Publications (19)
Attention U-Net: Learning Where to Look for the Pancreas
Ozan Oktay, Jo Schlemper, Loic Le Folgoc +9
We propose a novel attention gate (AG) model for medical imaging that automatically learns to focus on target structures of varying shapes and sizes. Models trained with AGs implic…
Learning to Represent Individual Differences for Choice Decision Making
Yan-Ying Chen, Yue Weng, Alexandre Filipowicz +7
Human decision making can be challenging to predict because decisions are affected by a number of complex factors. Adding to this complexity, decision-making processes can differ c…
Federated Learning Enables Big Data for Rare Cancer Boundary Detection
Sarthak Pati, Ujjwal Baid, Brandon Edwards +276
Although machine learning (ML) has shown promise in numerous domains, there are concerns about generalizability to out-of-sample data. This is currently addressed by centrally shar…
A Search for Low-mass Dark Matter via Bremsstrahlung Radiation and the Migdal Effect in SuperCDMS
SuperCDMS Collaboration, Musaab Al-Bakry, Imran Alkhatib +128
In this paper, we present a re-analysis of SuperCDMS data using a profile likelihood approach to search for sub-GeV dark matter particles (DM) through two inelastic scattering chan…
Probabilistic 3D surface reconstruction from sparse MRI information
KatarÃna Tóthová, Sarah Parisot, Matthew Lee +4
Surface reconstruction from magnetic resonance (MR) imaging data is indispensable in medical image analysis and clinical research. A reliable and effective reconstruction tool shou…
Universal Phone Recognition with a Multilingual Allophone System
Xinjian Li, Siddharth Dalmia, Juncheng Li +8
Multilingual models can improve language processing, particularly for low resource situations, by sharing parameters across languages. Multilingual acoustic models, however, genera…
Detecting Affective Flow States of Knowledge Workers Using Physiological Sensors
Matthew Lee
Flow-like experiences at work are important for productivity and worker well-being. However, it is difficult to objectively detect when workers are experiencing flow in their work.…
MusicFlow: Cascaded Flow Matching for Text Guided Music Generation
K R Prajwal, Bowen Shi, Matthew Lee +8
We introduce MusicFlow, a cascaded text-to-music generation model based on flow matching. Based on self-supervised representations to bridge between text descriptions and music aud…
Distance Metric Learning using Graph Convolutional Networks: Application to Functional Brain Networks
Sofia Ira Ktena, Sarah Parisot, Enzo Ferrante +4
Evaluating similarity between graphs is of major importance in several computer vision and pattern recognition problems, where graph representations are often used to model objects…
Disease Prediction using Graph Convolutional Networks: Application to Autism Spectrum Disorder and Alzheimer's Disease
Sarah Parisot, Sofia Ira Ktena, Enzo Ferrante +4
Graphs are widely used as a natural framework that captures interactions between individual elements represented as nodes in a graph. In medical applications, specifically, nodes c…
Spectral Graph Convolutions for Population-based Disease Prediction
Sarah Parisot, Sofia Ira Ktena, Enzo Ferrante +4
Exploiting the wealth of imaging and non-imaging information for disease prediction tasks requires models capable of representing, at the same time, individual features as well as…
Training Towards Critical Use: Learning to Situate AI Predictions Relative to Human Knowledge
Anna Kawakami, Luke Guerdan, Yanghuidi Cheng +6
A growing body of research has explored how to support humans in making better use of AI-based decision support, including via training and onboarding. Existing research has focuse…
Plug-and-Play Reweighting for Resilient Collaborative Decision-Making in Connected Autonomous Driving
Jiewen Liu, Rui Liu, Matthew Lee +3
Collaborative decision-making is a fundamental capability in multi-robot systems, such as connected autonomous vehicles. However, perceptual noise and adversarial attacks in collab…
A Summary of the First Workshop on Language Technology for Language Documentation and Revitalization
Graham Neubig, Shruti Rijhwani, Alexis Palmer +21
Despite recent advances in natural language processing and other language technology, the application of such technology to language documentation and conservation has been limited…
Pain Intensity Estimation from Mobile Video Using 2D and 3D Facial Keypoints
Matthew Lee, Lyndon Kennedy, Andreas Girgensohn +4
Managing post-surgical pain is critical for successful surgical outcomes. One of the challenges of pain management is accurately assessing the pain level of patients. Self-reported…
One-Shot Learning for Language Modelling
Talip Ucar, Adrian Gonzalez-Martin, Matthew Lee +1
Humans can infer a great deal about the meaning of a word, using the syntax and semantics of surrounding words even if it is their first time reading or hearing it. We can also gen…
Towards a Learner-Centered Explainable AI: Lessons from the learning sciences
Anna Kawakami, Luke Guerdan, Yang Cheng +8
In this short paper, we argue for a refocusing of XAI around human learning goals. Drawing upon approaches and theories from the learning sciences, we propose a framework for the l…
Measurement of Low Energy Nuclear Recoil Events with the phonon-mediated Voltage-Assisted Hybrid Detector for Rare Event Searches
Sandro Maludze, Mahdi Mirzakhani, William Baker +7
The phonon-mediated hybrid detector is made out of a monolithic silicon crystal characterized by two interconnected regions linked through a narrow neck. Operating solely on phonon…
Ensembles of Multiple Models and Architectures for Robust Brain Tumour Segmentation
Konstantinos Kamnitsas, Wenjia Bai, Enzo Ferrante +8
Deep learning approaches such as convolutional neural nets have consistently outperformed previous methods on challenging tasks such as dense, semantic segmentation. However, the v…