What is it about?
In this paper, we propose a novel approach, termed Probabilistic BNN (ProbBNN), which leverages probabilistic computing principles to streamline the inference process. Unlike traditional deterministic approaches, ProbBNN represents inputs and parameters as random variables governed by probability distributions, allowing for the propagation of uncertainty throughout the network. We employ Gaussian Mixture Models (GMMs) to represent the parameters of each neuron or convolutional kernel, enabling efficient encoding and processing of uncertainty.
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Why is it important?
Our approach simplifies the inference process by replacing complex deterministic computations with lightweight probabilistic operations, resulting in reduced computational complexity and improved scalability. Experimental results demonstrate the effectiveness of ProbBNN in achieving competitive accuracy to traditional BNNs while significantly reducing the number of parameters.
Perspectives
Our experimental results demonstrate the effectiveness of ProbBNNs in reducing the number of parameters while maintaining modeling accuracy, offering a promising avenue for enhancing the efficiency and robustness of BNNs in various applications. Additionally, we highlight the challenges and opportunities for future research, including further optimization of hyperparameters and exploration of probabilistic computing techniques for other neural network architectures.
Md Ishak
Wayne State University
Read the Original
This page is a summary of: Probabilistic Bayesian Neural Networks for Efficient Inference, June 2024, ACM (Association for Computing Machinery),
DOI: 10.1145/3649476.3658740.
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