David Fleet

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David Fleet

David Fleet

@fleet_dj

Google Research, Brain Team; and University of Toronto.

เข้าร่วม Kasım 2016
18 กำลังติดตาม450 ผู้ติดตาม
David Fleet รีทวีตแล้ว
Shayan Shekarforoush
Shayan Shekarforoush@s_shekarforoush·
We compare our method with the state-of-the-art on both synthetic and experimental benchmarks. Empirically, cryoSPIN outperforms in reconstruction quality and FSC. [6/7]
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David Fleet รีทวีตแล้ว
Daniel Watson
Daniel Watson@watson_nn·
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 รีทวีตแล้ว
Kevin Clark
Kevin Clark@clark_kev·
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)
Kevin Clark tweet media
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David Fleet รีทวีตแล้ว
Kevin Clark
Kevin Clark@clark_kev·
Our best variant, DRaFT-LV, learns 2x faster than ReFL (arxiv.org/abs/2304.05977) and 100x faster than RL. (3/5)
Kevin Clark tweet media
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David Fleet รีทวีตแล้ว
Priyank Jaini
Priyank Jaini@priyankjaini·
.@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 🧵👇
Priyank Jaini tweet media
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David Fleet รีทวีตแล้ว
Kevin Clark
Kevin Clark@clark_kev·
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)
Kevin Clark tweet media
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David Fleet รีทวีตแล้ว
Structura Biotechnology
Structura Biotechnology@structurabio·
📄 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 รีทวีตแล้ว
George E. Dahl
George E. Dahl@GeorgeEDahl·
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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Durk Kingma
Durk Kingma@dpkingma·
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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