📃A key review on graph data science: The power of graphs in scientific studies
📎 Study paper
@Machine_learn
📎 Study paper
@Machine_learn
Paper: Scalable Autoregressive Image Generation with Mamba
Paper: https://arxiv.org/pdf/2408.12245v1.pdf
Code: https://github.com/hp-l33/aim
Dataset: ImageNet
@Machine_learn
Paper: https://arxiv.org/pdf/2408.12245v1.pdf
Code: https://github.com/hp-l33/aim
Dataset: ImageNet
@Machine_learn
🎓 Graph Neural Networks in Intrusion Detection
📘A thesis submitted in fulfilment of the requirements for the degree of MSc. Computer Science
🗓Publish year: 2024
📎Study Thesis
@Machine_learn
📘A thesis submitted in fulfilment of the requirements for the degree of MSc. Computer Science
🗓Publish year: 2024
📎Study Thesis
@Machine_learn
DocsGPT
DocsGPT is a cutting-edge open-source solution that streamlines the process of finding information in project documentation. With its integration of the powerful GPT models, developers can easily ask questions about a project and receive accurate answers.
Say goodbye to time-consuming manual searches, and let DocsGPT help you quickly find the information you need. Try it out and see how it revolutionizes your project documentation experience. Contribute to its development and be a part of the future of AI-powered assistance.
Creator: Arc53
Stars ⭐️: 7.4k
Forked By: 769
https://github.com/arc53/DocsGPT
#DocsGPT #GPT
@Machine_learn
DocsGPT is a cutting-edge open-source solution that streamlines the process of finding information in project documentation. With its integration of the powerful GPT models, developers can easily ask questions about a project and receive accurate answers.
Say goodbye to time-consuming manual searches, and let DocsGPT help you quickly find the information you need. Try it out and see how it revolutionizes your project documentation experience. Contribute to its development and be a part of the future of AI-powered assistance.
Creator: Arc53
Stars ⭐️: 7.4k
Forked By: 769
https://github.com/arc53/DocsGPT
#DocsGPT #GPT
@Machine_learn
GitHub
GitHub - arc53/DocsGPT: Chatbot for documentation, that allows you to chat with your data. Privately deployable, provides AI knowledge…
Chatbot for documentation, that allows you to chat with your data. Privately deployable, provides AI knowledge sharing and integrates knowledge into your AI workflow - arc53/DocsGPT
An open source UI to train your own Flux LoRA just landed on Hugging Face 🚀 Also, probably the easiest and cheapest (local training also supported).
https://huggingface.co/spaces/autotrain-projects/train-flux-lora-ease
@Machine_learn
https://huggingface.co/spaces/autotrain-projects/train-flux-lora-ease
@Machine_learn
Forwarded from Papers
با عرض سلام مقاله اي تحت ريوايزد داريم که در حوزه Ultrasound Image Segmentation هستش. دوستانی که نیاز دارن نفر سومش رو می تونیم اختصاص بدیم.
@Raminmousa
@Paper4money
@Machine_learn
@Raminmousa
@Paper4money
@Machine_learn
Please open Telegram to view this post
VIEW IN TELEGRAM
Please open Telegram to view this post
VIEW IN TELEGRAM
Please open Telegram to view this post
VIEW IN TELEGRAM
# Clone repository
git clone https://github.com/01-ai/Yi-Coder.git
cd Yi-Coder
# Install requirements
pip install -r requirements.txt
@Machine_learn
Please open Telegram to view this post
VIEW IN TELEGRAM
Book of machine learning algorithms & concepts explained to simply, even a human can understand.
📓 Book
✅ @Machine_learn
📓 Book
Please open Telegram to view this post
VIEW IN TELEGRAM
Conformal prediction under ambiguous ground truth
Paper: https://arxiv.org/pdf/2307.09302v2.pdf
Codes:
https://github.com/google-deepmind/uncertain_ground_truth
https://github.com/alaalab/webcp
Dataset: Dermatology ddx dataset
✅ @Machine_learn
Paper: https://arxiv.org/pdf/2307.09302v2.pdf
Codes:
https://github.com/google-deepmind/uncertain_ground_truth
https://github.com/alaalab/webcp
Dataset: Dermatology ddx dataset
Please open Telegram to view this post
VIEW IN TELEGRAM
Forwarded from Papers
با عرض سلام
در ادامه فرایند نگارش مقالات سعی داریم چند گروه ۴ نفره برای مقالات با موضوعات مختلف ایجاد کنیم. چهار موضوع که می خواهیم در ان ها کار کنیم از قبیل زیر می باشند:
۱ - طبقه بندی تصاویر پزشکی
۲- پیش بینی ترافیک شبکه
۳- حل مشکلات شبکه های RNN در مساله سری زمانی
۴-پیش بینی بار مصرفی در شبکه های smart grid
جهت اطلاعات بیشتر کسانی که دوست دارند می تونن به بنده پیام
بدن.
✅ @Raminmousa
@Paper4money
@machine_learn
در ادامه فرایند نگارش مقالات سعی داریم چند گروه ۴ نفره برای مقالات با موضوعات مختلف ایجاد کنیم. چهار موضوع که می خواهیم در ان ها کار کنیم از قبیل زیر می باشند:
۱ - طبقه بندی تصاویر پزشکی
۲- پیش بینی ترافیک شبکه
۳- حل مشکلات شبکه های RNN در مساله سری زمانی
۴-پیش بینی بار مصرفی در شبکه های smart grid
جهت اطلاعات بیشتر کسانی که دوست دارند می تونن به بنده پیام
بدن.
@Paper4money
@machine_learn
Please open Telegram to view this post
VIEW IN TELEGRAM
Machine learning books and papers pinned «با عرض سلام در ادامه فرایند نگارش مقالات سعی داریم چند گروه ۴ نفره برای مقالات با موضوعات مختلف ایجاد کنیم. چهار موضوع که می خواهیم در ان ها کار کنیم از قبیل زیر می باشند: ۱ - طبقه بندی تصاویر پزشکی ۲- پیش بینی ترافیک شبکه ۳- حل مشکلات شبکه های RNN در مساله…»
Please open Telegram to view this post
VIEW IN TELEGRAM
This open-source RAG tool for chatting with your documents is Trending at Number-1 in Github from the past few days
🔍 Open-source RAG UI for document QA
🛠️ Supports local LLMs and API providers
📊 Hybrid RAG pipeline with full-text & vector retrieval
🖼️ Multi-modal QA with figures & tables support
📄 Advanced citations with in-browser PDF preview
🧠 Complex reasoning with question decomposition
⚙️ Configurable settings UI
🔧 Extensible Gradio-based architecture
Key features:
🌐 Host your own RAG web UI with multi-user login
🤖 Organize LLM & embedding models (local & API)
🔎 Hybrid retrieval + re-ranking for quality
📚 Multi-modal parsing and QA across documents
💡 Detailed citations with relevance scores
🧩 Question decomposition for complex queries
🎛️ Adjustable retrieval & generation settings
🔌 Customizable UI and indexing strategies
▪ Github
✅ @Machine_learn
🔍 Open-source RAG UI for document QA
🛠️ Supports local LLMs and API providers
📊 Hybrid RAG pipeline with full-text & vector retrieval
🖼️ Multi-modal QA with figures & tables support
📄 Advanced citations with in-browser PDF preview
🧠 Complex reasoning with question decomposition
⚙️ Configurable settings UI
🔧 Extensible Gradio-based architecture
Key features:
🌐 Host your own RAG web UI with multi-user login
🤖 Organize LLM & embedding models (local & API)
🔎 Hybrid retrieval + re-ranking for quality
📚 Multi-modal parsing and QA across documents
💡 Detailed citations with relevance scores
🧩 Question decomposition for complex queries
🎛️ Adjustable retrieval & generation settings
🔌 Customizable UI and indexing strategies
▪ Github
Please open Telegram to view this post
VIEW IN TELEGRAM
Data Structures and Information Retrieval in Python
https://greenteapress.com/wp/data-structures-and-information-retrieval-in-python/
✅ @Machine_learn
https://greenteapress.com/wp/data-structures-and-information-retrieval-in-python/
Please open Telegram to view this post
VIEW IN TELEGRAM
WavTokenizer: an Efficient Acoustic Discrete Codec Tokenizer for Audio Language Modeling
Paper: https://arxiv.org/pdf/2408.16532v1.pdf
Code: https://github.com/jishengpeng/wavtokenizer
Dataset: AudioSet LibriTTS SLURP
✅ @Machine_learn
Paper: https://arxiv.org/pdf/2408.16532v1.pdf
Code: https://github.com/jishengpeng/wavtokenizer
Dataset: AudioSet LibriTTS SLURP
Please open Telegram to view this post
VIEW IN TELEGRAM
Eagle: Exploring The Design Space for Multimodal LLMs with Mixture of Encoders
Paper: https://arxiv.org/pdf/2408.15998v1.pdf
Code: https://github.com/nvlabs/eagle
✅ @Machine_learn
Paper: https://arxiv.org/pdf/2408.15998v1.pdf
Code: https://github.com/nvlabs/eagle
Please open Telegram to view this post
VIEW IN TELEGRAM