Specifying meaningful weight priors for variational inference in Bayesian deep neural network (DNN) is a challenging problem, particularly for scaling to larger models involving high…
In this paper, we propose SwarmNet -- a neural network architecture that can learn to predict and imitate the behavior of an observed swarm of…
We present a deep learning system for testing graphics units by detecting novel visual corruptions in videos. Unlike previous work in which manual tagging was…
This demo presents a novel data visualization solution for exploring the results of time series anomaly detection systems. When anomalies are reported, there is a…
Query optimization is one of the most challenging problems in database systems. Despite the progress made over the past decades, query optimizers remain extremely complex…
In this paper, we propose SwarmNet – a neural network architecture that can learn to predict and imitate the behavior of an observed swarm of…
Scene graphs are a structured representation, with objects as nodes with attributes, and edges marking the semantic relationship between objects. Generating images from scene graphs,…
With the recent advancements in Artificial Intelligence (AI), Intelligent Virtual Assistants (IVA) such as Alexa, Google Home, etc., have become a ubiquitous part of every…
Data poisoning attacks compromise the integrity of machine-learning models by introducing malicious training samples to influence the results during test time. In this work, we…
Scene graphs have become an important form of structured knowledge for tasks such as for image generation, visual relation detection, visual question answering, and image…
