432 research outputs found
PhotoRedshift-MML: a multimodal machine learning method for estimating photometric redshifts of quasars
We propose a Multimodal Machine Learning method for estimating the
Photometric Redshifts of quasars (PhotoRedshift-MML for short), which has long
been the subject of many investigations. Our method includes two main models,
i.e. the feature transformation model by multimodal representation learning,
and the photometric redshift estimation model by multimodal transfer learning.
The prediction accuracy of the photometric redshift was significantly improved
owing to the large amount of information offered by the generated spectral
features learned from photometric data via the MML. A total of 415,930 quasars
from Sloan Digital Sky Survey (SDSS) Data Release 17, with redshifts between 1
and 5, were screened for our experiments. We used |{\Delta}z| =
|(z_phot-z_spec)/(1+z_spec)| to evaluate the redshift prediction and
demonstrated a 4.04% increase in accuracy. With the help of the generated
spectral features, the proportion of data with |{\Delta}z| < 0.1 can reach
84.45% of the total test samples, whereas it reaches 80.41% for single-modal
photometric data. Moreover, the Root Mean Square (RMS) of |{\Delta}z| is shown
to decreases from 0.1332 to 0.1235. Our method has the potential to be
generalized to other astronomical data analyses such as galaxy classification
and redshift prediction. The algorithm code can be found at
https://github.com/HongShuxin/PhotoRedshift-MML .Comment: 10 pages, 8 figures, accepted for publication in MNRA
Responsible and Regulatory Conform Machine Learning for Medicine: A Survey of Challenges and Solutions
Machine learning is expected to fuel significant improvements in medical
care. To ensure that fundamental principles such as beneficence, respect for
human autonomy, prevention of harm, justice, privacy, and transparency are
respected, medical machine learning systems must be developed responsibly. Many
high-level declarations of ethical principles have been put forth for this
purpose, but there is a severe lack of technical guidelines explicating the
practical consequences for medical machine learning. Similarly, there is
currently considerable uncertainty regarding the exact regulatory requirements
placed upon medical machine learning systems. This survey provides an overview
of the technical and procedural challenges involved in creating medical machine
learning systems responsibly and in conformity with existing regulations, as
well as possible solutions to address these challenges. First, a brief review
of existing regulations affecting medical machine learning is provided, showing
that properties such as safety, robustness, reliability, privacy, security,
transparency, explainability, and nondiscrimination are all demanded already by
existing law and regulations - albeit, in many cases, to an uncertain degree.
Next, the key technical obstacles to achieving these desirable properties are
discussed, as well as important techniques to overcome these obstacles in the
medical context. We notice that distribution shift, spurious correlations,
model underspecification, uncertainty quantification, and data scarcity
represent severe challenges in the medical context. Promising solution
approaches include the use of large and representative datasets and federated
learning as a means to that end, the careful exploitation of domain knowledge,
the use of inherently transparent models, comprehensive out-of-distribution
model testing and verification, as well as algorithmic impact assessments
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