Methods and Applications

By W. Arveson. 2002

365 citations recorded by the source corpus.

Subfields: Applied Mathematics, Mathematical Physics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/bc1c55e05560af12140919f1eb625a49911efcc7

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Applied Mathematics

In Mathematical Physics

In Statistics and Probability

Citations in the corpus

Managing Functional Biases in Organizational Forecasts: A Case Study of Consensus Forecasting in Supply Chain Planning (2009, methodology): “Some individual biases that have been shown to affect subjective forecasting include over-conﬁdence, availability, anchor and adjustment, and optimism (Makridakis et al. 1998).”
Community Structure in Time-Dependent, Multiscale, and Multiplex Networks (2009, methodology): “23 We include the following symmetrized ties from the first wave of this data (covering the first year of university attendance): (1) Facebook friendships; (2) picture friendships, in which a student has posted and tagged a photograph of another online; (3) roommates, in which two students share a f”
Image processing and Quality Control for the first 10,000 brain imaging datasets from UK Biobank (2017, methodology): “This optimal AP/PA pair is then fed into topup 22 (Andersson et al. (2003)) in order to estimate the b=0 image ﬁeldmap and associated dMRI EPI distortions.”
Particle swarm optimization algorithm embedded with maximum deviation theory for solving multi-objective flexible job shop scheduling problem (2015, background): “Many performance metrics have been suggested to evaluate the non-dominated solutions [34, 41].”
Deep Learning: A Comprehensive Overview on Techniques, Taxonomy, Applications and Research Directions (2021, background): “A fundamental distinction between DL and other machinelearning techniques is the attempt to extract high-level characteristics directly from data [22, 97].”
Environmental sound classification with convolutional neural networks (2015, background): “A thorough coverage of current deep learning methods and their applications in various contexts, including speech and audio processing, can be found in recently published books discussing this topic [21–23].”
Yuel: Compound-Protein Interaction Prediction with High Generalizability (2021, background): “= f ([ (ω(C)A1) ω(P)Ak+1 ⋯ (ω(C)A1) ω(P)An ⋯ ⋯ ⋯ (ω(C)Ak) ω(P)Ak+1 ⋯ (ω(C)Ak) ω(P)An ]) (2)”