A Clustering Method Based on Soft Learning of Model (Prototype) and Dissimilarity Metrics
Graph Chatbot
Chattez avec Graph Search
Posez n’importe quelle question sur les cours, conférences, exercices, recherches, actualités, etc. de l’EPFL ou essayez les exemples de questions ci-dessous.
AVERTISSEMENT : Le chatbot Graph n'est pas programmé pour fournir des réponses explicites ou catégoriques à vos questions. Il transforme plutôt vos questions en demandes API qui sont distribuées aux différents services informatiques officiellement administrés par l'EPFL. Son but est uniquement de collecter et de recommander des références pertinentes à des contenus que vous pouvez explorer pour vous aider à répondre à vos questions.
SARS-CoV-2 spreads via close contact during daily activities, forming clusters of cases mainly in households and workplaces. A crucial challenge to contain the spread lies in the early detection of these outbreak clusters, and the localisation and isolatio ...
This paper presents an early-stage application of the design science research (DSR) method to obtain a new idea selection approach, which uses clustering to filter ideas while taking into account the seeker’s goals and the learning dynamics. Most of previo ...
Modeling and predicting student learning in computer-based environments often relies solely on sequences of accuracy data. Previous research suggests that it does not only matter what we learn, but also how we learn. The detection and analysis of learning ...
Despite the importance of understanding the historical dynamics of ecosystem services (ES), littleresearch has focused on a historical, spatially explicit, assessment of ES supply. This research is aimed at understanding the spatial patterns and potential ...
We study the problem of constructing epsilon-coresets for the (k, z)-clustering problem in a doubling metric M(X, d). An epsilon-coreset is a weighted subset S subset of X with weight function w : S -> R->= 0, such that for any k-subset C is an element of ...
In Virtual Reality (VR) applications, understanding how users explore the omnidirectional content is important to optimize content creation, to develop user-centric services, or even to detect disorders in medical applications. Clustering users based on th ...
Clustering is a method for discovering structure in data, widely used across many scientific disciplines. The two main clustering problems this dissertation considers are K-means and K-medoids. These are NP-hard problems in the number of samples and cluste ...
EPFL2018
,
Galaxy cluster counts in bins of mass and redshift have been shown to be a competitive probe to test cosmological models. This method requires an efficient blind detection of clusters from surveys with a well-known selection function and robust mass estima ...
EDP Sciences2019
We prove a quantitative estimate on the number of certain singularities in almost minimizing clusters. In particular, we consider the singular points belonging to the lowest stratum of the Federer-Almgren stratification (namely, where each tangent cone doe ...
2019
,
This paper describes the speaker diarization systems developed for the Second DIHARD Speech Diarization Challenge (DIHARD II) by the Speed team. Besides describing the system, which considerably outperformed the challenge baselines, we also focus on the le ...