13 citations · 15 across the 6 of their papers we have counts for
6 papers
Point-Identification of a Robust Predictor Under Latent Shift with Imperfect Proxies
Zahra Rahiminasab, Reza Soumi, Arto Klami +1
Addressing the domain adaptation problem becomes more challenging when distribution shifts across domains stem from latent confounders that affect both covariates and outcomes. Exi…
Disentangled and Distilled Encoder for Out-of-Distribution Reasoning with Rademacher Guarantees
Zahra Rahiminasab, Michael Yuhas, Arvind Easwaran
Recently, the disentangled latent space of a variational autoencoder (VAE) has been used to reason about multi-label out-of-distribution (OOD) test samples that are derived from di…
Out of Distribution Reasoning by Weakly-Supervised Disentangled Logic Variational Autoencoder
Zahra Rahiminasab, Michael Yuhas, Arvind Easwaran
Out-of-distribution (OOD) detection, i.e., finding test samples derived from a different distribution than the training set, as well as reasoning about such samples (OOD reasoning)…
Efficient Out-of-Distribution Detection Using Latent Space of -VAE for Cyber-Physical Systems
Shreyas Ramakrishna, Zahra Rahiminasab, Gabor Karsai +2
Deep Neural Networks are actively being used in the design of autonomous Cyber-Physical Systems (CPSs). The advantage of these models is their ability to handle high-dimensional st…
Embedded out-of-distribution detection on an autonomous robot platform
Michael Yuhas, Yeli Feng, Daniel Jun Xian Ng +2
Machine learning (ML) is actively finding its way into modern cyber-physical systems (CPS), many of which are safety-critical real-time systems. It is well known that ML outputs ar…
Out-of-Distribution Detection in Multi-Label Datasets using Latent Space of -VAE
Vijaya Kumar Sundar, Shreyas Ramakrishna, Zahra Rahiminasab +2
Learning Enabled Components (LECs) are widely being used in a variety of perception based autonomy tasks like image segmentation, object detection, end-to-end driving, etc. These c…