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Admin: @Raminmousa ID: @Machine_learn link: https://t.me/Machine_learn
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JanusFlow: Harmonizing Autoregression and Rectified Flow for Unified Multimodal Understanding and Generation
We present JanusFlow, a powerful framework that unifies image understanding and generation in a single model. JanusFlow introduces a minimalist architecture that integrates autoregressive language models with rectified flow, a state-of-the-art method in generative modeling. Our key finding demonstrates that rectified flow can be straightforwardly trained within the large language model framework, eliminating the need for complex architectural modifications. To further improve the performance of our unified model, we adopt two key strategies: (i) decoupling the understanding and generation encoders, and (ii) aligning their representations during unified training. Extensive experiments show that JanusFlow achieves comparable or superior performance to specialized models in their respective domains, while significantly outperforming existing unified approaches across standard benchmarks. This work represents a step toward more efficient and versatile vision-language models.
Paper: https://arxiv.org/pdf/2411.07975v1.pdf
Code: https://github.com/deepseek-ai/janus
Datasets: GQA MMBench MM-Vet SEED-Bench
@Machine_learn
ChatGPT Cheat Sheet for Business - DataCamp
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🌟 SmolVLM:
SmolVLM -
⭐️ smolervlm: https://huggingface.co/blog/smolervlm
Model: https://huggingface.co/collections/HuggingFaceTB/smolvlm-256m-and-500m-6791fafc5bb0ab8acc960fb0
@Machine_learn
Foundations of Geometry. DAVID HILBERT, PH. D.
📚 Book
@Machine_learn
Discrete Matematics and applications
🔗 link
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با عرض سلام پروژه MedicalRec تنها نفر ٤ ام باقي مونده و امشب استارت کار میباشد.
🫥🫥🫥🫥
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
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@Raminmousa
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Deep Learning Handwritten Notes.
#DL
#CNN
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Physics IQ Benchmark: Do generative video models learn physical principles from watching videos
Book
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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
List of 500+AI Agent projects/UseCases
https://github.com/DataSpoof/500-AI-Agents-Projects
@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
DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
Paper submitted by #DeepSeek team has generated significant attention in the AI community.
This work addresses the enhancement of reasoning capabilities in Large Language Models (LLMs) through the application of reinforcement learning techniques. The authors introduce a novel framework, DeepSeek-R1, which aims to improve LLM reasoning abilities by incorporating incentives for logical reasoning processes within their training. This integration of reinforcement learning allows LLMs to go beyond basic linguistic processing, developing sophisticated reasoning methods that can boost performance across a wide array of complex applications.
This approach has cause lots of discussions in different communities, but it definitely opens up the whole new direction of development for the research.
Paper: https://arxiv.org/abs/2501.12948
#nn #LLM
@Machine_learn
📃 Perspectives on Computational Enzyme Modeling: From Mechanisms to Design and Drug Development
📎 Study the paper
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🎬 Random Graphs
🎞 Watch
▫️Part 1
▫️Part 2
@Machine_learn
در پروژه MedicalRec ما نياز به يه نفر جهت مشاركت داريم(جايگاه ٧)
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
🔸 7- 200$❌
جهت مشارکت می تونید به ایدی بنده پیام بدین.
🧠🧠🧠🧠🧠
@Raminmousa
⭐️ Fast Think-on-Graph: Wider, Deeper and Faster Reasoning of Large Language Model on Knowledge Graph
🖥 Github: https://github.com/dosonleung/fasttog
📕 Paper: https://arxiv.org/abs/2501.14300v1
@Machine_learn
در این پروژه امکان اموزش کامل کد نویسی مدل هم برای کسانی که مشارکت میکنن فراهم
Читать полностью…
📄A Survey of Genetic Programming Applications in Modern Biological Research
📎 Study the paper
@Machine_learn
📄 Deep Generative Models for Therapeutic Peptide Discovery: A Comprehensive Review
📎 Study the paper
@Machine_learn
Click-Calib: A Robust Extrinsic Calibration Method for Surround-View Systems
Surround-View System (SVS) is an essential component in Advanced Driver Assistance System (ADAS) and requires precise calibrations.
Paper: https://arxiv.org/pdf/2501.01557v2.pdf
Code: https://github.com/lwangvaleo/click_calib
Dataset: WoodScape
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Lots of math for CS & ML. Looks pretty interesting.
📚 Book
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📘 ABI Bioinformatics Guide
🌐 Study
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📃 Demystifying the black box: A survey on explainable artificial intelligence (XAI) in bioinformatics
📎 Study the paper
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با عرض سلام ما نياز به يك co-author براي مقاله زير داريم
Title : A Comprehensive Analysis on Machine Learning for Lung Cancer Level
Abstract
Lung cancer remains a significant global health concern, necessitating accurate and efficient diagnostic methodologies. This study undertakes a comprehensive exploration into the utilization of machine learning (ML) techniques for the precise classification of lung cancer levels. A meticulous examination is conducted to mitigate overfitting challenges while optimizing model efficacy, incorporating strategies such as monitoring minimum child weight and fine-tuning learning rates. Diverse ML models, encompassing XGBoost, LGBM, Adaboost, Logistic Regression, Decision Tree, Random Forest, CatBoost, and k-NN, are meticulously employed and rigorously evaluated. Furthermore, the study scrutinizes the intricate relationships between features and targets through the lens of Deep Neural Networks (DNN), elucidating their potential for complex pattern recognition. The findings underscore the efficacy of various ML models in accurately stratifying lung cancer levels. Despite the intricacies of DNN architectures, conventional ML models, notably XGBoost, LGBM, and Logistic Regression, demonstrate exceptional performance. Comprehensive comparative metrics including accuracy, precision, recall, and F-1 score underscore the superiority of select models in the realm of lung cancer prediction.
Keywords: Lung cancer prediction, Machine learning, Overfitting, Model performance, Deep Neural Networks.
جهت هماهنگي با ايدي بنده در ارتباط باشين
@Raminmous
@Machine_learn
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امكان ريكام در اين مقاله وجود دارد...!
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📚 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
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