Results 201 to 210 of about 649,643 (307)

An Optimization-based Framework to Learn Conditional Random Fields for Multi-label Classification. [PDF]

Proc SIAM Int Conf Data Min, 2014
Naeini MP, Batal I, Liu Z, Hong C, Hauskrecht M.   +4 more
europepmc   +1 more source

Deep-dense Conditional Random Fields for Object Co-segmentation

International Joint Conference on Artificial Intelligence, 2017
Ze-Huan Yuan, Tong Lu, Yirui Wu
semanticscholar   +1 more source

Polymerase Chain Reaction. Perturbation Theory and Machine Learning Artificial Intelligence‐Experimental Microbiome Analysis: Applications to Ancient DNA and Tree Soil Metagenomics Cases of Study

Advanced Intelligent Systems, EarlyView.
The polymerase chain reaction (PCR).Perturbation Theory and Machine Learning framework integrates perturbation theory and machine learning to classify genetic sequences, distinguishing ancient DNA from modern controls and predicting tree health from soil metagenomic data.
Jose L. Rodriguez, Estefania Ascencio, Jose M. Ageitos, Lucia Feijoo, Shan He, Amirreza Daghighi, Gerardo M. Casanola‐Martin, Cristian R. Munteanu, Santiago Rodriguez Yañez, Alejandro Pazos, Harbil Bediaga, Brenda Durand Ordiales, Raquel Esteban, Ana‐Maria Heres, Jorge Curiel Yuste, Lur Epelde, Bakhtiyor Rasulev, Tomas Gonzalez, Sonia Arrasate, Humberto González‐Diaz   +19 more
wiley   +1 more source

A comparison of conditional random fields and structured support vector machines for chemical entity recognition in biomedical literature. [PDF]

J Cheminform, 2015
Tang B, Feng Y, Wang X, Wu Y, Zhang Y, Jiang M, Wang J, Xu H.   +7 more
europepmc   +1 more source

Elastic Fast Marching Learning from Demonstration

Advanced Intelligent Systems, EarlyView.
This article presents Elastic Fast Marching Learning (EFML), a novel approach for learning from demonstration that combines velocity‐based planning with elastic optimization. EFML enables smooth, precise, and adaptable robot trajectories in both position and orientation spaces.
Adrian Prados, Brendan Hertel, Ramon Barber, Reza Azadeh   +3 more
wiley   +1 more source

Recognition and Evaluation of Clinical Section Headings in Clinical Documents Using Token-Based Formulation with Conditional Random Fields. [PDF]

Biomed Res Int, 2015
Dai HJ, Syed-Abdul S, Chen CW, Wu CC.
europepmc   +1 more source

Infinite Conditional Random Fields

IEEE Transactions on Neural Networks and Learning Systems, vol. 24, no. 1, pp. 170-177, 2013
K. Bousmalis, S. Zafeiriou, L.P. Morency & M. Pantic   +2 more
openaire  

gnSPADE: Incorporating Gene Network Structures Enhances Reference‐Free Deconvolution in Spatial Transcriptomics

Advanced Intelligent Systems, EarlyView.
gnSPADE integrates gene‐network structures into a probabilistic topic modeling framework to achieve reference‐free cell‐type deconvolution in spatial transcriptomics. By embedding gene connectivity within the generative process, gnSPADE enhances biological interpretability and accuracy across simulated and real datasets, revealing spatial organization ...
Aoqi Xie, Yuehua Cui
wiley   +1 more source

Parsing citations in biomedical articles using conditional random fields. [PDF]

Comput Biol Med, 2011
Zhang Q, Cao YG, Yu H.
europepmc   +1 more source

Soft Robotic Sim2Real via Conditional Flow Matching

Advanced Intelligent Systems, EarlyView.
A new framework based on conditional flow matching addresses the persistent Sim2Real gap in soft robotics. By learning a conditional probability path, the model directly transforms inaccurate simulation data to match physical reality, successfully capturing complex phenomena like hysteresis.
Ge Shi, Xing Wang, André Farinha, Joshua Pinskier, Xing Shen, Richard Scalzo, David Howard   +6 more
wiley   +1 more source