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Machine learning models (e.g., neural networks) achieve high accuracy in wind power forecasting, but they are usually regarded as black boxes that lack interpretability. To address this issue, the paper proposes a glass-box model that combines high accurac ...

This data set contains the data collected during the FNS project Green Piezo (Grant no. 179064) in association with the recent publication entitled “3D printing of customizable transient bioelectronics and sensors”. This work aims to study and demonstrate ...

Recent progress in computer vision has been driven by a simple yet transformative observation: the potential of 30-year-old neural networks can be unlocked by massively scaling their parameters and the amount of human-labeled training data. This breakthrou ...

There is an increasing interest in a fast-growing machine learning technique called Federated Learning (FL), in which the model training is distributed over mobile user equipment (UEs), exploiting UEs' local computation and training data. Despite its advan ...

The current machine learning paradigm relies on continuous representations and fixed neural network architectures to approximate environmental structures, leading to challenges with continual learning, internal structure design, and goal-directed behavior ...

Proteins are the molecular machines of life, driving essential biological processes such as enzymatic catalysis, cellular signaling, and immune responses. The ability to design entirely new proteins with desired structures and functions has immense implica ...

Over a third of the world 's population owns a smartphone. As generic computing devices that support a large and heterogeneous collection of mobile applications (apps), smartphones provide a plethora of functionalities and services to billions of users. B ...

Millions of users rely on mobile health (mHealth) apps to manage their wellness and medical conditions. Although the popularity of such apps continues to grow, several privacy and security challenges can hinder their potential. In particular, the simple fa ...

When arranged in a crossbar configuration, resistive memory devices can be used to execute Matrix-Vector Multiplications (MVMs), the most dominant operation of many Machine Learning (ML) algorithms, in constant time complexity. Nonetheless, when performing ...

This thesis focuses on understanding and improving the reasoning capabilities of neural networks. It develops theoretical results and empirical analyses to uncover reasoning potential and limitations, leveraging these insights to guide the design of improv ...

Deep learning has shown remarkable potential for industrial applications, particularly in predictive maintenance and condition monitoring. A key challenge in this domain is prognostics, specifically predicting the Remaining Useful Life (RUL) of assets - a ...

Motivation Many machine learning (ML) models developed to classify phenotype from gene expression data provide interpretations for their decisions, with the aim of understanding biological processes. For many models, including neural networks, interpretati ...

Cyclic peptides are attractive for drug discovery due to their excellent binding properties and the potential to cross cell membranes. However, by far not all cyclic peptides are cell permeable, and measuring or predicting their membrane permeability is no ...

Data augmentation is vital in deep learning for enhancing model robustness by artificially expanding training datasets. However, advanced methods like CutMix blend images and assign labels based on pixel ratios, often introducing label noise by neglecting ...

Machine learning and artificial intelligence methods have achieved remarkable success, matching and even surpassing human capabilities in various complex tasks. However, many demonstrations have generally neglected a critical part of the intelligence that ...

In this thesis, we explore the application of data mining and machine learning techniques to several practical problems. These problems have roots in various fields such as social science, economics, and political science. We show that computer science tec ...

An universal remote powering and communication system is presented for the implantable medical devices. The system be interfaced with different sensors or actuators. A mobile external unit controls the operation of the implantable chip and reads the sensor ...

Semi-supervised semantic segmentation methods leverage unlabeled data by pseudo-labeling them. Thus the success of these methods hinges on the reliability of the pseudo-labels. Existing methods mostly choose high-confidence pixels in an effort to avoid err ...

Where to go from here? Mobility prediction from instantaneous information

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Explainable modeling for wind power forecasting: A Glass-Box model with high accuracy

Dataset for '3D printing of customizable transient bioelectronics and sensors'

Exploiting Representation Similarities in Self-Supervised Learning for Vision Tasks

Federated Learning Over Wireless Networks: Convergence Analysis and Resource Allocation

Modelleyen: Continual Learning and Planning via Structured Modelling of Environment Dynamics

Computational design of complex protein folds and soluble analogs of membrane proteins

Privacy-Enhancing Technologies for Mobile Applications and Services

HideMyApp: Hiding the Presence of Sensitive Apps on Android

LIONHEART: A Layer-based Mapping Framework for Heterogeneous Systems with Analog In-Memory Computing Tiles

Learning to Reason with Neural Networks: Principles and Methods

HideMyApp : Hiding the Presence of Sensitive Apps on Android

Overcoming Distribution Shifts and Imbalance Challenges in Representation Learning for Deep Regression Models

HideMyApp : Hiding the Presence of Sensitive Apps on Android

A comparative analysis of gene expression profiling by statistical and machine learning approaches

Bulk measurement of membrane permeability for random cyclic peptides in living cells to guide drug development

SaliencyMix plus : Noise-Minimized Image Mixing Method With Saliency Map in Data Augmentation

Sequential Reasoning with Socially Caused Beliefs

Combine and Conquer : Mining Social Systems for Prediction

A Remotely Powered Implantable Biomedical System With Location Detector

Weighting Pseudo-labels via High-Activation Feature Index Similarity and Object Detection for Semi-supervised Segmentation