Discovering Interaction Patterns in Online Learning Environments : A Learning Analytics Research

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Recent advancements in deep learning have revolutionized 3D computer vision, enabling the extraction of intricate 3D information from 2D images and video sequences. This thesis explores the application of deep learning in three crucial challenges of 3D com ...

We present a new algorithm for imitation learning in infinite horizon linear MDPs dubbed ILARL which greatly improves the bound on the number of trajectories that the learner needs to sample from the environment. In particular, we re- move exploration assu ...

This paper presents a comparison of machine learning (ML) methods used for three-dimensional localization of partial discharges (PD) in a power transformer tank. The study examines ML and deep learning (DL) methods, ranging from support vector machines (SV ...

Clustering in education, particularly in large-scale online environments like MOOCs, is essential for understanding and adapting to diverse student needs. However, the effectiveness of clustering depends on its interpretability, which becomes challenging w ...

In the rapidly evolving landscape of machine learning research, neural networks stand out with their ever-expanding number of parameters and reliance on increasingly large datasets. The financial cost and computational resources required for the training p ...

Computing servers have played a key role in developing and processing emerging compute-intensive applications in recent years. Consolidating multiple virtual machines (VMs) inside one server to run various applications introduces severe competence for limi ...

In light of the challenges posed by climate change and the goals of the Paris Agreement, electricity generation is shifting to a more renewable and decentralized pattern, while the operation of systems like buildings is increasingly electrified. This calls ...

Machine learning has provided a means to accelerate early-stage drug discovery by combining molecule generation and filtering steps in a single architecture that leverages the experience and design preferences of medicinal chemists. However, designing mach ...

Designing novel materials is greatly dependent on understanding the design principles, physical mechanisms, and modeling methods of material microstructures, requiring experienced designers with expertise and several rounds of trial and error. Although rec ...

Computer simulations are often used as support material for science education, as they can engage students through inquiry-based learning, promote their active interaction in the experimentation phase, and help them visualize abstract concepts. For instanc ...

We discuss two extensions to a recently introduced theory of arrays, which are based on considerations coming from the model theory of power structures. First, we discuss how the ordering relation on the index set can be expressed succinctly by referring t ...

Discovering Interaction Patterns in Online Learning Environments : A Learning Analytics Research

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Advancing Self-Supervised Deep Learning for 3D Scene Understanding

Imitation Learning in Discounted Linear MDPs without exploration assumptions

Partial discharge localization in power transformer tanks using machine learning methods

Interpret3C: Interpretable Student Clustering Through Individualized Feature Selection

Random matrix methods for high-dimensional machine learning models

CloudProphet: A Machine Learning-Based Performance Prediction for Public Clouds

Quantifying the Unknown: Data-Driven Approaches and Applications in Energy Systems

Machine learning-aided generative molecular design

Text-to-Microstructure Generation Using Generative Deep Learning

Teacher-Mediated and Student-Led Interaction with a Physics Simulation: Effects on the Learning Experience

Succinct ordering and aggregation constraints in algebraic array theories