University of Waterloo

research

∙ 11/07/2021

Survey of Deep Learning Methods for Inverse Problems

In this paper we investigate a variety of deep learning strategies for s...

Shima Kamyab, et al. ∙

research

∙ 03/22/2020

COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest Radiography Images

The COVID-19 pandemic continues to have a devastating effect on the heal...

Linda Wang, et al. ∙

research

∙ 01/21/2020

SAUNet: Shape Attentive U-Net for Interpretable Medical Image Segmentation

Medical image segmentation is a difficult but important task for many cl...

Jesse Sun, et al. ∙

research

∙ 05/25/2020

Interpretable Contrastive Learning for Networks

Contrastive learning (CL) is an emerging analysis approach that aims to ...

Takanori Fujiwara, et al. ∙

research

∙ 12/14/2021

Performance or Trust? Why Not Both. Deep AUC Maximization with Self-Supervised Learning for COVID-19 Chest X-ray Classifications

Effective representation learning is the key in improving model performa...

Siyuan He, et al. ∙

research

∙ 10/06/2019

Improving Dataset Distillation

Dataset distillation is a method for reducing dataset sizes: the goal is...

Ilia Sucholutsky, et al. ∙

research

∙ 01/22/2021

A Review on Deep Learning in UAV Remote Sensing

Deep Neural Networks (DNNs) learn representation from data with an impre...

Lucas Prado Osco, et al. ∙

research

∙ 09/08/2020

COVIDNet-CT: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest CT Images

The coronavirus disease 2019 (COVID-19) pandemic continues to have a tre...

Hayden Gunraj, et al. ∙

research

∙ 06/25/2020

The Effect of Optimization Methods on the Robustness of Out-of-Distribution Detection Approaches

Deep neural networks (DNNs) have become the de facto learning mechanism ...

Vahdat Abdelzad, et al. ∙

research

∙ 12/02/2019

Deep Neural Network Fingerprinting by Conferrable Adversarial Examples

In Machine Learning as a Service, a provider trains a deep neural networ...

Nils Lukas, et al. ∙

research

∙ 03/09/2019

BayesOD: A Bayesian Approach for Uncertainty Estimation in Deep Object Detectors

One of the challenging aspects of incorporating deep neural networks int...

Ali Harakeh, et al. ∙

research

∙ 09/08/2021

Where Did You Learn That From? Surprising Effectiveness of Membership Inference Attacks Against Temporally Correlated Data in Deep Reinforcement Learning

While significant research advances have been made in the field of deep ...

Maziar Gomrokchi, 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

∙ 04/10/2020

Deep Learning for Image and Point Cloud Fusion in Autonomous Driving: A Review

Autonomous vehicles are experiencing rapid development in the past few y...

Yaodong Cui, et al. ∙

research

∙ 05/20/2020

Deep Learning for LiDAR Point Clouds in Autonomous Driving: A Review

Recently, the advancement of deep learning in discriminative feature lea...

Ying Li, et al. ∙

research

∙ 11/24/2019

AnoNet: Weakly Supervised Anomaly Detection in Textured Surfaces

Humans can easily detect a defect (anomaly) because it is different or s...

Manpreet Singh Minhas, et al. ∙

research

∙ 01/09/2020

Investigating the Impact of Inclusion in Face Recognition Training Data on Individual Face Identification

Modern face recognition systems leverage datasets containing images of h...

Chris Dulhanty, et al. ∙

research

∙ 08/25/2019

Principal Component Analysis Using Structural Similarity Index for Images

Despite the advances of deep learning in specific tasks using images, th...

Benyamin Ghojogh, et al. ∙

research

∙ 05/08/2020

Active Preference Learning using Maximum Regret

We study active preference learning as a framework for intuitively speci...

Nils Wilde, et al. ∙

research

∙ 04/16/2020

Representation Learning of Histopathology Images using Graph Neural Networks

Representation learning for Whole Slide Images (WSIs) is pivotal in deve...

Mohammed Adnan, et al. ∙

research

∙ 12/29/2021

MetaGraspNet: A Large-Scale Benchmark Dataset for Vision-driven Robotic Grasping via Physics-based Metaverse Synthesis

There has been increasing interest in smart factories powered by robotic...

Yuhao Chen, et al. ∙

research

∙ 04/29/2022

COVID-Net US-X: Enhanced Deep Neural Network for Detection of COVID-19 Patient Cases from Convex Ultrasound Imaging Through Extended Linear-Convex Ultrasound Augmentation Learn

As the global population continues to face significant negative impact b...

E. Zhixuan Zeng, et al. ∙

research

∙ 06/01/2022

LiDAR-MIMO: Efficient Uncertainty Estimation for LiDAR-based 3D Object Detection

The estimation of uncertainty in robotic vision, such as 3D object detec...

Matthew Pitropov, et al. ∙

research

∙ 03/20/2019

Affordance Learning In Direct Perception for Autonomous Driving

Recent development in autonomous driving involves high-level computer vi...

Chen Sun, et al. ∙

research

∙ 07/12/2019

Gated-SCNN: Gated Shape CNNs for Semantic Segmentation

Current state-of-the-art methods for image segmentation form a dense ima...

Towaki Takikawa, et al. ∙

research

∙ 06/28/2022

SSL-Lanes: Self-Supervised Learning for Motion Forecasting in Autonomous Driving

Self-supervised learning (SSL) is an emerging technique that has been su...

Prarthana Bhattacharyya, et al. ∙

research

∙ 05/17/2022

Text Detection Recognition in the Wild for Robot Localization

Signage is everywhere and a robot should be able to take advantage of si...

Zobeir Raisi, et al. ∙

research

∙ 06/08/2022

Adversarial Text Normalization

Text-based adversarial attacks are becoming more commonplace and accessi...

Joanna Bitton, et al. ∙

research

∙ 01/22/2022

Visual Representation Learning with Self-Supervised Attention for Low-Label High-data Regime

Self-supervision has shown outstanding results for natural language proc...

Prarthana Bhattacharyya, et al. ∙

research

∙ 01/14/2020

Unsupervised Domain Adaptation in Person re-ID via k-Reciprocal Clustering and Large-Scale Heterogeneous Environment Synthesis

An ongoing major challenge in computer vision is the task of person re-i...

Devinder Kumar, et al. ∙

research

∙ 05/04/2021

COVID-19 Detection from Chest X-ray Images using Imprinted Weights Approach

The COVID-19 pandemic has had devastating effects on the well-being of t...

Jianxing Zhang, et al. ∙

research

∙ 12/04/2021

SHAPr: An Efficient and Versatile Membership Privacy Risk Metric for Machine Learning

Data used to train machine learning (ML) models can be sensitive. Member...

Vasisht Duddu, et al. ∙

research

∙ 08/16/2018

Auto-Classification of Retinal Diseases in the Limit of Sparse Data Using a Two-Streams Machine Learning Model

Automatic clinical diagnosis of retinal diseases has emerged as a promis...

C. -H. Huck Yang, et al. ∙

research

∙ 03/21/2022

Better Language Model with Hypernym Class Prediction

Class-based language models (LMs) have been long devised to address cont...

He Bai, et al. ∙

research

∙ 06/09/2022

Building Spatio-temporal Transformers for Egocentric 3D Pose Estimation

Egocentric 3D human pose estimation (HPE) from images is challenging due...

Jinman Park, et al. ∙

research

∙ 02/25/2020

Multivariate time-series modeling with generative neural networks

Generative moment matching networks (GMMNs) are introduced as dependence...

Marius Hofert, et al. ∙

research

∙ 11/21/2018

Urban Driving with Multi-Objective Deep Reinforcement Learning

Autonomous driving is a challenging domain that entails multiple aspects...

Changjian Li, et al. ∙

research

∙ 07/09/2019

Improving the Performance of the LSTM and HMM Models via Hybridization

Language models based on deep neural neural networks and traditionalstoc...

Larkin Liu, et al. ∙

research

∙ 07/28/2020

RadarNet: Exploiting Radar for Robust Perception of Dynamic Objects

We tackle the problem of exploiting Radar for perception in the context ...

Bin Yang, et al. ∙

research

∙ 07/30/2020

Algorithmic Stability in Fair Allocation of Indivisible Goods Among Two Agents

We propose a notion of algorithmic stability for scenarios where cardina...

Vijay Menon, et al. ∙

research

∙ 12/19/2018

Learning beamforming in ultrasound imaging

Medical ultrasound (US) is a widespread imaging modality owing its popul...

Sanketh Vedula, et al. ∙

research

∙ 05/20/2021

DeepDarts: Modeling Keypoints as Objects for Automatic Scorekeeping in Darts using a Single Camera

Existing multi-camera solutions for automatic scorekeeping in steel-tip ...

William McNally, et al. ∙

research

∙ 10/16/2021

Deep Image Debanding

Banding or false contour is an annoying visual artifact whose impact is ...

Raymond Zhou, et al. ∙

research

∙ 11/01/2021

Don't Generate Me: Training Differentially Private Generative Models with Sinkhorn Divergence

Although machine learning models trained on massive data have led to bre...

Tianshi Cao, et al. ∙

research

∙ 11/18/2021

M2A: Motion Aware Attention for Accurate Video Action Recognition

Advancements in attention mechanisms have led to significant performance...

Brennan Gebotys, et al. ∙

research

∙ 12/15/2021

EDAssistant: Supporting Exploratory Data Analysis in Computational Notebooks with In-Situ Code Search and Recommendation

Using computational notebooks (e.g., Jupyter Notebook), data scientists ...

Xingjun Li, et al. ∙

research

∙ 05/18/2022

COVID-Net UV: An End-to-End Spatio-Temporal Deep Neural Network Architecture for Automated Diagnosis of COVID-19 Infection from Ultrasound Videos

Besides vaccination, as an effective way to mitigate the further spread ...

Hilda Azimi, et al. ∙

research

∙ 06/29/2019

Approximate Inference in Structured Instances with Noisy Categorical Observations

We study the problem of recovering the latent ground truth labeling of a...

Alireza Heidari, et al. ∙

research

∙ 05/20/2020

p-Norm Flow Diffusion for Local Graph Clustering

Local graph clustering and the closely related seed set expansion proble...

Shenghao Yang, et al. ∙

research

∙ 06/16/2020

Density Deconvolution with Normalizing Flows

Density deconvolution is the task of estimating a probability density fu...

Tim Dockhorn, et al. ∙

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