AIMC Topic: Machine Learning

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A Survey on Deep Learning Techniques for Stereo-Based Depth Estimation.

IEEE transactions on pattern analysis and machine intelligence
Estimating depth from RGB images is a long-standing ill-posed problem, which has been explored for decades by the computer vision, graphics, and machine learning communities. Among the existing techniques, stereo matching remains one of the most wide...

Depth Selection for Deep ReLU Nets in Feature Extraction and Generalization.

IEEE transactions on pattern analysis and machine intelligence
Deep learning is recognized to be capable of discovering deep features for representation learning and pattern recognition without requiring elegant feature engineering techniques by taking advantages of human ingenuity and prior knowledge. Thus it h...

Heterogeneous Graph Attention Network for Unsupervised Multiple-Target Domain Adaptation.

IEEE transactions on pattern analysis and machine intelligence
Domain adaptation, which transfers the knowledge from label-rich source domain to unlabeled target domains, is a challenging task in machine learning. The prior domain adaptation methods focus on pairwise adaptation assumption with a single source an...

A Neural Network Approach for Property Determination of Molecular Solar Cell Candidates.

The journal of physical chemistry. A
The dihydroazulene/vinylheptafulvene (DHA/VHF) photocouple is a promising candidate for molecular solar heat batteries, storing and releasing energy in a closed cycle. Much work has been done on improving the energy storage capacity and the half-life...

Effective Transfer Learning with Label-Based Discriminative Feature Learning.

Sensors (Basel, Switzerland)
The performance of natural language processing with a transfer learning methodology has improved by applying pre-training language models to downstream tasks with a large number of general data. However, because the data used in pre-training are irre...

A Systematic Review on Machine Learning and Deep Learning Models for Electronic Information Security in Mobile Networks.

Sensors (Basel, Switzerland)
Today's advancements in wireless communication technologies have resulted in a tremendous volume of data being generated. Most of our information is part of a widespread network that connects various devices across the globe. The capabilities of elec...

Active label cleaning for improved dataset quality under resource constraints.

Nature communications
Imperfections in data annotation, known as label noise, are detrimental to the training of machine learning models and have a confounding effect on the assessment of model performance. Nevertheless, employing experts to remove label noise by fully re...

A Broad Learning System to Predict the 28-Day Mortality of Patients Hospitalized with Community-Acquired Pneumonia: A Case-Control Study.

Computational and mathematical methods in medicine
This study was to conduct a model based on the broad learning system (BLS) for predicting the 28-day mortality of patients hospitalized with community-acquired pneumonia (CAP). A total of 1,210 eligible CAP cases from Chifeng Municipal Hospital were ...

Glycosylation-Related Genes Predict the Prognosis and Immune Fraction of Ovarian Cancer Patients Based on Weighted Gene Coexpression Network Analysis (WGCNA) and Machine Learning.

Oxidative medicine and cellular longevity
BACKGROUND: Ovarian cancer (OC) is a malignancy exhibiting high mortality in female tumors. Glycosylation is a posttranslational modification of proteins but research has failed to demonstrate a systematic link between glycosylation-related signature...

A brief review of machine learning methods for RNA methylation sites prediction.

Methods (San Diego, Calif.)
Thanks to the tremendous advancement of deep sequencing and large-scale profiling, epitranscriptomics has become a rapidly growing field. As one of the most important parts of epitranscriptomics, ribonucleic acid (RNA) methylation has been focused on...