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Wei-Keng Liao

Showing results (1-10 of 22) with videos related to

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Bioinformatics (Oxford, England)|November 20, 2010
Anatomy of a hash-based long read sequence mapping algorithm for next generation DNA sequencingSanchit Misra, Ankit Agrawal, Wei-keng Liao, et al.
Journal of Healthcare Informatics Research|April 14, 2022
Harnessing Psycho-lingual and Crowd-Sourced Dictionaries for Predicting Taboos in Written Emotional Disclosure in Anonymous Confession BoardsArindam Paul, Wei-Keng Liao, Alok Choudhary, et al.
Molecular Informatics|September 11, 2019
Property Prediction of Organic Donor Molecules for Photovoltaic Applications Using Extremely Randomized TreesArindam Paul, Alona Furmanchuk, Wei-Keng Liao, et al.
The Journal of Supercomputing|October 14, 2014
High Performance Data Clustering: A Comparative Analysis of Performance for GPU, RASC, MPI, and OpenMP ImplementationsLuobin Yang, Steve C Chiu, Wei-Keng Liao, et al.
Scientific Reports|July 13, 2022
Moving closer to experimental level materials property prediction using AIDipendra Jha, Vishu Gupta, Wei-Keng Liao, et al.
Scientific Reports|June 5, 2023
Improving deep learning model performance under parametric constraints for materials informatics applicationsVishu Gupta, Alec Peltekian, Wei-Keng Liao, et al.
Nature Communications|July 17, 2020
Author Correction: Enhancing materials property prediction by leveraging computational and experimental data using deep transfer learningDipendra Jha, Kamal Choudhary, Francesca Tavazza, et al.
Integrating Materials and Manufacturing Innovation|December 19, 2022
Generative Adversarial Networks and Mixture Density Networks-Based Inverse Modeling for Microstructural Materials DesignYuwei Mao, Zijiang Yang, Dipendra Jha, et al.
Scientific Reports|December 6, 2018
ElemNet: Deep Learning the Chemistry of Materials From Only Elemental CompositionDipendra Jha, Logan Ward, Arindam Paul, et al.
Nature Communications|November 24, 2019
Enhancing materials property prediction by leveraging computational and experimental data using deep transfer learningDipendra Jha, Kamal Choudhary, Francesca Tavazza, et al.
Pageof 3

Showing results (1-10 of 22) with videos related to

Sort By:
Pageof 3
Bioinformatics (Oxford, England)|November 20, 2010
Anatomy of a hash-based long read sequence mapping algorithm for next generation DNA sequencingSanchit Misra, Ankit Agrawal, Wei-keng Liao, et al.
Journal of Healthcare Informatics Research|April 14, 2022
Harnessing Psycho-lingual and Crowd-Sourced Dictionaries for Predicting Taboos in Written Emotional Disclosure in Anonymous Confession BoardsArindam Paul, Wei-Keng Liao, Alok Choudhary, et al.
Molecular Informatics|September 11, 2019
Property Prediction of Organic Donor Molecules for Photovoltaic Applications Using Extremely Randomized TreesArindam Paul, Alona Furmanchuk, Wei-Keng Liao, et al.
The Journal of Supercomputing|October 14, 2014
High Performance Data Clustering: A Comparative Analysis of Performance for GPU, RASC, MPI, and OpenMP ImplementationsLuobin Yang, Steve C Chiu, Wei-Keng Liao, et al.
Scientific Reports|July 13, 2022
Moving closer to experimental level materials property prediction using AIDipendra Jha, Vishu Gupta, Wei-Keng Liao, et al.
Scientific Reports|June 5, 2023
Improving deep learning model performance under parametric constraints for materials informatics applicationsVishu Gupta, Alec Peltekian, Wei-Keng Liao, et al.
Nature Communications|July 17, 2020
Author Correction: Enhancing materials property prediction by leveraging computational and experimental data using deep transfer learningDipendra Jha, Kamal Choudhary, Francesca Tavazza, et al.
Integrating Materials and Manufacturing Innovation|December 19, 2022
Generative Adversarial Networks and Mixture Density Networks-Based Inverse Modeling for Microstructural Materials DesignYuwei Mao, Zijiang Yang, Dipendra Jha, et al.
Scientific Reports|December 6, 2018
ElemNet: Deep Learning the Chemistry of Materials From Only Elemental CompositionDipendra Jha, Logan Ward, Arindam Paul, et al.
Nature Communications|November 24, 2019
Enhancing materials property prediction by leveraging computational and experimental data using deep transfer learningDipendra Jha, Kamal Choudhary, Francesca Tavazza, et al.
Pageof 3