Results 61 to 70 of about 256,437 (324)
Which graphical models are difficult to learn? [PDF]
, 2009 We consider the problem of learning the structure of Ising models (pairwise binary Markov random fields) from i.i.d. samples. While several methods have been proposed to accomplish this task, their relative merits and limitations remain somewhat obscure.
Bento, Jose, Montanari, Andrea
core +2 more sources
A Bayesian semiparametric latent variable model for mixed responses [PDF]
, 2006 In this article we introduce a latent variable model (LVM) for mixed ordinal and continuous responses, where covariate effects on the continuous latent variables are modelled through a flexible semiparametric predictor. We extend existing LVM with simple
Fahrmeir, Ludwig, Raach, Alexander
core +4 more sources
Advanced Science, EarlyView.Protein complexes like KIBRA‐PKMζ are crucial for maintaining memories, forming month‐long protein traces in memory‐tagged neurons, but conventional RNA‐seq analysis fails to detect their transcript changes, leaving memory molecules undetected in the shadows of abundantly‐expressed genes.
Jiyeon Han +10 more
wiley +1 more source
APLIKASI MARKOV RANDOM FIELD PADA MASALAH INDUSTRI
Jurnal Teknik Industri, 2002 Markov chain in the stochastic process is widely used in the industrial problems particularly in the problem of determining the market share of products.
Siana Halim
doaj
Sustainable Materials Design With Multi‐Modal Artificial Intelligence
Advanced Science, EarlyView.Critical mineral scarcity, high embodied carbon, and persistent pollution from materials processing intensify the need for sustainable materials design. This review frames the problem as multi‐objective optimization under heterogeneous, high‐dimensional evidence and highlights multi‐modal AI as an enabling pathway.
Tianyi Xu +8 more
wiley +1 more source
A Markov Random Field Model for the Restoration of Foggy Images
International Journal of Advanced Robotic Systems, 2014 This paper presents an algorithm to remove fog from a single image using a Markov random field (MRF) framework. The method estimates the transmission map of an image degradation model by assigning labels with a MRF model and then optimizes the map ...
Fan Guo, Jin Tang, Hui Peng
doaj +1 more source
Partially observed Markov random fields are variable neighborhood random fields
, 2012 The present paper has two goals. First to present a natural example of a new class of random fields which are the variable neighborhood random fields. The example we consider is a partially observed nearest neighbor binary Markov random field. The second
Cassandro, Marzio +2 more
core +1 more source
Ultra‐Wide‐Field Noninvasive Imaging Through Scattering Media Via Physics‐Guided Deep Learning
Advanced Science, EarlyView.We propose a physics‐guided adaptive dual‐domain learning method for ultra‐wide‐field noninvasive imaging through scattering media, namely UNI‐Net. Our method not only reduces the requirement for real experimental data by an order of magnitude but also enables clear imaging of complex scenes with an ultra‐large field of view, which is 164 times the OME
Lintao Peng +5 more
wiley +1 more source
Advanced Science, EarlyView.SMarT‐Diff introduces a multi‐objective generative paradigm that integrates scaffold hopping with structure‐aware scoring to enable controlled exploration beyond the training distribution. The framework consistently balances drug‐likeness, synthesizes accessibility and bioactivity, yielding chemically diverse candidates with enhanced properties.
Yuwei Yang +8 more
wiley +1 more source
Relational Neural Markov Random Fields
CoRR, 2021 Statistical Relational Learning (SRL) models have attracted significant attention due to their ability to model complex data while handling uncertainty. However, most of these models have been limited to discrete domains due to their limited potential functions.
Yuqiao Chen +2 more
openaire +3 more sources