AIMC Topic: Machine Learning

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Granite classification using machine learning and edge computing.

F1000Research
BACKGROUND: The outlook and the aura of any place are highly dependent on how a place is decorated and what materials are used in designing it. Granite is such a kind of rock which is vastly used for this purpose. Granite flooring and countershave a ...

Machine learning based outcome prediction of large vessel occlusion of the anterior circulation prior to thrombectomy in patients with wake-up stroke.

Interventional neuroradiology : journal of peritherapeutic neuroradiology, surgical procedures and related neurosciences
PURPOSE: Outcome prediction of large vessel occlusion of the anterior circulation in patients with wake-up stroke is important to identify patients that will benefit from thrombectomy. Currently, mismatch concepts that require MRI or CT-Perfusion (CT...

Patient-specific transfer learning for auto-segmentation in adaptive 0.35 T MRgRT of prostate cancer: a bi-centric evaluation.

Medical physics
BACKGROUND: Online adaptive radiation therapy (RT) using hybrid magnetic resonance linear accelerators (MR-Linacs) can administer a tailored radiation dose at each treatment fraction. Daily MR imaging followed by organ and target segmentation adjustm...

Efficient Perturbation Inference and Expandable Network for continual learning.

Neural networks : the official journal of the International Neural Network Society
Although humans are capable of learning new tasks without forgetting previous ones, most neural networks fail to do so because learning new tasks could override the knowledge acquired from previous data. In this work, we alleviate this issue by propo...

Representational Gradient Boosting: Backpropagation in the Space of Functions.

IEEE transactions on pattern analysis and machine intelligence
The estimation of nested functions (i.e., functions of functions) is one of the central reasons for the success and popularity of machine learning. Today, artificial neural networks are the predominant class of algorithms in this area, known as repre...

The Conditional Super Learner.

IEEE transactions on pattern analysis and machine intelligence
Using cross validation to select the best model from a library is standard practice in machine learning. Similarly, meta learning is a widely used technique where models previously developed are combined (mainly linearly) with the expectation of impr...

Factors of Influence for Transfer Learning Across Diverse Appearance Domains and Task Types.

IEEE transactions on pattern analysis and machine intelligence
Transfer learning enables to re-use knowledge learned on a source task to help learning a target task. A simple form of transfer learning is common in current state-of-the-art computer vision models, i.e., pre-training a model for image classificatio...

Generalizing Correspondence Analysis for Applications in Machine Learning.

IEEE transactions on pattern analysis and machine intelligence
Correspondence analysis (CA) is a multivariate statistical tool used to visualize and interpret data dependencies by finding maximally correlated embeddings of pairs of random variables. CA has found applications in fields ranging from epidemiology t...

Reducing Data Complexity Using Autoencoders With Class-Informed Loss Functions.

IEEE transactions on pattern analysis and machine intelligence
Available data in machine learning applications is becoming increasingly complex, due to higher dimensionality and difficult classes. There exists a wide variety of approaches to measuring complexity of labeled data, according to class overlap, separ...