Results 91 to 100 of about 58,822 (196)

The differences in essential facial areas for impressions between humans and deep learning models: An eye‐tracking and explainable AI approach

British Journal of Psychology, EarlyView.
Abstract This study explored the facial impressions of attractiveness, dominance and sexual dimorphism using experimental and computational methods. In Study 1, we generated face images with manipulated morphological features using geometric morphometrics. In Study 2, we conducted eye tracking and impression evaluation experiments using these images to
Takanori Sano, Jun Shi, Hideaki Kawabata
wiley   +1 more source

Affine Calculus for Constrained Minima of the Kullback–Leibler Divergence

Stats
The non-parametric version of Amari’s dually affine Information Geometry provides a practical calculus to perform computations of interest in statistical machine learning.
Giovanni Pistone
doaj   +1 more source

Correction : Random-forest model for drug-target interaction prediction via Kullback-Leibler divergence. [PDF]

J Cheminform, 2022
Ahn S, Lee SE, Kim MH.
europepmc   +1 more source

Inducing Features of Random Fields

, 1995
We present a technique for constructing random fields from a set of training samples. The learning paradigm builds increasingly complex fields by allowing potential functions, or features, that are supported by increasingly large subgraphs.
Della Pietra, S., Della Pietra, V., Lafferty, J.   +2 more
core   +1 more source

An investigation into in‐sample and out‐of‐sample model selection for nonstationary autoregressive models

British Journal of Mathematical and Statistical Psychology, EarlyView.
Abstract The stationary autoregressive model forms an important base of time‐series analysis in today's psychology research. Diverse nonstationary extensions of this model are developed to capture different types of changing temporal dynamics. However, researchers do not always have a solid theoretical base to rely on for deciding a‐priori which of ...
Yong Zhang, Anja F. Ernst, Ginette Lafit, Ward B. Eiling, Laura F. Bringmann   +4 more
wiley   +1 more source

Using Feature Engineering and Principal Component Analysis for Monitoring Spindle Speed Change Based on Kullback-Leibler Divergence with a Gaussian Mixture Model. [PDF]

Sensors (Basel), 2023
Huang YC, Hou CC.
europepmc   +1 more source

Inflation expectations and time variations in the oil price pass‐through

Economic Inquiry, EarlyView.
Abstract Previous literature suggests that the pass‐through of oil price shocks to inflation rates became weaker since the 1970s. I use a time‐varying parameter VAR to show that this trend has recently been reversed with headline and core inflation rates responding more sensitive to oil price shocks. Based on a counterfactual analysis, I offer evidence
Daniel Gründler
wiley   +1 more source

Generalizing machine learning models from clinical free text

Scientific Reports
To assess strategies for enhancing the generalizability of healthcare artificial intelligence models, we analyzed the impact of preprocessing approaches applied to medical free text, compared single- versus multiple-institution data models, and evaluated
Balaji Pandian, John Vandervest, Graciela Mentz, Jomy Varghese, Shavano D. Steadman, Sachin Kheterpal, Maggie Makar, V. G. Vinod Vydiswaran, Michael L. Burns   +8 more
doaj   +1 more source

E3 ligase Praja1 mediates ubiquitination and degradation of microtubule‐associated protein tau

The FEBS Journal, EarlyView.
E3 ligase Praja1, but not its paralogue Praja2, recognizes and ubiquitinates tau protein for proteasomal degradation. This newly identified function of Praja1‐mediated tau degradation suggests its role in protein quality control, which may provide insights into the pathogenesis of tauopathies.
Shiho Aoki, Wataru Onodera, Akihiko Takashima, Kotaro Kawasaki, Kazuki Imadegawa, Hikaru Kurahashi, Mizuho Oishi, Toru Asahi, Yoshiyuki Soeda   +8 more
wiley   +1 more source

Bregman–Hausdorff Divergence: Strengthening the Connections Between Computational Geometry and Machine Learning

Machine Learning and Knowledge Extraction
The purpose of this paper is twofold. On a technical side, we propose an extension of the Hausdorff distance from metric spaces to spaces equipped with asymmetric distance measures.
Tuyen Pham, Hana Dal Poz Kouřimská, Hubert Wagner   +2 more
doaj   +1 more source