sunil gupta

Incorporating expert prior in Bayesian optimisation via space warping

Publisher: Elsevier BV

Date: 05-2020

DOI: 10.1016/J.KNOSYS.2020.105663

A scrap-tolerant alloying concept based on high entropy alloys

Publisher: Elsevier BV

Date: 11-2020

DOI: 10.1016/J.ACTAMAT.2020.09.027

Verification of integrity of deployed deep learning models using Bayesian Optimization

Publisher: Elsevier BV

Date: 04-2022

DOI: 10.1016/J.KNOSYS.2022.108238

New Bayesian-Optimization-Based Design of High-Strength 7xxx-Series Alloys from Recycled Aluminum

Publisher: Springer Science and Business Media LLC

Date: 26-06-2018

DOI: 10.1007/S11837-018-2984-Z

Dual-frame spatio-temporal feature modulation for video enhancement

Publisher: Elsevier BV

Date: 10-2022

DOI: 10.1016/J.PATCOG.2022.108822

Stable Bayesian optimization

Publisher: Springer Science and Business Media LLC

Date: 09-04-2018

DOI: 10.1007/S41060-018-0119-9

Machine learning-based discovery of vibrationally stable materials

Publisher: Springer Science and Business Media LLC

Date: 11-01-2023

DOI: 10.1038/S41524-022-00943-Z

Abstract: The identification of the ground state phases of a chemical space in the convex hull analysis is a key determinant of the synthesizability of materials. Online material databases have been instrumental in exploring one aspect of the synthesizability of many materials, namely thermodynamic stability. However, the vibrational stability, which is another aspect of synthesizability, of new materials is not known. Applying first principles approaches to calculate the vibrational spectra of materials in online material databases is computationally intractable. Here, a dataset of vibrational stability for ~3100 materials is used to train a machine learning classifier that can accurately distinguish between vibrationally stable and unstable materials. This classifier has the potential to be further developed as an essential filtering tool for online material databases that can inform the material science community of the vibrational stability or instability of the materials queried in convex hulls.

Fairness Improvement for Black-box Classifiers with Gaussian Process

Publisher: Elsevier BV

Date: 10-2021

DOI: 10.1016/J.INS.2021.06.095

An Unified Recurrent Video Object Segmentation Framework for Various Surveillance Environments

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Date: 2021

DOI: 10.1109/TIP.2021.3108405

Bayesian strategy selection identifies optimal solutions to complex problems using an example from GP prescribing

Publisher: Springer Science and Business Media LLC

Date: 20-01-2020

DOI: 10.1038/S41746-019-0205-Y

Abstract: Complex health problems require multi-strategy, multi-target interventions. We present a method that uses machine learning techniques to choose optimal interventions from a set of possible interventions within a case study aiming to increase General Practitioner (GP) discussions of physical activity (PA) with their patients. Interventions were developed based on a causal loop diagram with 26 GPs across 13 clinics in Geelong, Australia. GPs prioritised eight from more than 80 potential interventions to increase GP discussion of PA with patients. Following a 2-week baseline, a multi-arm bandit algorithm was used to assign optimal strategies to GP clinics with the target outcome being GP PA discussion rates. The algorithm was updated weekly and the process iterated until the more promising strategies emerged (a duration of seven weeks). The top three performing strategies were continued for 3 weeks to improve the power of the hypothesis test of effectiveness for each strategy compared to baseline. GPs recorded a total of 11,176 conversations about PA. GPs identified 15 factors affecting GP PA discussion rates with patients including GP skills and awareness, fragmentation of care and fear of adverse outcomes. The two most effective strategies were correctly identified within seven weeks of the algorithm-based assignment of strategies. These were clinic reception staff providing PA information to patients at check in and PA screening questionnaires completed in the waiting room. This study demonstrates an efficient way to test and identify optimal strategies from multiple possible solutions.

Unified Multi-Weather Visibility Restoration

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Date: 2022

DOI: 10.1109/TMM.2022.3225712

Bayesian Optimization for Adaptive Experimental Design: A Review

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Date: 2020

DOI: 10.1109/ACCESS.2020.2966228

Filtering Bayesian optimization approach in weakly specified search space

Publisher: Springer Science and Business Media LLC

Date: 10-07-2019

DOI: 10.1007/S10115-018-1238-2

Deakin University

Location: Australia

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Curtin University

Location: Australia

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Discovery Projects - Grant ID: DP210102798

Start Date: 02-2021

End Date: 01-2024

Amount: $361,000.00

Funder: Australian Research Council

Linkage Infrastructure, Equipment And Facilities - Grant ID: LE230100039

Start Date: 2023

End Date: 12-2023

Amount: $440,145.00

Funder: Australian Research Council

Industrial Transformation Research Hubs - Grant ID: IH170100013

Start Date: 03-2018

End Date: 10-2023

Amount: $2,962,655.00

Funder: Australian Research Council