Topological Knowledge Distillation for Wearable Sensor Data-Reference-Cited by-同舟云学术

Topological Knowledge Distillation for Wearable Sensor Data

Published:2022-10-31 Issue: Volume: Page:
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Container-title:2022 56th Asilomar Conference on Signals, Systems, and Computers
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Author:

Jeon Eun Som¹,Choi Hongjun¹,Shukla Ankita¹,Wang Yuan²,Buman Matthew P.³,Turaga Pavan¹

Affiliation:

1. School of Arts, Media and Engineering and School of Electrical, Computer and Energy Engineering, Arizona State University,Geometric Media Lab,Tempe,AZ,USA,85281

2. University of South Carolina,Department of Epidemiology and Biostatistics,Columbia,SC,USA,29208

3. College of Health Solutions, Arizona State University,Phoenix,AZ,USA,85004

Funder

NIH

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10051833/10051818/10052019.pdf?arnumber=10052019

Reference30 articles.

1. Knowledge Distillation: A Survey

2. Distilling the knowledge in a neural network;hinton;Proceedings of the NeurIPS Deep Learning and Representation Learning Workshop,0

3. Role of Data Augmentation Strategies in Knowledge Distillation for Wearable Sensor Data

4. On the Efficacy of Knowledge Distillation

5. Wide residual networks;zagoruyko;Proceedings of the British Machine Vision Conference,2016

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1. Uncertainty-Aware Topological Persistence Guided Knowledge Distillation on Wearable Sensor Data;IEEE Internet of Things Journal;2024-09-15

2. Topological persistence guided knowledge distillation for wearable sensor data;Engineering Applications of Artificial Intelligence;2024-04

3. Constrained Adaptive Distillation Based on Topological Persistence for Wearable Sensor Data;IEEE Transactions on Instrumentation and Measurement;2023