To be confirmed


ID Presenter Title
001 Eliot Ayache (University of Bath) Machine learning applications in High-Energy Time-Domain Astrophysics
002 Pasquale Cascarano (Università di Bologna) Deep Plug-and-Play Gradient Method for Super-Resolution
003 Eric Baruch Gutierrez Castillo (University of Bath) On Primal-Dual Algorithms for Nonsmooth Large-Scale Machine Learning
004 Dongdong Chen (University of Edinburgh) Deep Plug-and-Play Network for Compressive MRF reconstruction
005 Maria Colomba Comes (University of Rome Tor Vergata) Deep Prior for Cells Video Restoration
006 Jacob Deasy (University of Cambridge) Closed-form differential entropy of a multi-layer perceptron variant
007 Margaret Duff (University of Bath) Solving Inverse Imaging Problems with Generative
008 David Fernandes (University of Bath) Unsupervised Learning with GPs and SDEs
009 Allen Hart (University of Bath) Machine Learning Models
010 Allard Hendriksen (Centrum Wiskunde & Informatica (CWI), Amsterdam) Noise2Inverse: Self-supervised deep convolutional denoising for linear inverse problems in imaging
011 Johannes Hertrich (TU Berlin) Parseval Proximal Neural Networks
012 Gabriele Incorvaia (University of Manchester) A deep learning application for Through-the-Wall Radar Imaging
013 Nicolas Keriven (CNRS, GIPSA-lab) coordinate inter-building electricity demand
014 Youngkyu Lee (KAIST) A parareal neural network emulating parallel-in-time algorithm
015 Peter Mathé (Weierstrass Institute) Inverse learning in Hilbert scales
016 Janith Petangoda (Imperial College London) Transfer Learning: An application of Foliations
017 Benedek Plosz (University of Bath) Learning with unexplored priors systemises error propagation in Bayesian hierarchical modelling
018 Ilan Price (University of Oxford) Trajectory growth through deep random ReLU networks
019 Abhishake Rastogi (University of Potsdam) Regularization schemes for statistical inverse problems
020 Salvatore Danilo Riccio (Queen Mary University of London) Robustness of Runge-Kutta networks against adversarial attacks
021 Paul Russell (University of Bath) Learning to solve Rubik’s cube
022 Malena Sabaté (University of Bath) Iteratively Reweighted Flexible Krylov methods for Sparse Reconstruction Poster Video
023 Silvester Sabathiel (NTNU Trondheim) A computational model of learning to count in a multimodal, interactive environment
024 Ferdia Sherry (University of Cambridge) Equivariant neural networks for inverse problems in imaging
025 Giuseppe Ughi (University of Oxford) A Model-Based Derivative-Free Approach to Black-Box Adversarial Examples
026 Tiffany Vlaar (University of Edinburgh) Partitioned Integrators for Thermodynamic Parameterization of NNs
027 Xiaoyu Wang (University of Cambridge) Gradient-based training of non-smooth neural networks
028 Stefan Wild (Argonne National Laboratory) Zero-order optimization for learning: How long can you wait?
029 Kelvin Shuangjian Zhang (ENS Paris) Wasserstein control of mirror Langevin Monte Carlo