Summary of Heal: Brain-inspired Hyperdimensional Efficient Active Learning, by Yang Ni et al.
HEAL: Brain-inspired Hyperdimensional Efficient Active Learning
by Yang Ni, Zhuowen Zou, Wenjun Huang, Hanning Chen, William Youngwoo Chung, Samuel Cho, Ranganath Krishnan, Pietro Mercati, Mohsen Imani
First submitted to arxiv on: 17 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces Hyperdimensional Efficient Active Learning (HEAL), a novel framework for actively annotating data points in supervised learning. HEAL is specifically designed for Hyperdimensional Computing (HDC) classification and leverages high-dimensional vector presentation and operations to boost the learning efficiency of HDC classifiers. Unlike conventional active learning methods, which only support classifiers built upon deep neural networks (DNN), HEAL operates without gradient or probabilistic computations, making it effortlessly integrable with any existing HDC classifier architecture. HEAL uses a novel approach for uncertainty estimation in HDC classifiers through a lightweight HDC ensemble with prior hypervectors and an extra metric to select diverse samples within each batch for annotation. The evaluation shows that HEAL surpasses a diverse set of baselines in AL quality and achieves a notable speedup in acquisition runtime per batch. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary HEAL is a new way to help computers learn from data without using deep neural networks (DNNs). It’s inspired by how our brains work, where we can quickly learn from small amounts of information. The paper shows that HEAL can be used with any existing computer program that uses high-dimensional vectors and operations. This means it can be easily combined with other machine learning techniques. HEAL works by choosing the most uncertain and diverse data points to label first. It uses a special type of vector called a hypervector, which is like a compact representation of the data. By using these hypervectors, HEAL can quickly find the most important data points to label. The paper tested HEAL with different machine learning techniques and showed that it’s much faster than other active learning methods. |
Keywords
* Artificial intelligence * Active learning * Classification * Machine learning * Supervised
