172 citations · 559 across the 44 of their papers we have counts for
14 papers · 1 filter
A Cautionary Tale of Self-Supervised Learning for Imaging Biomarkers: Alzheimer's Disease Case Study
Maxwell Reynolds, Chaitanya Srinivasan, Vijay Cherupally +6
Discovery of sensitive and biologically grounded biomarkers is essential for early detection and monitoring of Alzheimer's disease (AD). Structural MRI is widely available but typi…
Dividing and Conquering a BlackBox to a Mixture of Interpretable Models: Route, Interpret, Repeat
Shantanu Ghosh, Ke Yu, Forough Arabshahi +1
ML model design either starts with an interpretable model or a Blackbox and explains it post hoc. Blackbox models are flexible but difficult to explain, while interpretable models…
Semi-Implicit Denoising Diffusion Models (SIDDMs)
Yanwu Xu, Mingming Gong, Shaoan Xie +4
Despite the proliferation of generative models, achieving fast sampling during inference without compromising sample diversity and quality remains challenging. Existing models such…
Tackling Shortcut Learning in Deep Neural Networks: An Iterative Approach with Interpretable Models
Shantanu Ghosh, Ke Yu, Forough Arabshahi +1
We use concept-based interpretable models to mitigate shortcut learning. Existing methods lack interpretability. Beginning with a Blackbox, we iteratively carve out a mixture of in…
Beyond Distribution Shift: Spurious Features Through the Lens of Training Dynamics
Nihal Murali, Aahlad Puli, Ke Yu +2
Deep Neural Networks (DNNs) are prone to learning spurious features that correlate with the label during training but are irrelevant to the learning problem. This hurts model gener…
Augmentation by Counterfactual Explanation -- Fixing an Overconfident Classifier
Sumedha Singla, Nihal Murali, Forough Arabshahi +2
A highly accurate but overconfident model is ill-suited for deployment in critical applications such as healthcare and autonomous driving. The classification outcome should reflect…
Using Causal Analysis for Conceptual Deep Learning Explanation
Sumedha Singla, Stephen Wallace, Sofia Triantafillou +1
Model explainability is essential for the creation of trustworthy Machine Learning models in healthcare. An ideal explanation resembles the decision-making process of a domain expe…
Can contrastive learning avoid shortcut solutions?
Joshua Robinson, Li Sun, Ke Yu +3
The generalization of representations learned via contrastive learning depends crucially on what features of the data are extracted. However, we observe that the contrastive loss d…
Semi-Supervised Hierarchical Drug Embedding in Hyperbolic Space
Ke Yu, Shyam Visweswaran, Kayhan Batmanghelich
Learning accurate drug representation is essential for tasks such as computational drug repositioning and prediction of drug side-effects. A drug hierarchy is a valuable source tha…
Explanation by Progressive Exaggeration
Sumedha Singla, Brian Pollack, Junxiang Chen +1
As machine learning methods see greater adoption and implementation in high stakes applications such as medical image diagnosis, the need for model interpretability and explanation…
Robust Ordinal VAE: Employing Noisy Pairwise Comparisons for Disentanglement
Junxiang Chen, Kayhan Batmanghelich
Recent work by Locatello et al. (2018) has shown that an inductive bias is required to disentangle factors of interest in Variational Autoencoder (VAE). Motivated by a real-world p…
Twin Auxiliary Classifiers GAN
Mingming Gong, Yanwu Xu, Chunyuan Li +2
Conditional generative models enjoy remarkable progress over the past few years. One of the popular conditional models is Auxiliary Classifier GAN (AC-GAN), which generates highly…
Weakly Supervised Disentanglement by Pairwise Similarities
Junxiang Chen, Kayhan Batmanghelich
Recently, researches related to unsupervised disentanglement learning with deep generative models have gained substantial popularity. However, without introducing supervision, ther…
Generative-Discriminative Complementary Learning
Yanwu Xu, Mingming Gong, Junxiang Chen +3
Majority of state-of-the-art deep learning methods are discriminative approaches, which model the conditional distribution of labels given inputs features. The success of such appr…