[ad_1] Introduction: Social media has become an integral part of our daily lives, with millions of users sharing their thoughts, opinions and emotions on various platforms. Sentiment analysis, also known as opinion mining, is a…
Read More
[ad_1] Introduction: Imbalanced data refers to a situation where the distribution of classes within a dataset is skewed, with one class significantly outnumbering the other(s). This imbalance can pose a challenge for machine learning algorithms,…
Read More
[ad_1] Graph Neural Networks (GNNs) have gained significant attention in recent years for their ability to effectively model relational data. They are neural networks that operate on graph-structured data, allowing them to capture complex relationships…
Read More
[ad_1] Introduction: Reinforcement Learning (RL) is a popular machine learning technique that has been used in a variety of fields, including game playing, robotics, and recommendation systems. In recent years, RL has also been applied…
Read More
[ad_1] Introduction: Disentangled representation learning has emerged as a powerful tool in machine learning for disentangling underlying factors of variation in data. By learning representations that separate different sources of variation, disentangled representation learning can…
Read More
[ad_1] Introduction: Meta-Learning for Neural Architecture Search is an emerging field in machine learning that aims to automate the process of designing neural network architectures. This thesis will explore various meta-learning techniques for neural architecture…
Read More
[ad_1] Introduction: Causal inference is the process of determining the causal relationship between variables in a given system. Counterfactual reasoning is a powerful tool in causal inference, as it allows researchers to analyze what might…
Read More
[ad_1] Introduction: Uncertainty quantification is a crucial aspect of predictive maintenance, as it allows for the assessment of the reliability and accuracy of predictive models. By quantifying uncertainties, maintenance planners can make informed decisions about…
Read More
[ad_1] Introduction: Adversarial attacks have become a significant concern in the field of machine learning and artificial intelligence, as attackers can manipulate models to produce incorrect predictions by introducing small, carefully crafted perturbations to inputs.…
Read More
[ad_1] Introduction: Topological Data Analysis (TDA) has emerged as a powerful tool in the field of scientific visualization, allowing researchers to analyze complex datasets and extract meaningful patterns and structures. By studying the topological properties…
Read More