NeurIPS 2023 Research Highlights - Circuits, Collaborative Filtering, Clustering, and Privacy
Overview of NeurIPS 2023 poster presentations covering graph neural networks, collaborative filtering, semi-supervised learning, and privacy assessment in image reconstruction, with insights on cutting-edge machine learning research.
On and Beyond Total Variation Regularization in Imaging - The Role of Space Variance
Exploring space-variant regularisation models for inverse imaging problems, focusing on Total Variation regularisation and its adaptive improvements for local image structures.
Stochastic Primal Dual Splitting Algorithms for Convex and Nonconvex Composite Optimization in Imaging
Explore stochastic primal dual splitting algorithms for convex and nonconvex composite optimization in imaging, focusing on SPDFP and SVRG-PDFP methods and their applications.
Convergence and Non-Convergence of Algebraic Iterative Reconstruction Methods - Imaging and Inverse Problems Seminar
Exploring convergence issues in algebraic iterative reconstruction methods for X-ray CT, presenting theoretical results and novel approaches to address non-convergence in limited-angle and limited-data scenarios.
Less is More: Learning Signals to Learn Representations in Healthcare
Explore advancements in machine learning for MRI reconstruction and medical imaging. Discover how less data can yield more accurate representations in healthcare applications.
The Phantom of Radon
Explore open-source Python library for image reconstruction in Axial Computed Tomography using Radon transforms. Learn about mathematical phantoms, sinograms, and reconstruction algorithms.
Pre-Training U-Net Using Autoencoders - Autoencoders and Visualizing Features
Explore autoencoders, image reconstruction, and feature visualization in deep learning models through hands-on coding examples and practical demonstrations.
SIREN - Implicit Neural Representations with Periodic Activation Functions
Explore SIREN neural networks for representing complex signals like images and 3D shapes. Learn about their unique periodic activation functions, initialization, and applications in solving differential equations.
Divide (or Convolve) and Conquer: Data Comparison with Wiener Filters
Explore Wiener filters for data comparison in machine learning, enhancing image reconstruction and translation robustness in various applications like compression and generative modeling.
New Approaches to Image and Video Reconstruction Using Deep Learning
Explore cutting-edge deep learning techniques for image and video enhancement, including colorization and resolution improvement, using efficient PyTorch-based tools trainable on a single GPU.
Advanced Computational Imaging - Chassez le Naturel, Il Revient au Galop
Explorez les avancées en imagerie computationnelle, en mettant l'accent sur la stabilité et la fiabilité des réseaux neuronaux profonds pour la reconstruction d'images biomédicales.
Diffusion Models for Inverse Problems
Explore advanced techniques for solving inverse problems using diffusion models, focusing on posterior sampling and manifold constraints.
Computational Imaging for Realistic Computer Graphics and Beyond
Explore advanced techniques in computational imaging for creating realistic graphics and expanding applications beyond traditional computer graphics.
Lensless Imaging: Overview, Opportunities, and Challenges
Explore lensless imaging advancements, principles, and applications in 3D reconstruction, discussing challenges and future opportunities in camera design and computational algorithms.
Lensless Imaging: Overview, Opportunities, and Challenges
Explore lensless imaging advancements, principles, and applications in 3D reconstruction, discussing challenges and future opportunities in camera design and computational algorithms.
FDA AI/ML Radiology Approvals - Basis for Quantum Neural Network Neuroimaging
Explore FDA approvals for AI/ML in radiology and their implications for quantum neural networks in neuroimaging. Gain insights into regulatory processes and future prospects for quantum-based medical algorithms.
The Unfinished Epic of Discrete Tomography
Delve into the mathematical foundations of discrete tomography through an engaging exploration of its ongoing challenges and theoretical developments in modern computational geometry.