Zobrazeno 1 - 10
of 35
pro vyhledávání: '"Daniel M. Dunlavy"'
We propose a novel statistical inference methodology for multiway count data that is corrupted by false zeros that are indistinguishable from true zero counts. Our approach consists of zero-truncating the Poisson distribution to neglect all zero valu
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4d742fbe872929e927e4d2a51ad2f583
Autor:
Jeremy M. Myers, Daniel M. Dunlavy
Publikováno v:
2021 IEEE High Performance Extreme Computing Conference (HPEC).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a2cf08bfed9efad36736da74945b74cd
https://doi.org/10.1109/hpec49654.2021.9622795
https://doi.org/10.1109/hpec49654.2021.9622795
Publikováno v:
2021 IEEE High Performance Extreme Computing Conference (HPEC).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6591704b9c716c2d3e329c1d039144d2
https://doi.org/10.1109/hpec49654.2021.9622869
https://doi.org/10.1109/hpec49654.2021.9622869
Tensor decomposition models play an increasingly important role in modern data science applications. One problem of particular interest is fitting a low-rank Canonical Polyadic (CP) tensor decomposition model when the tensor has sparse structure and
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::043b16ca48f739ee391a346c5a358159
https://doi.org/10.2172/1706215
https://doi.org/10.2172/1706215
Publikováno v:
HPEC
Tensor decomposition models play an increasingly important role in modern data science applications. One problem of particular interest is fitting a low-rank Canonical Polyadic (CP) tensor decomposition model when the tensor has sparse structure and
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::21d1b304e177c8fa6826fa66f6707f74
https://doi.org/10.1109/hpec43674.2020.9286210
https://doi.org/10.1109/hpec43674.2020.9286210
We explore the use of multiple deep learning models for detecting flaws in software programs. Current, standard approaches for flaw detection rely on a single representation of a software program (e.g., source code or a program binary). We illustrate
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::fd70c89f88f29cb49cab807b8e92670c
https://doi.org/10.2172/1660805
https://doi.org/10.2172/1660805
Software flaw detection using multimodal deep learning models has been demonstrated as a very competitive approach on benchmark problems. In this work, we demonstrate that even better performance can be achieved using neural architecture search (NAS)
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d13f557580f180f2d834b7d65ee22b22
https://doi.org/10.2172/1668929
https://doi.org/10.2172/1668929