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The field of machine learning has witnessed a surge in research on generative models, as these models possess the ability to generate novel data similar to the training data. In this study, our primary objective is to explore various types of generative models, with a thorough analysis of their respective limitations and potential applications. Additionally, we will investigate several influence methods and explore their efficacy in addressing the issue of memorization in generative models.
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Data attribution methods help interpret how neural networks behave by linking the model behavior to their training data. We extend the first-order influence approximation, TracIn, to diffusion models by incorporating the denoising timestep dynamics. We demonstrate that this influence estimation may be biased due to dominating gradient norms. To this end, Diffusion-ReTrac with a renormalization technique is introduced, enabling notably more localized influence estimation and the targeted attribution of training samples.
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Short description of portfolio item number 2
Published in Journal 1, 2009
This paper is about the number 1. The number 2 is left for future work.
Recommended citation: Your Name, You. (2009). "Paper Title Number 1." Journal 1. 1(1). http://academicpages.github.io/files/paper1.pdf
Published in Journal 1, 2010
This paper is about the number 2. The number 3 is left for future work.
Recommended citation: Your Name, You. (2010). "Paper Title Number 2." Journal 1. 1(2). http://academicpages.github.io/files/paper2.pdf
Published in Journal 1, 2015
This paper is about the number 3. The number 4 is left for future work.
Recommended citation: Your Name, You. (2015). "Paper Title Number 3." Journal 1. 1(3). http://academicpages.github.io/files/paper3.pdf
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This is a description of your talk, which is a markdown files that can be all markdown-ified like any other post. Yay markdown!
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This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
Undergraduate course, University 1, Department, 2014
This is a description of a teaching experience. You can use markdown like any other post.
Workshop, University 1, Department, 2015
This is a description of a teaching experience. You can use markdown like any other post.