🎓 Empowering Graph Neural Networks for Real-World Tasks

📘 Doctoral thesis
🗓 Publish year: 2024

🧑‍💻 Author: Zhichun Guo
🏢 University: University of Notre Dame, Indiana

📎 Study Thesis 

🌐 http://t.me/codeprogrammer ✅️

Large Brain Model for Learning Generic Representations with Tremendous EEG Data in BCI

🖥 Github: https://github.com/935963004/labram

📕Paper: https://arxiv.org/abs/2405.18765v1

#machineLearning

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⚡️ Flash version of Scribble SDXL

Utilizes SDXL Flash and Scribble SDXL, making it even faster to get quality images from simple sketches and doodles

🤗 Launch on Hugging Face

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🌟 V-Express is a method of animating a static photograph of a face, taking into account pose, sound and the image itself

The V-Express method was recently introduced by Cong Wang, Kuan Tian, ​​Jun Zhang and others as one of the most efficient methods for generating such animations, the code and model are published

🖥 GitHub

🟡 V-Express page

🤗 Model on Hugging Face

https://news.1rj.ru/str/DataScienceT ✅

🌟 GPT Researcher is a GPT-based autonomous agent that performs comprehensive online research on any given topic

- pip install gpt-researcher

Example of using GPT Researcher:

from gpt_researcher import GPTResearcher

query = "why is Nvidia stock going up?"
researcher = GPTResearcher(query=query, report_type="research_report")
# Conduct research on the given query
research_result = await researcher.conduct_research()
# Write the report
report = await researcher.write_report()

🖥 GitHub
🟡 GPT Researcher Page

https://news.1rj.ru/str/DataScienceT ✅

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🌟 Dot is an open-source application for local deployment of LLM, RAG and TTS for working with your documents

Dot is designed to easily interact with documents and files (.pdf, .docx, .xlsx, .pptx, .md) using local LLMs and Retrieval Augmented Generation technology.
Dot was inspired by solutions like Nvidia's Chat with RTX
Defaults to Phi-3

🖥 GitHub
🟡 Dot Page

https://news.1rj.ru/str/DataScienceT ✅

InterpreTabNet: Distilling Predictive Signals from Tabular Data by Salient Feature Interpretation

🖥 Github: https://github.com/jacobyhsi/InterpreTabNet

📕 Paper: https://arxiv.org/abs/2406.00426v1

https://news.1rj.ru/str/DataScienceT ✅