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⚡ StreamMultiDiffusion: Real-Time Interactive Generation with Region-Based Semantic Control
▪Сode: https://github.com/ironjr/StreamMultiDiffusion
▪Paper: https://arxiv.org/abs/2403.09055
@Machine_learn
▪Сode: https://github.com/ironjr/StreamMultiDiffusion
▪Paper: https://arxiv.org/abs/2403.09055
@Machine_learn
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🧬 Evolving New Foundation Models: Unleashing the Power of Automating Model Development
▪Blog: https://sakana.ai/evolutionary-model-merge/
▪Paper: https://arxiv.org/abs/2403.13187
@Machine_learn
▪Blog: https://sakana.ai/evolutionary-model-merge/
▪Paper: https://arxiv.org/abs/2403.13187
@Machine_learn
AutoWebGLM: Bootstrap And Reinforce A Large Language Model-based Web Navigating Agent
🖥 Github: https://github.com/thudm/autowebglm
📕 Paper: https://arxiv.org/abs/2404.03648v1
🔥Dataset: https://paperswithcode.com/dataset/mind2web
@Machine_learn
🖥 Github: https://github.com/thudm/autowebglm
📕 Paper: https://arxiv.org/abs/2404.03648v1
🔥Dataset: https://paperswithcode.com/dataset/mind2web
@Machine_learn
Mixtral 8x22B weights are now available
📦model: https://dagshub.com/MistralAI/Mixtral-8x22B-v0.1
🌐page: https://mistral.ai
@Machine_learn
📦model: https://dagshub.com/MistralAI/Mixtral-8x22B-v0.1
🌐page: https://mistral.ai
@Machine_learn
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🌊 LaVague: automate automation with Large Action Model framework
▪Github: https://github.com/lavague-ai/LaVague
▪Docs: https://docs.lavague.ai/en/latest/docs/
▪Colab: https://colab.research.google.com/github/lavague-ai/LaVague/blob/main/docs/docs/get-
started/quick-tour.ipynb
@Machine_learn
▪Github: https://github.com/lavague-ai/LaVague
▪Docs: https://docs.lavague.ai/en/latest/docs/
▪Colab: https://colab.research.google.com/github/lavague-ai/LaVague/blob/main/docs/docs/get-
started/quick-tour.ipynb
@Machine_learn
Forwarded from Papers
Title:
CNN-based Labelled Crack Detection for Image Annotation
Short title:
Machine Learning, Convolutional Neural Networks (CNNs),Image Annotation, Crack Detection
Abstract
Numerous image processing techniques (IPTs) have been employed to detect crack defects, offering an alternative to human-conducted onsite inspections. These IPTs manipulate images to extract defect features, particularly cracks in surfaces produced through Additive Manufacturing (AM). This article presents a vision-based approach that utilizes deep convolutional neural networks (CNNs) for crack detection in AM surfaces. Traditional image processing techniques face challenges with diverse real-world scenarios and varying crack types. To overcome these challenges, our proposed method leverages CNNs, eliminating the need for extensive feature extraction. Annotation for CNN training is facilitated by LabelImg without the requirement for additional IPTs. The trained CNN, enhanced by OpenCV preprocessing techniques, achieves an outstanding 99.54% accuracy on a dataset of 14,982 annotated images with resolutions of 1536 × 1103 pixels. Evaluation metrics exceeding 96% precision, 98% recall, and a 97% F1-score highlight the precision and effectiveness of the entire process.
Field
Mechanical Engineering, Material Engineering, Industrial Engineering, Computer Engineering, Civil Engineering, Aerospace Engineering
Journal
1. Optics and Laser Technology (8.3 CiteScore, 5.0 Impact Factor)
2. Optics and Lasers in Engineering (9.3 CiteScore, 4.6 Impact Factor)
3. The International Journal of Advanced Manufacturing Technology (3.4 CiteScore, 3.226 Impact Factor)
با عرض سلام نفرات ١ تا ٤ اين مقاله جهت ارسال به ژورنال خالي مي باشد. دوستاني كه نياز دارند به ايدي بنده پيام بدن.
@Raminmousa
@paper4money
CNN-based Labelled Crack Detection for Image Annotation
Short title:
Machine Learning, Convolutional Neural Networks (CNNs),Image Annotation, Crack Detection
Abstract
Numerous image processing techniques (IPTs) have been employed to detect crack defects, offering an alternative to human-conducted onsite inspections. These IPTs manipulate images to extract defect features, particularly cracks in surfaces produced through Additive Manufacturing (AM). This article presents a vision-based approach that utilizes deep convolutional neural networks (CNNs) for crack detection in AM surfaces. Traditional image processing techniques face challenges with diverse real-world scenarios and varying crack types. To overcome these challenges, our proposed method leverages CNNs, eliminating the need for extensive feature extraction. Annotation for CNN training is facilitated by LabelImg without the requirement for additional IPTs. The trained CNN, enhanced by OpenCV preprocessing techniques, achieves an outstanding 99.54% accuracy on a dataset of 14,982 annotated images with resolutions of 1536 × 1103 pixels. Evaluation metrics exceeding 96% precision, 98% recall, and a 97% F1-score highlight the precision and effectiveness of the entire process.
Field
Mechanical Engineering, Material Engineering, Industrial Engineering, Computer Engineering, Civil Engineering, Aerospace Engineering
Journal
1. Optics and Laser Technology (8.3 CiteScore, 5.0 Impact Factor)
2. Optics and Lasers in Engineering (9.3 CiteScore, 4.6 Impact Factor)
3. The International Journal of Advanced Manufacturing Technology (3.4 CiteScore, 3.226 Impact Factor)
با عرض سلام نفرات ١ تا ٤ اين مقاله جهت ارسال به ژورنال خالي مي باشد. دوستاني كه نياز دارند به ايدي بنده پيام بدن.
@Raminmousa
@paper4money
Taming Stable Diffusion for Text to 360° Panorama Image Generation
🖥 Github: https://github.com/chengzhag/panfusion
📕 Paper: https://arxiv.org/abs/2404.07949v1
🔥Dataset: https://chengzhag.github.io/publication/panfusion/
@Machine_learn
🔥Dataset: https://chengzhag.github.io/publication/panfusion/
@Machine_learn
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Machine learning books and papers
Title: CNN-based Labelled Crack Detection for Image Annotation Short title: Machine Learning, Convolutional Neural Networks (CNNs),Image Annotation, Crack Detection Abstract Numerous image processing techniques (IPTs) have been employed to detect crack…
باعرض سلام نفرات ۱ تا ۳ از این مقاله باقی مونده
@paper4money
@paper4money
uilding Skills in Object-Oriented Design,
Step-by-Step Construction of A Complete Application
This is release 4.2003, published Mar 04, 2020.
Link:https://slott56.github.io/building-skills-oo-design-book/build/html/
@Machine_learn
Step-by-Step Construction of A Complete Application
This is release 4.2003, published Mar 04, 2020.
Link:https://slott56.github.io/building-skills-oo-design-book/build/html/
@Machine_learn
⚡️ DBRX, a groundbreaking open-source Large Language Model (LLM) with a staggering 132 billion parameters.
▪Github: https://github.com/databricks/dbrx
▪HF: https://huggingface.co/databricks/dbrx-base
▪Demo: https://huggingface.co/spaces/databricks/dbrx-instruct
▪Docs: https://docs.databricks.com/en/machine-learning/foundation-models/index.html
@Machine_learn
▪Github: https://github.com/databricks/dbrx
▪HF: https://huggingface.co/databricks/dbrx-base
▪Demo: https://huggingface.co/spaces/databricks/dbrx-instruct
▪Docs: https://docs.databricks.com/en/machine-learning/foundation-models/index.html
@Machine_learn
Forwarded from Papers
Title
A Comparative Analysis of Machine Learning Models on Cryptocurrency Encompassing Indicators of Gold, Dollar, And Technical Indicators
————————————
Short title
Time series forecasting, ML, Gradient Boost Machine, BTC, cryptocurrency.
————————————-
Abstract
In recent years, the application of machine learning models in financial forecasting has gained significant traction due to their ability to capture complex patterns in diverse datasets. This study presents a comprehensive comparison of several prominent machine learning algorithms, including XGBoost, AdaBoost, CatBoost, Random Forest, Decision Trees and LightGBM, across different datasets encompassing indicators of gold, dollar, and technical indicators. The evaluation is conducted on a range of performance metrics to ascertain the efficacy of each model in predicting financial trends and fluctuations. Through ML analysis, we examine the models' capabilities in handling the unique characteristics and dynamics inherent in each dataset, providing insights into their relative strengths and weaknesses. Furthermore, this research contributes to the existing literature by offering a comparative framework for assessing the suitability of machine learning algorithms in financial forecasting tasks. The findings of this study have implications for practitioners and researchers seeking to employ machine learning techniques in financial markets, aiding in informed decision-making and risk management strategies.
—————————————
Field
Business, Marketing, Industrial Engineering, Computer Engineering.
——————————————
journal
1. Annals of Operations Research (7.1 CiteScore, 4.8 Impact Factor)
2. Neural Computing and Applications ( 8.7 CiteScore, 6.0 Impact Factor)
3. IEEE Access (9.0 CiteScore, 3.9 Impact Factor)
با عرض سلام نفرات اول و دوم این مقاله رو خالی داریم . دوستانی که نیاز دارن با بنده هماهنگ کنند.
▶️ @Raminmousa
@Machine_learn
@Paper4money
A Comparative Analysis of Machine Learning Models on Cryptocurrency Encompassing Indicators of Gold, Dollar, And Technical Indicators
————————————
Short title
Time series forecasting, ML, Gradient Boost Machine, BTC, cryptocurrency.
————————————-
Abstract
In recent years, the application of machine learning models in financial forecasting has gained significant traction due to their ability to capture complex patterns in diverse datasets. This study presents a comprehensive comparison of several prominent machine learning algorithms, including XGBoost, AdaBoost, CatBoost, Random Forest, Decision Trees and LightGBM, across different datasets encompassing indicators of gold, dollar, and technical indicators. The evaluation is conducted on a range of performance metrics to ascertain the efficacy of each model in predicting financial trends and fluctuations. Through ML analysis, we examine the models' capabilities in handling the unique characteristics and dynamics inherent in each dataset, providing insights into their relative strengths and weaknesses. Furthermore, this research contributes to the existing literature by offering a comparative framework for assessing the suitability of machine learning algorithms in financial forecasting tasks. The findings of this study have implications for practitioners and researchers seeking to employ machine learning techniques in financial markets, aiding in informed decision-making and risk management strategies.
—————————————
Field
Business, Marketing, Industrial Engineering, Computer Engineering.
——————————————
journal
1. Annals of Operations Research (7.1 CiteScore, 4.8 Impact Factor)
2. Neural Computing and Applications ( 8.7 CiteScore, 6.0 Impact Factor)
3. IEEE Access (9.0 CiteScore, 3.9 Impact Factor)
با عرض سلام نفرات اول و دوم این مقاله رو خالی داریم . دوستانی که نیاز دارن با بنده هماهنگ کنند.
@Machine_learn
@Paper4money
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Pixart-Sigma, the first high-quality, transformer-based image generation training framework!
🖥 Github: https://github.com/PixArt-alpha/PixArt-sigma
🔥Demo: https://huggingface.co/spaces/PixArt-alpha/PixArt-Sigma
@Machine_learn
🔥Demo: https://huggingface.co/spaces/PixArt-alpha/PixArt-Sigma
@Machine_learn
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LongEmbed: Extending Embedding Models for Long Context Retrieval
🖥 Github: https://github.com/dwzhu-pku/longembed
📕 Paper: https://arxiv.org/abs/2404.12096v1
⚡️ Project: https://6img-to-3d.github.io/
@Machine_learn
⚡️ Project: https://6img-to-3d.github.io/
@Machine_learn
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⚡️ DesignEdit: Multi-Layered Latent Decomposition and Fusion for Unified & Accurate Image Editing
Microsoft представляет DesignEd it!
▪Github: https://github.com/design-edit/DesignEdit.git
▪Paper: https://arxiv.org/abs/2403.14487
▪Project: https://design-edit.github.io/
@Machine_learn
Microsoft представляет DesignEd it!
▪Github: https://github.com/design-edit/DesignEdit.git
▪Paper: https://arxiv.org/abs/2403.14487
▪Project: https://design-edit.github.io/
@Machine_learn
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⚡️Map-relative Pose Regression🔥(#CVPR2024 highlight)
▪Paper: https://arxiv.org/abs/2404.09884
▪Page: https://nianticlabs.github.io/marepo
@Machine_learn
▪Paper: https://arxiv.org/abs/2404.09884
▪Page: https://nianticlabs.github.io/marepo
@Machine_learn