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Found 8 papers, 1 papers with code
A Deep Learning Approach for Pixel-level Material Classification via Hyperspectral Imaging
This study evaluates the potential of combining HS imaging with deep learning for material characterization.
Trustworthy Intrusion Detection: Confidence Estimation Using Latent Space
This work introduces a novel method for enhancing confidence in anomaly detection in Intrusion Detection Systems (IDS) through the use of a Variational Autoencoder (VAE) architecture.
MAGNETO: Edge AI for Human Activity Recognition -- Privacy and Personalization
Human activity recognition (HAR) is a well-established field, significantly advanced by modern machine learning (ML) techniques.
A Latent Space Metric for Enhancing Prediction Confidence in Earth Observation Data
This study presents a new approach for estimating confidence in machine learning model predictions, specifically in regression tasks utilizing Earth Observation (EO) data, with a particular focus on mosquito abundance (MA) estimation.
Practical Insights on Incremental Learning of New Human Physical Activity on the Edge
Edge Machine Learning (Edge ML), which shifts computational intelligence from cloud-based systems to edge devices, is attracting significant interest due to its evident benefits including reduced latency, enhanced data privacy, and decreased connectivity reliance.
On Handling Catastrophic Forgetting for Incremental Learning of Human Physical Activity on the Edge
Human activity recognition (HAR) has been a classic research problem.
The Price of Local Fairness in Multistage Selection
In particular, our experiments show that the price of local fairness is generally smaller when the sensitive attribute is observed at the first stage; but globally fair selections are more locally fair when the sensitive attribute is observed at the second stage---hence in both cases it is often possible to have a selection that has a small price of local fairness and is close to locally fair.
Variational Information Bottleneck for Unsupervised Clustering: Deep Gaussian Mixture Embedding
In this paper, we develop an unsupervised generative clustering framework that combines the Variational Information Bottleneck and the Gaussian Mixture Model.
