Modeling and Analyzing Inquiry Strategies in Open-Ended Learning Environments

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Contemporary AI faces important limitations that remain unresolved despite significant successes. Chief among these are the inability to acquire new knowledge without destroying old, the incomprehensible and nonengineerable nature of internal representatio ...

Deploying energy-efficient deep neural networks on energy-constrained edge devices is an important research topic in both machine learning and circuit design communities. Both artificial neural networks (ANNs) and spiking neural networks (SNNs) have been p ...

High-dimensional data must be highly structured to be learnable. Although the compositional and hierarchical nature of data is often put forward to explain learnability, quantitative measurements establishing these properties are scarce. Likewise, accessin ...

We present a reinforcement learning framework for constructing assemblies composed of rigid parts, which are commonly seen in many historical masonry buildings and bridges. Traditional construction methods for such structures often depend on dense scaffold ...

As one of the major threats to women's health worldwide, breast cancer requires early diagnosis and accurate classification, since they are key to optimizing therapeutic interventions and ensuring precise prognosis. Recently, deep learning has demonstrated ...

Many empirical studies have provided evidence for the emergence of algorithmic mechanisms (abilities) in the learning of language models, that lead to qualitative improvements in the model capabilities. Yet, a theoretical characterization of how such mecha ...

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 ...

Modern neuroscience research is generating increasingly large datasets, from recording thousands of neurons over long timescales to behavioral recordings of animals spanning weeks, months, or even years. Despite a great variety in recording setups and expe ...

Machine learning (ML) enables artificial intelligent (AI) agents to learn autonomously from data obtained from their environment to perform tasks. Modern ML systems have proven to be extremely effective, reaching or even exceeding human intelligence. Altho ...

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 ...

Epilepsy, a major neurological disease, requires careful diagnosis and treatment. However, the detection of epileptic seizures remains a significant challenge. Current clinical practice relies on expert analysis of EEG signals, a process that is time-consu ...

Emotion recognition through artificial intelligence and smart sensing of physical and physiological signals (affective computing) is achieving very interesting results in terms of accuracy, inference times, and user-independent models. In this sense, there ...

Students learn more when they are actively engaged in the learning process. While hands-on activities, labs and projects are moments when students are active, the learning benefits can be amplified with coaching strategies. This activity will enable studen ...

Deploying compute-intensive deep neural networks (DNNs) on resource-constrained end devices has become a prominent trend, enabling localized intelligence. However, efficiently deploying these DNNs at scale poses challenges. To address this, extensive resea ...

There is a growing recognition that electronic band structure is a local property of materials and devices, and there is steep growth in capabilities to collect the relevant data. New photon sources, from small-laboratory-based lasers to free electron lase ...

Buildings significantly impact urban energy consumption. Mobile passive solutions, such as manual solar protections, can mitigate heat gains, but their effectiveness depends on occupants’ decisions. Analyzing occupant adjustments of manual systems is chall ...

In this thesis we present and analyze approximation algorithms for three different clustering problems. The formulations of these problems are motivated by fairness and explainability considerations, two issues that have recently received attention in the ...

This work studies the learning process over social networks under partial and random information sharing. In traditional social learning models, agents exchange full belief information with each other while trying to infer the true state of nature. We stud ...

Unsupervised graph representation learning aims to learn low-dimensional node embeddings without supervision while preserving graph topological structures and node attributive features. Previous Graph Neural Networks (GNN) require a large number of labeled ...

A range of behavioral and contextual factors, including eating and drinking behavior, mood, social context, and other daily activities, can significantly impact an individual's quality of life and overall well-being. Therefore, inferring everyday life aspe ...

Modeling and Analyzing Inquiry Strategies in Open-Ended Learning Environments

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Foundations of a New Learning Paradigm in AI Grounded in the Principles of Evolutionary Developmental Biology

Eflop: a Sparsity-aware Metric for Evaluating Computational Cost in Spiking and Non-spiking Neural Networks

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Learning to Assemble with Alternative Plans

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A phase transition between positional and semantic learning in a solvable model of dot-product attention

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

Robust machine learning for neuroscientific inference

Few-shot Learning for Efficient and Effective Machine Learning Model Adaptation

Interpret3C: Interpretable Student Clustering Through Individualized Feature Selection

Resource-Efficient Continual Learning for Personalized Online Seizure Detection

Personalized Clustering for Emotion Recognition Improvement

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RAPNet: Resolution-Adaptive and Predictive Early Exit Network for Efficient Image Recognition

Transfer learning application of self-supervised learning in ARPES

Machine-learning to analyze human-building interactions: How do people use mobile solar protections?

Fairness and Explainability in Clustering Problems

Social Opinion Formation and Decision Making Under Communication Trends

Unsupervised Graph Representation Learning with Cluster-aware Self-training and Refining

Generalization and Personalization of Machine Learning for Multimodal Mobile Sensing in Everyday Life