Noam Elata

Ph.D. Student at Technion - Israel Institute of Technology.
Interested in diffusion models, computer vision and deep learning.
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About Me

I am a Ph.D. Student in the Electric and Computer Engineering faculty at the Technion, researching computer vision and machine learning under the supervision of Prof. Michael Elad and Prof. Tomer Michaeli. I am interested in generative models and theoretical deep learning, focusing most of my recent work on diffusion models. As of summer 2024, I am also a research intern at Apple in Herzeliya.

Prior to my Ph.D. studies, I recieved my B.Sc., summa cum laude, in computer engineering from Technion. During my studies, I have worked as a computer vision engineer at Mobileye.


Publications

2024

Noam Elata, Tomer Michaeli, Michael Elad
Zero-Shot Image Compression with Diffusion-Based Posterior Sampling
Under Review.

Shahar Yadin, Noam Elata, Tomer Michaeli
Classification Diffusion Models: Revitalizing Density Ratio Estimation
NeurIPS 2024, in The 38th Conference on Neural Information Processing Systems.
[webpage]

Noam Elata, Tomer Michaeli, Michael Elad
Adaptive Compressed Sensing with Diffusion-Based Posterior Sampling
ECCV 2024, in The 18th European Conference on Computer Vision.
☆ Rothschild Academic Excellence Award
[code]

Bahjat Kawar*, Noam Elata*, Tomer Michaeli, Michael Elad
GSURE-Based Diffusion Model Training with Corrupted Data
TMLR 2024, in Transactions on Machine Learning Research.
[code]

Noam Elata, Bahjat Kawar, Tomer Michaeli, Michael Elad
Nested Diffusion Processes for Anytime Image Generation
WACV 2024, in The IEEE/CVF Winter Conference on Applications of Computer Vision.
[webpage] [code] [space]


Awards

I am a recipient of the following awards:
  • Rothschild Academic Excellence Award, 2024
  • Meyer Fellows prize 2022
  • EMET Excellence Program 2020-2022
  • Alfred and Anna Grey Excellence Scholarship 2021
  • Apple Excellence Award 2021
  • Technion Alumni Scholarship 2020

Teaching

I have taught the following courses as a TA:
  • Generative AI - Diffusion Models (CS236610) – Winter 2023-2024 (recorded lectures)
  • Deep Learning (ECE046211) – Spring 2023