but they are often incapable of recognizing when their predictions may be wrong. Closely related to this is the task of…
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…
Glimpse Clouds: Human Activity Recognition from Unstructured Feature Points
Fabien Baradel, Christian Wolf, Julien Mille +1
We propose a method for human activity recognition from RGB data that does not rely on any pose information during test time and does not explicitly calculate pose information inte…