Загрузка видео...

Не удалось загрузить видео

На главную

Scaling laws describe how loss changes with scale. Do neurons inside models change predictably too? We study vision and language models up to 30B params and find systematic scaling in neuron universality, specialization, and selectivity. Paper+code: 1/n

_AmilDravid's profile picture

Amil Dravid

1,292 subscribers

205,643 просмотров • 1 месяц назад •via X (Twitter)

ОбразованиеНаука и технологии
LIVE

Anya Rossi• Live Now

Private livecam show

Комментарии: 0

Нет доступных комментариев

Здесь появятся комментарии из оригинального поста

Похожие видео

neuron trying in vain to find and connect with other neurons
0:18

neuron trying in vain to find and connect with other neurons

microscopic images.

79,466 просмотров • 1 год назад

Can Vision-Language Models Solve the Shell Game? paper:
0:12

Can Vision-Language Models Solve the Shell Game? paper:

AK

38,459 просмотров • 3 месяцев назад

Does off-policy value-based RL scale? In LLMs, larger scale predictably improves performance. Value-based RL learns from arbitrary data and is sample-efficient, but folk wisdom says it doesn't scale 🧵⬇️We show predictability for scaling value-based RL!
0:16

Does off-policy value-based RL scale? In LLMs, larger scale predictably improves performance. Value-based RL learns from arbitrary data and is sample-efficient, but folk wisdom says it doesn't scale 🧵⬇️We show predictability for scaling value-based RL!

Oleg Rybkin

23,979 просмотров • 1 год назад

Ever wondered how training dynamics differ between LLMs 🖋️ and Vision 👁️ models? We explore this and close the gap between VMs and LLMs in our #NeurIPS2024 paper "TrAct: Making First-layer Pre-Activations Trainable". Paper📜 Video🎥
0:20

Ever wondered how training dynamics differ between LLMs 🖋️ and Vision 👁️ models? We explore this and close the gap between VMs and LLMs in our #NeurIPS2024 paper "TrAct: Making First-layer Pre-Activations Trainable". Paper📜 Video🎥

Felix Petersen

20,915 просмотров • 1 год назад

E11 Bio is excited to unveil PRISM technology for mapping brain wiring with simple light microscopes. Today, brain mapping in humans and other mammals is bottlenecked by accurate neuron tracing. PRISM uses molecular ID codes and AI to help neurons trace themselves. We discovered a new cell barcoding approach exceeding comparable methods by more than 750x. This is the heart of PRISM. We integrated this capability with microscopy and AI image analysis to automatically trace neurons at high resolution and annotate them with molecular features. This is a key advance towards economically viable brain mapping - 95% of costs stem from neuron tracing. It is also an important step towards democratizing neuron tracing for everyday neuroscience. Solving these problems is critical for curing brain disorders, building safer and human-like AI, and even simulating brain function. In our first pilot study, we acquired a unique dataset in mouse hippocampus. Barcodes improved the accuracy of tracing genetically labelled neurons by 8x – with a clear path to 100x or more. They also permit tracing across spatial gaps – essential for mitigating tissue section loss in whole-brain scaling. Using molecular annotation, we uncover an intriguing feature of synaptic organization, demonstrating how PRISM can be used for systematic discovery 🧵
0:11

E11 Bio is excited to unveil PRISM technology for mapping brain wiring with simple light microscopes. Today, brain mapping in humans and other mammals is bottlenecked by accurate neuron tracing. PRISM uses molecular ID codes and AI to help neurons trace themselves. We discovered a new cell barcoding approach exceeding comparable methods by more than 750x. This is the heart of PRISM. We integrated this capability with microscopy and AI image analysis to automatically trace neurons at high resolution and annotate them with molecular features. This is a key advance towards economically viable brain mapping - 95% of costs stem from neuron tracing. It is also an important step towards democratizing neuron tracing for everyday neuroscience. Solving these problems is critical for curing brain disorders, building safer and human-like AI, and even simulating brain function. In our first pilot study, we acquired a unique dataset in mouse hippocampus. Barcodes improved the accuracy of tracing genetically labelled neurons by 8x – with a clear path to 100x or more. They also permit tracing across spatial gaps – essential for mitigating tissue section loss in whole-brain scaling. Using molecular annotation, we uncover an intriguing feature of synaptic organization, demonstrating how PRISM can be used for systematic discovery 🧵

Andrew Payne

112,064 просмотров • 9 месяцев назад

How do time series foundation models forecast unseen dynamical systems? In new experiments, we find that small transformers learn to approximate transfer operators in-context. (1/N)
0:12

How do time series foundation models forecast unseen dynamical systems? In new experiments, we find that small transformers learn to approximate transfer operators in-context. (1/N)

William Gilpin

29,267 просмотров • 4 месяцев назад

(1/6) X-Humanoid 🤖: Scaling up data for Humanoid Robots. We convert human daily activity videos (from Ego-Exo4D) into humanoid videos (i.e., Tesla Optimus) performing tasks like cooking or fixing a bike. This data can be potentially used to train robot policies and world models. 🔥 Project page: Paper link:
0:11

(1/6) X-Humanoid 🤖: Scaling up data for Humanoid Robots. We convert human daily activity videos (from Ego-Exo4D) into humanoid videos (i.e., Tesla Optimus) performing tasks like cooking or fixing a bike. This data can be potentially used to train robot policies and world models. 🔥 Project page: Paper link:

Mike Shou

87,622 просмотров • 7 месяцев назад

Artificial Intelligence (#AI) models that process and interpret visual data are called large vision models. These models, like large language models, are trained on large amounts of data. #midjourney #dalle #stablediffusion (1/8) 🧵 ⤵️
0:10

Artificial Intelligence (#AI) models that process and interpret visual data are called large vision models. These models, like large language models, are trained on large amounts of data. #midjourney #dalle #stablediffusion (1/8) 🧵 ⤵️

Nuklai

13,614 просмотров • 2 лет назад

My first PhD paper!🎉We learn *diffusion* models for code generation that learn to directly *edit* syntax trees of programs. The result is a system that can incrementally write code, see the execution output, and debug it. 🧵1/n
0:12

My first PhD paper!🎉We learn *diffusion* models for code generation that learn to directly *edit* syntax trees of programs. The result is a system that can incrementally write code, see the execution output, and debug it. 🧵1/n

Shreyas Kapur

742,493 просмотров • 2 лет назад

NVIDIA's CUDA Moat is becoming a Copper Moat with scale-up networking. Bigger models demand more from the scale-up network, and we❤️ Copper
0:17

NVIDIA's CUDA Moat is becoming a Copper Moat with scale-up networking. Bigger models demand more from the scale-up network, and we❤️ Copper

SemiAnalysis

38,731 просмотров • 10 месяцев назад

🤖 Does VLA models really listen to language instructions? Maybe not 👀 🚀 Introducing our RSS paper: CodeDiffuser -- using VLM-generated code to bridge the gap between **high-level language** and **low-level visuomotor policy** 🎮 Try the live demo: (1/9)
0:16

🤖 Does VLA models really listen to language instructions? Maybe not 👀 🚀 Introducing our RSS paper: CodeDiffuser -- using VLM-generated code to bridge the gap between **high-level language** and **low-level visuomotor policy** 🎮 Try the live demo: (1/9)

Yixuan Wang

26,648 просмотров • 1 год назад

Imagine you want to learn about the technology behind LLMs. You instantly get an 100 page book with chapters, content, and structure. What if you find the language too technical? You can change the prompt and the book – all 100 pages – adapts to your needs. What if you want to learn how to code models in python? You can ask for that too, and in seconds, every chapter has code examples alongside the instructional content. That’s the power of an Infinite Bookshelf. Open source, Powered by Groq Inc
0:11

Imagine you want to learn about the technology behind LLMs. You instantly get an 100 page book with chapters, content, and structure. What if you find the language too technical? You can change the prompt and the book – all 100 pages – adapts to your needs. What if you want to learn how to code models in python? You can ask for that too, and in seconds, every chapter has code examples alongside the instructional content. That’s the power of an Infinite Bookshelf. Open source, Powered by Groq Inc

Ben Klieger

62,600 просмотров • 1 год назад

Introducing DeepSeek Coder! - SOTA large coding models with params ranging from 1.3B to 33B. - Building games, testing code, fixing bugs, and analyzing data... You dream it, we make it. - Free for commercial use and fully open-source. Try it out now at
0:19

Introducing DeepSeek Coder! - SOTA large coding models with params ranging from 1.3B to 33B. - Building games, testing code, fixing bugs, and analyzing data... You dream it, we make it. - Free for commercial use and fully open-source. Try it out now at

DeepSeek

162,557 просмотров • 2 лет назад

We just shipped React Native ExecuTorch v0.8.0 – our biggest release yet! 👏🏼 ➡️ Vision Camera integration that runs computer vision models directly on your camera feed, in realtime. Also new: multiple CV models (including RF-DETR (Roboflow) and Liquid AI's Vision Language), Bare React Native support, and more. Takes minutes to try – check out the repo! ⬇️
0:12

We just shipped React Native ExecuTorch v0.8.0 – our biggest release yet! 👏🏼 ➡️ Vision Camera integration that runs computer vision models directly on your camera feed, in realtime. Also new: multiple CV models (including RF-DETR (Roboflow) and Liquid AI's Vision Language), Bare React Native support, and more. Takes minutes to try – check out the repo! ⬇️

Software Mansion

37,099 просмотров • 3 месяцев назад

Chat GPT inside VS Code! Find bugs, add comments, explain code and more! (how to do it yourself in the next tweet)
0:17

Chat GPT inside VS Code! Find bugs, add comments, explain code and more! (how to do it yourself in the next tweet)

Jarrod Watts

269,839 просмотров • 3 лет назад

My favorite paper this year: "Video models are zero-shot learners and reasoners" It illustrates that video models show emergent visual reasoning at scale - they can solve vision tasks they weren't trained for. This may be the "GPT moment" for vision. Let's break it down 👇
0:19

My favorite paper this year: "Video models are zero-shot learners and reasoners" It illustrates that video models show emergent visual reasoning at scale - they can solve vision tasks they weren't trained for. This may be the "GPT moment" for vision. Let's break it down 👇

Justine Moore

185,001 просмотров • 6 месяцев назад

TOMORROW, 9:30AM PT We go live with an Odyssey announcement (or two) I've been looking forward to for some time. I think these releases will make where world models are headed, and how powerful they can become, much more obvious. So hyped. GPUs scaling as we speak.
0:10

TOMORROW, 9:30AM PT We go live with an Odyssey announcement (or two) I've been looking forward to for some time. I think these releases will make where world models are headed, and how powerful they can become, much more obvious. So hyped. GPUs scaling as we speak.

Oliver Cameron

79,586 просмотров • 1 месяц назад

🚨New paper! Generative models are often “miscalibrated”. We calibrate diffusion models, LLMs, and more to meet desired distributional properties. E.g. we finetune protein models to better match the diversity of natural proteins.
0:10

🚨New paper! Generative models are often “miscalibrated”. We calibrate diffusion models, LLMs, and more to meet desired distributional properties. E.g. we finetune protein models to better match the diversity of natural proteins.

Brian L Trippe

20,562 просмотров • 9 месяцев назад

Vision AI agents are becoming a practical way to automatically turn video data from the physical world into operational intelligence in factories, cities, warehouses and transportation systems. See how Roboflow, DeepHow, and Linker Vision use NVIDIA Cosmos and NVIDIA Metropolis to generate synthetic data, fine-tune vision models faster, and deploy vision agents at scale. 📰 Read the blog:
0:13

Vision AI agents are becoming a practical way to automatically turn video data from the physical world into operational intelligence in factories, cities, warehouses and transportation systems. See how Roboflow, DeepHow, and Linker Vision use NVIDIA Cosmos and NVIDIA Metropolis to generate synthetic data, fine-tune vision models faster, and deploy vision agents at scale. 📰 Read the blog:

NVIDIA Omniverse

21,412 просмотров • 12 дней назад

Working on a new topic and hardware for a while: Feet Language Action models. VLAs teach robots to act through vision and language FLAs teach legged robots to act through contact Given a task, terrain, body state, and instruction - decide where, when, and how to make contact
0:18

Working on a new topic and hardware for a while: Feet Language Action models. VLAs teach robots to act through vision and language FLAs teach legged robots to act through contact Given a task, terrain, body state, and instruction - decide where, when, and how to make contact

Irvin (in Japan 🇯🇵)

13,828 просмотров • 16 дней назад