NL4Opt Competition: Formulating Optimization Problems Based on Their Natural Language Descriptions

1 code implementation • 14 Mar 2023 • Rindranirina Ramamonjison, Timothy T. Yu, Raymond Li, Haley Li, Giuseppe Carenini, Bissan Ghaddar, Shiqi He, Mahdi Mostajabdaveh, Amin Banitalebi-Dehkordi, Zirui Zhou, Yong Zhang

The Natural Language for Optimization (NL4Opt) Competition was created to investigate methods of extracting the meaning and formulation of an optimization problem based on its text description.

Language Modelling Large Language Model

Spectral Bandwidth Recovery of Optical Coherence Tomography Images using Deep Learning

no code implementations • 2 Jan 2023 • Timothy T. Yu, Da Ma, Jayden Cole, Myeong Jin Ju, Mirza F. Beg, Marinko V. Sarunic

Optical coherence tomography (OCT) captures cross-sectional data and is used for the screening, monitoring, and treatment planning of retinal diseases.

Decision Making Generative Adversarial Network +1

Augmenting Operations Research with Auto-Formulation of Optimization Models from Problem Descriptions

1 code implementation • 30 Sep 2022 • Rindranirina Ramamonjison, Haley Li, Timothy T. Yu, Shiqi He, Vishnu Rengan, Amin Banitalebi-Dehkordi, Zirui Zhou, Yong Zhang

We describe an augmented intelligence system for simplifying and enhancing the modeling experience for operations research.

Domain Adaptation via CycleGAN for Retina Segmentation in Optical Coherence Tomography

no code implementations • 6 Jul 2021 • Ricky Chen, Timothy T. Yu, Gavin Xu, Da Ma, Marinko V. Sarunic, Mirza Faisal Beg

In this study, we investigated a learning-based approach of adapting the domain of a publicly available dataset, UK Biobank dataset (UKB).

Decision Making Domain Adaptation