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๐ŸŒ 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


๐Ÿ–ฅ Code: https://github.com/damo-vilab/AnyDoor

๐ŸŽ“ HF: https://huggingface.co/spaces/xichenhku/AnyDoor-online

๐Ÿ”ฎ Project Page: https://damo-vilab.github.io/AnyDoor-Page/

๐Ÿ“š ArXiv: https://arxiv.org/abs/2307.09481

@Machine_learn
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๐ŸŒน4DGen: Grounded 4D Content Generation with Spatial-temporal Consistency


๐Ÿ–ฅ Code: https://github.com/VITA-Group/4DGen

๐Ÿ”ฎ Project: https://vita-group.github.io/4DGen/

๐Ÿ“š ArXiv: https://arxiv.org/abs/2305.06456

@Machine_learn
Vaccine: Perturbation-aware Alignment for Large Language Model

๐Ÿ–ฅ Github: https://github.com/git-disl/vaccineT

๐Ÿ“• Paper: https://arxiv.org/pdf/2402.01109v1.pdf

๐Ÿ”ฅDatasets: https://paperswithcode.com/dataset/sst

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

@Machine_learn
OReilly.Training.Data.for.Machine.Learning.pdf
21.3 MB
Book: ๐Ÿ“šTraining 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
๐Ÿ’Š AMIE: A research AI system for diagnostic medical reasoning and conversations

๐Ÿ’ก Blog: https://blog.research.google/2024/01/amie-research-ai-system-for-diagnostic_12.html

๐Ÿ“š Paper: https://arxiv.org/abs/2401.05654

@Machine_learn
TimesFM is a forecasting model, pre-trained on a large time-series corpus of 100 billion real world time-points

https://blog.research.google/2024/02/a-decoder-only-foundation-model-for.html

@Machine_learn
๐Ÿ“ท InstructIR: High-Quality Image Restoration Following Human Instructions


๐Ÿ–ฅ Code: https://github.com/mv-lab/InstructIR

๐Ÿš€ Project: mv-lab.github.io/InstructIR/

๐ŸŽฎ Colab: https://colab.research.google.com/drive/1OrTvS-i6uLM2Y8kIkq8ZZRwEQxQFchfq

๐Ÿ“š Paper: https://arxiv.org/abs/2401.16468

@Machine_learn
SoftEDA: Rethinking Rule-Based Data Augmentation with Soft Labels

๐Ÿ–ฅ Github: https://github.com/IIPL-CAU/SoftEDA

๐Ÿ“• Paper: https://arxiv.org/pdf/2402.05591v1.pdf

๐Ÿ”ฅDatasets: https://paperswithcode.com/dataset/cola

@Machine_learn
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โšก๏ธ LGM: Large Multi-View Gaussian Model for High-Resolution 3D Content Creation

๐Ÿ–ฅ Github: https://github.com/3DTopia/LGM

๐Ÿ“š Paper: https://arxiv.org/abs/2402.05054

๐Ÿ”— Demo: https://huggingface.co/spaces/ashawkey/LGM

๐Ÿ’ป Weights: https://huggingface.co/ashawkey/LGM

โฉ Project: https://me.kiui.moe/lgm/

@Machine_learn
Successful Algorithmic Trading (1).pdf
2.2 MB
Book: ๐Ÿ“šSuccessful #AlgorithmicTrading
Authors: By Michael L. Halls-Moore
ISBN: Null
year: 2023
pages: 208
Tags: #Machine_learning# Trading
@Machine_learn
ุงู…ุดุจ ุงุฎุฑูŠู† ุชุฎููŠู ุงุฒ ุงูŠู† ูพูƒ ู‡ุงูŠ ูŠุงุฏฺฏูŠุฑูŠ ู…ูŠ ุจุงุดุฏ....!

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

๐Ÿ–ฅ Github: https://github.com/jiangyctarheel/sq-transformer

๐Ÿ“• Paper: https://arxiv.org/pdf/2402.06492v1.pdf

๐Ÿ”ฅDatasets: https://paperswithcode.com/dataset/wmt-2014

@Machine_learn
2308.04512.pdf
3.1 MB
Book: ๐Ÿ“šAn introduction to graph theory
Authors: Darij Grinberg
ISBN: Null
year: 2023
pages: 442
Tags: #Graph
@Machine_learn
2025/02/22 05:44:27
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