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2Fast Supervised Hashing with Decision Trees for High-Dimensional Data
G. Lin, C. Shen, Q. Shi, A. van den Hengel, and D. Suter. 2014 IEEE Conference on Computer Vision and Pattern Recognition, page 1971-1978. (June 2014)
4 years ago by @dsuter
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decision
retrieval;inference
trees;Kernel;Optimization;Support
problem;block
inference;sub-modular
hashing;hash
supervised
codes;graph-cut;hashing;image
trees;fast
data;large-scale
coding;image
code
formulations;Binary
inference
mechanisms;GraphCut;binary
machines;Training;Training
codes;cryptography;decision
retrieval
data;binary
functions;high-dimensional
trees;image
method;boosted
codes;Decision
block
vector
search
decisionretrieval;inferencetrees;Kernel;Optimization;Supportproblem;blockinference;sub-modularhashing;hashsupervisedcodes;graph-cut;hashing;imagetrees;fastdata;large-scalecoding;imagecodeformulations;Binaryinferencemechanisms;GraphCut;binarymachines;Training;Trainingcodes;cryptography;decisionretrievaldata;binaryfunctions;high-dimensionaltrees;imagemethod;boostedcodes;Decisionblockvectorsearch
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1Wisdom of crowds for robust gene network inference
D. Marbach, J. Costello, R. Küffner, N. Vega, R. Prill, D. Camacho, K. Allison, DREAM5 Consortium, M. Kellis, J. Collins and 1 other author(s). Nat Methods, 9 (8): 796-804 (July 2012)
6 years ago by @karthikraman
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gene-regulatory-networks
inference
DREAM
gene-regulatory-networksinferenceDREAM
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1Revealing strengths and weaknesses of methods for gene network inference
D. Marbach, R. Prill, T. Schaffter, C. Mattiussi, D. Floreano, and G. Stolovitzky. Proc Natl Acad Sci U S A, 107 (14): 6286-6291 (April 2010)
6 years ago by @karthikraman
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gene-regulatory-networks
inference
DREAM
gene-regulatory-networksinferenceDREAM
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1Inferring Generative Model Structure with Static Analysis
P. Varma, B. He, P. Bajaj, N. Khandwala, I. Banerjee, D. Rubin, and C. Ré. Advances in neural information processing systems, page 240--250. (2017)
7 years ago by @thoni
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snorkel
inference
citedby:scholar:count:7
model
citedby:scholar:timestamp:2018-11-8
generative
structure
snorkelinferencecitedby:scholar:count:7modelcitedby:scholar:timestamp:2018-11-8generativestructure
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1The fallacy of placing confidence in confidence intervals
R. Morey, R. Hoekstra, J. Rouder, M. Lee, and E. Wagenmakers. Psychonomic Bulletin & Review, 23 (1): 103--123 (February 2016)
9 years ago by @yourwelcome
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Bayesian
inference
Bayesianinference
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4A large annotated corpus for learning natural language inference
S. Bowman, G. Angeli, C. Potts, and C. Manning. Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP), Association for Computational Linguistics, (2015)
9 years ago by @thoni
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dataset
snli
inference
natural
language
corpus
datasetsnliinferencenaturallanguagecorpus
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1Demographic inference under a spatially continuous coalescent model
T. Joseph, M. Hickerson, and D. Alvarado-Serrano. Heredity, (April 2016)
10 years ago by @peter.ralph
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ABC
inference
spatial_coalescent
SLFV
ABCinferencespatial_coalescentSLFV
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1A Novel Approach for Troubleshooting Microprocessor based Systems using An Object Oriented Expert System
S. D V Kodavade. International Journal of Advanced Computer Science and Applications(IJACSA), (2012)
12 years ago by @thesaiorg
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diagnosis.
mechanism;
Inference
object;
Fault
diagnosis.mechanism;Inferenceobject;Fault
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2Simulation Approaches to General Probabilistic Inference on Belief Networks.
R. Shachter, and M. Peot. UAI, page 221-234. North-Holland, (1989)
14 years ago by @djain
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Bayesian
directedModels
inference
sampling
BayesiandirectedModelsinferencesampling
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1Exact and approximate sampling by systematic stochastic search
J. Tenenbaum, E. Jonas, D. Roy, V. Mansinghka, and others. (2009)
15 years ago by @djain
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inference
sampling
inferencesampling
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2A flexible semantic inference methodology to reason about user preferences in knowledge-based recommender systems
Y. Blanco-Fernández, J. Pazos-Arias, A. Gil-Solla, M. Ramos-Cabrer, M. López-Nores, J. Garc\'ıa-Duque, A. Fernández-Vilas, R. D\'ıaz-Redondo, and J. Bermejo-Muñoz. Knowledge-Based Systems, 21 (4): 305--320 (2008)
15 years ago by @mediadigits
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semantic
inference
preference
knowledge
base
semanticinferencepreferenceknowledgebase
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1Distinguishing Between Selective Sweeps and Demography Using DNA Polymorphism Data
J. Jensen, Y. Kim, V. DuMont, C. Aquadro, and C. Bustamante. Genetics, 170 (3): 1401-1410 (2005)
16 years ago by @peter.ralph
show all tags
inference
hitchhiking
inferencehitchhiking
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1ON DESIGN CONSIDERATIONS AND RANDOMIZATION-BASED INFERENCE FOR COMMUNITY INTERVENTION TRIALS
G. H., M. D., C. J., G. B., and P. DAVID. Statistics in Medicine, 15 (11): 1069--1092 (December 1998)
16 years ago by @jwbowers
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observational_studies
inference
research_design
statistics
observational_studiesinferenceresearch_designstatistics
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4A Collapsed Variational Bayesian Inference Algorithm for Latent Dirichlet Allocation
Y. Teh, D. Newman, and M. Welling. Advances in Neural Information Processing Systems, 19, (2007)
16 years ago by @folke
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variational
inference
collapsed
lda
variationalinferencecollapsedlda
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1Remote sensing data assimilation for a prognostic phenology model
R. Stöckli, T. Rutishauser, D. Dragoni, J. O'Keefe, P. Thornton, M. Jolly, L. Lu, and A. Denning. J. Geophys. Res., (November 2008)
16 years ago by @jgomezdans
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models
modeling
inference
bayes
phenology
vegetation
model
assimilation
remotesensing
statistics
modelsmodelinginferencebayesphenologyvegetationmodelassimilationremotesensingstatistics
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2A Bayesian Approach to Crop Model Calibration Under Unknown Error Covariance
C. Hue, M. Tremblay, and D. Wallach. Journal of Agricultural, Biological, and Environmental Statistics, 13 (3): 355--365 (2008)
16 years ago by @jgomezdans
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agriculture
models
inference
bayes
mcmc
crops
agronomic
model
metropolishastings
statistics
uncertainty
agriculturemodelsinferencebayesmcmccropsagronomicmodelmetropolishastingsstatisticsuncertainty
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1A decision making methodology in support of the business rules lifecycle
D. Rosca, S. Greenspan, M. Feblowitz, and C. Wild. Requirements Engineering, 1997., Proceedings of the Third IEEE International Symposium on, (January 1997)
17 years ago by @cschie
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making
decision
specification
formal
corporate
service
London
processing
support
requirements
methodology
case
modeling
study
reasoning
metamodel
inference
data
enterprise
emergency
rules
representation
Ambulance
systems
lifecycle
model
business
knowledge
mechanism
makingdecisionspecificationformalcorporateserviceLondonprocessingsupportrequirementsmethodologycasemodelingstudyreasoningmetamodelinferencedataenterpriseemergencyrulesrepresentationAmbulancesystemslifecyclemodelbusinessknowledgemechanism
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1Application of a decision support mechanism to the business rules lifecycle
D. Rosca, S. Greenspan, C. Wild, H. Reubenstein, K. Maly, and M. Feblowitz. Knowledge-Based Software Engineering Conference, 1995 .Proceedings., 10th, (November 1995)
17 years ago by @cschie
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based
conceptual
decision
operational
engineering
analysis
structure
framework
processing
support
modeling
knowledge-based
reasoning
inference
software
data
enterprise
lifetime
rules
re-engineering
systems
lifecycle
business
knowledge
mechanism
architecture
basedconceptualdecisionoperationalengineeringanalysisstructureframeworkprocessingsupportmodelingknowledge-basedreasoninginferencesoftwaredataenterpriselifetimerulesre-engineeringsystemslifecyclebusinessknowledgemechanismarchitecture
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1Ridge Regression in Practice
D. Marquardt, and R. Snee. The American Statistician, 29 (1): 3--20 (1975)
17 years ago by @jgomezdans
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imported
modeling
reflectance
inference
inversions
statistics
importedmodelingreflectanceinferenceinversionsstatistics
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1Sensitivity of Constrained Linear Inversions to the Selection of the Lagrange Multiplier
M. King. Journal of the Atmospheric Sciences, 39 (6): 1356-1369 (June 1981)
17 years ago by @jgomezdans
show all tags
optical
models
inference
inversions
sensitivity
BRDF
constrained
remotesensing
brdf
uncertainty
opticalmodelsinferenceinversionssensitivityBRDFconstrainedremotesensingbrdfuncertainty
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