533 citations · 623 across the 14 of their papers we have counts for
7 papers · 1 filter
Maintaining Stability and Plasticity for Predictive Churn Reduction
George Adam, Benjamin Haibe-Kains, Anna Goldenberg
Deployed machine learning models should be updated to take advantage of a larger sample size to improve performance, as more data is gathered over time. Unfortunately, even when mo…
Lymph Node Graph Neural Networks for Cancer Metastasis Prediction
Michal Kazmierski, Benjamin Haibe-Kains
Predicting outcomes, such as survival or metastasis for individual cancer patients is a crucial component of precision oncology. Machine learning (ML) offers a promising way to exp…
A Machine Learning Challenge for Prognostic Modelling in Head and Neck Cancer Using Multi-modal Data
Michal Kazmierski, Mattea Welch, Sejin Kim +12
Accurate prognosis for an individual patient is a key component of precision oncology. Recent advances in machine learning have enabled the development of models using a wider rang…
Deep-CR MTLR: a Multi-Modal Approach for Cancer Survival Prediction with Competing Risks
Sejin Kim, Michal Kazmierski, Benjamin Haibe-Kains
Accurate survival prediction is crucial for development of precision cancer medicine, creating the need for new sources of prognostic information. Recently, there has been signific…
Learning across label confidence distributions using Filtered Transfer Learning
Seyed Ali Madani Tonekaboni, Andrew E. Brereton, Zhaleh Safikhani +3
Performance of neural network models relies on the availability of large datasets with minimal levels of uncertainty. Transfer Learning (TL) models have been proposed to resolve th…
Reducing Adversarial Example Transferability Using Gradient Regularization
George Adam, Petr Smirnov, Benjamin Haibe-Kains +1
Deep learning algorithms have increasingly been shown to lack robustness to simple adversarial examples (AdvX). An equally troubling observation is that these adversarial examples…
Stochastic Combinatorial Ensembles for Defending Against Adversarial Examples
George A. Adam, Petr Smirnov, David Duvenaud +2
Many deep learning algorithms can be easily fooled with simple adversarial examples. To address the limitations of existing defenses, we devised a probabilistic framework that can…