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Found 6 papers, 2 papers with code
SMORE: Similarity-based Hyperdimensional Domain Adaptation for Multi-Sensor Time Series Classification
Many real-world applications of the Internet of Things (IoT) employ machine learning (ML) algorithms to analyze time series information collected by interconnected sensors.
Robust and Scalable Hyperdimensional Computing With Brain-Like Neural Adaptations
In this work, we present dynamic HDC learning frameworks that identify and regenerate undesired dimensions to provide adequate accuracy with significantly lowered dimensionalities, thereby accelerating both the training and inference.
DOMINO: Domain-invariant Hyperdimensional Classification for Multi-Sensor Time Series Data
With the rapid evolution of the Internet of Things, many real-world applications utilize heterogeneously connected sensors to capture time-series information.
RS2G: Data-Driven Scene-Graph Extraction and Embedding for Robust Autonomous Perception and Scenario Understanding
Effectively capturing intricate interactions among road users is of critical importance to achieving safe navigation for autonomous vehicles.
DistHD: A Learner-Aware Dynamic Encoding Method for Hyperdimensional Classification
Brain-inspired hyperdimensional computing (HDC) has been recently considered a promising learning approach for resource-constrained devices.
Late Breaking Results: Scalable and Efficient Hyperdimensional Computing for Network Intrusion Detection
Cybersecurity has emerged as a critical challenge for the industry.
