Graham W Taylor

is this you? claim profile

∙ 0 followers
University of Guelph

Associate Professor School of Engineering at University of Guelph and Vector Institute

Enabling Continual Learning with Differentiable Hebbian Plasticity

Continual learning is the problem of sequentially learning new tasks or ...
06/30/2020 ∙ by Vithursan Thangarasa, et al. ∙ 0 ∙ share

read it
Graph Density-Aware Losses for Novel Compositions in Scene Graph Generation

Scene graph generation (SGG) aims to predict graph-structured descriptio...
05/17/2020 ∙ by Boris Knyazev, et al. ∙ 0 ∙ share

read it
Sample-Efficient Model-based Actor-Critic for an Interactive Dialogue Task

Human-computer interactive systems that rely on machine learning are bec...
04/28/2020 ∙ by Katya Kudashkina, et al. ∙ 0 ∙ share

read it
ProxyNCA++: Revisiting and Revitalizing Proxy Neighborhood Component Analysis

We consider the problem of distance metric learning (DML), where the tas...
04/02/2020 ∙ by Eu Wern Teh, et al. ∙ 0 ∙ share

read it
Learning with less data via Weakly Labeled Patch Classification in Digital Pathology

In Digital Pathology (DP), labeled data is generally very scarce due to ...
11/27/2019 ∙ by Eu Wern Teh, et al. ∙ 0 ∙ share

read it
Skip-Clip: Self-Supervised Spatiotemporal Representation Learning by Future Clip Order Ranking

Deep neural networks require collecting and annotating large amounts of ...
10/28/2019 ∙ by Alaaeldin El-Nouby, et al. ∙ 12 ∙ share

read it
A Nonparametric Bayesian Model for Sparse Temporal Multigraphs

As the availability and importance of temporal interaction data–such as ...
10/11/2019 ∙ by Elahe Ghalebi, et al. ∙ 0 ∙ share

read it
Learning Temporal Attention in Dynamic Graphs with Bilinear Interactions

Graphs evolving over time are a natural way to represent data in many do...
09/23/2019 ∙ by Boris Knyazev, et al. ∙ 3 ∙ share

read it
Image Classification with Hierarchical Multigraph Networks

Graph Convolutional Networks (GCNs) are a class of general models that c...
07/21/2019 ∙ by Boris Knyazev, et al. ∙ 4 ∙ share

read it
On the Evaluation of Conditional GANs

Conditional Generative Adversarial Networks (cGANs) are finding increasi...
07/11/2019 ∙ by Terrance DeVries, et al. ∙ 8 ∙ share

read it
Understanding attention in graph neural networks

We aim to better understand attention over nodes in graph neural network...
05/08/2019 ∙ by Boris Knyazev, et al. ∙ 0 ∙ share

read it
Batch Normalization is a Cause of Adversarial Vulnerability

Batch normalization (batch norm) is often used in an attempt to stabiliz...
05/06/2019 ∙ by Angus Galloway, et al. ∙ 0 ∙ share

read it
Similarity Learning Networks for Animal Individual Re-Identification - Beyond the Capabilities of a Human Observer

The ability of a researcher to re-identify (re-ID) an animal individual ...
02/21/2019 ∙ by Stefan Schneider, et al. ∙ 0 ∙ share

read it
SISC: End-to-end Interpretable Discovery Radiomics-Driven Lung Cancer Prediction via Stacked Interpretable Sequencing Cells

Objective: Lung cancer is the leading cause of cancer-related death worl...
01/15/2019 ∙ by Vignesh Sankar, et al. ∙ 2 ∙ share

read it
Adversarial Examples as an Input-Fault Tolerance Problem

We analyze the adversarial examples problem in terms of a model's fault ...
11/30/2018 ∙ by Angus Galloway, et al. ∙ 0 ∙ share

read it
Keep Drawing It: Iterative language-based image generation and editing

Conditional text-to-image generation approaches commonly focus on genera...
11/24/2018 ∙ by Alaaeldin El-Nouby, et al. ∙ 0 ∙ share

read it
Spectral Multigraph Networks for Discovering and Fusing Relationships in Molecules

Spectral Graph Convolutional Networks (GCNs) are a generalization of con...
11/23/2018 ∙ by Boris Knyazev, et al. ∙ 0 ∙ share

read it
Past, Present, and Future Approaches Using Computer Vision for Animal Re-Identification from Camera Trap Data

The ability of a researcher to re-identify (re-ID) an individual animal ...
11/19/2018 ∙ by Stefan Schneider, et al. ∙ 0 ∙ share

read it
Self-Paced Learning with Adaptive Deep Visual Embeddings

Selecting the most appropriate data examples to present a deep neural ne...
07/24/2018 ∙ by Vithursan Thangarasa, et al. ∙ 0 ∙ share

read it
Stochastic Layer-Wise Precision in Deep Neural Networks

Low precision weights, activations, and gradients have been proposed as ...
07/03/2018 ∙ by Griffin Lacey, et al. ∙ 0 ∙ share

read it
Leveraging Uncertainty Estimates for Predicting Segmentation Quality

The use of deep learning for medical imaging has seen tremendous growth ...
07/02/2018 ∙ by Terrance DeVries, et al. ∙ 4 ∙ share

read it
Adversarial Training Versus Weight Decay

Performance-critical machine learning models should be robust to input p...
04/10/2018 ∙ by Angus Galloway, et al. ∙ 0 ∙ share

read it
Deep Learning Object Detection Methods for Ecological Camera Trap Data

Deep learning methods for computer vision tasks show promise for automat...
03/28/2018 ∙ by Stefan Schneider, et al. ∙ 0 ∙ share

read it
Generalized Hadamard-Product Fusion Operators for Visual Question Answering

We propose a generalized class of multimodal fusion operators for the ta...
03/26/2018 ∙ by Brendan Duke, et al. ∙ 0 ∙ share

read it
Real-Time End-to-End Action Detection with Two-Stream Networks

Two-stream networks have been very successful for solving the problem of...
02/23/2018 ∙ by Alaaeldin El-Nouby, et al. ∙ 0 ∙ share

read it
Glimpse Clouds: Human Activity Recognition from Unstructured Feature Points

We propose a method for human activity recognition from RGB data which d...
02/22/2018 ∙ by Fabien Baradel, et al. ∙ 0 ∙ share

read it
Learning Confidence for Out-of-Distribution Detection in Neural Networks

Modern neural networks are very powerful predictive models, but they are...
02/13/2018 ∙ by Terrance DeVries, et al. ∙ 0 ∙ share

read it
Predicting Adversarial Examples with High Confidence

It has been suggested that adversarial examples cause deep learning mode...
02/13/2018 ∙ by Angus Galloway, et al. ∙ 0 ∙ share

read it
Attacking Binarized Neural Networks

Neural networks with low-precision weights and activations offer compell...
11/01/2017 ∙ by Angus Galloway, et al. ∙ 0 ∙ share

read it
Discovery Radiomics with CLEAR-DR: Interpretable Computer Aided Diagnosis of Diabetic Retinopathy

Objective: Radiomics-driven Computer Aided Diagnosis (CAD) has shown con...
10/29/2017 ∙ by Devinder Kumar, et al. ∙ 0 ∙ share

read it
Opening the Black Box of Financial AI with CLEAR-Trade: A CLass-Enhanced Attentive Response Approach for Explaining and Visualizing Deep Learning-Driven Stock Market Prediction

Deep learning has been shown to outperform traditional machine learning ...
09/05/2017 ∙ by Devinder Kumar, et al. ∙ 0 ∙ share

read it
Structure Optimization for Deep Multimodal Fusion Networks using Graph-Induced Kernels

A popular testbed for deep learning has been multimodal recognition of h...
07/03/2017 ∙ by Dhanesh Ramachandram, et al. ∙ 0 ∙ share

read it
Explaining the Unexplained: A CLass-Enhanced Attentive Response (CLEAR) Approach to Understanding Deep Neural Networks

In this work, we propose CLass-Enhanced Attentive Response (CLEAR): an a...
04/13/2017 ∙ by Devinder Kumar, et al. ∙ 0 ∙ share

read it
Dataset Augmentation in Feature Space

Dataset augmentation, the practice of applying a wide array of domain-sp...
02/17/2017 ∙ by Terrance DeVries, et al. ∙ 0 ∙ share

read it
An Integrated Simulator and Dataset that Combines Grasping and Vision for Deep Learning

Deep learning is an established framework for learning hierarchical data...
02/07/2017 ∙ by Matthew Veres, et al. ∙ 0 ∙ share

read it
Modeling Grasp Motor Imagery through Deep Conditional Generative Models

Grasping is a complex process involving knowledge of the object, the sur...
01/11/2017 ∙ by Matthew Veres, et al. ∙ 0 ∙ share

read it
Learning a metric for class-conditional KNN

Naive Bayes Nearest Neighbour (NBNN) is a simple and effective framework...
07/11/2016 ∙ by Daniel Jiwoong Im, et al. ∙ 0 ∙ share

read it
Deep Learning on FPGAs: Past, Present, and Future

The rapid growth of data size and accessibility in recent years has inst...
02/13/2016 ∙ by Griffin Lacey, et al. ∙ 0 ∙ share

read it
ModDrop: adaptive multi-modal gesture recognition

We present a method for gesture detection and localisation based on mult...
12/31/2014 ∙ by Natalia Neverova, et al. ∙ 0 ∙ share

read it
Neural Network Regularization via Robust Weight Factorization

Regularization is essential when training large neural networks. As deep...
12/20/2014 ∙ by Jan Rudy, et al. ∙ 0 ∙ share

read it
Scoring and Classifying with Gated Auto-encoders

Auto-encoders are perhaps the best-known non-probabilistic methods for r...
12/20/2014 ∙ by Daniel Jiwoong Im, et al. ∙ 0 ∙ share

read it
"Mental Rotation" by Optimizing Transforming Distance

The human visual system is able to recognize objects despite transformat...
06/11/2014 ∙ by Weiguang Ding, et al. ∙ 0 ∙ share

read it
Learning Human Pose Estimation Features with Convolutional Networks

This paper introduces a new architecture for human pose estimation using...
12/27/2013 ∙ by Arjun Jain, et al. ∙ 0 ∙ share

read it
Products of Hidden Markov Models: It Takes N>1 to Tango

Products of Hidden Markov Models(PoHMMs) are an interesting class of gen...
05/09/2012 ∙ by Graham W Taylor, et al. ∙ 0 ∙ share

read it

Graham W Taylor

Featured Co-authors