Results 81 to 90 of about 2,491,078 (295)
Visualizing stability: a sensitivity analysis framework for t-SNE embeddings
Frontiers in Bioinformaticst-distributed Stochastic Neighbour Embedding (t-SNE) is a cornerstone for visualizing high-dimensional biological data, where each high-dimensional data point is represented as a point in a two-dimensional map.
Susanne Zabel +2 more
doaj +1 more source
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective Progressive brain atrophy has now been widely observed in the chronic stages of moderate–severe traumatic brain injury (msTBI), but there is only coarse information regarding its prevalence and magnitude, and whether rate of atrophy accelerates or attenuates across time. We examined these questions in a Canadian inception cohort from
Robin E. A. Green +6 more
wiley +1 more source
Tackling Error Propagation through Reinforcement Learning: A Case of Greedy Dependency Parsing [PDF]
, 2017 Error propagation is a common problem in NLP. Reinforcement learning explores erroneous states during training and can therefore be more robust when mistakes are made early in a process. In this paper, we apply reinforcement learning to greedy dependency
Antske Fokkens +3 more
core +2 more sources
The Association of Amyloid‐β and Tau With Synaptic Dysfunction in Alzheimer's Disease
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective To clarify the relative contributions of amyloid and tau to synaptic dysfunction in Alzheimer's disease (AD). Methods We included 109 cognitively unimpaired and impaired individuals from the Geneva Memory Center with available CSF synaptic biomarkers (neurogranin, SNAP‐25, GAP‐43). Participants were stratified according to amyloid (A)
Fausto Roveta +11 more
wiley +1 more source
ComputationMolecular dynamics (MD) simulations rely on accurate numerical integration of the equations of motion, where the choice of the timestep (Δt) critically affects stability and precision.
Luis A. Selis +2 more
doaj +1 more source
Error-propagation in weakly nonlinear inverse problems [PDF]
, 1996 In applications of inversion methods to real data, nonlinear inverse problems are often simplied to more easily solvable linearized inverse problems. By doing so one introduces an error made by the linearization.
Snieder, R.K. +2 more
core
Error back-propagation in multi-valued logic systems
, 2007 Error back-propagation—and its many variations—has been used extensively to train neural networks. A multi-layer system cannot be trained in a supervised learning scheme because data are usually provided only as end-to-end input-output pairs for the ...
Georgios Apostolikas +1 more
core +1 more source
Arthritis Care &Research, EarlyView.Generative artificial intelligence (AI) is increasingly being used in rheumatology practice, to assist with tasks such as documentation, record summarization, and clinical decision support. Rheumatologists need a practical approach to determining what evidence and oversight each application requires.
Koushan Kouranloo +2 more
wiley +1 more source
A Q‐Learning Algorithm to Solve the Two‐Player Zero‐Sum Game Problem for Nonlinear Systems
International Journal of Adaptive Control and Signal Processing, Volume 39, Issue 3, Page 566-581, March 2025.A Q‐learning algorithm to solve the two‐player zero‐sum game problem for nonlinear systems. ABSTRACT This paper deals with the two‐player zero‐sum game problem, which is a bounded L2$$ {L}_2 $$‐gain robust control problem. Finding an analytical solution to the complex Hamilton‐Jacobi‐Issacs (HJI) equation is a challenging task.
Afreen Islam +2 more
wiley +1 more source
The early identification of detector locations in dependable software [PDF]
, 2011 The dependability properties of a software system are usually assessed and refined towards the end of the software development lifecycle. Problems pertaining to software dependability may necessitate costly system redesign. Hence, early insights into the
Jhumka, Arshad +3 more
core +1 more source