Quantization for Bayesian Deep Learning: Low-Precision Characterization and Robustness-Reference-Cited by-同舟云学术

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Quantization for Bayesian Deep Learning: Low-Precision Characterization and Robustness

Published:2023-10-01 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE International Symposium on Workload Characterization (IISWC)
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Author:

Lin Jun-Liang¹,Krishnan Ranganath²,Ranipa Keyur Ruganathbhai²,Subedar Mahesh²,Sanghavi Vrushabh²,Arunachalam Meena³,Tickoo Omesh²,Iyer Ravishankar²,Kandemir Mahmut Taylan¹

Affiliation:

1. The Pennsylvania State University

2. Intel

3. AMD

Funder

National Science Foundation

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10288627/10288623/10289271.pdf?arnumber=10289271

Reference42 articles.

1. A review of uncertainty quantification in deep learning: Techniques, applications and challenges

2. Towards Dependable Autonomous Systems Based on Bayesian Deep Learning Components

3. Benchmarking bayesian deep learning on diabetic retinopathy detection tasks;Band

4. From Detection of Individual Metastases to Classification of Lymph Node Status at the Patient Level: The CAMELYON17 Challenge

5. Uncertainty as a Form of Transparency: Measuring, Communicating, and Using Uncertainty

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