Signal Processor Using Deterministic Neural Network for Generative Modeling
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Solution Overview
Problem
Existing signal processing systems, such as those employing neural networks and Boltzmann machines, face challenges in computational intensity and slow learning rates, particularly in generating output examples that match previously learned distributions, and require significant processing power.
Innovation Solution
The implementation of a deterministic neural network with a shared mapping system that translates between probability and category vectors, allowing for efficient data compression and facilitating faster learning by enabling closed-form weight calculation, along with a chain of signal processors that refine output examples based on context vectors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If Boltzmann machines or deep generative models are used to generate output examples from learned distributions, then the system can provide generative capabilities, but the processing power required becomes excessively high
Solution Approach 1:
The patent segments the generative model into distinct functional components: a probability vector generation system that computes output probabilities, a category vector system that represents compressed categories, and a mapping system that connects them. This segmentation allows each component to be optimized independently, reducing overall computational requirements while maintaining generative capabilities.
Solution Approach 2:
The patent changes the parameter representation by introducing category vectors as compressed representations of probability vectors. Instead of working directly with full probability distributions requiring extensive computation, the system uses condensed category vectors that capture essential information with fewer parameters, thereby reducing processing power requirements.
2Adaptability or versatility
If Helmholtz machines are used to provide generative models, then the system can learn from training examples, but the learning rate becomes unacceptably slow
Solution Approach 1:
The patent replaces the slow, iterative learning mechanism of traditional Helmholtz machines with a more efficient deterministic neural network-based probability vector generation system. This substitution enables faster weight calculations and quicker convergence to learned distributions, dramatically reducing learning time while preserving the ability to learn from training examples.
3Ease of operation
If traditional neural networks are used without a shared mapping system, then the system can process data, but the number of parameters to be learned becomes excessively large
Solution Approach 1:
The patent introduces a shared mapping system that serves multiple functions: it maps category vectors to probability vectors, enables compression of probability distributions into category representations, and facilitates efficient parameter sharing across different parts of the system. This universal mapping mechanism dramatically reduces the total number of parameters that need to be learned while maintaining full data processing capability.
Data Source
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AI summary
We describe a signal processor, the signal processor comprising: a probability vector generation system, wherein said probability vector generation system has an input to receive a category vector for a category of output example and an output to provide a probability vector for said category of output example, wherein said output example comprises a set of data points, and wherein said probability vector defines a probability of each of said set of data points for said category of output example; a memory storing a plurality of said category vectors, one for each of a plurality of said categories of output example; and a stochastic selector to select a said stored category of output example for presentation of the corresponding category vector to said probability vector generation system; wherein said signal processor is configured to output data for an output example corresponding to said selected stored category.