Machine learning books and papers
با عرض سلام پروژه جدیدمون شروع شد. هدف اصلی این پروژه اموزش یک مدل پیشنهاد دهنده ی مدل برای مسائله طبقه بندی تصاویر پزشکی میباشد که از اموزش مجدد مدل ها جلوگیری میکند. این مسائله با جنبه جلوگیری از مصرف انرژی اموزشی و زمان اموزش مدل ها ارائه می شود. برای…
نفرات ٤ و ٥ از اين پروژه باقي موندن دوستاني كه حاضر به همكاري هستن به ايدي بنده مراجعه كنند.
@Raminmousa
@Raminmousa
📄 A comprehensive bibliometric analysis on social network anonymization: current approaches and future directions
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Title: Breast Cancer Ultrasound Image Segmentation Using Improved 3DUnet++
🔹 🔹 🔹 🔹 🔹 🔹 🔹 🔹
Author: @Raminmousa
🔹 🔹 🔹 🔹 🔹 🔹 🔹 🔹
Cite: https://doi.org/10.1016/j.wfumbo.2024.100068
ABSTRACT: Breast cancer is the most common cancer and the main cause of cancer-related deaths in women around the world. Early detection reduces the number of deaths. Automated breast ultrasound (ABUS) is a new and promising screening method for examining the entire breast. Volumetric ABUS examination is time-consuming, and lesions may be missed during the examination. Therefore, computer-aided cancer diagnosis in ABUS volume is highly expected to help the physician for breast cancer screening. In this research, we presented 3D structures based on UNet, ResUNet, and UNet++ for the automatic detection of cancer in ABUS volume to speed up examination while providing high detection sensitivity with low false positives (FPs). The three investigated approaches were evaluated on equal datasets in terms of training and testing as well as with proportional hyperparameters. Among the proposed approaches in classification and segmentation problems, the UNet++ approach was able to achieve more acceptable results. The UNet++ approach on the dataset of the Tumor Segmentation, Classification, and Detection Challenge on Automated 3D Breast Ultrasound 2023 (Named TSCD-ABUS2023) was able to achieve Accuracy=0.9911 and AUROC=0.9761 in classification and Dice=0.4930 in segmentation.
#Accepted✅
@Machine_learn
Author: @Raminmousa
Cite: https://doi.org/10.1016/j.wfumbo.2024.100068
ABSTRACT: Breast cancer is the most common cancer and the main cause of cancer-related deaths in women around the world. Early detection reduces the number of deaths. Automated breast ultrasound (ABUS) is a new and promising screening method for examining the entire breast. Volumetric ABUS examination is time-consuming, and lesions may be missed during the examination. Therefore, computer-aided cancer diagnosis in ABUS volume is highly expected to help the physician for breast cancer screening. In this research, we presented 3D structures based on UNet, ResUNet, and UNet++ for the automatic detection of cancer in ABUS volume to speed up examination while providing high detection sensitivity with low false positives (FPs). The three investigated approaches were evaluated on equal datasets in terms of training and testing as well as with proportional hyperparameters. Among the proposed approaches in classification and segmentation problems, the UNet++ approach was able to achieve more acceptable results. The UNet++ approach on the dataset of the Tumor Segmentation, Classification, and Detection Challenge on Automated 3D Breast Ultrasound 2023 (Named TSCD-ABUS2023) was able to achieve Accuracy=0.9911 and AUROC=0.9761 in classification and Dice=0.4930 in segmentation.
#Accepted
@Machine_learn
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MiniCPM-V: A GPT-4V Level MLLM on Your Phone
The recent surge of Multimodal Large Language Models (MLLMs) has fundamentally reshaped the landscape of #AI research and industry, shedding light on a promising path toward the next AI milestone. However, significant challenges remain preventing MLLMs from being practical in real-world applications. The most notable challenge comes from the huge cost of running an MLLM with a massive number of parameters and extensive computation. As a result, most MLLMs need to be deployed on high-performing cloud servers, which greatly limits their application scopes such as mobile, offline, energy-sensitive, and privacy-protective scenarios. In this work, we present MiniCPM-V, a series of efficient #MLLMs deployable on end-side devices. By integrating the latest MLLM techniques in architecture, pretraining and alignment, the latest MiniCPM-Llama3-V 2.5 has several notable features: (1) Strong performance, outperforming GPT-4V-1106, Gemini Pro and Claude 3 on OpenCompass, a comprehensive evaluation over 11 popular benchmarks, (2) strong #OCR capability and 1.8M pixel high-resolution #image perception at any aspect ratio, (3) trustworthy behavior with low hallucination rates, (4) multilingual support for 30+ languages, and (5) efficient deployment on mobile phones. More importantly, MiniCPM-V can be viewed as a representative example of a promising trend: The model sizes for achieving usable (e.g., GPT-4V) level performance are rapidly decreasing, along with the fast growth of end-side computation capacity. This jointly shows that GPT-4V level MLLMs deployed on end devices are becoming increasingly possible, unlocking a wider spectrum of real-world AI applications in the near future.
Paper: https://arxiv.org/pdf/2408.01800v1.pdf
Codes:
https://github.com/OpenBMB/MiniCPM-o
https://github.com/openbmb/minicpm-v
Datasets: Video-MME
@Machine_learn
The recent surge of Multimodal Large Language Models (MLLMs) has fundamentally reshaped the landscape of #AI research and industry, shedding light on a promising path toward the next AI milestone. However, significant challenges remain preventing MLLMs from being practical in real-world applications. The most notable challenge comes from the huge cost of running an MLLM with a massive number of parameters and extensive computation. As a result, most MLLMs need to be deployed on high-performing cloud servers, which greatly limits their application scopes such as mobile, offline, energy-sensitive, and privacy-protective scenarios. In this work, we present MiniCPM-V, a series of efficient #MLLMs deployable on end-side devices. By integrating the latest MLLM techniques in architecture, pretraining and alignment, the latest MiniCPM-Llama3-V 2.5 has several notable features: (1) Strong performance, outperforming GPT-4V-1106, Gemini Pro and Claude 3 on OpenCompass, a comprehensive evaluation over 11 popular benchmarks, (2) strong #OCR capability and 1.8M pixel high-resolution #image perception at any aspect ratio, (3) trustworthy behavior with low hallucination rates, (4) multilingual support for 30+ languages, and (5) efficient deployment on mobile phones. More importantly, MiniCPM-V can be viewed as a representative example of a promising trend: The model sizes for achieving usable (e.g., GPT-4V) level performance are rapidly decreasing, along with the fast growth of end-side computation capacity. This jointly shows that GPT-4V level MLLMs deployed on end devices are becoming increasingly possible, unlocking a wider spectrum of real-world AI applications in the near future.
Paper: https://arxiv.org/pdf/2408.01800v1.pdf
Codes:
https://github.com/OpenBMB/MiniCPM-o
https://github.com/openbmb/minicpm-v
Datasets: Video-MME
@Machine_learn
Transformers 2: Self-adaptive LLMs
Paper: https://arxiv.org/pdf/2501.06252v2.pdf
Code:
https://github.com/SakanaAI/self-adaptive-llms
https://github.com/codelion/adaptive-classifier
Datasets: GSM8K - HumanEval - MATH
MBPP - TextVQA - OK-VQA - ARC (AI2 Reasoning Challenge)
@Machine_learn
Paper: https://arxiv.org/pdf/2501.06252v2.pdf
Code:
https://github.com/SakanaAI/self-adaptive-llms
https://github.com/codelion/adaptive-classifier
Datasets: GSM8K - HumanEval - MATH
MBPP - TextVQA - OK-VQA - ARC (AI2 Reasoning Challenge)
@Machine_learn
🧑🍳 New Cookbook guide: How to use the Usage API and Cost API to monitor your OpenAI usage
📚 Book
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📚 Book
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git clone https://github.com/DepthAnything/Video-Depth-Anything
cd Video-Depth-Anything
pip install -r requirements.txt▪GitHub
▪Paper
▪Model Small
▪Model Large
▪Demo
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Mathematics of Machine Learning.pdf
3.9 MB
📚 Mathematics of Machine Learning
👨🏻🏫 Philipp Christian Petersen
📝 Table of Contents:
● Language of Machine Learning
● ML Mathematical Framework
● Rademacher Complexities
● Rademacher Complexities Applications
●The Mysterious Machine
● Lower Bounds on Learning
● Model Selection
● Regression and Regularization
● Freezing Fritz
● Support Vector Machines
● Kernel Methods
● Nearest Neighbour
● Neural Networks
● Boosting
● Clustering
● Dimensionality Reduction
@Machine_learn
👨🏻🏫 Philipp Christian Petersen
📝 Table of Contents:
● Language of Machine Learning
● ML Mathematical Framework
● Rademacher Complexities
● Rademacher Complexities Applications
●The Mysterious Machine
● Lower Bounds on Learning
● Model Selection
● Regression and Regularization
● Freezing Fritz
● Support Vector Machines
● Kernel Methods
● Nearest Neighbour
● Neural Networks
● Boosting
● Clustering
● Dimensionality Reduction
@Machine_learn
با عرض سلام پروژه جدیدمون شروع شد.
هدف اصلی این پروژه اموزش یک مدل پیشنهاد دهنده ی مدل برای مسائله طبقه بندی تصاویر پزشکی میباشد که از اموزش مجدد مدل ها جلوگیری میکند. این مسائله با جنبه جلوگیری از مصرف انرژی اموزشی و زمان اموزش مدل ها ارائه می شود. برای این منظور ۵۰۰۰ مقاله در این زمینه جمع اوری شده است. جزئیات بیشتر در لینک گیت قرار دارد.
Project Title: MedRec: Medical recommender system for image classification without retraining
Github: https://github.com/Ramin1Mousa/MedicalRec
Journal: IEEE Transactions on Pattern Analysis and Machine Intelligence
Impact factor: 20.8
۷ نفر دیگر امکان اضافه شدن به این پروژه رو دارند. هر شخص نیاز هست که حدودا داده های ۴۰۰ مقاله رو بررسی کند. زمان تقریبی هر مقاله ۵-۱۰ دقیقه می باشد. هزینه مشارکت در مقاله:
🔹 2- 600$
🔺 3- 500$
💠 4- 400$
🔺 5- 300$
🔹 6- 200$
🔸 7- 200$
جهت مشارکت می تونید به ایدی بنده پیام بدین.
🔹 شنبه شروع این پروژه هست🔹
@Raminmousa
هدف اصلی این پروژه اموزش یک مدل پیشنهاد دهنده ی مدل برای مسائله طبقه بندی تصاویر پزشکی میباشد که از اموزش مجدد مدل ها جلوگیری میکند. این مسائله با جنبه جلوگیری از مصرف انرژی اموزشی و زمان اموزش مدل ها ارائه می شود. برای این منظور ۵۰۰۰ مقاله در این زمینه جمع اوری شده است. جزئیات بیشتر در لینک گیت قرار دارد.
Project Title: MedRec: Medical recommender system for image classification without retraining
Github: https://github.com/Ramin1Mousa/MedicalRec
Journal: IEEE Transactions on Pattern Analysis and Machine Intelligence
Impact factor: 20.8
۷ نفر دیگر امکان اضافه شدن به این پروژه رو دارند. هر شخص نیاز هست که حدودا داده های ۴۰۰ مقاله رو بررسی کند. زمان تقریبی هر مقاله ۵-۱۰ دقیقه می باشد. هزینه مشارکت در مقاله:
جهت مشارکت می تونید به ایدی بنده پیام بدین.
@Raminmousa
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List of 500+AI Agent projects/UseCases
https://github.com/DataSpoof/500-AI-Agents-Projects
@Machine_learn
https://github.com/DataSpoof/500-AI-Agents-Projects
@Machine_learn
Forecasting of Bitcoin Prices Using Hashrate Features: Wavelet and Deep Stacking Approach
✅ NEW PAPER
Link: https://arxiv.org/abs/2501.13136
Abstract: Digital currencies have become popular in the last decade due to their non-dependency and decentralized nature. The price of these currencies has seen a lot of fluctuations at times, which has increased the need for prediction. As their most popular, Bitcoin(BTC) has become a research hotspot. The main challenge and trend of digital currencies, especially BTC, is price fluctuations, which require studying the basic price prediction model. This research presents a classification and regression model based on stack deep learning that uses a wavelet to remove noise to predict movements and prices of BTC at different time intervals. The proposed model based on the stacking technique uses models based on deep learning, especially neural networks and transformers, for one, seven, thirty and ninety-day forecasting. Three feature selection models, Chi2, RFE and Embedded, were also applied to the data in the pre-processing stage. The classification model achieved 63\% accuracy for predicting the next day and 64\%, 67\% and 82\% for predicting the seventh, thirty and ninety days, respectively. For daily price forecasting, the percentage error was reduced to 0.58, while the error ranged from 2.72\% to 2.85\% for seven- to ninety-day horizons. These results show that the proposed model performed better than other models in the literature.
@Machine_learn
Link: https://arxiv.org/abs/2501.13136
Abstract: Digital currencies have become popular in the last decade due to their non-dependency and decentralized nature. The price of these currencies has seen a lot of fluctuations at times, which has increased the need for prediction. As their most popular, Bitcoin(BTC) has become a research hotspot. The main challenge and trend of digital currencies, especially BTC, is price fluctuations, which require studying the basic price prediction model. This research presents a classification and regression model based on stack deep learning that uses a wavelet to remove noise to predict movements and prices of BTC at different time intervals. The proposed model based on the stacking technique uses models based on deep learning, especially neural networks and transformers, for one, seven, thirty and ninety-day forecasting. Three feature selection models, Chi2, RFE and Embedded, were also applied to the data in the pre-processing stage. The classification model achieved 63\% accuracy for predicting the next day and 64\%, 67\% and 82\% for predicting the seventh, thirty and ninety days, respectively. For daily price forecasting, the percentage error was reduced to 0.58, while the error ranged from 2.72\% to 2.85\% for seven- to ninety-day horizons. These results show that the proposed model performed better than other models in the literature.
@Machine_learn
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arXiv.org
Forecasting of Bitcoin Prices Using Hashrate Features: Wavelet and...
Digital currencies have become popular in the last decade due to their non-dependency and decentralized nature. The price of these currencies has seen a lot of fluctuations at times, which has...
📃 Demystifying the black box: A survey on explainable artificial intelligence (XAI) in bioinformatics
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📎 Study the paper
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Physics IQ Benchmark: Do generative video models learn physical principles from watching videos
Book
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Book
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