AIMC Topic: Algorithms

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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 ...

Low-latency label-free image-activated cell sorting using fast deep learning and AI inferencing.

Biosensors & bioelectronics
Classification and sorting of cells using image-activated cell sorting (IACS) systems can bring significant insight to biomedical sciences. Incorporating deep learning algorithms into IACS enables cell classification and isolation based on complex an...

PrivacyMask: Real-world privacy protection in face ID systems.

Mathematical biosciences and engineering : MBE
Recent works have illustrated that many facial privacy protection methods are effective in specific face recognition algorithms. However, the COVID-19 pandemic has promoted the rapid innovation of face recognition algorithms for face occlusion, espec...

Multimodal medical image fusion algorithm based on pulse coupled neural networks and nonsubsampled contourlet transform.

Medical & biological engineering & computing
Combining two medical images from different modalities is more helpful for using the resulting image in the healthcare field. Medical image fusion means combining two or more images coming from multiple sensors. This technology obtains an output imag...

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...

Hyperbolic Deep Neural Networks: A Survey.

IEEE transactions on pattern analysis and machine intelligence
Recently, hyperbolic deep neural networks (HDNNs) have been gaining momentum as the deep representations in the hyperbolic space provide high fidelity embeddings with few dimensions, especially for data possessing hierarchical structure. Such a hyper...

Non-Local Graph Neural Networks.

IEEE transactions on pattern analysis and machine intelligence
Modern graph neural networks (GNNs) learn node embeddings through multilayer local aggregation and achieve great success in applications on assortative graphs. However, tasks on disassortative graphs usually require non-local aggregation. In addition...

Structured Multimodal Attentions for TextVQA.

IEEE transactions on pattern analysis and machine intelligence
Text based Visual Question Answering (TextVQA) is a recently raised challenge requiring models to read text in images and answer natural language questions by jointly reasoning over the question, textual information and visual content. Introduction o...

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...