Zobrazeno 1 - 10
of 33
pro vyhledávání: '"Samuel Schmid"'
Autor:
Samuel Schmid
Publikováno v:
Verfassungsblog, Iss 2366-7044 (2022)
As part of their broader agenda to “modernize” its immigration laws, Germany’s government has proposed to ease immigrants’ access to citizenship. The opposition – especially the CDU – as well as the liberal government coalition partner FD
Externí odkaz:
https://doaj.org/article/91c0c2421a5e4862bacf2bd8c8e1908b
In this paper, we explore the extraction of recursive nested structure in the processing of self-similar binary sequences generated by two Lindenmayer grammars: the Fibonacci grammar and the Skip grammar. In each of these grammars only sequential ord
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0ad0c305c925c5eaf59bcc4262da3412
https://doi.org/10.31234/osf.io/mre4h
https://doi.org/10.31234/osf.io/mre4h
Trainees’ perspectives and recommendations for catalyzing the next generation of NeuroAI researchers
Autor:
Andrea I. Luppi, Jascha Achterberg, Samuel Schmidgall, Isil Poyraz Bilgin, Peer Herholz, Maximilian Sprang, Benjamin Fockter, Andrew Siyoon Ham, Sushrut Thorat, Rojin Ziaei, Filip Milisav, Alexandra M. Proca, Hanna M. Tolle, Laura E. Suárez, Paul Scotti, Helena M. Gellersen
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-7 (2024)
At this critical juncture in the development of NeuroAI, we outline challenges and training needs of junior researchers working across AI and neuroscience. We also provide advice and resources to help trainees plan their NeuroAI careers.
Externí odkaz:
https://doaj.org/article/4d00e88d4168494f95302ff60b8133c9
Autor:
Samuel Schmidgall, Carl Harris, Ime Essien, Daniel Olshvang, Tawsifur Rahman, Ji Woong Kim, Rojin Ziaei, Jason Eshraghian, Peter Abadir, Rama Chellappa
Publikováno v:
npj Digital Medicine, Vol 7, Iss 1, Pp 1-9 (2024)
Abstract Increasing interest in applying large language models (LLMs) to medicine is due in part to their impressive performance on medical exam questions. However, these exams do not capture the complexity of real patient–doctor interactions becau
Externí odkaz:
https://doaj.org/article/8fe0a749a2ee4756b334bf9db44db74e
In this article, we explore the extraction of recursive nested structure in the processing of binary sequences. Our aim was to determine whether the brain learns the higher order regularities of a highly simplified input where only sequential order i
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7b4314d72b6d5851365276a237ccdb0e
https://doi.org/10.31234/osf.io/fp5zb
https://doi.org/10.31234/osf.io/fp5zb
Autor:
Emilien Fargues, Giacomo Solano, Thomas Huddleston, Maarten Vink, Samuel Schmid, Rainer Baubock, Luicy Pedroza, Pau Palop-García, Jelena Dzankic, Ashley Mantha-Hollands
Publikováno v:
SSRN Electronic Journal.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0a72bb7b8b4832609a1be40ed4dacb79
https://doi.org/10.2139/ssrn.4148470
https://doi.org/10.2139/ssrn.4148470
Macrophytes are an integral component of lake communities, therefore understanding the factors that affect macrophyte community structure is important for conservation and management of lakes. In Sibley County, Minnesota, USA, five lakes were surveye
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0bde08b5bfcef28b43fbfe5e39d66b25
https://doi.org/10.21203/rs.3.rs-667792/v1
https://doi.org/10.21203/rs.3.rs-667792/v1
Autor:
Samuel Schmidgall, Rojin Ziaei, Jascha Achterberg, Louis Kirsch, S. Pardis Hajiseyedrazi, Jason Eshraghian
Publikováno v:
APL Machine Learning, Vol 2, Iss 2, Pp 021501-021501-14 (2024)
Artificial neural networks (ANNs) have emerged as an essential tool in machine learning, achieving remarkable success across diverse domains, including image and speech generation, game playing, and robotics. However, there exist fundamental differen
Externí odkaz:
https://doaj.org/article/574eb6c147894e3994e9606c6e86191e
Autor:
Irene Moskowitz, Eric Gawiser, John Franklin Crenshaw, Brett H. Andrews, Alex I. Malz, Samuel Schmidt, The LSST Dark Energy Science Collaboration
Publikováno v:
The Astrophysical Journal Letters, Vol 967, Iss 1, p L6 (2024)
Large imaging surveys will rely on photometric redshifts (photo- z 's), which are typically estimated through machine-learning methods. Currently planned spectroscopic surveys will not be deep enough to produce a representative training sample for Le
Externí odkaz:
https://doaj.org/article/4d1d6cf1f74f45f3a2b470e95d6cc4bd
Autor:
Samuel Schmidgall, Joe Hays
Publikováno v:
Frontiers in Neuroscience, Vol 17 (2023)
We propose that in order to harness our understanding of neuroscience toward machine learning, we must first have powerful tools for training brain-like models of learning. Although substantial progress has been made toward understanding the dynamics
Externí odkaz:
https://doaj.org/article/6aec47bb5fdc4dafb277546c7bd5eca8