[ad_1] Introduction: Multi-Task Learning (MTL) is an approach in machine learning where multiple tasks are solved jointly to improve the prediction performance of each individual task. Transfer Learning is a related concept, where knowledge learned…
Read More
[ad_1] Introduction: Reinforcement learning has shown significant potential for autonomous driving applications, allowing vehicles to learn complex driving tasks through trial and error. This technology has the capability to improve driving safety, efficiency, and overall…
Read More
[ad_1] Introduction: Bayesian non-parametric models have gained popularity in recent years as a flexible approach to modeling complex data sets. Unlike traditional parametric models, Bayesian non-parametric models do not assume a fixed number of parameters,…
Read More
[ad_1] Introduction to Gaussian Processes for Regression and Classification: Gaussian Processes (GPs) are a powerful machine learning technique for regression and classification tasks. Unlike traditional methods that assume a specific functional form for the data,…
Read More
[ad_1] Introduction: Kernel methods are powerful tools in machine learning and data analysis that enable the modeling of non-linear relationships in data. These methods transform data into a higher-dimensional space where it may be easier…
Read More
[ad_1] Collaborative filtering is a popular technique used in recommendation systems to provide personalized recommendations to users based on their preferences and behaviors. This approach involves collecting and analyzing user data to identify patterns and…
Read More
[ad_1] Introduction: Meta-Reinforcement Learning (Meta-RL) is a cutting-edge technique that empowers agents to rapidly adapt to new tasks and environments through learning from past experiences. This thesis explores the application of Meta-RL for rapid adaptation…
Read More
[ad_1] Introduction: Secure Multi-Party Computation (SMPC) is a cryptographic technique that allows multiple parties to jointly compute a function over their private inputs without revealing any individual input to the other parties. This technology has…
Read More
[ad_1] Introduction: Curriculum learning is a machine learning technique that focuses on training models in a sequential manner where the complexity of the tasks increases gradually. This approach has been shown to be effective in…
Read More
[ad_1] Introduction: Automated Feature Engineering for Machine Learning is a field of study that focuses on developing algorithms and techniques to automatically extract and create predictive features from raw data. By automating this process, researchers…
Read More