Personalized Machine Learning Models via Dynamic Weight Transformation
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Solution Overview
Problem
Existing machine learning models for speech recognition, particularly those using conditional neural networks, are inefficient and resource-intensive, making them unsuitable for deployment on resource-constrained devices due to increased model size, dynamic memory, and latency.
Innovation Solution
A method that trains a first neural network to learn personalized weights for a second neural network based on conditioning vectors, eliminating the need for concatenation and reducing model size, dynamic memory, and latency by dynamically assigning weights and biases tailored to individual users and device constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conditional neural networks use concatenation or modulation with conditioning vectors, then personalization capability is improved, but model size and computational resources increase dramatically
Solution Approach 1:
The patent introduces a weight transformation network as an intermediary component that receives the conditioning vector and transforms the fixed weights into personalized weights. This mediator enables personalization without directly modifying the main network structure or concatenating vectors, thus achieving adaptability while controlling model size.
Solution Approach 2:
The patent segments the weight parameters into a fixed base weight component and a dynamic transformed weight component. The fixed weights remain unchanged and shared across all users, while the transformed weights are generated dynamically from the conditioning vector through the weight transformation network, enabling personalization without duplicating the entire network for each user.
2Adaptability or versatility
If conditional neural networks use concatenation or modulation with conditioning vectors, then personalization capability is improved, but latency increases
Solution Approach 1:
The patent pre-computes and stores fixed base weights that are optimized for the general task. During inference, only the lightweight weight transformation network needs to process the conditioning vector to generate personalized weights, rather than processing the entire network from scratch. This preliminary preparation of base weights significantly reduces inference latency.
Solution Approach 2:
The weight transformation network acts as a lightweight intermediary that quickly transforms fixed weights into personalized weights through a simple neural network transformation. This intermediary approach avoids the computational burden of concatenating conditioning vectors with input features at each network layer, thereby reducing overall inference time.
3Adaptability or versatility
If conditional neural networks use concatenation or modulation with conditioning vectors, then personalization capability is improved, but dynamic memory requirements increase
Solution Approach 1:
The patent creates a lightweight copy of the weight transformation logic that operates independently from the main network. Instead of duplicating the entire network for each user or concatenating conditioning vectors throughout the network, a single compact transformation network generates personalized weights that are applied to the fixed base network, minimizing memory overhead.
Solution Approach 2:
The patent extracts the personalization function into a separate weight transformation network that takes the conditioning vector as input and outputs personalized weight adjustments. This extraction separates the personalization mechanism from the main network processing, allowing the conditioning vector to be processed once rather than being concatenated and processed repeatedly through multiple network layers.
Data Source
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AI summary
Broadly speaking, embodiments of the present techniques provide a method and system for personalising machine learning models on resource-constrained devices by using conditional neural networks. In particular, the present techniques allow for resource-efficient use of a conditioning vector by incorporating the conditioning vector into weights learned during training. This reduces the computational resources required at inference time.