Emille E. O. Ishida

Orcid: 0000-0002-0406-076X

According to our database1, Emille E. O. Ishida authored at least 17 papers between 2015 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Online presence:

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Bibliography

2024
Multi-View Symbolic Regression.
CoRR, 2024

2023
A graph-based spectral classification of Type II supernovae.
Astron. Comput., July, 2023

From Images to Features: Unbiased Morphology Classification via Variational Auto-Encoders and Domain Adaptation.
CoRR, 2023

Sidestepping the inversion of the weak-lensing covariance matrix with Approximate Bayesian Computation.
Astron. Comput., 2023

2022
Explainable classification of astronomical uncertain time series.
CoRR, 2022

2021
Fink: early supernovae Ia classification using active learning.
CoRR, 2021

The Role of the Human Expert in the Era of Big Data (keynote abstract).
Proceedings of the Supplementary Proceedings of the XXIII International Conference on Data Analytics and Management in Data Intensive Domains, 2021

2020
Active learning with RESSPECT: Resource allocation for extragalactic astronomical transients.
Proceedings of the 2020 IEEE Symposium Series on Computational Intelligence, 2020

2019
Active Anomaly Detection for time-domain discoveries.
CoRR, 2019

Machine Learning and the future of Supernova Cosmology.
CoRR, 2019

Use of Machine Learning for Anomaly Detection Problem in Large Astronomical Databases.
Proceedings of the Selected Papers of the XXI International Conference on Data Analytics and Management in Data Intensive Domains (DAMDID/RCDL 2019), 2019

Realization of Different Techniques for Anomaly Detection in Astronomical Databases.
Proceedings of the Data Analytics and Management in Data Intensive Domains, 2019

2017
Photometric redshift estimation: An active learning approach.
Proceedings of the 2017 IEEE Symposium Series on Computational Intelligence, 2017

2016
Automated supernova Ia classification using adaptive learning techniques.
Proceedings of the 2016 IEEE Symposium Series on Computational Intelligence, 2016

Exploring the spectroscopic diversity of type Ia supernovae with Deep Learning and Unsupervised Clustering.
Proceedings of the Astroinformatics 2016, Sorrento, Italy, October 19-25, 2016, 2016

2015
cosmoabc: Likelihood-free inference via Population Monte Carlo Approximate Bayesian Computation.
Astron. Comput., 2015

The overlooked potential of Generalized Linear Models in astronomy-II: Gamma regression and photometric redshifts.
Astron. Comput., 2015


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