Neural Network Function Approximator with Auxiliary Memory for Rapid Adaptation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing machine learning systems, particularly neural networks, face challenges in rapidly adapting to new information while maintaining high performance and generalization.
Innovation Solution
The implementation of a neural network function approximator that combines a neural network with an auxiliary or episodic memory. This setup allows the neural network to slowly learn an approximate function of an input, while the auxiliary memory quickly adapts the network's parameters to new information, enabling rapid inference and learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a neural network is trained to learn from new information, then the network can adapt to new data, but the training process requires significant computational resources and time
Solution Approach 1:
The system segments the learning process into two distinct components: a slowly adapting neural network that maintains generalization and an auxiliary memory that rapidly adapts to new information. This segmentation allows each component to specialize - the neural network learns fundamental patterns over time while the memory handles quick adaptations without requiring full retraining
Solution Approach 2:
The auxiliary memory acts as an intermediary between new information and the neural network. Instead of directly training the neural network on new data (which is time-consuming), the system stores new information in the auxiliary memory which then influences the neural network's outputs, enabling rapid adaptation without direct retraining
2Measurement precision
If a neural network is trained with more data to improve performance, then the network achieves better accuracy, but the computational resources and training time increase
Solution Approach 1:
The system divides the knowledge storage function between the neural network (which stores general patterns) and the auxiliary memory (which stores specific new information). This segmentation allows the system to achieve high accuracy on new data by querying the auxiliary memory rather than requiring the neural network to learn all specifics during training, reducing computational resource requirements
Solution Approach 2:
The auxiliary memory stores copies of new information and experiences that can be quickly retrieved during inference. Instead of requiring the neural network to relearn or reprocess large amounts of training data, the system copies relevant information into the auxiliary memory for rapid access, maintaining accuracy while reducing computational overhead
3Adaptability or versatility
If the neural network parameters are frequently updated to adapt to new data, then the network responds quickly to new information, but the network loses its ability to generalize
Solution Approach 1:
The system segments the adaptation function from the generalization function. The auxiliary memory handles rapid adaptation to new information by storing and retrieving specific experiences, while the neural network maintains stable weights that ensure generalization. This segmentation prevents the conflict between adapting quickly and maintaining generalization ability
Solution Approach 2:
The auxiliary memory serves as an intermediary that buffers new information before it reaches the neural network. During inference, the system queries the auxiliary memory for relevant stored information and combines it with the neural network's general predictions. This intermediary approach allows the neural network to maintain its generalization ability while the system as a whole responds quickly to new information through the memory component
Data Source
AI summary
There is described herein a computer-implemented method of processing an input data item. The method comprises processing the input data item using a parametric model to generate output data, wherein the parametric model comprises a first sub-model and a second sub-model. The processing comprises processing, by the first sub-model, the input data to generate a query data item, retrieving, from a memory storing data point-value pairs, at least one data point-value pair based upon the query data item and modifying weights of the second sub-model based upon the retrieved at least one data point-value pair. The output data is then generated based upon the