❇️ AniGS: Animatable Gaussian Avatar from a Single Image with Inconsistent Gaussian Reconstruction 🔥


🔗 Discover More:
  *  Github Link
  *  Project Page: AniGS
  *  Paper: Read the paper

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This channels is for Programmers, Coders, Software Engineers.

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2DMatGMM: An open-source robust machine learning platform for real-time detection and classification of 2D material flakes

🖥 Github: https://github.com/jaluus/2dmatgmm

📕 Paper: https://arxiv.org/abs/2412.09333v1

⭐️ Dataset: https://paperswithcode.com/task/instance-segmentation

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

OASIS Alzheimer's Detection

Large-scale brain MRI dataset for deep neural network analysis

About Dataset
The dataset used is the OASIS MRI dataset (https://sites.wustl.edu/oasisbrains/), which consists of 80,000 brain MRI images. The images have been divided into four classes based on Alzheimer's progression. The dataset aims to provide a valuable resource for analyzing and detecting early signs of Alzheimer's disease.

To make the dataset accessible, the original .img and .hdr files were converted into Nifti format (.nii) using FSL (FMRIB Software Library). The converted MRI images of 461 patients have been uploaded to a GitHub repository, which can be accessed in multiple parts.
For the neural network training, 2D images were used as input. The brain images were sliced along the z-axis into 256 pieces, and slices ranging from 100 to 160 were selected from each patient. This approach resulted in a comprehensive dataset for analysis.

Patient classification was performed based on the provided metadata and Clinical Dementia Rating (CDR) values, resulting in four classes: demented, very mild demented, mild demented, and non-demented. These classes enable the detection and study of different stages of Alzheimer's disease progression.

During the dataset preparation, the .nii MRI scans were converted to .jpg files. Although this conversion presented some challenges, the files were successfully processed using appropriate tools. The resulting dataset size is 1.3 GB.

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⚡️ Byte Latent Transformer: Patches Scale Better Than Tokens

Byte Latent Transformer architecture (BLTs), a new byte-level LLM architecture that for the first time, matches tokenization-based LLM performance at scale, with significant improvements in inference efficiency and robustness.

🖥 Github: https://github.com/facebookresearch/blt

📕 Paper: https://arxiv.org/abs/2412.09871v1

🌟 Dataset: https://paperswithcode.com/dataset/mmlu

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🀄 GuoFeng Webnovel: A Discourse-Level and Multilingual Corpus of Web Fiction

🖥 Github: https://github.com/longyuewangdcu/guofeng-webnovel

📕 Paper: https://arxiv.org/abs/2412.11732v1

🌟 Dataset: www2.statmt.org/wmt24/literary-trans

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

Large Language Models Course: Learn by Doing LLM Projects

🖥 Github: https://github.com/peremartra/Large-Language-Model-Notebooks-Course

📕 Paper: https://doi.org/10.31219/osf.io/qgxea

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KAG: Boosting LLMs in Professional Domains via Knowledge Augmented Generation

Paper: https://arxiv.org/pdf/2409.13731v3.pdf

Code: https://github.com/openspg/kag

Dataset: 2WikiMultiHopQA

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

CogAgent: A Visual Language Model for GUI Agents

Paper: https://arxiv.org/pdf/2312.08914v3.pdf

CVPR 2024: http://openaccess.thecvf.com//content/CVPR2024/papers/Hong_CogAgent_A_Visual_Language_Model_for_GUI_Agents_CVPR_2024_paper.pdf

Code1: https://github.com/thudm/cogvlm
Code2: https://github.com/digirl-agent/digirl
Code3: https://github.com/THUDM/CogAgent

Dataset: TextVQA

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Automating the Search for Artificial Life with Foundation Models

paper: https://arxiv.org/pdf/2412.17799v1.pdf

Code: https://github.com/sakanaai/asal

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

This channels is for Programmers, Coders, Software Engineers.

0️⃣ Python
1️⃣ Data Science
2️⃣ Machine Learning
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5️⃣ Data Analysis
6️⃣ Statistics
7️⃣ Deep Learning
8️⃣ programming Languages

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Are They the Same? Exploring Visual Correspondence Shortcomings of Multimodal LLMs

🖥 Github: https://github.com/zhouyiks/CoLVA/tree/main

📕 Paper: https://arxiv.org/pdf/2501.04670v1.pdf

⭐️ Dataset: https://paperswithcode.com/dataset/bdd100k

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

💻 ACU - Awesome Agents for Computer Use

A project that contains a carefully selected list of resources about AI agents designed to run autonomously on your computers.

It includes research studies, projects, frameworks, guides and various tools.

Agents support task analysis and decision making functions for interacting with any interface.

▪️ Github

#aiagents #awesome #agents

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