Graham W Taylor

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University of Guelph

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

Instance Selection for GANs

Recent advances in Generative Adversarial Networks (GANs) have led to th...
07/30/2020 ∙ by Terrance DeVries, et al. ∙ 0 ∙ share

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Generative Graph Perturbations for Scene Graph Prediction

Inferring objects and their relationships from an image is useful in man...
07/11/2020 ∙ by Boris Knyazev, et al. ∙ 0 ∙ share

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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

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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

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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

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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

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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

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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

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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

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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

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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

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On the Evaluation of Conditional GANs

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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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Attacking Binarized Neural Networks

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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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"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

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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

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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

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Graham W Taylor

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