Learning Consistent Deep Generative Models from Sparse Data via Prediction Constraints

no code implementations • 12 Dec 2020 • Gabriel Hope, Madina Abdrakhmanova, Xiaoyin Chen, Michael C. Hughes, Erik B. Sudderth

We develop a new framework for learning variational autoencoders and other deep generative models that balances generative and discriminative goals.

General Classification Image Classification

Learning Consistent Deep Generative Models from Sparsely Labeled Data

no code implementations • pproximateinference AABI Symposium 2022 • Gabriel Hope, Madina Abdrakhmanova, Xiaoyin Chen, Michael C Hughes, Erik B. Sudderth

We consider training deep generative models toward two simultaneous goals: discriminative classification and generative modeling using an explicit likelihood.

Image Classification

On the current failure -- but bright future -- of topology-driven biological network alignment

1 code implementation • 25 Apr 2022 • Siyue Wang, Xiaoyin Chen, Brent J. Frederisy, Benedict A. Mbakogu, Amy D. Kanne, Pasha Khosravi, Wayne B. Hayes

The function of a protein is defined by its interaction partners.

BM25 Query Augmentation Learned End-to-End

no code implementations • 23 May 2023 • Xiaoyin Chen, Sam Wiseman

Given BM25's enduring competitiveness as an information retrieval baseline, we investigate to what extent it can be even further improved by augmenting and re-weighting its sparse query-vector representation.

Information Retrieval Retrieval

Efficient Causal Graph Discovery Using Large Language Models

1 code implementation • 2 Feb 2024 • Thomas Jiralerspong, Xiaoyin Chen, Yash More, Vedant Shah, Yoshua Bengio

We propose a novel framework that leverages LLMs for full causal graph discovery.