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Aurelien Lucchi

Aurelien Lucchi

D-Index & Metrics

Computer Science

D-Index
32
Citations
15705
World Ranking
12840
National Ranking
208

Overview

Aurelien Lucchi is affiliated with ETH Zurich in Switzerland, focusing on research primarily within the field of Computer Science. Their work spans several subfields including Artificial Intelligence, Computational Mechanics, Computer Vision and Pattern Recognition, Management Science and Operations Research, and Statistical and Nonlinear Physics.

The researcher's main areas of study encompass a variety of topics, notably:

  • Stochastic Gradient Optimization Techniques
  • Neural Networks and Applications
  • Sparse and Compressive Sensing Techniques
  • Generative Adversarial Networks and Image Synthesis
  • Advanced Bandit Algorithms Research
  • Model Reduction and Neural Networks
  • Galaxies: Formation, Evolution, Phenomena

Aurelien Lucchi has contributed to several recent publications in distinguished venues. Key papers include:

  • "A machine learning-based surrogate model to approximate optimal building retrofit solutions," 2020, Applied Energy
  • "Variational quantum Boltzmann machines," 2021, Quantum Machine Intelligence
  • "A convolutional neural network for classifying cloud particles recorded by imaging probes," 2020, Atmospheric Measurement Techniques
  • "Full wCDM analysis of KiDS-1000 weak lensing maps using deep learning," 2022, Physical Review. D/Physical Review. D.
  • "Learning Generative Models of Textured 3D Meshes from Real-World Images," 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV)

Frequent publication venues where Aurelien Lucchi's work appears include:

  • arXiv (Cornell University)
  • Physical Review. D/Physical Review. D.
  • Repository for Publications and Research Data (ETH Zurich)
  • Zenodo (CERN European Organization for Nuclear Research)
  • Applied Energy

Among frequent co-authors with whom Aurelien Lucchi has collaborated are:

  • Antonio Orvieto
  • Thomas Hofmann
  • Frank Proske
  • Hans Kersting
  • Dario Pavllo

Best Publications

  • SLIC Superpixels Compared to State-of-the-Art Superpixel Methods

    R. Achanta;A. Shaji;K. Smith;A. Lucchi

  • Quantum Generative Adversarial Networks for Learning and Loading Random Distributions

    Christa Zoufal;Christa Zoufal;Aurélien Lucchi;Stefan Woerner

  • Stabilizing Training of Generative Adversarial Networks through Regularization

    Kevin Roth;Aurelien Lucchi;Sebastian Nowozin;Thomas Hofmann

  • Learning Aerial Image Segmentation From Online Maps

    Pascal Kaiser;Jan Dirk Wegner;Aurelien Lucchi;Martin Jaggi

  • Supervoxel-Based Segmentation of Mitochondria in EM Image Stacks With Learned Shape Features

    A. Lucchi;K. Smith;R. Achanta;G. Knott

  • The power of quantum neural networks

    Amira Abbas;David Sutter;Christa Zoufal;Aurélien Lucchi

  • Radio frequency interference mitigation using deep convolutional neural networks

    Joël Akeret;Chihway L. Chang;Aurélien Lucchi;Alexandre Réfrégier

  • Topological Map Extraction From Overhead Images

    Zuoyue Li;Jan Dirk Wegner;Aurelien Lucchi

  • Cosmological constraints with deep learning from KiDS-450 weak lensing maps

    Janis Fluri;Tomasz Kacprzak;Aurelien Lucchi;Alexandre Refregier

  • Fast cosmic web simulations with generative adversarial networks

    Andres C. Rodríguez;Tomasz Kacprzak;Aurelien Lucchi;Adam Amara

  • Probabilistic Bag-Of-Hyperlinks Model for Entity Linking

    Octavian-Eugen Ganea;Marina Ganea;Aurelien Lucchi;Carsten Eickhoff

  • Leveraging Large Amounts of Weakly Supervised Data for Multi-Language Sentiment Classification

    Jan Deriu;Aurelien Lucchi;Valeria De Luca;Aliaksei Severyn

  • SwissCheese at SemEval-2016 Task 4: Sentiment Classification Using an Ensemble of Convolutional Neural Networks with Distant Supervision

    Jan Deriu;Maurice Gonzenbach;Fatih Uzdilli;Aurélien Lucchi

  • A fully automated approach to segmentation of irregularly shaped cellular structures in EM images

    Aurélien Lucchi;Kevin Smith;Radhakrishna Achanta;Vincent Lepetit

  • Stabilizing Training of Generative Adversarial Networks through Regularization

    Kevin Roth;Aurelien Lucchi;Sebastian Nowozin;Thomas Hofmann

  • Learning for Structured Prediction Using Approximate Subgradient Descent with Working Sets

    Aurelien Lucchi;Yunpeng Li;Pascal Fua

  • Variance reduced stochastic gradient descent with neighbors

    Thomas Hofmann;Aurelien Lucchi;Simon Lacoste-Julien;Brian McWilliams

  • A machine learning-based surrogate model to approximate optimal building retrofit solutions

    Emmanouil Thrampoulidis;Emmanouil Thrampoulidis;Georgios Mavromatidis;Aurelien Lucchi;Kristina Orehounig

  • Sub-sampled Cubic Regularization for Non-convex Optimization

    Jonas Moritz Kohler;Aurelien Lucchi

  • Local Saddle Point Optimization: A Curvature Exploitation Approach

    Leonard Adolphs;Hadi Daneshmand;Aurelien Lucchi;Thomas Hofmann

  • Structured Image Segmentation Using Kernelized Features

    Aurélien Lucchi;Yunpeng Li;Kevin Smith;Pascal Fua

  • Variational Quantum Boltzmann Machines

    Christa Zoufal;Aurélien Lucchi;Stefan Woerner

  • Are spatial and global constraints really necessary for segmentation

    Aurelien Lucchi;Yunpeng Li;Xavier Boix;Kevin Smith

  • A convolutional neural network for classifying cloud particles recorded by imaging probes

    Georgios Touloupas;Annika Lauber;Jan Henneberger;Alexander Beck

Frequent Co-Authors

Thomas Hofmann
Thomas Hofmann ETH Zurich
Pascal Fua
Pascal Fua École Polytechnique Fédérale de Lausanne
Andreas Krause
Andreas Krause ETH Zurich
Jan Dirk Wegner
Jan Dirk Wegner University of Zurich
Graham Knott
Graham Knott École Polytechnique Fédérale de Lausanne
Sebastian Nowozin
Sebastian Nowozin Microsoft (United States)
Francis Bach
Francis Bach École Normale Supérieure
Pierre Dillenbourg
Pierre Dillenbourg École Polytechnique Fédérale de Lausanne
Simon Lacoste-Julien
Simon Lacoste-Julien University of Montreal

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