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Admin: @Raminmousa ID: @Machine_learn link: https://t.me/Machine_learn
Больше📈 Аналитический обзор Telegram-канала Machine learning books and papers
Канал Machine learning books and papers (@machine_learn) языкового сегмента Английский является активным участником. Сейчас сообщество объединяет 24 521 подписчиков, занимая 8 070 место в категории Образование и 13 778 место в регионе Иран.
📊 Показатели аудитории и динамика
С момента создания невідомо проект демонстрирует стремительный рост, собрав аудиторию из 24 521 подписчиков.
Согласно последним данным от 23 июня, 2026, канал показывает стабильную активность. За последние 30 дней изменение числа участников составило -162, а за последние 24 часа — -13, при этом общий охват остаётся высоким.
- Статус верификации: Не верифицирован
- Уровень вовлечённости (ER): Средний показатель вовлечённости аудитории составляет 8.28%. В первые 24 часа после публикации контент обычно набирает 1.90% реакций от общего числа подписчиков.
- Охват публикаций: В среднем каждый пост получает 2 031 просмотров. В течение первых суток публикация набирает 465 просмотров.
- Реакции и взаимодействия: Аудитория активно поддерживает контент: среднее количество реакций на один пост — 4.
- Тематические интересы: Контент сосредоточен на ключевых темах, таких как disorder, psy, مقاله, framework, graph.
📝 Описание и контентная политика
Автор описывает ресурс как площадку для выражения субъективного мнения:
“Admin: @Raminmousa
ID: @Machine_learn
link: https://t.me/Machine_learn”
Благодаря высокой частоте обновлений (последние данные получены 24 июня, 2026) канал поддерживает актуальность и высокий уровень охвата публикаций. Аналитика показывает, что аудитория активно взаимодействует с контентом, что делает его важной точкой влияния в категории Образование.
24 521
Подписчики
-1324 часа
-547 дней
-16230 день
Архив постов
Repost from Papers
با عرض سلام براي يكي از مقالاتمون در حوزه Multi-modal wound image classification با Heuristic algorithms نياز به نفر دوم مقاله داريم. هزينه مشاركت ٤٠٠$ هستش و ژورنال مد نظر ما
https://www.nature.com/srep/
مي باشد. جهت مشاركت با ايدي بنده ارتباط باشنين
@Raminmousa
Repost from Github LLMs
Tutorial: Train your own Reasoning model with GRPO
📓 Tutorial
https://t.me/deep_learning_proj
Repost from Papers
یکی از ابزارهای خوبی که بنده تونستم توسعه بدم ابزار Stock Ai می باشد. در این ابزار از ۳۶۰ اندیکاتور استفاده کردم. گزارشات back test این ابزار در ویدیو های زیر موجود می باشد.
May 2024 :
https://youtu.be/aSS99lynMFQ?si=QSk8VVKhLqO_2Qi3
July 2014:
https://youtu.be/ThyZ0mZwsGk?si=FKPK7Hkz-mRx-752&t=209
از این رو سعی میکنیم مقاله ای این کار رو بنویسیم. شروع مقاله ی این کار ۲۰ اسفند خواهد بود.
دوستانی که می تونن به هر نحوی کمک کنند تا شروع مقاله می تونن نام نویسی کنند.
نفرات ٣ و ٥ اين كار باقي مونده.
@Raminmousa
OSUM: Advancing Open Speech Understanding Models with Limited Resources in Academia
Large Language Models (LLMs) have made significant progress in various downstream tasks, inspiring the development of Speech Understanding Language Models (SULMs) to enable comprehensive speech-based interactions. However, most advanced SULMs are developed by the industry, leveraging large-scale datasets and computational resources that are not readily available to the academic community. Moreover, the lack of transparency in training details creates additional barriers to further innovation. In this study, we present OSUM, an Open Speech Understanding Model designed to explore the potential of training SLUMs under constrained academic resources. The OSUM model combines a Whisper encoder with a Qwen2 LLM and supports a wide range of speech tasks, including speech recognition (ASR), speech recognition with timestamps (SRWT), vocal event detection (VED), speech emotion recognition (SER), speaking style recognition (SSR), speaker gender classification (SGC), speaker age prediction (SAP), and speech-to-text chat (STTC). By employing an ASR+X training strategy, OSUM achieves efficient and stable multi-task training by simultaneously optimizing ASR alongside target tasks. Beyond delivering strong performance, OSUM emphasizes transparency by providing openly available data preparation and training methodologies, offering valuable insights and practical guidance for the academic community. By doing so, we aim to accelerate research and innovation in advanced SULM technologies.
Paper: https://arxiv.org/pdf/2501.13306v2.pdf
Code: https://github.com/aslp-lab/osum
Datasets: LibriSpeech - IEMOCAP
@Machine_learn
Mathematics of Backpropagation Through Time.
📕 Paper
@Machine_learn
📃 Methods of decomposition theory and graph labeling in the study of social network structure
📎 Study the paper
@Machine_learn
Repost from Papers
با عرض سلام
نیاز به یک نفر داریم که در موضوع زیرکمکمون کنه (نفر اول)
🔸🔸🔸🔸🔸🔸🔸🔸🔸
Title: Chronic kidney disease classification: Deep ansemble approach
کنفرانس مد نظر :
⭐️https://saiconference.com/IntelliSys
⚙️Abstract: Chronic kidney disease (CKD) is a progressive disease that may lead to kidney failure, so early diagnosis is crucial for proper management. This condition has a high mortality rate, especially in developing countries. CKD is often overlooked because there are no apparent symptoms in the early stages. Meanwhile, early diagnosis and timely clinical intervention are essential to reduce the progression of the disease. CKD diagnosis using deep learning (DL) and feature selection (FS) methods can be a useful application of artificial intelligence (AI) in healthcare. DL algorithms can provide cost-effective and efficient computer-aided diagnosis (CAD) to assist physicians. DL models are based on automatic feature selection.
In some cases, manual feature extraction can improve the results before the network learning process. This study aims to present an ensemble deep-learning model for CKD classification. The proposed method used Deep Embedded Clustering (DEC) as a similarity feature. Also, latent features obtained from the Gaussian Mixture Model (GMM) process were used. The proposed method on UCI databases achieved an accuracy of 1.0 using the Synthetic Minority Over-Sampling technique (SMOTE).
دوستانی که مشارکت میکنم بخشی از هزینه چاپ رو هم تقبل میکنن. بخش related work and introduction, هم بر عهده ی مشارکت کنندست.
@Raminmousa
Papers channel: https://t.me/+SP9l58Ta_zZmYmY0
Bayesian Sample Inference
🖥 Github: https://github.com/martenlienen/bsi
📕 Paper: https://arxiv.org/abs/2502.07580
🌟 Dataset: https://paperswithcode.com/dataset/cifar-10
@Machine_learn
Enhance-A-Video: Better Generated Video for Free
11 Feb 2025 · Yang Luo, Xuanlei Zhao, Mengzhao Chen, Kaipeng Zhang, Wenqi Shao, Kai Wang, Zhangyang Wang, Yang You
DiT-based video generation has achieved remarkable results, but research into enhancing existing models remains relatively unexplored. In this work, we introduce a training-free approach to enhance the coherence and quality of DiT-based generated videos, named Enhance-A-Video. The core idea is enhancing the cross-frame correlations based on non-diagonal temporal attention distributions. Thanks to its simple design, our approach can be easily applied to most DiT-based video generation frameworks without any retraining or fine-tuning. Across various DiT-based video generation models, our approach demonstrates promising improvements in both temporal consistency and visual quality. We hope this research can inspire future explorations in video generation enhancement.
Paper: https://arxiv.org/pdf/2502.07508v1.pdf
Code: https://github.com/NUS-HPC-AI-Lab/Enhance-A-Video
@Machine_learn
Competitive Programming with Large Reasoning Models
OpenAI∗
▪ link
@Machine_learn
Repost from Papers
با عرض سلام
نیاز به یک نفر داریم که در موضوع زیرکمکمون کنه
Title: Chronic kidney disease classification: wavelet ensemble approach
کنفرانس مد نظر :
https://saiconference.com/IntelliSys
دوستانی که مشارکت میکنم بخشی از هزینه چاپ رو هم تقبل میکنن. بخش related work , introduction, and conclusion هم بر عهده ی مشارکت کنندست.
@Raminmousa
Papers channel: https://t.me/+SP9l58Ta_zZmYmY0
Repost from Papers
با عرض سلام
نیاز به یک نفر داریم که در موضوع زیرکمکمون کنه
Title: Chronic kidney disease classification: wavelet ansemble approach
کنفرانس مد نظر :
https://saiconference.com/IntelliSys
دوستانی که مشارکت میکنم بخشی از هزینه چاپ رو هم تقبل میکنن. بخش related work , introduction, and conclusion هم بر عهده ی مشارکت کنندست.
@Raminmousa
OmniParser for Pure Vision Based GUI Agent
1 Aug 2024 · Yadong Lu, Jianwei Yang, Yelong Shen, Ahmed Awadallah
The recent success of large vision language models shows great potential in driving the agent system operating on user interfaces. However, we argue that the power multimodal models like GPT-4V as a general agent on multiple operating systems across different applications is largely underestimated due to the lack of a robust screen parsing technique capable of: 1) reliably identifying interactable icons within the user interface, and 2) understanding the semantics of various elements in a screenshot and accurately associate the intended action with the corresponding region on the screen. To fill these gaps, we introduce \textsc{OmniParser}, a comprehensive method for parsing user interface screenshots into structured elements, which significantly enhances the ability of #GPT-4V to generate actions that can be accurately grounded in the corresponding regions of the interface. We first curated an interactable icon detection dataset using popular webpages and an icon description dataset. These datasets were utilized to fine-tune specialized models: a detection model to parse interactable regions on the screen and a caption model to extract the functional semantics of the detected elements. \textsc{#OmniParser} significantly improves GPT-4V's performance on ScreenSpot benchmark. And on #Mind2Web and AITW benchmark, \textsc{OmniParser} with screenshot only input #outperforms the GPT-4V baselines requiring additional information outside of screenshot.
Paper: https://arxiv.org/pdf/2408.00203v1.pdf
Code: https://github.com/microsoft/omniparser
Dataset: ScreenSpot
@Machine_learn
