High Dimension Data Analysis - A tutorial and review for Dimensionality Reduction Techniques
High Dimension Data Analysis - A tutorial and review for Dimensionality Reduction Techniques
This article explains and provides a comparative study of a few techniques for dimensionality reduction. It dives into the mathematical explanation of several feature selection and feature transformation techniques, while also providing the algorithmic representation and implementation of some other techniques. Lastly, it also provides a very brief review of various other works done in this space.
The use of Mathpix OCR with EDICO scientific editor to help blind Students in STEM education
The use of Mathpix OCR with EDICO scientific editor to help blind Students in STEM education
In this tutorial we'll show how Mathpix OCR is helpful to instantly transpose math and science assignments both in braille and speech. We'll use the free EDICO Scientific Editor to demonstrate how a math assignment can be imported using Mathpix technology, and how it can be solved using a Refreshable Braille Display.
Event Camera: the Next Generation of Visual Perception System
Event Camera: the Next Generation of Visual Perception System
Event camera can extend computer vision to scenarios where it is currently incompetent. In the following decades, it is hopeful that event cameras will be mature enough to be mass-produced, to have dedicated algorithms, and to show up in widely-used products.
Group Equivariant Convolutional Networks in Medical Image Analysis
Group Equivariant Convolutional Networks in Medical Image Analysis
This is a brief review of G-CNNs' applications in medical image analysis, including fundamental knowledge of group equivariant convolutional networks, and applications in medical images' classification and segmentation.
Co-Tuning: An easy but effective trick to improve transfer learning
Co-Tuning: An easy but effective trick to improve transfer learning
Transfer learning is a popular method in the deep learning community, but it is usually implemented naively (eg. copying weights as initialization). Co-Tuning is a recently proposed technique to improve transfer learning that is easy to implement, and effective to a wide variety of tasks.
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