Zobrazeno 1 - 10
of 10
pro vyhledávání: '"Stephen Giguere"'
Publikováno v:
EURO Journal on Decision Processes. 11:100031
Autor:
Philip S. Thomas, Emma Brunskill, Bruno da Silva, Andrew G. Barto, Stephen Giguere, Yuriy Brun
Publikováno v:
Science. 366:999-1004
Making well-behaved algorithmsMachine learning algorithms are being used in an ever-increasing number of applications, and many of these applications affect quality of life. Yet such algorithms often exhibit undesirable behavior, from various types o
Publikováno v:
Spectrochimica Acta Part B: Atomic Spectroscopy. 126:53-64
This project examines the causes, effects, and optimization of continuum removal in laser-induced breakdown spectroscopy (LIBS) to produce the best possible prediction accuracy of elemental composition in geological samples. We compare prediction acc
Autor:
Stephen Giguere, Sarah Byrne, M. Darby Dyar, CJ Carey, K. H. Lepore, T. Boucher, Caleb I. Fassett, Sridhar Mahadevan
Publikováno v:
Spectrochimica Acta Part B: Atomic Spectroscopy. 123:93-104
This study uses 1356 spectra from 452 geologically-diverse samples, the largest suite of LIBS rock spectra ever assembled, to compare the accuracy of elemental predictions in models that use only spectral regions thought to contain peaks arising from
Autor:
T. Boucher, Chloe H Anderson, J. Michael Rhodes, E. A. Breves, Sarah Byrne, Stephen Giguere, Caleb I. Fassett, Richard W. Murray, M. Darby Dyar, Michael J. Vollinger, K. H. Lepore
Publikováno v:
Applied spectroscopy. 71(4)
Obtaining quantitative chemical information using laser-induced breakdown spectroscopy is challenging due to the variability in the bulk composition of geological materials. Chemical matrix effects caused by this variability produce changes in the pe
Publikováno v:
Proceedings of the AAAI Conference on Artificial Intelligence. 27:654-660
This paper presents a new approach to representation discovery in reinforcement learning (RL) using basis adaptation. We introduce a general framework for basis adaptation as {\em nonlinear separable least-squares value function approximation} based
Publikováno v:
UIST
We present AttribIt, an approach for people to create visual content using relative semantic attributes expressed in linguistic terms. During an off-line processing step, AttribIt learns semantic attributes for design components that reflect the high
Publikováno v:
Social Computing, Behavioral-Cultural Modeling and Prediction ISBN: 9783642290466
SBP
SBP
Today, the responsibility for U.S. cyber defense is divided asymmetrically between a large population of cyber-naive end-users and a small cadre of cyber-savvy security experts in government and the private sector. We foresee the rise of "Cyber Civil
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::71cad6a35dc86d3f123b07cd5d915341
https://doi.org/10.1007/978-3-642-29047-3_5
https://doi.org/10.1007/978-3-642-29047-3_5
Publikováno v:
Intelligent Tutoring Systems ISBN: 9783642134364
Intelligent Tutoring Systems (2)
Intelligent Tutoring Systems (2)
This paper examines the problem of modeling when students are engaged in “gaming the system.” We propose and partially validate an approach that uses a hidden Markov model, as is used in knowledge tracing, to estimate whether the student is gamin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7fe7076a87815b925c7fc51a55246ac2
https://doi.org/10.1007/978-3-642-13437-1_58
https://doi.org/10.1007/978-3-642-13437-1_58
Autor:
Aaron P. Mitchell, Linda R. Kauffman, Albert T. Corbett, Benjamin A. MacLaren, Angela Z. Wagner, Stephen Giguere, Sujith M. Gowda, Ryan S. Baker
Publikováno v:
User Modeling, Adaptation, and Personalization ISBN: 9783642134692
UMAP
UMAP
Intelligent tutoring systems that utilize Bayesian Knowledge Tracing have achieved the ability to accurately predict student performance not only within the intelligent tutoring system, but on paper post-tests outside of the system Recent work has su
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d3b942e216d30f024a8d995296c48b9b
https://doi.org/10.1007/978-3-642-13470-8_7
https://doi.org/10.1007/978-3-642-13470-8_7