[ad_1] Introduction: Tensor factorization is a powerful tool used in signal processing to extract relevant information from high-dimensional data. By decomposing a tensor into a set of lower-dimensional factors, tensor factorization allows for efficient representation,…
Read More
[ad_1] Introduction: Graph Neural Networks (GNNs) have gained popularity in recent years for their ability to effectively model graph-structured data. One of the emerging applications of GNNs is in traffic prediction, where they can be…
Read More
[ad_1] Introduction: Gaussian Processes (GPs) are a powerful tool for modeling spatial data. They allow for the flexible modeling of complex spatial patterns and relationships, making them particularly well-suited for tasks such as spatial interpolation,…
Read More
[ad_1] Introduction: Bayesian Deep Learning has emerged as a powerful tool for uncertainty quantification in machine learning models. By incorporating Bayesian principles into deep learning algorithms, researchers can not only make more accurate predictions but…
Read More
[ad_1] Introduction: Distributed representation learning for multimodal data is a cutting-edge research area that aims to develop efficient and effective algorithms for extracting meaningful representations from data that combine information from multiple modalities, such as…
Read More
[ad_1] Introduction: Multi-Agent Reinforcement Learning (MARL) has gained significant attention in recent years due to its ability to enable autonomous agents to learn and adapt in dynamic and complex environments. In the field of swarm…
Read More
[ad_1] Introduction: Graph embedding techniques have gained popularity in the field of molecular data analysis due to their ability to capture complex relationships and patterns within molecular structures. These techniques involve transforming molecular data into…
Read More
[ad_1] Introduction: Domain generalization is a critical component of robust machine learning, allowing models to perform well on unseen data from different domains. In this thesis, we will explore the concept of domain generalization and…
Read More
[ad_1] Introduction: Adversarial attacks and defenses for image recognition have become increasingly important in the field of artificial intelligence and computer vision. Adversarial attacks refer to the manipulation of input data in order to trick…
Read More
[ad_1] Introduction: Few-shot learning is an emerging area in machine learning that focuses on training models with only a small amount of labeled data. This approach is particularly valuable for applications where collecting extensive labeled…
Read More