9 accepted papers
Multimodal deep learning systems are deployed in dynamic scenarios due to the robustness afforded by multiple sensing modalities. Nevertheless, they struggle with varying compute resource availability (due to multi-tenancy, device heterogeneity, etc.) and fluctuating quality of inputs (from sensor f…
We study the problem of quickest change detection (QCD) when the post-change distribution has parametric uncertainty. The generalized likelihood ratio (GLR) cumulative sum (CuSum) procedure is known to be asymptotically optimum in this setting. However, this rule requires significant memory and comp…
Localization is a critical technology for various applications ranging from navigation and surveillance to assisted living. Localization systems typically fuse information from sensors viewing the scene from different perspectives to estimate the target location while also employing multiple modalit…
Deep neural networks (DNNs) have been shown to perform well on exclusive, multi-class classification tasks. However, when different classes have similar visual features, it becomes challenging for human annotators to differentiate them. When an image is ambiguous, such as a blurry one where an annot…
This paper studies the problem of event detection in highly dynamic and uncertain environments where training data is limited and the number of observations is small. The objective is to determine if the training data and observations are drawn from the same or different distributions. The tradition…
Early event detection aims to detect events even before the event is complete. However, most of the existing methods focus on an event with a single label but fail to be applied to cases with multiple labels. Another non-negligible issue for early event detection is a prediction with overconfidence…
When collaborating with an artificial intelligence (AI) system, we need to assess when to trust its recommendations. Suppose we mistakenly trust it in regions where it is likely to err. In that case, catastrophic failures may occur, hence the need for Bayesian approaches for reasoning and learning t…
We consider the problem of distributed inference of a group of agents in a social network, where the agents construct, share, and update beliefs in a non-Bayesian framework to identify the underlying true state of the world. We build upon the concept of uncertain models that accurately represents ea…
Estimation of an unknown deterministic vector from quantized sensor data is considered in the presence of spoofing and man-in-the-middle attacks. First, asymptotically optimum processing, which identifies and categorizes the attacked sensors into different groups according to distinct types of attac…