Automated Variational Inference for Stochastic Deep Neural Networks
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Solution Overview
Problem
Current automated machine learning (AutoML) methods for deep neural networks (DNNs) face challenges in optimizing architecture and hyperparameters, particularly in capturing data statistics and probabilistic uncertainty when the underlying model is unspecified, leading to inefficient search processes and suboptimal performance due to the explosion of search space and reliance on homogeneous statistics.
Innovation Solution
An automated variational Bayesian inference framework that explores different combinations of posterior, prior, and likelihood beliefs, as well as discrepancy measures, allowing for irregular and heterogeneous assignments of these beliefs across stochastic nodes to enhance modeling capacity and robustness, particularly through the use of mismatched pairs and ensemble methods, and incorporates adversarial disentanglement for nuisance-robust feature extraction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If homogeneous statistics (normal distribution) are used for latent representations, then computational convenience is improved, but modeling accuracy deteriorates when data statistics are unspecified
Solution Approach 1:
The patent changes the distributional parameters of latent representations from homogeneous normal distribution to heterogeneous distributions (including normal, Laplace, Cauchy, logistic, Gumbel, student-t, uniform, exponential, and hyper-exponential distributions). This allows the model to adapt to unspecified data statistics while maintaining tractable inference through variational Bayesian methods, resolving the contradiction between computational convenience and modeling accuracy.
2Adaptability or versatility
If automated exploration of different DNN architectures is performed, then adaptability is improved, but search time increases due to search space explosion
Solution Approach 1:
The patent implements self-service through automated variational Bayesian inference that automatically selects appropriate distributional forms and hyperparameters for latent representations. The system performs self-adaptation to data characteristics without requiring exhaustive manual architecture search, thereby improving adaptability while reducing search time through intelligent automation.
3Adaptability or versatility
If irregular and heterogeneous assignments of beliefs are explored, then modeling capacity is improved, but system complexity increases
Solution Approach 1:
The patent applies local quality by allowing different distributional beliefs (posterior, prior, likelihood) to be assigned to different latent variables and network layers based on local data characteristics. This heterogeneous assignment enables the model to capture diverse data statistics in different parts of the network, improving modeling capacity while managing complexity through localized adaptation rather than global uniformity.
Data Source
AI summary
A system and method for automated construction of a stochastic deep neural network (DNN) architecture is provided. The framework of invention automatically searches for most relevant stochastic modes underlaying datasets for variational Bayesian inference. The invention provides a way to use heterogenous, irregular, and mismatched beliefs in stochastic sampling for intermediate representation in DNNs with a capability of an automatically tuning mechanism of posterior, prior, and likelihood models to enable accurate generative models and uncertainty models for machine learning tasks. The system further allows adjustable discrepancy measure to regularize intermediate representation by variants of divergence metrics including Renyi's alpha, beta, and gamma divergences. The invention enables diverse mixture combinations of stochastic models for misspecified and unspecified probabilistic relations in an automatic fashion. Accordingly, the representation capability of variational autoencoders, variational information bottlenecks, denoising diffusion probabilistic models and other stochastic DNNs are improved.