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Found 6 papers, 0 papers with code
Efficient Model Adaptation for Continual Learning at the Edge
This paper presents the Encoder-Adaptor-Reconfigurator (EAR) framework for efficient continual learning under domain shifts.
Learning with Local Gradients at the Edge
To enable learning on edge devices with fast convergence and low memory, we present a novel backpropagation-free optimization algorithm dubbed Target Projection Stochastic Gradient Descent (tpSGD).
Real-time Hyper-Dimensional Reconfiguration at the Edge using Hardware Accelerators
In this paper we present Hyper-Dimensional Reconfigurable Analytics at the Tactical Edge (HyDRATE) using low-SWaP embedded hardware that can perform real-time reconfiguration at the edge leveraging non-MAC (free of floating-point MultiplyACcumulate operations) deep neural nets (DNN) combined with hyperdimensional (HD) computing accelerators.
A general approach to bridge the reality-gap
Employing machine learning models in the real world requires collecting large amounts of data, which is both time consuming and costly to collect.
Reducing audio membership inference attack accuracy to chance: 4 defenses
It is critical to understand the privacy and robustness vulnerabilities of machine learning models, as their implementation expands in scope.
Robust or Private? Adversarial Training Makes Models More Vulnerable to Privacy Attacks
Adversarial training was introduced as a way to improve the robustness of deep learning models to adversarial attacks.
