From 42c8ed0c229e8cfb87b7dfaf3c361332816904a1 Mon Sep 17 00:00:00 2001 From: Dane Sabo Date: Wed, 30 Oct 2024 15:17:08 -0400 Subject: [PATCH] vault backup: 2024-10-30 15:17:08 --- 201 Metadata/My Library.bib | 9 +++++++++ 4 Qualifying Exam/2 Writing/3. QE Research Approach.md | 2 +- 2 files changed, 10 insertions(+), 1 deletion(-) diff --git a/201 Metadata/My Library.bib b/201 Metadata/My Library.bib index b6313773..e7bb15a7 100644 --- a/201 Metadata/My Library.bib +++ b/201 Metadata/My Library.bib @@ -11227,6 +11227,15 @@ Subject\_term: Careers, Politics, Policy}, isbn = {1-4612-0577-8} } +@online{SoraCreatingVideo, + title = {Sora: {{Creating}} Video from Text}, + shorttitle = {Sora}, + url = {https://openai.com/index/sora/}, + urldate = {2024-10-30}, + langid = {american}, + file = {/home/danesabo/Zotero/storage/YUQHRZUS/sora.html} +} + @misc{sorensenLecturesCurryHowardIsomorphism, title = {Lectures on the {{Curry-Howard Isomorphism}}}, author = {Sorensen, Morten Heine B. and Urzyczyn, Pawel}, diff --git a/4 Qualifying Exam/2 Writing/3. QE Research Approach.md b/4 Qualifying Exam/2 Writing/3. QE Research Approach.md index c3481bc0..5dc3b074 100644 --- a/4 Qualifying Exam/2 Writing/3. QE Research Approach.md +++ b/4 Qualifying Exam/2 Writing/3. QE Research Approach.md @@ -43,4 +43,4 @@ Something to justify, why diffusion model as opposed to other generative AI ## Writin some stuff -The purpose of this proposal is to suggest that using a generative network to create unstructured perturbations can be a viable way to advance the state of the art. But to do this, the current state of diffusion models and their place must be introduced. The generative diffusion model is a recent breakthrough in generative models. Diffusion models \ No newline at end of file +The purpose of this proposal is to suggest that using a generative network to create unstructured perturbations can be a viable way to advance the state of the art. But to do this, the current state of diffusion models and their place must be introduced. The generative diffusion model is a recent breakthrough in generative models [@sohl-dicksteinDeepUnsupervisedLearning2015]. Diffusion generative models are the state of the art for image and video generation, and have demonstrated promise for audio generation and noise removal [@kongDiffWaveVersatileDiffusion2020] [@SoraCreatingVideo]. Diffusion models do this through a forward noise-inducing process, and a backwards \ No newline at end of file