Results 11 to 20 of about 30,914,687 (313)
Active Learning with Statistical Models [PDF]
Neural Information Processing Systems, 1995 For many types of machine learning algorithms, one can compute the statistically `optimal' way to select training data. In this paper, we review how optimal data selection techniques have been used with feedforward neural networks.
Cohn, D. A. +2 more
core +11 more sources
performance: An R Package for Assessment, Comparison and Testing of Statistical Models
Journal of Open Source Software, 2021 A crucial part of statistical analysis is evaluating a model's quality and fit, or performance. During analysis, especially with regression models, investigating the fit of models to data also often involves selecting the best fitting model amongst many ...
D. Lüdecke +4 more
semanticscholar +1 more source
Manual for Theoretical Chemistry, 2021 Professor Chen Ning Yang has made seminal and influential contributions in many different areas in theoretical physics. This talk focuses on his contributions in statistical mechanics, a field in which Professor Yang has held a continual interest for ...
Uwe-Jens Wiese, Albert Einstein
semanticscholar +1 more source
Head-Driven Statistical Models for Natural Language Parsing
Computational Linguistics, 2021 This article describes three statistical models for natural language parsing. The models extend methods from probabilistic context-free grammars to lexicalized grammars, leading to approaches in which a parse tree is represented as the sequence of ...
Michael Collins
doaj +2 more sources
Using Adversarial Attacks to Reveal the Statistical Bias in Machine Reading Comprehension Models [PDF]
Annual Meeting of the Association for Computational Linguistics, 2021 Pre-trained language models have achieved human-level performance on many Machine Reading Comprehension (MRC) tasks, but it remains unclear whether these models truly understand language or answer questions by exploiting statistical biases in datasets ...
Jieyu Lin, Jiajie Zou, N. Ding
semanticscholar +1 more source
An introduction to statistical learning with applications in R
Statistical Theory and Related Fields, 2021 The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics.
Fariha Sohil +2 more
semanticscholar +1 more source
Comparison and evaluation of statistical error models for scRNA-seq
Genome Biology, 2021 Heterogeneity in single-cell RNA-seq (scRNA-seq) data is driven by multiple sources, including biological variation in cellular state as well as technical variation introduced during experimental processing.
S. Choudhary, R. Satija
semanticscholar +1 more source
Tropical Geometry of Statistical Models [PDF]
, 2004 This paper presents a unified mathematical framework for inference in graphical models, building on the observation that graphical models are algebraic varieties.
Allman +4 more
core +4 more sources
On direct sequential analysis of heart rate variability signals [PDF]
Archive of Oncology, 2005 Heart rate variability analysis represents one of the most promising and the most commonly used quantitative measures of the cardiovascular autonomic regulatory system.
Bajić Dragana +2 more
doaj +1 more source
Statistical indices from bifactor models
Interacciones, 2017 Many instruments are created with the primary purpose of scaling individuals on a single trait. However psychological traits are often complex and contain domain specific manifestations.
Sergio Alexis Dominguez-Lara +1 more
doaj +1 more source