Dual-Latent Space ML Mapping for Reliable Uncertainty Estimation
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Solution Overview
Problem
Existing machine learning systems, particularly neural networks, struggle to provide accurate uncertainty estimates in safety-critical applications like self-driving cars, due to challenges in choosing appropriate priors and performing inference over high-dimensional parameter spaces.
Innovation Solution
A machine learning system that maps input data instances to outputs by constructing a second latent input vector from reference instances related to the input, using two latent spaces and stochastic mappings to improve learning of input distribution structure and provide better uncertainty estimates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If Bayesian neural networks are used to model uncertainty, then uncertainty estimation capability is improved, but device complexity and difficulty of inference increase due to high-dimensional parameter spaces and posterior complexity
Solution Approach 1:
The patent segments the complex inference problem into two separate latent spaces: a first latent space for representing input data and a second latent space for representing output distributions. By dividing the high-dimensional parameter space into these two manageable latent representations, the system avoids the computational burden of direct Bayesian inference over all parameters while still capturing uncertainty through the second latent space.
Solution Approach 2:
The patent introduces a stochastic mapping function as an intermediary between the first latent space (input representation) and the second latent space (output distribution representation). This intermediary mapping allows the system to propagate uncertainty from inputs to outputs without requiring direct inference over the complex posterior distribution, thereby simplifying the inference process while maintaining reliability.
2Reliability
If Gaussian processes are used as a stochastic process, then uncertainty modeling is improved, but training and inference cost increases significantly due to cubic scaling with dataset size
Solution Approach 1:
The patent segments the computationally expensive Gaussian process inference into a two-stage process using separate latent spaces. The first latent space handles input data representation efficiently, while the second latent space models output uncertainty. This segmentation reduces the computational burden from cubic scaling to a more manageable complexity by avoiding direct computation over the entire dataset in a single step.
Solution Approach 2:
The patent changes the parameter representation by introducing stochastic mappings between latent spaces instead of directly modeling the full covariance matrix required by Gaussian processes. This parameter transformation allows uncertainty modeling to be performed in a lower-dimensional latent space, significantly reducing the computational cost of training and inference while maintaining the ability to model uncertainty effectively.
3Speed
If standard neural networks are used for prediction, then predictive speed is improved, but reliability decreases due to lack of accurate uncertainty estimates in safety-critical applications
Solution Approach 1:
The patent segments the neural network into two functional components represented by separate latent spaces: one for fast deterministic prediction (first latent space) and one for uncertainty estimation (second latent space). This segmentation allows the system to maintain the speed advantages of standard neural networks while adding uncertainty estimation capability through the stochastic mapping to the second latent space.
Solution Approach 2:
The patent makes the neural network multi-functional by having it simultaneously perform both fast prediction and uncertainty estimation through the two latent spaces. The first latent space handles rapid deterministic predictions, while the second latent space provides uncertainty estimates, allowing the single system to serve both speed-critical and reliability-critical functions without requiring separate models.
Data Source
AI summary
A machine learning system configured to map an input data instance to an output according to a system mapping. The system mapping may be composed from multiple functions. A latent input vector may be determined in a second latent space for the input data instance, from latent reference vectors related to a number of reference data instances identified as parents.