Zobrazeno 1 - 10
of 226
pro vyhledávání: '"M. Blei"'
Publikováno v:
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XLVIII-2-W8-2024, Pp 31-36 (2024)
This paper proposes a handheld mapping system consisting of a stereo camera setup combined with low-cost automotive LiDAR. The prototype system is applicable to various types of mobile monocular, stereo vision and LiDAR data collection and processing
Externí odkaz:
https://doaj.org/article/5da86feacc49416eab0e7b322aa33c59
Publikováno v:
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XLVIII-2-W7-2024, Pp 57-64 (2024)
In this paper, we present an implementation and publish an open source library for binary correspondence search (BICOS), an efficient method for accurate 3D reconstruction from structured light stereo imagery. Starting with two stacks of stereo-recti
Externí odkaz:
https://doaj.org/article/d783851aba43433593d3accb3e99bf2b
Publikováno v:
61. Jahrestagung der Deutschen Gesellschaft für Nuklearmedizin.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6c5f5f3cf9f67ae9d18491aff6478e50
https://doi.org/10.1055/s-0043-1766339
https://doi.org/10.1055/s-0043-1766339
Publikováno v:
Quantitative Marketing and Economics. 19:369-407
This paper proposes a method for estimating consumer preferences among discrete choices, where the consumer chooses at most one product in a category, but selects from multiple categories in parallel. The consumer's utility is additive in the differe
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b418e856bfcb8a39783597bafe0ee02e
https://doi.org/10.1007/s11129-021-09241-2
https://doi.org/10.1007/s11129-021-09241-2
Autor:
Dhanya Sridhar, David M. Blei
Publikováno v:
Science advances. 8(42)
Statistical and machine learning methods help social scientists and other researchers make causal inferences from texts.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::97119af60b60473ea4ba6b393a6c70d2
https://pubmed.ncbi.nlm.nih.gov/36260667
https://pubmed.ncbi.nlm.nih.gov/36260667
Autor:
Venkateswaran Shekar, Vincent Yu, Benjamin J. Garcia, David Benjamin Gordon, Gemma E. Moran, David M. Blei, Loïc M. Roch, Alberto García-Durán, Mansoor Ani Najeeb, Margaret Zeile, Philip W. Nega, Zhi Li, Mina A. Kim, Emory M. Chan, Alexander J. Norquist, Sorelle Friedler, Joshua Schrier
Machine learning is a useful tool for accelerating materials discovery, however it is a challenge to develop accurate methods that successfully transfer between domains while also broadening the scope of reaction conditions considered. In this paper,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f9b7d8eb73803574e204f28241acb281
https://doi.org/10.26434/chemrxiv-2022-l1wpf-v2
https://doi.org/10.26434/chemrxiv-2022-l1wpf-v2
Akademický článek
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Autor:
David M. Blei, Scott W. Linderman, Chris H. Wiggins, Wesley Tansey, Haoran Zhang, Raul Rabadan, Kathy Li
Publikováno v:
Biostatistics
Summary Personalized cancer treatments based on the molecular profile of a patient’s tumor are an emerging and exciting class of treatments in oncology. As genomic tumor profiling is becoming more common, targeted treatments for specific molecular
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::48959f679422fde492247f9d96fa8495
https://doi.org/10.1093/biostatistics/kxaa047
https://doi.org/10.1093/biostatistics/kxaa047
Publikováno v:
Transactions of the Association for Computational Linguistics, Vol 8, Pp 439-453 (2020)
Topic modeling analyzes documents to learn meaningful patterns of words. However, existing topic models fail to learn interpretable topics when working with large and heavy-tailed vocabularies. To this end, we develop the Embedded Topic Model (ETM),
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3a365570870f777b490d36dc51dc1806
https://www.mitpressjournals.org/doi/abs/10.1162/tacl_a_00325
https://www.mitpressjournals.org/doi/abs/10.1162/tacl_a_00325
Publikováno v:
Int Stat Rev
Analyzing data from large-scale, multi-experiment studies requires scientists to both analyze each experiment and to assess the results as a whole. In this article, we develop double empirical Bayes testing (DEBT), an empirical Bayes method for analy
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f53195cdc06ff40dbff7a9d280c45a1b
https://pubmed.ncbi.nlm.nih.gov/35356801
https://pubmed.ncbi.nlm.nih.gov/35356801