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Research Publications

Outside Research

  1. Non-contractual churn prediction using Hierarchical Temporal Memory
    Bakkevig, Jone K.; Methi, Magnus
    Sequence Learning
    Master's theses in Information and Communication Technology2018/09/21

    In this Master's thesis from the University of Agder, the authors examine using Hierarchical Temporal Memory (HTM) to predict future purchase events of customers in a non-contractual setting. HTM was chosen specifically because of a desire to research algorithms that may be useful in churn analysis by utilizing temporal structure of data in prediction based models. The authors also compare HTM results to other techniques.

  2. Neuromorphic Architecture for the Hierarchical Temporal Memory
    Abdullah M. Zyarah, Dhireesha Kudithipudi
    Hardware implementations of HTM
    Pre-print of journal submission 2018/07/31

    The authors present a full-scale Hierarchical Temporal Memory (HTM) architecture for spatial pooler and temporal memory and verify it for two data sets: MNIST and the European number plate font (EUNF), with and without noise. These results suggest that the proposed architecture, using a novel form of synthetic synapses, can serve as a digital core to build HTM in hardware.

  3. Structural Plasticity on the SpiNNaker Many-Core Neuromorphic System
    Petruț A. Bogdan*, Andrew G. D. Rowley, Oliver Rhodes and Steve B. Furber
    Hardware implementations of HTM
    Published in Frontiers in Neuroscience2018/07/02

    Structural plasticity is required by HTM models, but not addressed in many hardware architectures. This paper presents a model of topographic map formation that is implemented on the SpiNNaker neuromorphic platform, running in real time using point neurons, and making use of both synaptic rewiring and spike-timing dependent plasticity (STDP). The authors use our papers as one of the motivations for working on this architecture.

  4. Detecting performance anomalies in scientific workflows using hierarchical temporal memory
    Maria A.Rodriguez, Ramamohanarao Kotagiri, Rajkumar Buyya
    Anomaly Detection
    Published in Future Generation Computer Systems2018/05/18

    This paper proposes an anomaly detection framework for scientific workflows that is based on our Hierarchical Temporal Memory (HTM) technology. The paper also cites the Numenta Anomaly Benchmark (NAB) scoring code and methodology; the researchers use NAB to evaluate performance of algorithms.

  5. A Distributed Anomaly Detection System for In-Vehicle Network Using HTM Anomaly Detection papers
    Chundong Wang, Zhentang Zhao, Liangyi Gong, Likun Zhu, Zheli Liu, Xiaochun Cheng
    Anomaly Detection
    IEEE Access2018/01/30

    The authors proposes a distributed anomaly detection system using hierarchical temporal memory (HTM) that predicts the flow of data in real time to increase security for a vehicular controller area network bus. The HTM-based anomaly detection system achieved better results than systems based on recurrent neural networks and hidden Markov model detection.

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