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I frequently get contacted by students inquiring about projects, so this is an outline of the conditions and opportunities for UW students. That is, this article focuses on students who are currently enrolled in undergraduate or graduate studies at Madison.
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In the landscape of modern Machine Learning, Large Language Models (LLMs) have risen to prominence, capturing the attention of both the public and researchers alike. These versatile tools, like ChatGPT, hold great promise for enhancing your paper-writing journey. However, a word of caution: always ensure compliance with conference guidelines regarding their use. Below, we unveil the art of leveraging LLMs for research while maintaining a professional and impactful approach:
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As part of our latest research work, we have collected and processed one large database, coined 2MF^2. Our dataset includes over 19 million frames of human faces (over 11 thousand videos in total).
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Instead of creating another tutorial about Variational Autoencoders (VAE), I will share my experience on the parts that introduced me to the topic.
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This post is devoted to constructing a simple binary classifier from scratch. A classifier is a tool that enables the algorithm to categorise different objects in their respective ‘class’. A binary classifier is a classifier with two categories, e.g. is this animal cat or dog?
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While preparing for a seminar of the EESTech Challenge (official site, EESTEC news), it required a while to find some reliable sources for the history of Machine Learning. Even though Machine Learning has become a buzzword recently, there are surprisingly scarce material on its history, so I will share these references here accompanied with few comments.