Results 181 to 190 of about 286,362 (268)

Addressing the unmet need for visualizing conditional random fields in biological data. [PDF]

BMC Bioinformatics, 2014
Ray WC, Wolock SL, Callahan NW, Dong M, Li QQ, Liang C, Magliery TJ, Bartlett CW.   +7 more
europepmc   +1 more source

Accelerating Discovery of Organic Molecular Crystals via Materials Informatics and Autonomous Experiments

Advanced Intelligent Discovery, EarlyView.
Materials informatics and autonomous experimentation are transforming the discovery of organic molecular crystals. This review presents an integrated molecule–crystal–function–optimization workflow combining machine learning, crystal structure prediction, and Bayesian optimization with robotic platforms.
Takuya Taniguchi, Kazuki Ishizaki, Ryo Fukasawa   +2 more
wiley   +1 more source

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

VAE+DDPG: An Attention‐Enhanced Variational Autoencoder for Deep Reinforcement Learning‐Based Autonomous Navigation in Low‐Light Environments

Advanced Intelligent Systems, EarlyView.
Variational Autoencoder+Deep Deterministic Policy Gradient addresses low‐light failures of infrared depth sensing for indoor robot navigation. Stage 1 pretrains an attention‐enhanced Variational Autoencoder (Convolutional Block Attention Module+Feature Pyramid Network) to map dark depth frames to a well‐lit reconstruction, yielding a 128‐D latent code ...
Uiseok Lee, Chanhong Hwang, Hyunseok Kang, Yechan Park, Hangwoo Jin, Tuan Anh Nguyen, Eunmi Choi, Dugki Min   +7 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

An Attention‐Assisted Machine Learning System for Deep Microorganism Image Classification

Advanced Intelligent Systems, EarlyView.
An attention‐assisted DenseNet201 framework was developed for the classification of eight microorganism classes from microscopic images. The proposed model improved classification performance and achieved an accuracy of 87.38%. Advances in microbiology and environmental health fundamentally depend on precise and timely microorganism identification ...
Yujie Li, Ailing Gao, Guoxu Liu, Chunlei Chen, Yonghui Zhang, Sunkyoung Kang, M. Khosravi   +6 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

Bayesian Optimisation for the Experimental Sciences: A Practical Guide to Data‐Efficient Optimisation of Laboratory Workflows

Advanced Intelligent Systems, EarlyView.
This study provides an introduction to Bayesian optimisation targeted for experimentalists. It explains core concepts, surrogate modelling, and acquisition strategies, and addresses common real‐world challenges such as noise, constraints, mixed variables, scalability, and automation.
Chuan He, Martin Singull, T. Jesper Jacobsson   +2 more
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

Adversarial Erasing Enhanced Multiple Instance Learning (siMILe): Discriminative Identification of Oligomeric Protein Structures in Single Molecule Localization Microscopy

Advanced Intelligent Systems, EarlyView.
Hallgrimson et al. introduce a machine learning algorithm, siMILe, that takes features of single‐molecule localization microscopy localization clusters (e.g., size and sphericity) and finds the clusters that are associated with certain cell conditions (such as differential protein expression or drug treatment).
Christian Hallgrimson, Y. Lydia Li, Claire A. Shou, Ben Cardoen, John Lim, Timothy H. Wong, Ismail M. Khater, Ivan Robert Nabi, Ghassan Hamarneh   +8 more
wiley   +1 more source