6 citations · 15 across the 5 of their papers we have counts for
8 papers
Pick up the PACE: Fast and Simple Domain Adaptation via Ensemble Pseudo-Labeling
Christopher Liao, Theodoros Tsiligkaridis, Brian Kulis
Domain Adaptation (DA) has received widespread attention from deep learning researchers in recent years because of its potential to improve test accuracy with out-of-distribution l…
Fourier-Based Augmentations for Improved Robustness and Uncertainty Calibration
Ryan Soklaski, Michael Yee, Theodoros Tsiligkaridis
Diverse data augmentation strategies are a natural approach to improving robustness in computer vision models against unforeseen shifts in data distribution. However, the ability t…
Ultrasound Diagnosis of COVID-19: Robustness and Explainability
Jay Roberts, Theodoros Tsiligkaridis
Diagnosis of COVID-19 at point of care is vital to the containment of the global pandemic. Point of care ultrasound (POCUS) provides rapid imagery of lungs to detect COVID-19 in pa…
Failure Prediction by Confidence Estimation of Uncertainty-Aware Dirichlet Networks
Theodoros Tsiligkaridis
Reliably assessing model confidence in deep learning and predicting errors likely to be made are key elements in providing safety for model deployment, in particular for applicatio…
Second Order Optimization for Adversarial Robustness and Interpretability
Theodoros Tsiligkaridis, Jay Roberts
Deep neural networks are easily fooled by small perturbations known as adversarial attacks. Adversarial Training (AT) is a technique aimed at learning features robust to such attac…
Information Aware Max-Norm Dirichlet Networks for Predictive Uncertainty Estimation
Theodoros Tsiligkaridis
Precise estimation of uncertainty in predictions for AI systems is a critical factor in ensuring trust and safety. Deep neural networks trained with a conventional method are prone…