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teaching:st24:master-seminar [2024/04/13 22:37] jschwarz created |
teaching:st24:master-seminar [2024/06/11 21:00] (current) jschwarz [Dates] |
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- | ===== (Master) Seminar: Score-based Generative Models for Machine Learning ===== | + | ===== Neural ODE and Generative Modelling (Master Seminar) ===== |
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Additionally, we will explore a broader generalization involving an infinite number of time steps for noise levels, studying the process using stochastic differential equations. This formulation, known as score SDEs, leverages SDEs for noise perturbation and sample generation. The seminar will conclude with a comparison to other possible diffusion models and a discussion of further enhancements in sample generation. | Additionally, we will explore a broader generalization involving an infinite number of time steps for noise levels, studying the process using stochastic differential equations. This formulation, known as score SDEs, leverages SDEs for noise perturbation and sample generation. The seminar will conclude with a comparison to other possible diffusion models and a discussion of further enhancements in sample generation. | ||
- | The seminar is scheduled for the second half of the winter term. Participants interested in reviewing concepts of stochastic differential equations have the option to attend a previous seminar titled [[teaching:st23:seminar| Stochastic Differential Equations and Generative Modelling (Proseminar/Seminar)]], which takes place in the first half of the winter term. | + | The seminar is scheduled for the second half of the winter term. |
==== Organization ==== | ==== Organization ==== | ||
* **Prerequisites:** Basic knowledge in probability theory and statistics | * **Prerequisites:** Basic knowledge in probability theory and statistics | ||
- | * **Registration:** Via Müsli. [[https://muesli.mathi.uni-heidelberg.de/lecture/view/1757|Link]] | + | * **Registration:** Via email to Jonathan |
- | * **First (organizational) meeting:** Tuesday, 17 October at 14:00 c.t. | + | * **First (organizational) meeting:** **Friday, the 26th of April** at 14:00 c.t. |
- | * **Time and Location:** Tuesdays 14:00 c.t. in SR 6 | + | * **Time and Location:** Friday 14:00 c.t. (SR 6 in INF205) |
- | Further information on the seminar will be announced in the first organizational meeting. For any specific question you can contact [[:people | Daniel Gonzalez, Jonas Cassel]]. | + | Further information on the seminar will be announced in the first organizational meeting. For any specific question you can contact [[:people | Jonathan Schwarz]]. |
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+ | ==== Dates ==== | ||
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+ | * ** 14th of June 2024 ** | ||
+ | * **Neural ordinary differential equations** with Hao | ||
+ | * **An introduction to deep generative modeling** with Ying | ||
+ | * ** 21st of June 2024 ** | ||
+ | * **Score-based generative modeling through stochastic differential equations** with Philipp | ||
+ | * **Generative modeling by estimating gradients of the data distribution** with Sanmati | ||
+ | * **Flow matching for generative modeling** with Shijie | ||
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* **Score-based generative modeling through stochastic differential equations**,// Song, Yang and Sohl-Dickstein, Jascha and Kingma, Diederik P and Kumar, Abhishek and Ermon, Stefano and Poole, Ben//, ICLR (2021) | * **Score-based generative modeling through stochastic differential equations**,// Song, Yang and Sohl-Dickstein, Jascha and Kingma, Diederik P and Kumar, Abhishek and Ermon, Stefano and Poole, Ben//, ICLR (2021) | ||
* **Gotta go fast when generating data with score-based models**,// olicoeur-Martineau, Alexia and Li, Ke and Piché-Taillefer, Rémi and Kachman, Tal and Mitliagkas, Ioannis//, arXiv preprint (2021) | * **Gotta go fast when generating data with score-based models**,// olicoeur-Martineau, Alexia and Li, Ke and Piché-Taillefer, Rémi and Kachman, Tal and Mitliagkas, Ioannis//, arXiv preprint (2021) | ||
+ | * **Flow matching for generative modeling**,// Lipman, Yaron and Chen, Ricky TQ and Ben-Hamu, Heli and Nickel, Maximilian and Le, Matt//, arXiv preprint arXiv:2210.02747 |