🔝 ByteDance presents SDXL-Lightning: a lightning fast 1024px text-to-image generation model

☄️ HF: https://huggingface.co/ByteDance/SDXL-Lightning

👀 Telegram: https://news.1rj.ru/str/DataScienceT

📁 Advancing Biomedicine with Graph Representation Learning: Recent Progress, Challenges, and Future Directions

🗓 Publish year: 2023

🫥 Authors: Fang Li, Yi Nian, Zenan Sun, Cui Tao

📎 Study the paper

👀 Telegram: https://news.1rj.ru/str/DataScienceT

💎 Visually Dehallucinative Instruction Generation: Know What You Don't Know

🍏 Github: https://github.com/ncsoft/idk

📕 Paper: https://arxiv.org/pdf/2402.09717v1.pdf

🔥 Dataset: https://paperswithcode.com/dataset/visual-question-answering

⭐ Tasks: https://paperswithcode.com/task/hallucination

👁 Telegram: https://news.1rj.ru/str/DataScienceT

SOTA🚀 YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information

☄️ Github
☄️ Paper
☄️ Hugging face

👀 Telegram: https://news.1rj.ru/str/DataScienceT

👺 Pose via Ray Diffusion 😻

🤨 Novel distributed representation of camera pose that treats a camera as a bundle of rays. Naturally suited for set-level transformers, it's the new SOTA on camera pose estimation. Source code released 📱

👉 Review: https://t.ly/qBsFK

🥺 Paper: arxiv.org/pdf/2402.14817.pdf

👉Project: jasonyzhang.com/RayDiffusion

👉 Code: github.com/jasonyzhang/RayDiffusion

👀 Telegram: https://news.1rj.ru/str/DataScienceT

⭐ MATH-Vision Dataset 🕹

😏 MATH-V is a curated dataset of 3,040 HQ mat problems with visual contexts sourced from real math competitions. Dataset released 📱

😏 Review: https://t.ly/gmIAu

🤨 Paper: arxiv.org/pdf/2402.14804.pdf

🥺 Project: mathvision-cuhk.github.io/

👉 Code: github.com/mathvision-cuhk/MathVision

🔞 Telegram: https://news.1rj.ru/str/DataScienceT

📄 Graph-Theoretical Analysis of Biological Networks: A Survey

🗓 Publish year: 2023

🧑‍💻Author: Kayhan Erciyes

📎 Study the paper

🗣️ Telegram: https://news.1rj.ru/str/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://news.1rj.ru/str/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://news.1rj.ru/str/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://news.1rj.ru/str/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://news.1rj.ru/str/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://news.1rj.ru/str/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://news.1rj.ru/str/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://news.1rj.ru/str/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://news.1rj.ru/str/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://news.1rj.ru/str/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: http://find.xyzou.net/

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

🆗 Demo: http://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://news.1rj.ru/str/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://news.1rj.ru/str/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://news.1rj.ru/str/DataScienceT