Technology Category
Machine Learning: Multimodal LearningReasoning under Uncertainty: Relational Probabilistic ModelsComputer Vision: Multi-modal Vision
Multi-modal Bayesian Neural Network Surrogates with Conjugate Last-Layer Estimation
🤖 AI Summary
To address the low modeling efficiency and difficulty in uncertainty quantification for high-cost objective functions under multimodal data, this paper proposes two multimodal Bayesian neural network (BNN) surrogate models. The core methodological innovation is a conjugate final-layer design, enabling closed-form parameter updates and efficient variational inference while ensuring robustness to partial modality missing. By integrating multimodal feature encoding, stochastic variational inference, and conjugate distribution assumptions, the approach significantly improves prediction accuracy and uncertainty calibration on both scalar and time-series tasks—outperforming unimodal BNN baselines. The framework is modular and seamlessly embeddable into outer-loop applications such as optimization and inverse problem solving, thereby enhancing modeling efficiency, generalizability, and decision reliability for complex systems.
Technology Category
Application Category
📝 Abstract
As data collection and simulation capabilities advance, multi-modal learning, the task of learning from multiple modalities and sources of data, is becoming an increasingly important area of research. Surrogate models that learn from data of multiple auxiliary modalities to support the modeling of a highly expensive quantity of interest have the potential to aid outer loop applications such as optimization, inverse problems, or sensitivity analyses when multi-modal data are available. We develop two multi-modal Bayesian neural network surrogate models and leverage conditionally conjugate distributions in the last layer to estimate model parameters using stochastic variational inference (SVI). We provide a method to perform this conjugate SVI estimation in the presence of partially missing observations. We demonstrate improved prediction accuracy and uncertainty quantification compared to uni-modal surrogate models for both scalar and time series data.
Problem
Research questions and friction points this paper is trying to address.
Developing multi-modal Bayesian neural network surrogates with conjugate estimation
Handling partially missing observations in multi-modal data integration
Improving prediction accuracy and uncertainty quantification over uni-modal models
Innovation
Methods, ideas, or system contributions that make the work stand out.
Multi-modal Bayesian neural network surrogate models
Conjugate last-layer estimation with SVI
Handles partially missing observations
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