📄 Graph-Theoretical Analysis of Biological Networks: A Survey

🗓 Publish year: 2023

🧑‍💻Author: Kayhan Erciyes

📎 Study the paper

🗣️ Telegram: https://t.iss.one/DataScienceT

​​😶‍🌫️ DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

🖥 Github: https://github.com/deepseek-ai/deepseek-math

📚 Paper: https://arxiv.org/abs/2402.03300v1

🗣 Dataset: https://paperswithcode.com/dataset/math

🗣️ Telegram: https://t.iss.one/DataScienceT

Energy-Time-series-anomaly-detection

⛓ Github: https://github.com/HardikPrabhu/Energy-Time-series-anomaly-detection

🔖 Paper: https://arxiv.org/pdf/2402.14384v1.pdf

✨ Tasks: https://paperswithcode.com/task/anomaly-detection

🗣️ Telegram: https://t.iss.one/DataScienceT

🎓 Multi-HMR: Multi-Person Whole-Body Human Mesh Recovery in a Single Shot.

Multi-HMR is a simple but powerful model that takes an RGB image as input and performs 3D-reconstruction of multiple people in space.

👑 Github

💍 Paper

🐻 Dataset

✈️ Telegram: https://t.iss.one/DataScienceT

​​🧠 EasyVolcap: Accelerating Neural Volumetric Video Research

🧑‍💻 Code: https://github.com/zju3dv/easyvolcap

👩‍🎨 Metrics: https://short.llm360.ai/amber-metrics

🌹 Paper: https://arxiv.org/abs/2312.06575v1

👀 Dataset: https://paperswithcode.com/dataset/nerf

🎰 Telegram: https://t.iss.one/DataScienceT

😀 OOTDiffusion: Outfitting Fusion based Latent Diffusion for Controllable Virtual Try-on.

🤡 Github: https://github.com/levihsu/OOTDiffusion

👻 Demo: https://ootd.ibot.cn

😀 Jupyter: https://github.com/camenduru/OOTDiffusion-jupyter

🔵 Telegram: https://t.iss.one/DataScienceT

🔒 Graph Diffusion Policy Optimization

🧑‍💻 Github: https://github.com/sail-sg/gdpo

📕 Paper: https://arxiv.org/pdf/2402.16302v1.pdf

💔 Dataset: https://paperswithcode.com/dataset/zinc

✨ Tasks: https://paperswithcode.com/task/graph-generation

🎲 Telegram: https://t.iss.one/DataScienceT

DCVSMNet: Double Cost Volume Stereo Matching Network

🖥 Github: https://github.com/m2219/dcvsmnet

⚙️ Paper: https://arxiv.org/pdf/2402.16473v1.pdf

🔥 Dataset: https://paperswithcode.com/dataset/kitti

⭐️ Tasks: https://paperswithcode.com/task/stereo-matching-1

🎲 Telegram: https://t.iss.one/DataScienceT

🔥 New free course: Prompt Engineering with Llama 2 from Andrew YNg and and DeepLearning.AI

Llama 2 has become a very important model for the entire AI world.

Llama is not one model, but a whole collection of models. In this course you will learn: - Learn the differences between the different types of Llama 2 and when to use each one.

⭐️ You'll also learn how prompt tags for Llama work - how they can help you with everyday tasks.

⭐️ Learn to use advanced prompts, such as multiple screenshot prompts for classification or chain-of-thought prompts for solving logic problems.

😡 Learn to use specialized models from the Llama collection to solve specific problems, such as Code Llama, which helps you write, analyze and improve code, and Llama Guard , which checks model prompts and responses for malicious content.

The course also covers how to run Llama 2 locally on your own computer.

📌 https://deeplearning.ai/short-courses/prompt-engineering-with-llama-2

🎲 Telegram: https://t.iss.one/DataScienceT

⏺ZHEM: An Integrated Data Processing Framework for Pretraining Foundation Models

🖥 Github: https://github.com/emanual20/zhem

💤 Paper: https://arxiv.org/pdf/2402.16358v1.pdf

🔥 Dataset: https://paperswithcode.com/dataset/wikitext-2

✅ Telegram: https://t.iss.one/DataScienceT

📚 NATURAL LANGUAGE PROCESSING (2023)

👁 Price: 5$

🔄 Download it: https://www.patreon.com/DataScienceBooks/shop/natural-language-processing-textbook-64525

💬 Tags: #NLP

​​🧐 FIND: Interface Foundation Models' Embeddings

🍏 Code: https://github.com/UX-Decoder/FIND

🧑‍🎓 Demo: https://find.xyzou.net/

🔮 Project Page: https://x-decoder-vl.github.io

🆗 Demo: https://find.xyzou.net

💐 ArXiv: https://arxiv.org/pdf/2312.07532.pdf

Placing Objects in Context via Inpainting for Out-of-distribution Segmentation

⌨️ Github: https://github.com/naver/poc

🔖 Paper: https://arxiv.org/pdf/2402.16392v1.pdf

🔥 Dataset: https://paperswithcode.com/dataset/cityscapes

💫 Tasks: https://paperswithcode.com/task/segmentation

🌈 Telegram: https://t.iss.one/DataScienceT

🔥SOTA: Stable Diffusion 3: out ! 🔥

Stable Diffusion 3 is SOTA's new text to image technology.

The new Multimodal Diffusion Transformer (MM Bit) architecture uses separate sets of weights for images and language, improving text/spelling comprehension capabilities.

✅ New scalable architecture for text-to-image synthesis
✅ Bi-directional mixing of text and image token streams
✅ Largest models are superior to open SOTA models such as SDXL

🤥 Blog : https://stability.ai/news/stable-diffusion-3-research-paper

✅️ Paper : https://stabilityai-public-packages.s3.us-west-2.amazonaws.com/Stable+Diffusion+3+Paper.pdf

🎲 Telegram: https://t.iss.one/DataScienceT

RENT (Dirichlet-based Per-Sample Weighting by Transition Matrix for Noisy Label Learning, ICLR 2024)

🖥 Github: https://github.com/BaeHeeSun/RENT

🔖 Paper: https://arxiv.org/pdf/2403.02690v1.pdf

🔥 Dataset: https://paperswithcode.com/dataset/cifar-10

✨ Tasks: https://paperswithcode.com/task/learning-with-noisy-labels

🔄 Telegram: https://t.iss.one/DataScienceT

🖼️ Differential Diffusion: Giving Each Pixel Its Strength 🔥

✅ code: github.com/exx8/differential-diffusion

✅ page: differential-diffusion.github.io

✅ paper: arxiv.org/abs/2306.00950

⚜️ Telegram: https://t.iss.one/DataScienceT

💡 ResAdapter: Domain Consistent Resolution Adapter for Diffusion Models

New framework designed for diffusion models (eg SD) to create images at any resolution and aspect ratio.
Unlike other resolution generation methods that process images with post-processing, ResAdapter directly generates images at a given resolution.

💥 page : https://res-adapter.github.io

🫧 paper : https://arxiv.org/abs/2403.02084

⭐️ code : https://github.com/bytedance/res-adapter

⚜️ Telegram: https://t.iss.one/DataScienceT

📁Machine Learning Techniques Applied to the Study of Drug Transporters

📕 Journal: Molecules (I.F.= 4.6)
📅 Publish year: 2023

🧑‍💻 Authors: Xiaorui Kong, Kexin Lin,Gaolei Wu
🤖 University: Department of Pharmacy, Dalian Medical University, China

📎 Study the paper

⚜️ Telegram: https://t.iss.one/DataScienceT

📣 Are you looking for a channel to download software and engineering programs in the form of Telegram files and all free, paid and customized versions ❓

🥹 We present to you our new channel related to programs and applications 🙊

https://t.iss.one/ProgramsStore

Recall-Oriented-CL-Framework

⌨️ Github: https://github.com/bigdata-inha/recall-oriented-cl-framework

📕 Paper: https://arxiv.org/pdf/2403.03082v1.pdf

🔥 Dataset: https://paperswithcode.com/dataset/cifar-10

✨ Tasks: https://paperswithcode.com/task/continual-learning

⚡️⚡️⚡️⚡️⚡️⚡️⚡️⚡️