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Alexander Lavin
Researcher at Northwestern University
Publications - 30
Citations - 1502
Alexander Lavin is an academic researcher from Northwestern University. The author has contributed to research in topics: Computer science & Generative model. The author has an hindex of 7, co-authored 26 publications receiving 1049 citations. Previous affiliations of Alexander Lavin include Carnegie Mellon University.
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Journal ArticleDOI
Unsupervised real-time anomaly detection for streaming data
TL;DR: A novel anomaly detection algorithm is proposed that is based on an online sequence memory algorithm called Hierarchical Temporal Memory (HTM) and presented using the Numenta Anomaly Benchmark (NAB), a benchmark containing real-world data streams with labeled anomalies.
Proceedings ArticleDOI
Evaluating Real-Time Anomaly Detection Algorithms -- The Numenta Anomaly Benchmark
Alexander Lavin,Subutai Ahmad +1 more
TL;DR: The Numenta Anomaly Benchmark (NAB) is proposed, which attempts to provide a controlled and repeatable environment of open-source tools to test and measure anomaly detection algorithms on streaming data.
Journal ArticleDOI
A generative vision model that trains with high data efficiency and breaks text-based CAPTCHAs
Dileep George,Wolfgang Lehrach,Ken Kansky,Miguel Lázaro-Gredilla,Christopher Laan,Bhaskara Marthi,Xinghua Lou,Zhaoshi Meng,Yi Liu,Huayan Wang,Alexander Lavin,D. Scott Phoenix +11 more
TL;DR: This work introduces recursive cortical network (RCN), a probabilistic generative model for vision in which message-passing–based inference handles recognition, segmentation, and reasoning in a unified manner and outperforms deep neural networks on a variety of benchmarks while being orders of magnitude more data-efficient.
Journal ArticleDOI
Clustering time-series energy data from smart meters
Alexander Lavin,Diego Klabjan +1 more
TL;DR: The results show accurate grouping of accounts similar in their energy usage patterns, and potential for the method to be utilized in energy efficiency programs.
Posted ContentDOI
Technology Readiness Levels for Machine Learning Systems.
Alexander Lavin,Ciarán M. Gilligan-Lee,Alessya Visnjic,Siddha Ganju,Dava J. Newman,Sujoy Ganguly,Danny Lange,Atılım Güneş Baydin,Amit Sharma,Adam Gibson,Yarin Gal,Eric P. Xing,Chris A. Mattmann,James Parr +13 more
TL;DR: The Machine Learning Technology Readiness Levels (MLTRL) framework defines a principled process to ensure robust, reliable, and responsible systems while being streamlined for ML workflows, including key distinctions from traditional software engineering.