VMem: Consistent Interactive Video Scene Generation with Surfel-Indexed View Memory
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Edwin Santiago
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Necessity is mother of invention. To understand ⚡FlashMLA⚡ by
🐋DeepSeek 🐋, the first question to ask is why.
The keyword here is H800, a lower-end product tailored for export control. The purpose here is to squeeze out as much performance as possible.
But here is the most important takeaway: this invention benefits EVERYONE.
🐋DeepSeek 🐋, the first question to ask is why.
The keyword here is H800, a lower-end product tailored for export control. The purpose here is to squeeze out as much performance as possible.
But here is the most important takeaway: this invention benefits EVERYONE.
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🚀 Introducing MOUSE: Space Research Thinking on HuggingFace Spaces
🚀 How to Get Started
ginipick/spaces-research-think
Welcome to **MOUSE: Space Research Thinking** – an innovative HuggingFace Spaces project designed to transform how you analyze and interact with Python code. Whether you're a developer, researcher, or simply passionate about coding, this tool provides state-of-the-art analysis, summarization, and usage guidance, all powered by advanced AI.
---
## 🌟 Key Features
- **Real-Time Code Analysis**
Instantly dissect your Python code to reveal its structure, functionality, and potential applications. Our tool delivers:
- **Background & Necessity**: Understand the context behind the code.
- **Functional Utility & Value**: Highlight core functionalities and benefits.
- **Distinctive Features**: Discover what sets the project apart.
- **Target Audience & Applications**: Identify who can benefit and how.
- **Expected Impact**: Envision the improvements and innovations the code can drive.
🔍
- **Visual File Structure Overview**
Navigate your project with ease! A dynamic tree-view displays your file hierarchy in a clear, intuitive format, allowing you to explore directories and files effortlessly. 🌲
- **Interactive Usage Guide**
Receive step-by-step instructions and practical tips on using the tool effectively. Our AI assistant explains everything in an engaging, user-friendly manner, ensuring a smooth learning curve. 💡
- **AI-Powered Code Chat**
Engage in real-time conversations with our AI. Ask questions, request detailed explanations, or dive deeper into code specifics with a chat interface that makes complex topics accessible. 🤖💬
- **Customizable Experience**
Tailor the analysis to your needs with adjustable parameters like token limits and response temperatures, enabling both concise summaries and in-depth explorations. ⚙️
🚀 How to Get Started
ginipick/spaces-research-think
Welcome to **MOUSE: Space Research Thinking** – an innovative HuggingFace Spaces project designed to transform how you analyze and interact with Python code. Whether you're a developer, researcher, or simply passionate about coding, this tool provides state-of-the-art analysis, summarization, and usage guidance, all powered by advanced AI.
---
## 🌟 Key Features
- **Real-Time Code Analysis**
Instantly dissect your Python code to reveal its structure, functionality, and potential applications. Our tool delivers:
- **Background & Necessity**: Understand the context behind the code.
- **Functional Utility & Value**: Highlight core functionalities and benefits.
- **Distinctive Features**: Discover what sets the project apart.
- **Target Audience & Applications**: Identify who can benefit and how.
- **Expected Impact**: Envision the improvements and innovations the code can drive.
🔍
- **Visual File Structure Overview**
Navigate your project with ease! A dynamic tree-view displays your file hierarchy in a clear, intuitive format, allowing you to explore directories and files effortlessly. 🌲
- **Interactive Usage Guide**
Receive step-by-step instructions and practical tips on using the tool effectively. Our AI assistant explains everything in an engaging, user-friendly manner, ensuring a smooth learning curve. 💡
- **AI-Powered Code Chat**
Engage in real-time conversations with our AI. Ask questions, request detailed explanations, or dive deeper into code specifics with a chat interface that makes complex topics accessible. 🤖💬
- **Customizable Experience**
Tailor the analysis to your needs with adjustable parameters like token limits and response temperatures, enabling both concise summaries and in-depth explorations. ⚙️
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Now the Hugging Face agent course is getting real! With frameworks like smolagents, LlamaIndex, and LangChain.
🔗 Follow the org for updates
agents-course
This week we are releasing the first framework unit in the course and it’s on smolagents. This is what the unit covers:
- why should you use smolagents vs another library?
- how to build agents that use code
- build multiagents systems
- use vision language models for browser use
The team has been working flat out on this for a few weeks. Led by @sergiopaniego and supported by smolagents author @m-ric .
🔗 Follow the org for updates
agents-course
This week we are releasing the first framework unit in the course and it’s on smolagents. This is what the unit covers:
- why should you use smolagents vs another library?
- how to build agents that use code
- build multiagents systems
- use vision language models for browser use
The team has been working flat out on this for a few weeks. Led by @sergiopaniego and supported by smolagents author @m-ric .
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📢 Old Research Alert: Making Computer Vision Models Smaller & Smarter!
Years ago, I coded an optimization in the first layers of a convolutional neural network (computer vision) and ended never posting here. The optimization decreases the number of parameters while increasing accuracy. The optimization relies in separating (branching) chromatic and achromatic information through the layers of a neural network.
YouTube videos:
https://www.youtube.com/watch?v=u4vZZmBMFLw
https://www.youtube.com/watch?v=-BD293yqdKI
Source codes:
https://github.com/joaopauloschuler/two-branch-plant-disease
https://github.com/joaopauloschuler/two-path-noise-lab-plant-disease
Research papers:
https://www.researchgate.net/publication/361511874_Color-Aware_Two-Branch_DCNN_for_Efficient_Plant_Disease_Classification
https://www.researchgate.net/publication/355215213_Reliable_Deep_Learning_Plant_Leaf_Disease_Classification_Based_on_Light-Chroma_Separated_Branches
May the force be with you.
Years ago, I coded an optimization in the first layers of a convolutional neural network (computer vision) and ended never posting here. The optimization decreases the number of parameters while increasing accuracy. The optimization relies in separating (branching) chromatic and achromatic information through the layers of a neural network.
YouTube videos:
https://www.youtube.com/watch?v=u4vZZmBMFLw
https://www.youtube.com/watch?v=-BD293yqdKI
Source codes:
https://github.com/joaopauloschuler/two-branch-plant-disease
https://github.com/joaopauloschuler/two-path-noise-lab-plant-disease
Research papers:
https://www.researchgate.net/publication/361511874_Color-Aware_Two-Branch_DCNN_for_Efficient_Plant_Disease_Classification
https://www.researchgate.net/publication/355215213_Reliable_Deep_Learning_Plant_Leaf_Disease_Classification_Based_on_Light-Chroma_Separated_Branches
May the force be with you.
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