Challenges and Opportunities in Approximate Bayesian Deep Learning for Intelligent IoT Systems
Approximate Bayesian deep learning methods hold significant promise for addressing several issues that occur when deploying deep learning components in intelligent systems, including mitigating the occurrence of over-confident errors and providing enhanced robustness to out of distribution examples. However, the computational requirements of existing approximate Bayesian inference methods can make them ill-suited for deployment in intelligent IoT systems that include lower-powered edge devices. In this paper, we present a range of approximate Bayesian inference methods for supervised deep learning and highlight the challenges and opportunities when applying these methods on current edge hardware. We highlight several potential solutions to decreasing model storage requirements and improving computational scalability, including model pruning and distillation methods.
Code (0)
등록된 구현이 없습니다.
Tasks
Bayesian InferenceDeep LearningMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Bayes in the age of intelligent machines
The success of methods based on artificial neural networks in creating intelligent machines seems like it might pose a challenge to explanations of human cognition in terms of Bayesian inference. We argue that this is no…
Bayesian InferenceUAVs with Reconfigurable Intelligent Surfaces: Applications, Challenges, and Opportunities
A reconfigurable intelligent surface (RIS) is a metamaterial that can be integrated into walls and influence the propagation of electromagnetic waves. This, typically passive radio frequency (RF) technology is emerging f…
Bayesian optimization as a flexible and efficient design framework for sustainable process systems
Bayesian optimization (BO) is a powerful technology for optimizing noisy expensive-to-evaluate black-box functions, with a broad range of real-world applications in science, engineering, economics, manufacturing, and bey…
Bayesian OptimizationBayesian Optimization in Materials Science: A Survey
Bayesian optimization is used in many areas of AI for the optimization of black-box processes and has achieved impressive improvements of the state of the art for a lot of applications. It intelligently explores large an…
Bayesian OptimizationSurveyLLMs in Education: Novel Perspectives, Challenges, and Opportunities
The role of large language models (LLMs) in education is an increasing area of interest today, considering the new opportunities they offer for teaching, learning, and assessment. This cutting-edge tutorial provides an o…