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Mastering Tensor Dimensions in Transformers
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reacted to
not-lain's
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about 10 hours ago
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Published a new blogpost ๐
In this blogpost I have gone through the transformers' architecture emphasizing how shapes propagate throughout each layer.
๐ https://huggingface.co/blog/not-lain/tensor-dims
some interesting takeaways :
In this blogpost I have gone through the transformers' architecture emphasizing how shapes propagate throughout each layer.
๐ https://huggingface.co/blog/not-lain/tensor-dims
some interesting takeaways :
reacted to
ezgikorkmaz's
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about 10 hours ago
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197
If you are interested in reinforcement learning, I am organizing the AAAI 2025 Tutorial! Find the details below:
Link: https://sites.google.com/view/aisafety-aaai2025
Link: https://sites.google.com/view/aisafety-aaai2025
reacted to
m-ric's
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about 10 hours ago
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182
๐ข๐ฆ-๐๐ฒ๐ป๐ฒ๐๐ถ๐: ๐ป๐ฒ๐ ๐ฟ๐ฒ๐๐ฒ๐ฎ๐ฟ๐ฐ๐ต ๐ฝ๐ฎ๐ฝ๐ฒ๐ฟ ๐ฝ๐ฟ๐ผ๐ฝ๐ผ๐๐ฒ๐ ๐ฎ ๐ป๐ผ๐๐ฒ๐น ๐๐ฟ๐ฎ๐ถ๐ป๐ถ๐ป๐ด ๐ฑ๐ฎ๐๐ฎ ๐ด๐ฒ๐ป๐ฒ๐ฟ๐ฎ๐๐ถ๐ผ๐ป ๐บ๐ฒ๐๐ต๐ผ๐ฑ ๐ณ๐ผ๐ฟ ๐๐น๐ฎ๐๐ฑ๐ฒ-๐๐ผ๐บ๐ฝ๐๐๐ฒ๐ฟ-๐จ๐๐ฒ-๐น๐ถ๐ธ๐ฒ ๐ฎ๐ด๐ฒ๐ป๐๐, ๐๐ถ๐๐ต ๐ถ๐บ๐ฝ๐ฟ๐ฒ๐๐๐ถ๐๐ฒ ๐ฟ๐ฒ๐๐๐น๐๐! ๐ฅ
The main bottleneck in building GUI agents it to find training data.
GUI Agent trajectories are not easy to get by. Crowdsourcing trajectories, then manually annotating them, could be an option, but at scale, it's hard to do
You could use synthetic data generation (ask 1000s small existing GUI agents to solve tasks, keep only successful runs). But then it's hard to come up with many high level-tasks.
โก๏ธ Well, a novel technique was just published that creates a new promising paradigm for synthetic data generation: Shanghai AI Lab researchers propose OS-Genesis, a novel way to create training data for GUI agents that flips the traditional approach on its head. Instead of starting with predefined tasks and having humans or machines execute them, OS-Genesis first explores the interface naturally, then derives meaningful tasks from those interactions.
๐ Exploration-driven vs task-driven approach:
โฃ Instead of starting with tasks, OS-Genesis first explores GUIs by clicking and interacting
โฃ It then reverse-engineers high-level tasks from successful interaction patterns
โฃ This leads to more natural and diverse training data than predefined tasks
๐ฏ Novel reward model for trajectory quality:
โฃ Rather than discarding incomplete trajectories, OS-Genesis scores them based on coherence and completion
โฃ This preserves valuable partial successes that would otherwise be wasted
๐ Superior results across environments:
โฃ Nearly doubles performance on AndroidWorld (9.8% โ 17.4%)
By the way, this field of GUI agents is still in infancy, so you can still make a difference with "low-cost" setups: their paper gets SOTA results with only 8xA100!
Read the paper here ๐ OS-Genesis: Automating GUI Agent Trajectory Construction via Reverse Task Synthesis (2412.19723)
The main bottleneck in building GUI agents it to find training data.
GUI Agent trajectories are not easy to get by. Crowdsourcing trajectories, then manually annotating them, could be an option, but at scale, it's hard to do
You could use synthetic data generation (ask 1000s small existing GUI agents to solve tasks, keep only successful runs). But then it's hard to come up with many high level-tasks.
โก๏ธ Well, a novel technique was just published that creates a new promising paradigm for synthetic data generation: Shanghai AI Lab researchers propose OS-Genesis, a novel way to create training data for GUI agents that flips the traditional approach on its head. Instead of starting with predefined tasks and having humans or machines execute them, OS-Genesis first explores the interface naturally, then derives meaningful tasks from those interactions.
๐ Exploration-driven vs task-driven approach:
โฃ Instead of starting with tasks, OS-Genesis first explores GUIs by clicking and interacting
โฃ It then reverse-engineers high-level tasks from successful interaction patterns
โฃ This leads to more natural and diverse training data than predefined tasks
๐ฏ Novel reward model for trajectory quality:
โฃ Rather than discarding incomplete trajectories, OS-Genesis scores them based on coherence and completion
โฃ This preserves valuable partial successes that would otherwise be wasted
๐ Superior results across environments:
โฃ Nearly doubles performance on AndroidWorld (9.8% โ 17.4%)
By the way, this field of GUI agents is still in infancy, so you can still make a difference with "low-cost" setups: their paper gets SOTA results with only 8xA100!
Read the paper here ๐ OS-Genesis: Automating GUI Agent Trajectory Construction via Reverse Task Synthesis (2412.19723)
reacted to
Kseniase's
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about 10 hours ago
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267
10 AI Systems for Scientific Research
Almost every AI researcher has studied or conducted a large number of AI research papers. So, it's quite logical that researchers are trying to create AI systems to help conduct research. Creating scientific research could be much easier and more varied if we use LLMs and AI assistants tailored for this purpose. Just imagine how interesting it would be to read high-quality research about AI made by an AI agent.
Today, we offer you to explore these 10 AI systems for scientific research:
1. Agent Laboratory framework helps researchers input their ideas by generating a research report and code repository: Agent Laboratory: Using LLM Agents as Research Assistants (2501.04227)
2. AI Scientist performs fully automated scientific discovery including creating ideas: The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery (2408.06292)
3. SciMON generates new ideas derived from the scientific literature: Learning to Generate Novel Scientific Directions with Contextualized Literature-based Discovery (2305.14259)
4. ResearchAgent implements LLMs to automate idea generation, methods, and experiment design, and ReviewingAgents' feedback to refine ideas: ResearchAgent: Iterative Research Idea Generation over Scientific Literature with Large Language Models (2404.07738)
5. Scientific Generative Agent (SGA) discovers novel, coherent solutions in physics and molecular design: LLM and Simulation as Bilevel Optimizers: A New Paradigm to Advance Physical Scientific Discovery (2405.09783)
6. MLRCopilot boosts machine learning research: MLR-Copilot: Autonomous Machine Learning Research based on Large Language Models Agents (2408.14033)
7. SciAgents accelerates material science discovery through combining knowledge graphs, LLMs, and multi-agent systems. SciAgents: Automating scientific discovery through multi-agent intelligent graph reasoning (2409.05556)
8. VirSci multi-agent system mimics teamwork among scientists. Two Heads Are Better Than One: A Multi-Agent System Has the Potential to Improve Scientific Idea Generation (2410.09403)
9. Chain-of-Ideas (CoI) agent organizes research into a chain structure. Chain of Ideas: Revolutionizing Research in Novel Idea Development with LLM Agents (2410.13185)
10. A system with CycleResearcher and CycleReviewer generates research papers and peer reviews: CycleResearcher: Improving Automated Research via Automated Review (2411.00816)
LLM4SR: A Survey on Large Language Models for Scientific Research (2501.04306) is worth exploring to study and analyze more systems for scientific research
Almost every AI researcher has studied or conducted a large number of AI research papers. So, it's quite logical that researchers are trying to create AI systems to help conduct research. Creating scientific research could be much easier and more varied if we use LLMs and AI assistants tailored for this purpose. Just imagine how interesting it would be to read high-quality research about AI made by an AI agent.
Today, we offer you to explore these 10 AI systems for scientific research:
1. Agent Laboratory framework helps researchers input their ideas by generating a research report and code repository: Agent Laboratory: Using LLM Agents as Research Assistants (2501.04227)
2. AI Scientist performs fully automated scientific discovery including creating ideas: The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery (2408.06292)
3. SciMON generates new ideas derived from the scientific literature: Learning to Generate Novel Scientific Directions with Contextualized Literature-based Discovery (2305.14259)
4. ResearchAgent implements LLMs to automate idea generation, methods, and experiment design, and ReviewingAgents' feedback to refine ideas: ResearchAgent: Iterative Research Idea Generation over Scientific Literature with Large Language Models (2404.07738)
5. Scientific Generative Agent (SGA) discovers novel, coherent solutions in physics and molecular design: LLM and Simulation as Bilevel Optimizers: A New Paradigm to Advance Physical Scientific Discovery (2405.09783)
6. MLRCopilot boosts machine learning research: MLR-Copilot: Autonomous Machine Learning Research based on Large Language Models Agents (2408.14033)
7. SciAgents accelerates material science discovery through combining knowledge graphs, LLMs, and multi-agent systems. SciAgents: Automating scientific discovery through multi-agent intelligent graph reasoning (2409.05556)
8. VirSci multi-agent system mimics teamwork among scientists. Two Heads Are Better Than One: A Multi-Agent System Has the Potential to Improve Scientific Idea Generation (2410.09403)
9. Chain-of-Ideas (CoI) agent organizes research into a chain structure. Chain of Ideas: Revolutionizing Research in Novel Idea Development with LLM Agents (2410.13185)
10. A system with CycleResearcher and CycleReviewer generates research papers and peer reviews: CycleResearcher: Improving Automated Research via Automated Review (2411.00816)
LLM4SR: A Survey on Large Language Models for Scientific Research (2501.04306) is worth exploring to study and analyze more systems for scientific research
reacted to
roseking's
post with ๐
about 10 hours ago
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I have an idea that I'm currently working on โ developing a standard API for large models. This standard would ensure compatibility with all known protocols, enabling large models worldwide to be accessed through a unified API. For instance, it could connect models like qwen, deepseek, and GLM from China. However, I haven't found a suitable forum for democratic discussion on this yet. I'm unsure of the next steps. ๐ฅ๐ฅ๐ฅ
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