Multi-Modal Imaging with Deep Learning and Modeling 2022

Multi-Modal Imaging with Deep Learning and Modeling 2022

Institute for Pure & Applied Mathematics (IPAM) via YouTube Direct link

Kevin Kelly - Machine Learning Enhanced Compressive Hyperspectral Imaging - IPAM at UCLA

19 of 20

19 of 20

Kevin Kelly - Machine Learning Enhanced Compressive Hyperspectral Imaging - IPAM at UCLA

Class Central Classrooms beta

YouTube videos curated by Class Central.

Classroom Contents

Multi-Modal Imaging with Deep Learning and Modeling 2022

Automatically move to the next video in the Classroom when playback concludes

  1. 1 Naomi Ginsberg - Formation and function of assembled nanomaterials with multimodal X-ray scattering
  2. 2 Anna Little - Unbiasing Procedures for Scale-invariant Multi-reference Alignment - IPAM at UCLA
  3. 3 Hanbaek Lyu - Mesoscale reconstruction of images and networks using tensor decomposition
  4. 4 Michael Lustig - Multi-modal Motion Imaging using Microwave tones in an MRI scanner - IPAM at UCLA
  5. 5 Daniel Cremers - Deep Learning: Challenges and Perspectives - IPAM at UCLA
  6. 6 Rama Vasudevan - Advancing Microscopy with Machine Learning: Lessons from Scanning Probe Microscopy
  7. 7 Piotr Indyk - Learning-Based Low-Rank Approximations - IPAM at UCLA
  8. 8 Robert Wolkow - Atom-Defined Devices, Ultra-Fast Classical Devices, and Diverse Quantum Devices
  9. 9 Paul Hand - Signal Recovery with Generative Priors - IPAM at UCLA
  10. 10 Sarah Haigh - Probing atomic behaviour in liquids with STEM : opportunities for machine learning
  11. 11 Juan Carlos Idrobo - Quantum Phenomena & Electron Microscopy: New Possibilities & Limitations
  12. 12 Ben Recht - Splitting the difference between deep and shallow solutions of inverse problems
  13. 13 Mahdi Soltanolkotabi - Medical image reconstruction via deep learning: architectures, data reduction
  14. 14 Mary Scott - Supervised and Unsupervised approaches for Electron Microscopy Data Analysis
  15. 15 Elizaveta Rebrova - Low-rank tensor recovery from memory-efficient measurements - IPAM at UCLA
  16. 16 Mark Iwen - Accurate Recovery of Compactly Supported Smooth Functions from Spectrogram Measurements
  17. 17 Reinhard Heckel - The role of data and models for deep-learning based image reconstruction
  18. 18 Palina Salanevich - STFT Phase retrieval: robustness and generative priors - IPAM at UCLA
  19. 19 Kevin Kelly - Machine Learning Enhanced Compressive Hyperspectral Imaging - IPAM at UCLA
  20. 20 Jamie Haddock - Hierarchical and neural nonnegative tensor factorizations - IPAM at UCLA

Never Stop Learning.

Get personalized course recommendations, track subjects and courses with reminders, and more.

Someone learning on their laptop while sitting on the floor.