This study identifies the feasibility of leveraging deep learning anomaly detection to identify event-driven traveling ionospheric disturbances (TIDs). DR. JIHYE PARK, FIONA LUHRMANN, DR. WENG-KEEN WONG, OREGON STATE UNIVERSITY Ionospheric responses to various geophysical events have been studi
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1990: gradient descent learns subgoals. 1991: multiple time scales and levels of abstraction. 1997: world models learn predictable abstract representations.
Conclusion (~1,700 words).
All backed up by over 200 references (~6,500 words).
We must stop crediting the wrong people for inventions made by others.
Instead let s heed the recent call in the journal
Nature: Let 2020 be the year in which we value those who ensure that
science is self-correcting [SV20].
Like those who know me can testify, finding and citing original sources of scientific and technological innovations is important to me, whether they are mine or other people s [DL1][DL2][HIN][NASC1-9]. The present page is offered as a resource for computer scientists who share this inclination.
By grounding research in its true intellectual foundations and crediting the original inventors,