Publications related to Personalizing Learning and Empowering Educators through Multi-Dimensional Clustering, Teacher Dashboards, and AI-Hybrid Learning Systems

Related publications (20)

Machine learning enhanced atom probe tomography analysis

Atom probe tomography (APT) is a burgeoning characterization technique that provides compositional mapping of materials in three-dimensions at near-atomic scale. Since its significant expansion in the past 30 years, we estimate that one million APT dataset ...

2026

Hadamard product in deep learning: Introduction, Advances and Challenges

Volkan Cevher, Yongtao Wu

While convolution and self-attention mechanisms have dominated architectural design in deep learning, this survey examines a fundamental yet understudied primitive: the Hadamard product. Despite its widespread implementation across various applications, th ...

2025

synMARSS—An End-To-End Platform for the Parametric Generation of Synthetic In Vivo Magnetic Resonance Spectra

André Döring

Synthetic magnetic resonance spectra (MRS) are mathematically generated spectra which can be used to investigate the assumptions of data analysis strategies, optimize experimental design, and as training data for the development and validation of machine l ...

2025

A High Dimensional Statistical Model for Adversarial Training: Geometry and Trade-Offs

Florent Gérard Krzakala, Bruno Loureiro, Matteo Vilucchio

This work investigates adversarial training in the context of margin-based linear classifiers in the high-dimensional regime where the dimension d and the number of data points n diverge with a fixed ratio α = n/d. We introduce a tractable mathematical mod ...

2025

Semantic Segmentation of Benthic Classes in Reef Environments Using a Large Vision Transformer

Devis Tuia, Jonathan Paul Sauder, Charlotte Louise Sertic

Coral reefs are crucial for biodiversity and provide vital resources for humankind. But despite such a central role, they are confronted to increasing threats linked to climate change, pollution, and local stressors. To ensure effective conservation, effic ...

Springer Science and Business Media Deutschland GmbH2025

Identifying storytelling in job interviews using deep learning

Mutian He

Structured interviews often include past-behavior questions inviting applicants to recount a past work experience. While optimal responses to these questions should take the form of a story, applicants struggle to produce them extemporaneously. Asynchronou ...

2025

Learning-Related Emotions in First-Year International Students: Investigating Affective, Cognitive, Motivational, and Physiological Components

Alexandra Corina Niculescu

The component process model of achievement emotions views learning emotions as interconnected psychological processes comprising affective, cognitive, motivational, and physiological components. Based on this framework, the Achievement Emotion Questionnair ...

2025

Assessment of fine-tuned large language models for real-world chemistry and material science applications

Berend Smit, Anne-Clémence Corminboeuf, Rubén Laplaza Solanas, Kevin Maik Jablonka, Sauradeep Majumdar, Joren Jozef M. Van Herck, Beatriz Bueno Mouriño, Seyed Amirmohammad Elahi

The current generation of large language models (LLMs) has limited chemical knowledge. Recently, it has been shown that these LLMs can learn and predict chemical properties through fine-tuning. Using natural language to train machine learning models opens ...

ROYAL SOC CHEMISTRY2024

Enhancing Explainability and Performance in AI-Driven Face Recognition and Deepfake Detection

Yuhang Lu

The rapid advancement of artificial intelligence (AI) has greatly influenced numerous research areas, leading to significant breakthroughs in human face-related technologies, particularly in face recognition and deepfake detection. While offering substanti ...

EPFL2024

LiDAR-NeRF: Novel LiDAR View Synthesis via Neural Radiance Fields

Mathieu Salzmann

We introduce a new task, novel view synthesis for LiDAR sensors. While traditional model-based LiDAR simulators with style-transfer neural networks can be applied to render novel views, they fall short of producing accurate and realistic LiDAR patterns bec ...

Association for Computing Machinery, Inc2024

Higher-order knowledge-enhanced recommendation with heterogeneous hypergraph multi-attention

Thanh Trung Huynh

Recent advancements in recommender systems have focused on integrating knowledge graphs (KGs) to leverage their auxiliary information. The core idea of KG-enhanced recommenders is to incorporate rich semantic information for more accurate recommendations. ...

2024

Towards Language Learning From Passive Exposures To In-Context Examples

Lars Henning Klein

This thesis explores innovative methods for language learning through passive exposure to in-context examples. We aim to reduce the discipline, willpower, and time investment required to learn a new language by integrating language learning into existing d ...

EPFL2024

Function Learning with Financial Applications

Paolo Colusso

This thesis sets itself the goal of investigating function learning approaches. The topic is extremely relevant today, since the abundance of data, jointly with the technological progress which has enhanced computing power, now allow to represent functions ...

EPFL2024

Positive-Unlabeled Constraint Learning for Inferring Nonlinear Continuous Constraints Functions From Expert Demonstrations

Baiyu Peng, Aude Billard

Planning for diverse real-world robotic tasks necessitates to know and write all constraints. However, instances exist where these constraints are either unknown or challenging to specify accurately. A possible solution is to infer the unknown constraints ...

2024

Knowledge-Aware Visual Question Generation for Remote Sensing Images

Devis Tuia, Javiera Francisca Castillo Navarro, Li Mi

With the rapid development of remote sensing image archives, asking questions about images has become an effective way of gathering specific information or performing image retrieval. However, automatically generated image-based questions tend to be simpli ...

IEEE2024

Unsupervised quality monitoring of metal additive manufacturing using Bayesian adaptive resonance

Patrik Willi Hoffmann

Metal additive manufacturing is a recent breakthrough technology that promises automated production of complex geometric shapes at low operating costs. However, its potential is not yet fully exploited due to the low reproducibility of quality in mass prod ...

CELL PRESS2024

Truly No-Regret Learning in Constrained MDPs

Volkan Cevher

Constrained Markov decision processes (CMDPs) are a common way to model safety constraints in reinforcement learning. State-of-the-art methods for efficiently solving CMDPs are based on primal-dual algorithms. For these algorithms, all currently known regr ...

2024

Optimizing Context-Enhanced Relational Joins

Anastasia Ailamaki, Viktor Sanca

Collecting data, extracting value, and combining insights from relational and context-rich sources of many modalities in data processing pipelines presents a challenge for traditional relational DBMS. While relational operators enable declarative and optim ...

IEEE2024

Combining general and personal models for epilepsy detection with hyperdimensional computing

David Atienza Alonso, Tomas Teijeiro Campo, Una Pale

Epilepsy is a highly prevalent chronic neurological disorder with great negative impact on patients' daily lives. Despite this there is still no adequate technological support to enable epilepsy detection and continuous outpatient monitoring in everyday li ...

Elsevier2024

AMAE: Adaptation of Pre-trained Masked Autoencoder for Dual-Distribution Anomaly Detection in Chest X-Rays

Jean-Philippe Thiran, Seyedbehzad Bozorgtabar

Unsupervised anomaly detection in medical images such as chest radiographs is stepping into the spotlight as it mitigates the scarcity of the labor-intensive and costly expert annotation of anomaly data. However, nearly all existing methods are formulated ...

Springer International Publishing Ag2023