David Fleet retweetledi
We compare our method with the state-of-the-art on both synthetic and experimental benchmarks. Empirically, cryoSPIN outperforms in reconstruction quality and FSC.
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David Fleet
37 posts
David Fleet
@fleet_dj
Google Research, Brain Team; and University of Toronto.
Katılım Kasım 2016
18 Takip Edilen450 Takipçiler
David Fleet retweetledi
🎉🎉Excited to share that “CryoSPIN” has been accepted to #NeurIPS2024!
Special thanks to my amazing collaborators and supervisors @DaveLindell, @marcusabrubaker, and @fleet_dj
Paper: arxiv.org/abs/2406.10455
Webpage: shekshaa.github.io/semi-amortized…
Code is also released!
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David Fleet retweetledi
Why use RLHF when u can backprop through the sampler with any differentiable reward🥹🥹🥹
Paul Vicol@PaulVicol
Check out @clark_kev’s and my paper on fine-tuning diffusion models on differentiable rewards! We present DRaFT, which computes gradients through diffusion sampling. DRaFT is efficient & works across many reward functions. With @kswersk, @fleet_dj arXiv: arxiv.org/abs/2309.17400
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David Fleet retweetledi
DRaFT backpropagates the reward directly into LoRA parameters – we don’t need to use RL because diffusion sampling is differentiable. We improve efficiency by truncating the BPTT; even truncating to one step still works! (2/5)
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David Fleet retweetledi
Our best variant, DRaFT-LV, learns 2x faster than ReFL (arxiv.org/abs/2304.05977) and 100x faster than RL. (3/5)
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David Fleet retweetledi
.@clark_kev & I are excited to share our new work on studying Imagen by evaluating it as a zero-shot classifier! Highlights include Imagen achieving SoTA on Stylized Imagenet and being able to perform attribute binding in certain settings unlike CLIP
arxiv.org/abs/2303.15233
🧵👇
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David Fleet retweetledi
We can reduce the effect of the fine-tuning or mix different reward functions post-training simply by scaling down or mixing LoRAs. (4/5)
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David Fleet retweetledi
📄 An updated paper describing 3D Flexible Refinement is now out in @naturemethods!
Paper: nature.com/articles/s4159…
It describes further experimental #cryoEM results and the improved 3DFlex method that was released in #CryoSPARC v4.1 ❄️⚡
Tutorial: guide.cryosparc.com/processing-dat…
Structura Biotechnology@structurabio
1/ We’re thrilled to announce that 3D Flexible Refinement, a motion-based deep generative model for continuous heterogeneity in #cryoEM structures, is available today in #CryoSPARC v4.1 Beta! ❄️⚡ Read more about v4.1: cryosparc.com/updates
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David Fleet retweetledi
Monocular Depth Estimation using Diffusion Models
abs: arxiv.org/abs/2302.14816
project page: depth-gen.github.io
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David Fleet retweetledi
Awesome work by Hshmat. The work showcases yet another evidence of how effective noise conditioning augmentation can be.
Hshmat Sahak@HshmatSahak
Thrilled to announce SR3+, our new model that establishes a new state-of-the-art on diffusion-based super-resolution for images in the wild! arxiv.org/abs/2302.07864 Joint work w/ @watson_nn @Chitwan_Saharia @fleet_dj
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David Fleet retweetledi
Thrilled to announce SR3+, our new model that establishes a new state-of-the-art on diffusion-based super-resolution for images in the wild!
arxiv.org/abs/2302.07864
Joint work w/ @watson_nn @Chitwan_Saharia @fleet_dj
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David Fleet retweetledi
We've just released the first version of our Deep Learning Tuning Playbook! This is our attempt to distill our process for actually getting good results with deep learning. We emphasize hyperparameter tuning since it has been a large pain point. github.com/google-researc…
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David Fleet retweetledi
Excited to announce our work on novel view synthesis with diffusion models! Our model can lift a single 2d image into 3d.
3d-diffusion.github.io
Joint work w/ @wchan212 @rmbrualla @hojonathanho @taiyasaki @mo_norouzi
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David Fleet retweetledi
Excited to announce Imagen Video, our new text-conditioned video diffusion model that generates 1280x768 24fps HD videos! #ImagenVideo
imagen.research.google/video/
Work w/ @wchan212 @Chitwan_Saharia @jaywhang_ @RuiqiGao @agritsenko @dpkingma @poolio @mo_norouzi @fleet_dj @TimSalimans
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David Fleet retweetledi
Happy to announce DreamFusion, our new method for Text-to-3D!
dreamfusion3d.github.io
We optimize a NeRF from scratch using a pretrained text-to-image diffusion model. No 3D data needed!
Joint work w/ the incredible team of @BenMildenhall @ajayj_ @jon_barron
#dreamfusion
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David Fleet retweetledi
If you don’t think DallE-2 and Imagen are an Alexnet level moment in the machine learning world you aren’t paying attention enough. Very impressive visual results coming out of these. Getting similar chills to when I saw first web browser, iPhone, etc.
hardmaru@hardmaru
“Oriental painting of tigers wearing VR headsets during the Song dynasty” generated using #Imagen
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David Fleet retweetledi
Daniel has created some of the most amazing #Imagen images so far. Keep an eye out.
Daniel Smith@motionphi
"A photo of a giant sloth drinking a cup of coffee in the dawn light" Generated with #Imagen, a new text-to-image diffusion model from the very smart folks at Google Brain. I'll be sharing much more, so stay tuned!
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David Fleet retweetledi
If you have any kind of feedback for the #imagen team, we'd love to hear it! imagen.research.google
@Chitwan_Saharia, @wchan212, @srbhsxn, Lala Li, @jaywhang_, @cephaloponderer, @coolboi95, Burcu Karagol Ayan, Sara Mahdavi, @iraphas13, @TimSalimans, @hojonathanho, @fleet_dj.
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David Fleet retweetledi
It was 16 years ago, in 2006, that @geoffreyhinton et al released their demo of deep belief nets. Undergrad me was highly impressed, and helped convince me that deep learning was the way to go. I refreshed Geoff's website almost every day checking for new papers... (1/n)
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David Fleet retweetledi
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