🔍 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

@Machine_learn

pix2gestalt: Amodal Segmentation by Synthesizing Wholes

🖥 Github: https://github.com/cvlab-columbia/pix2gestalt

📕 Paper: https://arxiv.org/pdf/2401.14398v1.pdf

✨ Tasks: https://paperswithcode.com/task/3d-reconstruction

@Machine_learn

DVT

🖥 Github: https://github.com/zhangzm0128/DVT

📕 Paper: https://www.ieee-jas.net/en/article/id/ec6a16fa-d348-417a-af0f-dd734c60439c

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

✨ Tasks: https://paperswithcode.com/task/image-classification

@Machine_learn

🌠AnyDoor: Zero-shot Object-level Image Customization

pip install git+https://github.com/cocodataset/panopticapi.git

pip install pycocotools -i https://pypi.douban.com/simple

pip install lvis

🖥 Code: https://github.com/damo-vilab/AnyDoor

🎓 HF: https://huggingface.co/spaces/xichenhku/AnyDoor-online

🔮 Project Page: https://damo-vilab.github.io/AnyDoor-Page/

📚 ArXiv: https://arxiv.org/abs/2307.09481

@Machine_learn

🌹4DGen: Grounded 4D Content Generation with Spatial-temporal Consistency


🖥 Code: https://github.com/VITA-Group/4DGen

🔮 Project: https://vita-group.github.io/4DGen/

📚 ArXiv: https://arxiv.org/abs/2305.06456

@Machine_learn

Vaccine: Perturbation-aware Alignment for Large Language Model

🖥 Github: https://github.com/git-disl/vaccineT

📕 Paper: https://arxiv.org/pdf/2402.01109v1.pdf

🔥Datasets: https://paperswithcode.com/dataset/sst

✨ Tasks: https://paperswithcode.com/task/language-modelling

@Machine_learn

🔥Physics-based Text-to-Motion🔥

🖥 Code: github.com/jiawei-ren/insactor

📚 Paper: arxiv.org/abs/2312.17135

⚡️ Project: https://jiawei-ren.github.io/projects/insactor/

@Machine_learn

Book: 📚Training Data for Machine Learning: Human Supervision from Annotation to Data Science (2023)
Authors: Anthony Sarkis
ISBN: null
year: 2023
pages: 332
Tags: #Machine_learning#Data
@Machine_learn

💊 AMIE: A research AI system for diagnostic medical reasoning and conversations

💡 Blog: https://blog.research.google/2024/01/amie-research-ai-system-for-diagnostic_12.html

📚 Paper: https://arxiv.org/abs/2401.05654

@Machine_learn

📷 InstructIR: High-Quality Image Restoration Following Human Instructions


🖥 Code: https://github.com/mv-lab/InstructIR

🚀 Project: mv-lab.github.io/InstructIR/

🎮 Colab: https://colab.research.google.com/drive/1OrTvS-i6uLM2Y8kIkq8ZZRwEQxQFchfq

📚 Paper: https://arxiv.org/abs/2401.16468

@Machine_learn

SoftEDA: Rethinking Rule-Based Data Augmentation with Soft Labels

🖥 Github: https://github.com/IIPL-CAU/SoftEDA

📕 Paper: https://arxiv.org/pdf/2402.05591v1.pdf

🔥Datasets: https://paperswithcode.com/dataset/cola

@Machine_learn

⭐️ YOLO-World Real-Time Open-Vocabulary Object Detection


🖥 Github: https://github.com/AILab-CVC/YOLO-World

📚 Paper: https://arxiv.org/abs/2401.17270

⚡️Demo: https://www.yoloworld.cc

🤗Hf: https://huggingface.co/spaces/stevengrove/YOLO-World

@Machine_learn

⚡️ LGM: Large Multi-View Gaussian Model for High-Resolution 3D Content Creation

🖥 Github: https://github.com/3DTopia/LGM

📚 Paper: https://arxiv.org/abs/2402.05054

🔗 Demo: https://huggingface.co/spaces/ashawkey/LGM

💻 Weights: https://huggingface.co/ashawkey/LGM

⏩ Project: https://me.kiui.moe/lgm/

@Machine_learn

EasyKV

🖥 Github: https://github.com/drsy/easykv

📕 Paper: https://arxiv.org/pdf/2402.06262v1.pdf

🔥Datasets: https://paperswithcode.com/dataset/webtext

@Machine_learn

Book: 📚Successful #AlgorithmicTrading
Authors: By Michael L. Halls-Moore
ISBN: Null
year: 2023
pages: 208
Tags: #Machine_learning# Trading
@Machine_learn

امشب اخرين تخفيف از اين پك هاي يادگيري مي باشد....!

@Raminmousa

SQ-Transformer: Inducing Systematicity in Transformers by Attending to Structurally Quantized Embeddings

🖥 Github: https://github.com/jiangyctarheel/sq-transformer

📕 Paper: https://arxiv.org/pdf/2402.06492v1.pdf

🔥Datasets: https://paperswithcode.com/dataset/wmt-2014

@Machine_learn

Book: 📚An introduction to graph theory
Authors: Darij Grinberg
ISBN: Null
year: 2023
pages: 442
Tags: #Graph
@Machine_learn