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Holistic Features For Real-Time Crowd Behaviour Anomaly Detection
This paper presents a new approach to crowd behaviour anomaly detection ...
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Incorporating Privileged Information to Unsupervised Anomaly Detection
We introduce a new unsupervised anomaly detection ensemble called SPI wh...
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Further Exploring Communal Technology Use in Smart Homes: Social Expectations
Device use in smart homes is becoming increasingly communal, requiring c...
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What do we expect from Multiple-choice QA Systems?
The recent success of machine learning systems on various QA datasets co...
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Neural Memory Plasticity for Anomaly Detection
In the domain of machine learning, Neural Memory Networks (NMNs) have re...
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An Adaptive Training-less System for Anomaly Detection in Crowd Scenes
Anomaly detection in crowd videos has become a popular area of research ...
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Two equalities expressing the determinant of a matrix in terms of expectations over matrix-vector products
We introduce two equations expressing the inverse determinant of a full ...
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The Profiling Machine: Active Generalization over Knowledge
The human mind is a powerful multifunctional knowledge storage and management system that performs generalization, type inference, anomaly detection, stereotyping, and other tasks. A dynamic KR system that appropriately profiles over sparse inputs to provide complete expectations for unknown facets can help with all these tasks. In this paper, we introduce the task of profiling, inspired by theories and findings in social psychology about the potential of profiles for reasoning and information processing. We describe two generic state-of-the-art neural architectures that can be easily instantiated as profiling machines to generate expectations and applied to any kind of knowledge to fill gaps. We evaluate these methods against Wikidata and crowd expectations, and compare the results to gain insight in the nature of knowledge captured by various profiling methods. We make all code and data available to facilitate future research.
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