Collecting Experience Data from Remotely Hosted Learning Applications

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One major challenge in distributed learning is to efficiently learn for each client when the data across clients is heterogeneous or non i.i.d (not independent or identically distributed). This provides a significant challenge as the data of the other clie ...

We introduce an approach to supporting high-dimensional data cubes at interactive query speeds and moderate storage cost. Our approach is based on binary(-domain) data cubes that are judiciously partially materialized; the missing information can be quickl ...

Human babies have a natural desire to interact with new toys and objects, through which they learn how the world around them works, e.g., that glass shatters when dropped, but a rubber ball does not. When their predictions are proven incorrect, such as whe ...

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 ...

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 ...

In this thesis we explore the applications of projective geometry, a mathematical theory of the relation between 3D scenes and their 2D images, in modern learning-based computer vision systems. This is an interesting research question which contradicts the ...

Wearable and implantable medical devices are of great importance in diagnosis and treatment as they provide continuous monitoring and data collection. Considering the comfort of the patient and ease-of-operation, these devices require wireless data transmi ...

Traditional example-based learning methods are often limited by static, expert-created content. Hence, they face challenges in scalability, engagement, and effectiveness, as some learners might struggle to relate to the examples or find them relevant. To a ...

We introduce the concept of "universal"password model - a password model that, once pre-trained, can automatically adapt its guessing strategy based on the target system. To achieve this, the model does not need to access any plaintext passwords from the t ...

This repository contains microphysics routines, scripts, and processed data from the Weather Research and Forecasting (WRF) model simulations presented in the paper "RaFSIP: Parameterizing ice multiplication in models using a machine learning approach", by ...

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 ...

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 ...

Modern computing has enhanced our understanding of how social interactions shape collective behaviour in animal societies. Although analytical models dominate in studying collective behaviour, this study introduces a deep learning model to assess social in ...

Dataset corresponding to the following manuscript: Slagter, A., Setyadji, J.A., Vogt, E.L. et al. Nanoindentation Hardness and Modulus of Al2O3–SiO2–CaO and MnO–SiO2–FeO Inclusions in Iron. Metall Mater Trans A (2024). https://doi.org/10.1007/s11661-024-0 ...

Distributed learning is the key for enabling training of modern large-scale machine learning models, through parallelising the learning process. Collaborative learning is essential for learning from privacy-sensitive data that is distributed across various ...

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 ...

This note describes a method to train a Neural Network so that it approximates a control Lyapunov function for a nonlinear system in affine form. The network is trained in a physics-informed fashion, as the training data are generated by enforcing the nega ...

This document contains all the data and the details of the analysis used in the manuscript titled " Control of Ge island coalescence for the formation of nanowires on silicon." https://doi.org/10.1039/D3NH00573A ...

Earth scientists study a variety of problems with remote sensing data, but they most often consider them in isolation from each other, which limits information flows across disciplines. In this work, we present METEOR, a meta-learning methodology for Earth ...

Collecting Experience Data from Remotely Hosted Learning Applications

Chattez avec Graph Search

Data Heterogeneity in Linear Distributed Estimation and Learning

High-dimensional Data Cubes

Seeking the new, learning from the unexpected: Computational models of surprise and novelty in the brain

Robust machine learning for neuroscientific inference

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

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

Incorporating Projective Geometry into Deep Learning

An area efficient inductorless super-regenerative receiver front-end for biomedical implant devices

GELEX: Generative AI-Hybrid System for Example-Based Learning

Universal Neural-Cracking-Machines: Self-Configurable Password Models from Auxiliary Data

Data and scripts for the RaFSIP scheme

Resource-Efficient Continual Learning for Personalized Online Seizure Detection

Social Opinion Formation and Decision Making Under Communication Trends

Predicting the long-term collective behaviour of fish pairs with deep learning

Nanoindentation hardness and modulus of Al2O3-SiO2-CaO and MnO-SiO2-FeO inclusions in iron

Optimization Algorithms for Decentralized, Distributed and Collaborative Machine Learning

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

Stabilization of nonlinear systems by neural Lyapunov approximators and Sontag's formula

Data set for control of Ge island coalescence for the formation of nanowires on silicon

Meta-learning to address diverse Earth observation problems across resolutions