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Admin: @Raminmousa ID: @Machine_learn link: https://t.me/Machine_learn
Mostrar más📈 Análisis del canal de Telegram Machine learning books and papers
El canal Machine learning books and papers (@machine_learn) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 24 521 suscriptores, ocupando la posición 8 070 en la categoría Educación y el puesto 13 778 en la región Irán.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 24 521 suscriptores.
Según los últimos datos del 23 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de -162, y en las últimas 24 horas de -13, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 8.28%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 1.90% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 2 031 visualizaciones. En el primer día suele acumular 465 visualizaciones.
- Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 4.
- Intereses temáticos: El contenido se centra en temas clave como disorder, psy, مقاله, framework, graph.
📝 Descripción y política de contenido
El autor describe el recurso como un espacio para expresar opiniones subjetivas:
“Admin: @Raminmousa
ID: @Machine_learn
link: https://t.me/Machine_learn”
Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 24 junio, 2026), el canal mantiene la vigencia y un amplio alcance. La analítica demuestra que la audiencia interactúa activamente con el contenido, lo que lo convierte en un punto de referencia dentro de la categoría Educación.
24 521
Suscriptores
-1324 horas
-547 días
-16230 días
Archivo de publicaciones
Seg-Zero: Reasoning-Chain Guided Segmentation via Cognitive Reinforcement
🖥 Github: https://github.com/yunncheng/MMRL
📕 Paper: https://arxiv.org/abs/2503.08497v1
🌟 Dataset: https://paperswithcode.com/dataset/imagenet-s
@Machine_learn
MonSter: Marry Monodepth to Stereo Unleashes Power
15 Jan 2025 · Junda Cheng, Longliang Liu, Gangwei Xu, Xianqi Wang, Zhaoxing Zhang, Yong Deng, Jinliang Zang, Yurui Chen, Zhipeng Cai, Xin Yang ·
Stereo matching recovers depth from image correspondences. Existing methods struggle to handle ill-posed regions with limited matching cues, such as occlusions and textureless areas. To address this, we propose MonSter, a novel method that leverages the complementary strengths of monocular depth estimation and stereo matching. MonSter integrates monocular depth and stereo matching into a dual-branch architecture to iteratively improve each other. Confidence-based guidance adaptively selects reliable stereo cues for monodepth scale-shift recovery. The refined monodepth is in turn guides stereo effectively at ill-posed regions. Such iterative mutual enhancement enables MonSter to evolve monodepth priors from coarse object-level structures to pixel-level geometry, fully unlocking the potential of stereo matching. As shown in Fig.1, MonSter ranks 1st across five most commonly used leaderboards -- SceneFlow, KITTI 2012, KITTI 2015, Middlebury, and ETH3D. Achieving up to 49.5% improvements (Bad 1.0 on ETH3D) over the previous best method. Comprehensive analysis verifies the effectiveness of MonSter in ill-posed regions. In terms of zero-shot generalization, MonSter significantly and consistently outperforms state-of-the-art across the board. The code is publicly available at: https://github.com/Junda24/MonSter.
Paper: https://arxiv.org/pdf/2501.08643v1.pdf
Code: https://github.com/junda24/monster
Datasets: KITTI - TartanAir
@Machine_learn
Everything You Always Wanted To Know About Mathematics*
📓 book
@Machine_learn
Repost from Papers
با عرض سلام براي مقاله بالا نياز به نفر سوم ام هستيم.
مجله پيشنهادي جهت سابميت.
https://www.springerprofessional.de/financial-innovation/50101254
If6️⃣. 5
هزینه نفر سوم ۱۵ میلیون می باشد
🔺🔺🔺🔸🔸🔸🔺🔺🔺
جهت ثبت اسم با ايدي بنده در ارتباط باشين
@Raminmousa
@Machine_learn
@paper4money
Controlling Latent Diffusion Using Latent CLIP
📚 Read
@Machine_learn
با عرض سلام برای مقاله زیر نیاز به کسی داریم که هزینه سرور با ما شریک بشه.
Multi-modal wound classification using wound image and location by vit-wavelet and transformer
🔸🔸🔸🔸🔸🔸🔸
Jouranl: scientific reports(nature)
هزینه مشارکت نفر ۵ ام ۳۰۰$ می باشد.
🔻@Raminmousa
A SURVEY ON POST-TRAINING OF LARGE LANGUAGE MODELS
📚 Read
@Machine_learn
باعرض سلام امشب اخرین فرصت برای مشارکت در این مقاله می باشد.
@Raminmousa
Repost from Papers
با عرض سلام براي مقاله بالا نياز به نفر 1 یا سوم ام هستيم.
مجله پيشنهادي جهت سابميت.
https://www.springerprofessional.de/financial-innovation/50101254
If6️⃣. 5
هزینه نفر اول ۵۰۰$ و هزینه نفر سوم ۳۰۰$ می باشد
🔺🔺🔺🔸🔸🔸🔺🔺🔺
جهت ثبت اسم با ايدي بنده در ارتباط باشين
@Raminmousa
@Machine_learn
@paper4money
Know You First and Be You Better: Modeling Human-Like User Simulators via Implicit Profiles
26 Feb 2025 · Kuang Wang, Xianfei Li, Shenghao Yang, Li Zhou, Feng Jiang, Haizhou Li ·
User simulators are crucial for replicating human interactions with dialogue systems, supporting both collaborative training and automatic evaluation, especially for large language models (LLMs). However, existing simulators often rely solely on text utterances, missing implicit user traits such as personality, speaking style, and goals. In contrast, persona-based methods lack generalizability, as they depend on predefined profiles of famous individuals or archetypes. To address these challenges, we propose User Simulator with implicit Profiles (#USP), a framework that infers implicit user profiles from human-machine conversations and uses them to generate more personalized and realistic dialogues. We first develop an LLM-driven extractor with a comprehensive profile schema. Then, we refine the simulation through conditional supervised fine-tuning and reinforcement learning with cycle consistency, optimizing it at both the utterance and conversation levels. Finally, we adopt a diverse profile sampler to capture the distribution of real-world user profiles. Experimental results demonstrate that USP outperforms strong baselines in terms of authenticity and diversity while achieving comparable performance in consistency. Furthermore, dynamic multi-turn evaluations based on USP strongly align with mainstream benchmarks, demonstrating its effectiveness in real-world applications
.
Paper: https://arxiv.org/pdf/2502.18968v1.pdf
Code: https://github.com/wangkevin02/USP
Dataset: LMSYS-USP
@Machine_learn
Kiss3DGen: Repurposing Image Diffusion Models for 3D Asset Generation
🖥 Github: https://github.com/EnVision-Research/Kiss3DGen
📕 Paper: https://arxiv.org/abs/2503.01370v1
🌟 Dataset: https://paperswithcode.com/dataset/nerf
@Machine_learn
Repost from Papers
با عرض سلام براي مقاله بالا نياز به نفر ٣ ام هستيم.
مجله هاي پيشنهادي جهت سابميت.
🔺🔺🔺🔸🔸🔸🔺🔺🔺
-Soft computing
- Computational Economics
- Multimedia Tools and Applicaion
جهت ثبت اسم با ايدي بنده در ارتباط باشين
@Raminmousa
@Machine_learn
@paper4money
CS229 Lecture Notes
Andrew Ng and Tengyu Ma
📚 Link
@Machine_learn
ViDoRAG: Visual Document Retrieval-Augmented Generation via Dynamic Iterative Reasoning Agents
25 Feb 2025 · Qiuchen Wang, Ruixue Ding, Zehui Chen, Weiqi Wu, Shihang Wang, Pengjun Xie, Feng Zhao ·
Understanding information from visually rich documents remains a significant challenge for traditional Retrieval-Augmented Generation (RAG) methods. Existing benchmarks predominantly focus on image-based question answering (QA), overlooking the fundamental challenges of efficient retrieval, comprehension, and reasoning within dense visual documents. To bridge this gap, we introduce ViDoSeek, a novel dataset designed to evaluate RAG performance on visually rich documents requiring complex reasoning. Based on it, we identify key limitations in current RAG approaches: (i) purely visual retrieval methods struggle to effectively integrate both textual and visual features, and (ii) previous approaches often allocate insufficient reasoning tokens, limiting their effectiveness. To address these challenges, we propose #ViDoRAG, a novel multi-agent RAG framework tailored for complex reasoning across visual documents. ViDoRAG employs a Gaussian Mixture Model (GMM)-based hybrid strategy to effectively handle multi-modal retrieval. To further elicit the model's reasoning capabilities, we introduce an iterative agent workflow incorporating exploration, summarization, and reflection, providing a framework for investigating test-time scaling in RAG domains. Extensive experiments on ViDoSeek validate the effectiveness and generalization of our approach. Notably, ViDoRAG outperforms existing methods by over 10% on the competitive #ViDoSeek benchmark.
Paper: https://arxiv.org/pdf/2502.18017v1.pdf
Code: https://github.com/Alibaba-NLP/ViDoRAG
@Machine_learn
📄The role and application of bioinformatics techniques and tools in drug discovery
📎 Study the paper
@Machine_learn
با عرض سلام این مقاله امشب فرایند سابمیتش دوشتانی که نیاز به مشارکت دارن می تونن شرکت کنند.
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