black-forest-labs/FLUX.1-dev
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alpik
Salvor
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Reacted to
fdaudens's
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about 2 months ago
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3044
The Nobel Prize background for Hopfield and Hinton's work on neural networks is pure gold. It's a masterclass in explaining AI basics.
Key takeaways from the conclusion:
- ML applications are expanding rapidly. We're still figuring out which will stick.
- Ethical discussions are crucial as the tech develops.
- Physics 🤝 AI: A two-way street of innovation.
Some mind-blowing AI applications in physics:
- Discovering the Higgs particle
- Cleaning up gravitational wave data
- Hunting exoplanets
- Predicting molecular structures
- Designing better solar cells
We're just scratching the surface. The interplay between AI and physics is reshaping both fields.
Bonus: The illustrations accompanying the background document are really neat. (Credit: Johan Jarnestad/The Royal Swedish Academy of Sciences)
#AI #MachineLearning #Physics #Ethics #Innovation
Key takeaways from the conclusion:
- ML applications are expanding rapidly. We're still figuring out which will stick.
- Ethical discussions are crucial as the tech develops.
- Physics 🤝 AI: A two-way street of innovation.
Some mind-blowing AI applications in physics:
- Discovering the Higgs particle
- Cleaning up gravitational wave data
- Hunting exoplanets
- Predicting molecular structures
- Designing better solar cells
We're just scratching the surface. The interplay between AI and physics is reshaping both fields.
Bonus: The illustrations accompanying the background document are really neat. (Credit: Johan Jarnestad/The Royal Swedish Academy of Sciences)
#AI #MachineLearning #Physics #Ethics #Innovation
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Reacted to
merve's
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about 2 months ago
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3747
Meta AI vision has been cooking
@facebook
They shipped multiple models and demos for their papers at @ECCV 🤗
Here's a compilation of my top picks:
- Sapiens is family of foundation models for human-centric depth estimation, segmentation and more, all models have open weights and demos 👏
All models have their demos and even torchscript checkpoints!
A collection of models and demos: facebook/sapiens-66d22047daa6402d565cb2fc
- VFusion3D is state-of-the-art consistent 3D generation model from images
Model: facebook/vfusion3d
Demo: facebook/VFusion3D
- CoTracker is the state-of-the-art point (pixel) tracking model
Demo: facebook/cotracker
Model: facebook/cotracker
They shipped multiple models and demos for their papers at @ECCV 🤗
Here's a compilation of my top picks:
- Sapiens is family of foundation models for human-centric depth estimation, segmentation and more, all models have open weights and demos 👏
All models have their demos and even torchscript checkpoints!
A collection of models and demos: facebook/sapiens-66d22047daa6402d565cb2fc
- VFusion3D is state-of-the-art consistent 3D generation model from images
Model: facebook/vfusion3d
Demo: facebook/VFusion3D
- CoTracker is the state-of-the-art point (pixel) tracking model
Demo: facebook/cotracker
Model: facebook/cotracker
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m-ric's
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2 months ago
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1066
🧠 Stanford paper might be the key to OpenAI o1’s performance: What’s so effective about Chain of Thought? ⇒ it unlocks radically different sequential tasks!
💭 Reminder: A Chain of Thought (CoT) means that you instruct the model to “think step by step”. Often it’s literally just putting in the prompt “let’s think step by step.”
🤔 This method has been shown to be unreasonably effective to increase perf on benchmarks. However why it works so well remains unclear.
Here's the scoop: Transformers are amazing at parallel processing, but they've always struggled with tasks that require sequential reasoning.
⛔️ For instance if you ask them the result of 3^2^2^2^…, with 20 iterations, they’ll nearly always fail.
💡 Indeed, researchers prove mathematically, by assimilating transformers networks to logical circuits, that effectively they cannot solve sequential tasks that require more than a certain threshold of sequences.
But CoT enables sequential reasoning:
- 🧱 Each step in the CoT corresponds to simulating one operation in a complex circuit.
- 🔄 This allows the transformer to "reset" the depth of intermediate outputs, overcoming previous limitations.
- 🚀 Thus, with CoT, constant-depth transformers can now solve ANY problem computable by polynomial-size circuits! (That's a huge class of problems in computer science.)
- 🔑 Transformers can now handle tricky tasks like iterated squares (computing 3^2^2^2^2) composed permutations and evaluating circuits - stuff that requires serial computation.
- 📊 The improvement is especially dramatic for transformers with a limited depth. Empirical tests on four arithmetic problems showed massive accuracy gains with CoT on inherently serial tasks.
Main takeaway: Chain-of-thought isn't just a neat trick - it fundamentally expands what transformer models can do!
Read the paper 👉 Chain of Thought Empowers Transformers to Solve Inherently Serial Problems (2402.12875)
💭 Reminder: A Chain of Thought (CoT) means that you instruct the model to “think step by step”. Often it’s literally just putting in the prompt “let’s think step by step.”
🤔 This method has been shown to be unreasonably effective to increase perf on benchmarks. However why it works so well remains unclear.
Here's the scoop: Transformers are amazing at parallel processing, but they've always struggled with tasks that require sequential reasoning.
⛔️ For instance if you ask them the result of 3^2^2^2^…, with 20 iterations, they’ll nearly always fail.
💡 Indeed, researchers prove mathematically, by assimilating transformers networks to logical circuits, that effectively they cannot solve sequential tasks that require more than a certain threshold of sequences.
But CoT enables sequential reasoning:
- 🧱 Each step in the CoT corresponds to simulating one operation in a complex circuit.
- 🔄 This allows the transformer to "reset" the depth of intermediate outputs, overcoming previous limitations.
- 🚀 Thus, with CoT, constant-depth transformers can now solve ANY problem computable by polynomial-size circuits! (That's a huge class of problems in computer science.)
- 🔑 Transformers can now handle tricky tasks like iterated squares (computing 3^2^2^2^2) composed permutations and evaluating circuits - stuff that requires serial computation.
- 📊 The improvement is especially dramatic for transformers with a limited depth. Empirical tests on four arithmetic problems showed massive accuracy gains with CoT on inherently serial tasks.
Main takeaway: Chain-of-thought isn't just a neat trick - it fundamentally expands what transformer models can do!
Read the paper 👉 Chain of Thought Empowers Transformers to Solve Inherently Serial Problems (2402.12875)
updated
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collection
2 months ago
Reacted to
DmitryRyumin's
post with 🔥
2 months ago
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2552
🔥🎭🌟 New Research Alert - HeadGAP (Avatars Collection)! 🌟🎭🔥
📄 Title: HeadGAP: Few-shot 3D Head Avatar via Generalizable Gaussian Priors 🔝
📝 Description: HeadGAP introduces a novel method for generating high-fidelity, animatable 3D head avatars from few-shot data, using Gaussian priors and dynamic part-based modelling for personalized and generalizable results.
👥 Authors: @zxz267 , @walsvid , @zhaohu2 , Weiyi Zhang, @hellozhuo , Xu Chang, Yang Zhao, Zheng Lv, Xiaoyuan Zhang, @yongjie-zhang-mail , Guidong Wang, and Lan Xu
📄 Paper: HeadGAP: Few-shot 3D Head Avatar via Generalizable Gaussian Priors (2408.06019)
🌐 Github Page: https://headgap.github.io
🚀 CVPR-2023-24-Papers: https://github.com/DmitryRyumin/CVPR-2023-24-Papers
🚀 WACV-2024-Papers: https://github.com/DmitryRyumin/WACV-2024-Papers
🚀 ICCV-2023-Papers: https://github.com/DmitryRyumin/ICCV-2023-Papers
📚 More Papers: more cutting-edge research presented at other conferences in the DmitryRyumin/NewEraAI-Papers curated by @DmitryRyumin
🚀 Added to the Avatars Collection: DmitryRyumin/avatars-65df37cdf81fec13d4dbac36
🔍 Keywords: #HeadGAP #3DAvatar #FewShotLearning #GaussianPriors #AvatarCreation #3DModeling #MachineLearning #ComputerVision #ComputerGraphics #GenerativeAI #DeepLearning #AI
📄 Title: HeadGAP: Few-shot 3D Head Avatar via Generalizable Gaussian Priors 🔝
📝 Description: HeadGAP introduces a novel method for generating high-fidelity, animatable 3D head avatars from few-shot data, using Gaussian priors and dynamic part-based modelling for personalized and generalizable results.
👥 Authors: @zxz267 , @walsvid , @zhaohu2 , Weiyi Zhang, @hellozhuo , Xu Chang, Yang Zhao, Zheng Lv, Xiaoyuan Zhang, @yongjie-zhang-mail , Guidong Wang, and Lan Xu
📄 Paper: HeadGAP: Few-shot 3D Head Avatar via Generalizable Gaussian Priors (2408.06019)
🌐 Github Page: https://headgap.github.io
🚀 CVPR-2023-24-Papers: https://github.com/DmitryRyumin/CVPR-2023-24-Papers
🚀 WACV-2024-Papers: https://github.com/DmitryRyumin/WACV-2024-Papers
🚀 ICCV-2023-Papers: https://github.com/DmitryRyumin/ICCV-2023-Papers
📚 More Papers: more cutting-edge research presented at other conferences in the DmitryRyumin/NewEraAI-Papers curated by @DmitryRyumin
🚀 Added to the Avatars Collection: DmitryRyumin/avatars-65df37cdf81fec13d4dbac36
🔍 Keywords: #HeadGAP #3DAvatar #FewShotLearning #GaussianPriors #AvatarCreation #3DModeling #MachineLearning #ComputerVision #ComputerGraphics #GenerativeAI #DeepLearning #AI
Reacted to
DmitryRyumin's
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2 months ago
Post
1853
🔥🎭🌟 New Research Alert - ECCV 2024 (Avatars Collection)! 🌟🎭🔥
📄 Title: MeshAvatar: Learning High-quality Triangular Human Avatars from Multi-view Videos 🔝
📝 Description: MeshAvatar is a novel pipeline that generates high-quality triangular human avatars from multi-view videos, enabling realistic editing and rendering through a mesh-based approach with physics-based decomposition.
👥 Authors: Yushuo Chen, Zerong Zheng, Zhe Li, Chao Xu, and Yebin Liu
📅 Conference: ECCV, 29 Sep – 4 Oct, 2024 | Milano, Italy 🇮🇹
📄 Paper: MeshAvatar: Learning High-quality Triangular Human Avatars from Multi-view Videos (2407.08414)
🌐 Github Page: https://shad0wta9.github.io/meshavatar-page
📁 Repository: https://github.com/shad0wta9/meshavatar
📺 Video: https://www.youtube.com/watch?v=Kpbpujkh2iI
🚀 CVPR-2023-24-Papers: https://github.com/DmitryRyumin/CVPR-2023-24-Papers
🚀 WACV-2024-Papers: https://github.com/DmitryRyumin/WACV-2024-Papers
🚀 ICCV-2023-Papers: https://github.com/DmitryRyumin/ICCV-2023-Papers
📚 More Papers: more cutting-edge research presented at other conferences in the DmitryRyumin/NewEraAI-Papers curated by @DmitryRyumin
🚀 Added to the Avatars Collection: DmitryRyumin/avatars-65df37cdf81fec13d4dbac36
🔍 Keywords: #MeshAvatar #3DAvatars #MultiViewVideo #PhysicsBasedRendering #TriangularMesh #AvatarCreation #3DModeling #NeuralRendering #Relighting #AvatarEditing #MachineLearning #ComputerVision #ComputerGraphics #DeepLearning #AI #ECCV2024
📄 Title: MeshAvatar: Learning High-quality Triangular Human Avatars from Multi-view Videos 🔝
📝 Description: MeshAvatar is a novel pipeline that generates high-quality triangular human avatars from multi-view videos, enabling realistic editing and rendering through a mesh-based approach with physics-based decomposition.
👥 Authors: Yushuo Chen, Zerong Zheng, Zhe Li, Chao Xu, and Yebin Liu
📅 Conference: ECCV, 29 Sep – 4 Oct, 2024 | Milano, Italy 🇮🇹
📄 Paper: MeshAvatar: Learning High-quality Triangular Human Avatars from Multi-view Videos (2407.08414)
🌐 Github Page: https://shad0wta9.github.io/meshavatar-page
📁 Repository: https://github.com/shad0wta9/meshavatar
📺 Video: https://www.youtube.com/watch?v=Kpbpujkh2iI
🚀 CVPR-2023-24-Papers: https://github.com/DmitryRyumin/CVPR-2023-24-Papers
🚀 WACV-2024-Papers: https://github.com/DmitryRyumin/WACV-2024-Papers
🚀 ICCV-2023-Papers: https://github.com/DmitryRyumin/ICCV-2023-Papers
📚 More Papers: more cutting-edge research presented at other conferences in the DmitryRyumin/NewEraAI-Papers curated by @DmitryRyumin
🚀 Added to the Avatars Collection: DmitryRyumin/avatars-65df37cdf81fec13d4dbac36
🔍 Keywords: #MeshAvatar #3DAvatars #MultiViewVideo #PhysicsBasedRendering #TriangularMesh #AvatarCreation #3DModeling #NeuralRendering #Relighting #AvatarEditing #MachineLearning #ComputerVision #ComputerGraphics #DeepLearning #AI #ECCV2024
Reacted to
asoria's
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2 months ago
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2383
📝 I wrote a tutorial on how to get started with the fine-tuning process using Hugging Face tools, providing an end-to-end workflow.
The tutorial covers creating a new dataset using the new SQL Console 🛢 and fine-tuning a model with SFT, guided by the Notebook Creator App 📙.
👉 You can read the full article here:
https://huggingface.co/blog/asoria/easy-fine-tuning-with-hf
asoria/auto-notebook-creator
The tutorial covers creating a new dataset using the new SQL Console 🛢 and fine-tuning a model with SFT, guided by the Notebook Creator App 📙.
👉 You can read the full article here:
https://huggingface.co/blog/asoria/easy-fine-tuning-with-hf
asoria/auto-notebook-creator
upvoted
a
collection
2 months ago
Reacted to
fdaudens's
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2 months ago
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3296
A big day for multimodal models!
Llama 3.2 is out with a major update: it can now process images.
Key highlights:
• 11B and 90B vision models
• Small 1B and 3B text models for mobile devices
Eval results already on the leaderboard: open-llm-leaderboard/open_llm_leaderboard
Collection: meta-llama/llama-32-66f448ffc8c32f949b04c8cf
Llama 3.2 is out with a major update: it can now process images.
Key highlights:
• 11B and 90B vision models
• Small 1B and 3B text models for mobile devices
Eval results already on the leaderboard: open-llm-leaderboard/open_llm_leaderboard
Collection: meta-llama/llama-32-66f448ffc8c32f949b04c8cf
- 1 reply
liked
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jeffboudier's
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2 months ago
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4015
Pro Tip - if you're a Firefox user, you can set up Hugging Chat as integrated AI Assistant, with contextual links to summarize or simplify any text - handy!
In this short video I show how to set it up
In this short video I show how to set it up
- 2 replies