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1BioTransformer: a comprehensive computational tool for small molecule metabolism prediction and metabolite identification
Y. Djoumbou-Feunang, J. Fiamoncini, A. Gil-de-la Fuente, R. Greiner, C. Manach, and D. Wishart. Journal of Cheminformatics, 11 (1): 2 (January 2019)
2 years ago by @jascal_panetzky
show all tags
pathway,
degradation,
spectrometry,
Enzyme-substrate
Metabolic
Metabolism
system,
classification
Machine
learning,
identification,
prediction,
Knowledge-based
Mass
Microbial
Metabolite
specificity,
Structure-based
Biotransformation,
ecomodelling
pathway,degradation,spectrometry,Enzyme-substrateMetabolicMetabolismsystem,classificationMachinelearning,identification,prediction,Knowledge-basedMassMicrobialMetabolitespecificity,Structure-basedBiotransformation,ecomodelling
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1Tiny Transformers for Environmental Sound Classification at the Edge
D. Elliott, C. Otero, S. Wyatt, and E. Martino. (March 2021)arXiv:2103.12157 cs, eess.
2 years ago by @jascal_panetzky
show all tags
Learning,
Sound,
Systems
-
Processing
Science
Machine
Engineering
Electrical
and
Computer
Speech
Audio
ecomodelling
Learning,Sound,Systems-ProcessingScienceMachineEngineeringElectricalandComputerSpeechAudioecomodelling
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2Reconstruction of offsets of an electron gun using deep learning and an optimization algorithm
D. Meier, G. Hartmann, J. Völker, J. Viefhaus, and B. Sick. Advances in Computational Methods for X-Ray Optics V, page 71--77. SPIE, (2020)
4 years ago by @ies
show all tags
optimization
learning,
deep
machine
electron
global
offsets,
gun
itegpub
optimizationlearning,deepmachineelectronglobaloffsets,gunitegpub
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1Self-Adaptation of Activity Recognition Systems to New Sensors
D. Bannach, M. Jänicke, V. Rey, S. Tomforde, B. Sick, and P. Lukowicz. arXiv e-prints, (2017)
4 years ago by @ies
show all tags
Learning,
K.3.2
68T05,
and
Vision
Computer
-
Statistics
Pattern
Science
Machine
Recognition,
Learning,K.3.268T05,andVisionComputer-StatisticsPatternScienceMachineRecognition,
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1Behaviour Suite for Reinforcement Learning
I. Osband, Y. Doron, M. Hessel, J. Aslanides, E. Sezener, A. Saraiva, K. McKinney, T. Lattimore, C. Szepesvári, S. Singh and 4 other author(s). ICLR, (2020)
6 years ago by @csaba
show all tags
learning,
evaluation
reinforcement
empirical
learning,evaluationreinforcementempirical
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3Private traits and attributes are predictable from digital records of human behavior
M. Kosinski, D. Stillwell, and T. Graepel. Proceedings of the National Academy of Sciences, (March 2013)
9 years ago by @yourwelcome
show all tags
psychology,
social
learning,
data
machine
mining,
humans,
networks
Big
data,
psychology,sociallearning,datamachinemining,humans,networksBigdata,
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1A Split-Merge MCMC Algorithm for the Hierarchical Dirichlet Process
C. Wang, and D. Blei. arXiv:1201.1657, (January 2012)
9 years ago by @yourwelcome
show all tags
dirichlet,
Learning,
Markov
algorithms,
chain
Bayesian
hierarchical
model,
Carlo
Monte
inference,
Machine
dirichlet,Learning,Markovalgorithms,chainBayesianhierarchicalmodel,CarloMonteinference,Machine
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1Discussion: Stability selection
D. Draper. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 72 (4): 461--462 (2010)
9 years ago by @yourwelcome
show all tags
Learning,
High
Bayesian
dimensional
Lasso,
inference,
data,
Machine
simulations,
selection
sampling,
importance,
variable
Learning,HighBayesiandimensionalLasso,inference,data,Machinesimulations,selectionsampling,importance,variable
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1Comment: Boosting Algorithms: Regularization, Prediction and Model Fitting
A. Buja, D. Mease, and A. Wyner. Statistical Science, 22 (4): 506--512 (November 2007)Mathematical Reviews number (MathSciNet): MR2420455; Zentralblatt MATH identifier: 06075137.
9 years ago by @yourwelcome
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Boosting,
Learning,
refutation
classification,
Machine
Boosting,Learning,refutationclassification,Machine
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4Mastering the game of Go with deep neural networks and tree search
D. Silver, A. Huang, C. Maddison, A. Guez, L. Sifre, G. Van Den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot and 1 other author(s). Nature, 529 (7587): 484--489 (2016)
9 years ago by @yourwelcome
show all tags
Learning,
Intelligence,
Artificial
learning,
deep
neural
networks
gameplay,
Machine
Learning,Intelligence,Artificiallearning,deepneuralnetworksgameplay,Machine
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13Generative Adversarial Networks
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio. (June 2014)
9 years ago by @yourwelcome
show all tags
Learning,
learning,
deep
unsupervised
model,
learning
generative
Machine
Learning,learning,deepunsupervisedmodel,learninggenerativeMachine
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1Intellectual aptitude tests and A levels for selecting UK school leaver entrants for medical school
I. Mcmanus, D. Powis, R. Wakeford, E. Ferguson, D. James, and P. Richards. BMJ, 331 (7516): 555--559 (September 2005)
17 years ago by @earthfare
show all tags
strugglers,
personality,
learning,
selection,
style
admissions,
strugglers,personality,learning,selection,styleadmissions,
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1Pilot study of the roles of personality, references, and personal statements in relation to performance over the five years of a medical degree * Commentary: How to derive causes from correlations in educational studies
E. Ferguson, D. James, F. O'Hehir, A. Sanders, and I. Mcmanus. BMJ, 326 (7386): 429--432 (February 2003)
17 years ago by @earthfare
show all tags
strugglers,
personality,
learning,
selection,
style
admissions,
strugglers,personality,learning,selection,styleadmissions,
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1Autism spectrum disorder is not as certain as implied
D. Simpson. BMJ, 326 (7396): 986+ (May 2003)
17 years ago by @earthfare
show all tags
personality,
learning,
style
personality,learning,style
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2Understanding Learning Culturally: Overcoming the Dualism Between Social and Individual Views of Learning
P. Hodkinson, G. Biesta, and D. James. Vocations and Learning, 1 (1): 27-47 (2008/03/30/)
17 years ago by @actualal
show all tags
learning,
learning-theory,
pedagogy
learning,learning-theory,pedagogy
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2Probabilistic Model Building and Competent Genetic Programming
K. Sastry, and D. Goldberg. Genetic Programming Theory and Practise, chapter 13, Kluwer, (2003)
18 years ago by @brazovayeye
show all tags
genetic
Linkage
compact
Probabilistic
Extended
adaptation,
algorithms,
program,
learning,
programming,
Competent
model
algorithm
building,
geneticLinkagecompactProbabilisticExtendedadaptation,algorithms,program,learning,programming,Competentmodelalgorithmbuilding,
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2Using genetic programming to classify node positive patients in bladder cancer
A. Almal, A. Mitra, R. Datar, P. Lenehan, D. Fry, R. Cote, and W. Worzel. GECCO 2006: Proceedings of the 8th annual conference on Genetic and evolutionary computation, 1, page 239--246. Seattle, Washington, USA, ACM Press, (8-12 July 2006)
18 years ago by @brazovayeye
show all tags
cancer,
genetic
synthesis
Biological
classification
measures,
bladder
synthesis,
Nodal
induction,
learning,
machine
evaluation,
rules,
algorithms
concept
learning
selection,
program
staging,
algorithms,
design
programming,
pattern
feature
and
similarity
analysis,
classifier
Applications,
cancer,geneticsynthesisBiologicalclassificationmeasures,bladdersynthesis,Nodalinduction,learning,machineevaluation,rules,algorithmsconceptlearningselection,programstaging,algorithms,designprogramming,patternfeatureandsimilarityanalysis,classifierApplications,
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4A Comparison of Classifiers and Document Representations for the Routing Problem
H. Schütze, D. Hull, and J. Pedersen. Research and Development in Information Retrieval, page 229--237. (1995)
18 years ago by @pprett
show all tags
connectionist,
feedback,
routing
machine,
learning,
ann,
relevance,
connectionist,feedback,routingmachine,learning,ann,relevance,
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4Automatic web query classification using labeled and unlabeled training data
S. Beitzel, E. Jensen, O. Frieder, D. Grossman, D. Lewis, A. Chowdhury, and A. Kolcz. SIGIR '05: Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval, page 581--582. New York, NY, USA, ACM, (2005)
18 years ago by @pprett
show all tags
aol,
machine,
learning,
supervised
log,
query,
mining,
aol,machine,learning,supervisedlog,query,mining,
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