1. Supervised learning algorithms.Insupervised learning, the algorithm learns from a labeled data set, where the input data is associated with the correct output. This approach is used for tasks such as classification and regression problems such as linear regression, time series regression and logis...
We study the behavior of two kernel based sensor fusion algorithms, nonparametric canonical correlation analysis (NCCA) and alternating diffusion (AD), under the nonnull setting that the clean datasets collected from two sensors are modeled by a common low-dimensional manifold embe...
The use of pseudo-labels can benefit various learning-based algorithms since it can significantly reduce the labelling cost. The key to the proposed method is to take advantage of the intrinsic temporal consistency between consecutive frames to improve the generated labels by refining them with a ...
Extensive knowledge of statistics, calculus or algebra to work withalgorithmsand an understanding of probability to interact with some of AI's most common machine learning models, including naive Bayes, hidden Markov and Gaussian mixture models. Proficiency with popular programming languages, such as Py...
In this reinforcement learning tutorial, I’ll show how we can use PyTorch to teach a reinforcement learning neural network how to play Flappy Bird. But first, we’ll need to cover a number of building blocks. Machine learning algorithms can roughly be divided into two parts: Traditional learn...
(Mach Learn 79(1–2):151–175, 2010) and the well-known domain-aligning algorithm based on this work using Domain Adversarial Neural Networks (DANN) presented by Ganin and Lempitsky (in International conference on machine learning, pp 1180–1189). Recently, multiple variants of DANN have ...
Generally speaking, the larger the buffer size, the better the read performance. But do not set a value too large, especially in container environments where the maximum memory is limited. It’s necessary to set the buffer size based on the actual workload and find a relatively reasonable val...
It provides access to recent research in cryptology and explores many subjects of security (e.g. Ciphers, Algorithms, SSL/TLS protocols). General disclaimer When I was studying architecture of HTTP servers I became interested in NGINX. I found a lot of information about it but I've never ...
Deep learning models for general computer vision tasks need to perform well on a large diversity of test images, and therefore require a large diversity of training images. This is not the case for a typical cell segmentation application, where a model only has to work well on a narrow ...