Uncertainty‐Guided Selective Adaptation Enables Cross‐Platform Predictive Fluorescence Microscopy
Deep learning models often fail when transferred to new microscopes. A novel framework overcomes this by selectively adapting the early layers governing low‐level image statistics, while freezing deep layers that encode morphology. This uncertainty‐guided approach enables robust, label‐free virtual staining across diverse systems, democratizing ...
Kai‐Wen K. Yang +9 more
wiley +1 more source
Semantic labelling of road scenes using supervised and unsupervised machine learning with lidar-stereo sensor fusion [PDF]
At the highest level the aim of this thesis is to review and develop reliable and efficient algorithms for classifying road scenery primarily using vision based technology mounted on vehicles. The purpose of this technology is to enhance vehicle safety
Osgood, Thomas J.
core
Unsupervised learning of spatially varying regularization for diffeomorphic image registration. [PDF]
Chen J +7 more
europepmc +1 more source
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 +2 more
wiley +1 more source
Unsupervised clustering approach for network anomaly detection
This paper describes the advantages of using the anomaly detection approach over the misuse detection technique in detecting unknown network intrusions or attacks.
Syarif, Iwan +2 more
core
Supervised and Unsupervised Learning with Numerical Computation for the Wolfram Cellular Automata. [PDF]
Tuo K +6 more
europepmc +1 more source
A Critical Assessment of Bonding Descriptors for Predicting Materials Properties
The impact of new bonding descriptors in machine learning models for predicting material properties is assessed. Improvements are validated using significance tests, and new, intuitive descriptors for screening lattice thermal conductivity and projected force constants are introduced.
Aakash Ashok Naik +6 more
wiley +1 more source
COMBINED UNSUPERVISED AND SEMI-SUPERVISED LEARNING FOR DATA CLASSIFICATION
Semi-supervised learning methods exploit both labeled and unlabeled data items in their training process, requiring only a small subset of labeled items.
Breve, Fabricio Aparecido [UNESP] +2 more
core
Unsupervised learning reveals novel disease-associated proteins in high-dimensional human proteomic data. [PDF]
Bernard E, Wang Y, Chen M, Xu S.
europepmc +1 more source
Matrix‐assisted laser desorption/ionization imaging‐based identification of reliable small molecule markers across heterogeneous glioblastoma cohorts is challenging with intensity‐only methods. We present spatially informed feature selection (SIFS), a spatially informed framework that prioritizes molecules consistently colocalizing with histopathology.
Shad A. Mohammed +15 more
wiley +1 more source

