Bayesian Neural Network Weight Sharing for Combined-Data Inference
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing neural networks face challenges in efficiently sharing knowledge across different environments and datasets, leading to suboptimal performance and increased computational costs due to catastrophic forgetting and the inability to accurately infer on combined data sets.
Innovation Solution
Implementing Bayesian neural networks (BNNs) with knowledge sharing controllers that mix weight distributions using Gaussian Mixture Models and fully connected neural networks to generate a combined posterior distribution, allowing for efficient knowledge sharing and improved inference accuracy without retraining on complete datasets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If neural networks are trained on separate subsets of data independently, then each network can be trained efficiently on its own data, but the networks cannot share knowledge and perform poorly on combined datasets due to catastrophic forgetting
Solution Approach 1:
The patent combines multiple independently trained Bayesian neural networks into a single ensemble system that shares knowledge through mixed weight distributions. The knowledge sharing controller merges the separate networks by mixing their weight distributions to create a combined posterior distribution, enabling the system to maintain performance on all training subsets while achieving high accuracy on combined datasets.
Solution Approach 2:
The knowledge sharing controller acts as an intermediary between independently trained Bayesian neural networks. It receives weight distributions from multiple networks, mixes them using Gaussian Mixture Models, and generates a combined posterior distribution that is distributed back to the networks. This intermediary mechanism enables knowledge sharing without requiring the networks to be retrained on complete datasets.
2Reliability
If neural networks share knowledge across different environments and datasets, then inference accuracy on combined data improves, but computational overhead increases due to the complexity of mixing weight distributions
Solution Approach 1:
The patent changes the parameter representation from fixed weights to probability distributions (mean and variance) in Bayesian neural networks. This parameter transformation enables the knowledge sharing controller to mix weight distributions mathematically using Gaussian Mixture Models, achieving knowledge sharing through parameter manipulation rather than complex retraining processes.
Solution Approach 2:
The patent replaces the mechanical process of retraining networks on complete datasets with a mathematical operation of mixing weight distributions. Instead of performing computationally intensive gradient descent and forward-backward propagation across all data, the system uses analytical operations on probability distributions to achieve knowledge sharing, significantly reducing computational overhead.
3Productivity
If Bayesian neural networks use mixed weight distributions from multiple networks, then knowledge sharing efficiency improves, but the complexity of managing multiple weight distributions increases
Solution Approach 1:
The knowledge sharing controller performs multiple functions using a unified approach: it receives weight distributions from different networks, mixes them using Gaussian Mixture Models, generates combined posterior distributions, and distributes them back to networks. This multi-functional design simplifies management by providing a universal interface and process for handling weight distributions from multiple sources.
Data Source
AI summary
Methods, apparatus, systems and articles of manufacture are disclosed to facilitate knowledge sharing among neural networks. An example apparatus includes a trainer to train, at a first computing system, a first Bayesian Neural Network (BNN) on a first subset of training data to generate a first weight distribution, and train, at a second computing system, a second BNN on a second subset of the training data to generate a second weight distribution, the second subset of the training data different from the first subset of training data. The example apparatus includes a knowledge sharing controller to generate a third BNN based on the first weight distribution and the second weight distribution.