Automated Stochastic DNN Architecture Search with Irregular Beliefs
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Solution Overview
Problem
Existing automated machine learning (AutoML) frameworks for deep neural networks (DNNs) face challenges in efficiently identifying the best probabilistic model and hyperparameters for stochastic DNNs, particularly when underlying data statistics and uncertainty are unspecified, leading to suboptimal performance and increased exploration time.
Innovation Solution
The system employs an automated variational Bayesian inference framework that explores irregular combinations of posterior, prior, and likelihood beliefs, along with mismatched discrepancy measures, using hypergradient methods to optimize stochastic DNNs for unspecified datasets, allowing for heterogenous and mismatched pairings of distributions and connectivity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If homogeneous normal distribution is used for latent representations, then computational convenience is improved, but model 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 (normal, Laplace, Cauchy, logistic, Gumbel, student-t, uniform, exponential, hyper-exponential) selected based on data characteristics. This allows the model to adapt to unspecified data statistics while maintaining computational tractability through automated selection mechanisms.
Solution Approach 2:
The patent introduces dynamic selection of distribution types for latent representations based on automated analysis of data statistics. The system dynamically determines which distribution family (normal, Laplace, Cauchy, etc.) best fits the underlying data characteristics, enabling adaptability rather than static homogeneous assumptions.
2Adaptability or versatility
If automated exploration of different DNN architectures is performed, then adaptability is improved, but exploration time increases
Solution Approach 1:
The patent applies local quality by allowing different latent representations to have different distribution types (normal, Laplace, Cauchy, logistic, Gumbel, student-t, uniform, exponential, or hyper-exponential) based on their specific data characteristics. This localized adaptation reduces the need for exhaustive global architecture search while maintaining optimality for each specific representation.
Solution Approach 2:
The system performs self-service by automatically analyzing data statistics and selecting appropriate distribution types for latent representations without requiring manual architecture design or extensive hyperparameter tuning. The automated selection mechanism serves the model configuration needs internally, reducing external exploration requirements.
Data Source
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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.