Introduction The explosion of data in recent years has led to an ever-increasing need for effective recommender systems. These systems are designed to analyze user behavior and preferences and provide recommendations for products, services, or content that are likely to be of interest to them. One of the most promising approaches to building these systems …
Building Recommender Systems with Graph Generative Models Read More »
Introduction In today’s digital age, data is everywhere, and with the rise of big data and machine learning, the number of features that can be collected is increasing rapidly. However, while having more data may seem like an advantage, it can often lead to feature overload, which can negatively impact the performance of models. Feature …
Visual anomaly detection is the process of identifying unusual or abnormal events or patterns within visual data. It is a critical technology that enables various industries to detect, diagnose, and solve problems that may go unnoticed otherwise. The ability to detect visual anomalies can provide significant benefits across multiple sectors, including manufacturing, healthcare, finance, and …
The Future of Visual Anomaly Detection: Emerging Trends and Technologies Read More »
Manifold learning is a class of techniques used to reduce the dimensionality of high-dimensional data. It does this by identifying and representing the underlying structure of the data in a lower-dimensional space, known as a manifold. These techniques are particularly useful for visualizing and analyzing complex datasets that cannot be easily understood in their raw …
Manifold Learning for Nonlinear Dimensionality Reduction Read More »
A conditional variational autoencoder (CVAE) is a type of generative model that can learn to generate new data samples that are similar to a training dataset, while also allowing you to specify certain desired properties or attributes of the generated samples. Here’s how a CVAE works: First, the CVAE encodes the input data into a …
