Michael Lomnitz | DeepAI

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∙ 08/03/2023

Efficient Model Adaptation for Continual Learning at the Edge

Most machine learning (ML) systems assume stationary and matching data d...

0 Zachary A. Daniels, et al. ∙

∙ 08/17/2022

Learning with Local Gradients at the Edge

To enable learning on edge devices with fast convergence and low memory,...

0 Michael Lomnitz, et al. ∙

∙ 06/10/2022

Real-time Hyper-Dimensional Reconfiguration at the Edge using Hardware Accelerators

In this paper we present Hyper-Dimensional Reconfigurable Analytics at t...

0 Indhumathi Kandaswamy, et al. ∙

∙ 09/03/2020

A general approach to bridge the reality-gap

Employing machine learning models in the real world requires collecting ...

0 Michael Lomnitz, et al. ∙

∙ 10/31/2019

Reducing audio membership inference attack accuracy to chance: 4 defenses

It is critical to understand the privacy and robustness vulnerabilities ...

0 Michael Lomnitz, et al. ∙

∙ 06/15/2019

Robust or Private? Adversarial Training Makes Models More Vulnerable to Privacy Attacks

Adversarial training was introduced as a way to improve the robustness o...

5 Felipe A. Mejia, et al. ∙

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