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Title Description Supervisors Status
Adaptive geometric neural networks Background: Networks are powerful mathematical objects which are widely used to model systems composed of interacting constituents such as those … Adám Gosztolai Finished
A Generalized Representation of Chemical Entities for Machine Learning Tasks What's it about. Machine learning on big chemical data has recently produced intriguing results and was shown to be capable … Daniel Probst Finished
Graph Neural Networks applied to team sports Project Description Team sports rely on highly tactical and complex strategies often suggested by coaching staffs. For instance, basketball is … Ali Hariri Finished
Heterogeneous Message Passing Neural Network for Semi-Supervised Learning Task Node attribute completion in knowledge graphs. Description In recent years, only a few graph representation learning (GRL) approaches have … Eda Bayram Finished
Anomaly detection on large scale distributed sensors, combining signal processing and machine learning This project may be done as a company internship for a master student, for the summer 2021 instead of a … Benjamin Ricaud Finished
Deep Learning on Neuronal Morphologies Project description at https://www.epfl.ch/research/domains/bluebrain/deep-learning-on-neuronal-morphologies-internship/. Michaël Defferrard, Lida Kanari, Francesco Casalegno, Stanislav Schmidt Finished
Is my deep neural network good at memorizing? Project Description How much information have deep neural network weights memorized after training? One way of answering this question is … Konstantinos Pitas Finished
How flat is my minimum? Project Description Predicting how a deep neural network will perform on new datapoints after training is important in real applications. … Konstantinos Pitas Finished
Better Uncertainty Estimates for DNNs v2! Project Description Deriving uncertainty estimates for feedforward DNN predictions is critical in a number of tasks. For example you might … Konstantinos Pitas Finished
Development, assessment and optimization of an Active Nonlinear Electroacoustic Resonator Acoustic metamaterials (AMMs) and metasurfaces (AMSs) are engineered structures aimed at achieving unprecedented acoustic properties not available in nature. They … Xinxin Guo Finished
Room modes identification and characterization and its potential application Room modes can significantly impair the quality of sound diffusion, in particular at low-frequencies and in small auditoria. These low-frequency … Thach Pham Vu Finished
Development of a microphone array to track mobile noise sources Microphone arrays can be used to locate and track moving sound sources, thanks to advanced signal processing techniques. Such techniques … Benjamin Ricaud, Hervé Lissek Finished
Multi-actuated panel for active sound absorption The distributed mode loudspeaker (DML) technology is a flat panel loudspeaker from which sound is generated a set of electromechanical … Hervé Lissek Finished
Implementation of a low-cost real-time reverberation algorithm The LTS2-Acoustics is working on binaural audio synthesis for applications in hearing aid technologies. Our activities deal with the improvement … Vincent Grimaldi Finished
Geometric Deep Learning for Climate and Weather Modeling Climate change is one of the grand challenges facing humanity in the 21st century. Humankind can mainly act on two … Michaël Defferrard, Gionata Ghiggi Finished
Protein reconstruction from multiple images with Deep Learning Single-particle cryo-electron microscopy (cryo-EM) is a Nobel-prized technology that aims to characterize the 3D structure of proteins at the atomic … Michaël Defferrard, Laurène Donati Finished
Learning on manifolds In many applications, data lie on non-Euclidean manifolds. Examples on the sphere include planetary data (such as temperature, wind, aerial … Michaël Defferrard, Martino Milani Finished
Data-driven research on graph neural networks I am looking for motivated students that wish to work on the development of deep learning algorithms for graph data. … Andreas Loukas Finished
An empirical study of rotation equivariant neural networks The goal of the project is to empirically study the difference in performance of Neural Networks that are either equivariant … Michaël Defferrard Finished
Assessment of Low-rank approximation techniques in sound field reconstruction Room modes can significantly impair the quality of sound diffusion, in particular at low-frequencies and in small auditoria. In practice, … Thach Pham Vu Finished
Better uncertainty estimates for DNNs DNNs are increasingly used in sensitive environments such as healthcare. In these sensitive environments a number of important questions arise. … Konstantinos Pitas Finished
Geometric Deep Learning for Fluid Dynamics Use Machine Learning on graphs and manifolds to solve problems in computational fluid dynamics. Michaël Defferrard Finished
Music outside the head In nature and most daily life situations, sounds of the world are naturally perceived as coming from outside the head. … Vincent Grimaldi Finished
Spherical Convolutional Neural Networks In the last 5 years, the field of Machine Learning has been revolutionized by the success of Deep Learning. Thanks … Michaël Defferrard Finished
Implementation of a low computational cost reverb algorithm The LTS2-Acoustics is working on binaural audio synthesis for applications in hearing aid technologies. Our activities deal with the improvement … Gilles André Courtois, Vincent Grimaldi Finished