21 citations · 21 across the 3 of their papers we have counts for
6 papers
Multi-Variate Time Series Forecasting on Variable Subsets
Jatin Chauhan, Aravindan Raghuveer, Rishi Saket +2
We formulate a new inference task in the domain of multivariate time series forecasting (MTSF), called Variable Subset Forecast (VSF), where only a small subset of the variables is…
Distributional Shifts in Automated Diabetic Retinopathy Screening
Jay Nandy, Wynne Hsu, Mong Li Lee
Deep learning-based models are developed to automatically detect if a retina image is `referable' in diabetic retinopathy (DR) screening. However, their classification accuracy deg…
Towards Bridging the gap between Empirical and Certified Robustness against Adversarial Examples
Jay Nandy, Sudipan Saha, Wynne Hsu +2
The current state-of-the-art defense methods against adversarial examples typically focus on improving either empirical or certified robustness. Among them, adversarially trained (…
Towards Maximizing the Representation Gap between In-Domain & Out-of-Distribution Examples
Jay Nandy, Wynne Hsu, Mong Li Lee
Among existing uncertainty estimation approaches, Dirichlet Prior Network (DPN) distinctly models different predictive uncertainty types. However, for in-domain examples with high…
Approximate Manifold Defense Against Multiple Adversarial Perturbations
Jay Nandy, Wynne Hsu, Mong Li Lee
Existing defenses against adversarial attacks are typically tailored to a specific perturbation type. Using adversarial training to defend against multiple types of perturbation re…
Normal Similarity Network for Generative Modelling
Jay Nandy, Wynne Hsu, Mong Li Lee
Gaussian distributions are commonly used as a key building block in many generative models. However, their applicability has not been well explored in deep networks. In this paper,…