Graham W Taylor

research

∙ 07/19/2022

Bounding generalization error with input compression: An empirical study with infinite-width networks

Estimating the Generalization Error (GE) of Deep Neural Networks (DNNs) ...

Angus Galloway, et al. ∙

research

∙ 06/27/2022

Monitoring Shortcut Learning using Mutual Information

The failure of deep neural networks to generalize to out-of-distribution...

Mohammed Adnan, et al. ∙

research

∙ 04/29/2022

Understanding the impact of image and input resolution on deep digital pathology patch classifiers

We consider annotation efficient learning in Digital Pathology (DP), whe...

Eu Wern Teh, et al. ∙

research

∙ 01/29/2022

DeepRNG: Towards Deep Reinforcement Learning-Assisted Generative Testing of Software

Although machine learning (ML) has been successful in automating various...

Chuan-Yung Tsai, et al. ∙

research

∙ 01/24/2022

On Evaluation Metrics for Graph Generative Models

In image generation, generative models can be evaluated naturally by vis...

Rylee Thompson, et al. ∙

research

∙ 01/07/2022

Learning with less labels in Digital Pathology via Scribble Supervision from natural images

A critical challenge of training deep learning models in the Digital Pat...

Eu Wern Teh, et al. ∙

research

∙ 11/23/2021

Domain-Agnostic Clustering with Self-Distillation

Recent advancements in self-supervised learning have reduced the gap bet...

Mohammed Adnan, et al. ∙

research

∙ 11/05/2021

An Empirical Study of Neural Kernel Bandits

Neural bandits have enabled practitioners to operate efficiently on prob...

Michal Lisicki, et al. ∙

research

∙ 10/29/2021

Brick-by-Brick: Combinatorial Construction with Deep Reinforcement Learning

Discovering a solution in a combinatorial space is prevalent in many rea...

Hyunsoo Chung, et al. ∙

research

∙ 10/25/2021

Parameter Prediction for Unseen Deep Architectures

Deep learning has been successful in automating the design of features i...

Boris Knyazev, et al. ∙

research

∙ 04/01/2021

Unconstrained Scene Generation with Locally Conditioned Radiance Fields

We tackle the challenge of learning a distribution over complex, realist...

Terrance DeVries, et al. ∙

research

∙ 03/31/2021

The GIST and RIST of Iterative Self-Training for Semi-Supervised Segmentation

We consider the task of semi-supervised semantic segmentation, where we ...

Eu Wern Teh, et al. ∙

research

∙ 03/05/2021

LOHO: Latent Optimization of Hairstyles via Orthogonalization

Hairstyle transfer is challenging due to hair structure differences in t...

Rohit Saha, et al. ∙

research

∙ 01/21/2021

SSTVOS: Sparse Spatiotemporal Transformers for Video Object Segmentation

In this paper we introduce a Transformer-based approach to video object ...

Brendan Duke, et al. ∙

research

∙ 12/21/2020

Building LEGO Using Deep Generative Models of Graphs

Generative models are now used to create a variety of high-quality digit...

Rylee Thompson, et al. ∙

research

∙ 11/12/2020

Evaluating Curriculum Learning Strategies in Neural Combinatorial Optimization

Neural combinatorial optimization (NCO) aims at designing problem-indepe...

Michal Lisicki, et al. ∙

research

∙ 11/05/2020

Identifying and interpreting tuning dimensions in deep networks

In neuroscience, a tuning dimension is a stimulus attribute that account...

Nolan S. Dey, et al. ∙

research

∙ 07/30/2020

Instance Selection for GANs

Recent advances in Generative Adversarial Networks (GANs) have led to th...

Terrance DeVries, et al. ∙

research

∙ 07/11/2020

Generative Graph Perturbations for Scene Graph Prediction

Inferring objects and their relationships from an image is useful in man...

Boris Knyazev, et al. ∙

research

∙ 06/30/2020

Enabling Continual Learning with Differentiable Hebbian Plasticity

Continual learning is the problem of sequentially learning new tasks or ...

Vithursan Thangarasa, et al. ∙

research

∙ 05/17/2020

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

Scene graph generation (SGG) aims to predict graph-structured descriptio...

Boris Knyazev, et al. ∙

research

∙ 04/28/2020

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

Human-computer interactive systems that rely on machine learning are bec...

Katya Kudashkina, et al. ∙

research

∙ 04/02/2020

ProxyNCA++: Revisiting and Revitalizing Proxy Neighborhood Component Analysis

We consider the problem of distance metric learning (DML), where the tas...

Eu Wern Teh, et al. ∙

research

∙ 11/27/2019

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

In Digital Pathology (DP), labeled data is generally very scarce due to ...

Eu Wern Teh, et al. ∙

research

∙ 10/28/2019

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

Deep neural networks require collecting and annotating large amounts of ...

Alaaeldin El-Nouby, et al. ∙

research

∙ 10/11/2019

A Nonparametric Bayesian Model for Sparse Temporal Multigraphs

As the availability and importance of temporal interaction data–such as ...

Elahe Ghalebi, et al. ∙

research

∙ 09/23/2019

Learning Temporal Attention in Dynamic Graphs with Bilinear Interactions

Graphs evolving over time are a natural way to represent data in many do...

Boris Knyazev, et al. ∙

research

∙ 07/21/2019

Image Classification with Hierarchical Multigraph Networks

Graph Convolutional Networks (GCNs) are a class of general models that c...

Boris Knyazev, et al. ∙

research

∙ 07/11/2019

On the Evaluation of Conditional GANs

Conditional Generative Adversarial Networks (cGANs) are finding increasi...

Terrance DeVries, et al. ∙

research

∙ 05/08/2019

Understanding attention in graph neural networks

We aim to better understand attention over nodes in graph neural network...

Boris Knyazev, et al. ∙

research

∙ 05/06/2019

Batch Normalization is a Cause of Adversarial Vulnerability

Batch normalization (batch norm) is often used in an attempt to stabiliz...

Angus Galloway, et al. ∙

research

∙ 02/21/2019

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 ...

Stefan Schneider, et al. ∙

research

∙ 01/15/2019

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...

Vignesh Sankar, et al. ∙

research

∙ 11/30/2018

Adversarial Examples as an Input-Fault Tolerance Problem

We analyze the adversarial examples problem in terms of a model's fault ...

Angus Galloway, et al. ∙

research

∙ 11/24/2018

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

Conditional text-to-image generation approaches commonly focus on genera...

Alaaeldin El-Nouby, et al. ∙

research

∙ 11/23/2018

Spectral Multigraph Networks for Discovering and Fusing Relationships in Molecules

Spectral Graph Convolutional Networks (GCNs) are a generalization of con...

Boris Knyazev, et al. ∙

research

∙ 11/19/2018

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 ...

Stefan Schneider, et al. ∙

research

∙ 07/24/2018

Self-Paced Learning with Adaptive Deep Visual Embeddings

Selecting the most appropriate data examples to present a deep neural ne...

Vithursan Thangarasa, et al. ∙

research

∙ 07/03/2018

Stochastic Layer-Wise Precision in Deep Neural Networks

Low precision weights, activations, and gradients have been proposed as ...

Griffin Lacey, et al. ∙

research

∙ 07/02/2018

Leveraging Uncertainty Estimates for Predicting Segmentation Quality

The use of deep learning for medical imaging has seen tremendous growth ...

Terrance DeVries, et al. ∙

research

∙ 04/10/2018

Adversarial Training Versus Weight Decay

Performance-critical machine learning models should be robust to input p...

Angus Galloway, et al. ∙

research

∙ 03/28/2018

Deep Learning Object Detection Methods for Ecological Camera Trap Data

Deep learning methods for computer vision tasks show promise for automat...

Stefan Schneider, et al. ∙

research

∙ 03/26/2018

Generalized Hadamard-Product Fusion Operators for Visual Question Answering

We propose a generalized class of multimodal fusion operators for the ta...

Brendan Duke, et al. ∙

research

∙ 02/23/2018

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

Two-stream networks have been very successful for solving the problem of...

Alaaeldin El-Nouby, et al. ∙

research

∙ 02/22/2018

Glimpse Clouds: Human Activity Recognition from Unstructured Feature Points

We propose a method for human activity recognition from RGB data which d...

Fabien Baradel, et al. ∙

research

∙ 02/13/2018

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

Modern neural networks are very powerful predictive models, but they are...

Terrance DeVries, et al. ∙

research

∙ 02/13/2018

Predicting Adversarial Examples with High Confidence

It has been suggested that adversarial examples cause deep learning mode...

Angus Galloway, et al. ∙

research

∙ 11/01/2017

Attacking Binarized Neural Networks

Neural networks with low-precision weights and activations offer compell...

Angus Galloway, et al. ∙

research

∙ 10/29/2017

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

Objective: Radiomics-driven Computer Aided Diagnosis (CAD) has shown con...

Devinder Kumar, et al. ∙

research

∙ 09/05/2017

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 ...

Devinder Kumar, et al. ∙

Graham W Taylor

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