DistriBayes: A Distributed Platform for Learning, Inference and Attribution on Large Scale Bayesian Network.

Yi Ding,Jun Zhou,Qing Cui,Lin Wang, Mengqi Zhang,Yang Dong

WSDM（2023）

Ant Group | Zhejiang University & Ant Group

Cited 0|Views26

Abstract

To improve the marketing performance in the financial scenario, it is necessary to develop a trustworthy model to analyze and select promotion-sensitive customers. Bayesian Network (BN) is suitable for this task because of its interpretability and flexibility, but it usually suffers the exponentially growing computation complexity as the number of nodes grows. To tackle this problem, we present a comprehensive distributed platform named DistriBayes, which can efficiently learn, infer and attribute on a large-scale BN all-in-one platform. It implements several score-based structure learning methods, loopy belief propagation with backdoor adjustment for inference, and a carefully optimized search procedure for attribution. Leveraging the distributed cluster, DistriBayes can finish the learning and attribution on Bayesian Network with hundreds of nodes and millions of samples in hours.

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Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

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