Machine learning books and papers

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

📽 Genomics in Cancer Care 🎞 Watch @Machine_learn

Everything You Always Wanted To Know About Mathematics* 📓 book @Machine_learn

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

The Matrix Cookbook 📚 Link @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

🔥 Exercises in Machine Learning Book @Machine_learn

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

Attention from Beginners Point of View 📚 Reed @Machine_learn

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

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. @Raminmousa