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…
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In this paper, we propose SwarmNet -- a neural network architecture that can learn to predict and imitate the behavior of an observed swarm of…
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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…
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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…
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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…
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In this paper, we propose SwarmNet – a neural network architecture that can learn to predict and imitate the behavior of an observed swarm of…
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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,…
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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…
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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…
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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…
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