Machine learning books and papers

BashBook 📚 Book @Machine_learn

دوستانی که مایل به شرکت در این مقاله می باشند اخرین فرصت تا فردا شب...!

با عرض سلام برای یکی از مقالاتمون نفر دوم رو لازم داریم زمان سابمیت امشب تا فردا شب Time-series Forecasting of Bitcoin Prices and Illiquidity using High-dimensional Features: XGBoostLSTM Approach Corresponding author: Ramin Mousa Abstract Liquidity is the ease of converting an asset into cash or another asset without loss, and is shown by the relationship between the time scale and the price scale of an investment. This article examines the relationship between Bitcoin’s price prediction and illiquidity. Bitcoin Hash Rate information was col- lected in three different intervals, and three techniques of feature selection (FS) Filter, Wrapper, and Embedded were used. Considering the regression nature of illiquidity prediction, an approach based on LSTM network and XGBoost was proposed. LSTM was used to extract time series features, and XGBoost was used to learn these features. The proposed LSTMXGBoost approach was evaluated in two modes: price prediction and illiquidity prediction. This approach achieved MAE 1.60 in the next-day forecast and MAE 3.46 in the next-day illiquidity forecast. In the cross-validation of the proposed approach on the FS approaches, the best result was obtained in the prediction by the filter approach and in the classification by the wrapper approach. These obtained results indicate that the presented models outperform the existing models in the literature. Examin- ing the confusion matrices indicates that the two tasks of price prediction and illiquidity prediction have no correlation and harm each other. Keywords: illiquidity prediction, Bitcoin hash rate, hybrid model, price pre- diction, LSTMXGBoost ژورنال سابمیت Journal : Finanace innovation(springer) If: 6.5 دوستانی که در سری زمانی کار می کنن می تونن در این مقاله شرکت کنن. @Raminmousa

با عرض سلام برای یکی از مقالاتمون نفر دوم رو اازم داریم زمان سابمیت امشب تا فردا شب Time-series Forecasting of Bitcoin Prices and Illiquidity using High-dimensional Features: XGBoostLSTM Approach Corresponding author: Ramin Mousa Abstract Liquidity is the ease of converting an asset into cash or another asset without loss, and is shown by the relationship between the time scale and the price scale of an investment. This article examines the relationship between Bitcoin’s price prediction and illiquidity. Bitcoin Hash Rate information was col- lected in three different intervals, and three techniques of feature selection (FS) Filter, Wrapper, and Embedded were used. Considering the regression nature of illiquidity prediction, an approach based on LSTM network and XGBoost was proposed. LSTM was used to extract time series features, and XGBoost was used to learn these features. The proposed LSTMXGBoost approach was evaluated in two modes: price prediction and illiquidity prediction. This approach achieved MAE 1.60 in the next-day forecast and MAE 3.46 in the next-day illiquidity forecast. In the cross-validation of the proposed approach on the FS approaches, the best result was obtained in the prediction by the filter approach and in the classification by the wrapper approach. These obtained results indicate that the presented models outperform the existing models in the literature. Examin- ing the confusion matrices indicates that the two tasks of price prediction and illiquidity prediction have no correlation and harm each other. Keywords: illiquidity prediction, Bitcoin hash rate, hybrid model, price pre- diction, LSTMXGBoost ژورنال سابمیت Journal : Finanace innovation(springer) If: 6.5 دوستانی که در سری زمانی کار می کنن می تونن در این مقاله شرکت کنن. @Raminmousa

نفر ۴ ام برداشته شد...!

با عرض سلام نفر سوم و چهارم از مقاله زیر رو جهت ثبت اسم نیاز داریم. این مقاله ۸ ماه داریم روش کار میکنیم. Title: Gaussian Mixture latent for Recurrent Neural Networks Basic deficiencies The problem of time series prediction analyzes patterns in past data to predict the future. Traditional machine learning algorithms, despite achieving impressive results, require manual feature selection. Automatic feature selection along with the addition of time concept in deep recurrent networks has led to the provision of more suitable solutions. The selection of feature order in deep recurrent networks leads to the provision of different results due to the use of Back-propagation. The problem of selecting feature order is an NP-complete problem. In this research, the aim is to provide a solution to improve this problem.......! Jouranl: Expert system with application هزینه نفر سوم ۵۰۰ دلار و هزینه نفر چهارم ۴۰۰ دلار می باشد. جهت ثبت اسم با ایدی بنده در ارتباط باشین. @Raminmousa @Machine_learn

Efficient Reasoning with Hidden Thinking Chain-of-Thought (CoT) reasoning has become a powerful framework for improving complex problem-solving capabilities in Multimodal Large Language Models (MLLMs). However, the verbose nature of textual reasoning introduces significant inefficiencies. In this work, we propose (as hidden llama), an efficient reasoning framework that leverages reasoning CoTs at hidden latent space. We design the Heima Encoder to condense each intermediate CoT into a compact, higher-level hidden representation using a single thinking token, effectively minimizing verbosity and reducing the overall number of tokens required during the reasoning process. Meanwhile, we design corresponding Heima Decoder with traditional Large Language Models (LLMs) to adaptively interpret the hidden representations into variable-length textual sequence, reconstructing reasoning processes that closely resemble the original CoTs. Experimental results across diverse reasoning MLLM benchmarks demonstrate that Heima model achieves higher generation efficiency while maintaining or even better zero-shot task accuracy. Moreover, the effective reconstruction of multimodal reasoning processes with Heima Decoder validates both the robustness and interpretability of our approach. Paper: https://arxiv.org/pdf/2501.19201v1.pdf Code: https://github.com/shawnricecake/heima Datasets: MMBench - MM-Vet - MathVista - MMStar - HallusionBench @Machine_learn

Detecting Backdoor Samples in Contrastive Language Image Pretraining 3 Feb 2025 · Hanxun Huang, Sarah Erfani, Yige Li, Xingjun Ma, James Bailey · Contrastive language-image pretraining (CLIP) has been found to be vulnerable to poisoning backdoor attacks where the adversary can achieve an almost perfect attack success rate on CLIP models by poisoning only 0.01\% of the training dataset. This raises security concerns on the current practice of pretraining large-scale models on unscrutinized web data using CLIP. In this work, we analyze the representations of backdoor-poisoned samples learned by CLIP models and find that they exhibit unique characteristics in their local subspace, i.e., their local neighborhoods are far more sparse than that of clean samples. Based on this finding, we conduct a systematic study on detecting CLIP backdoor attacks and show that these attacks can be easily and efficiently detected by traditional density ratio-based local outlier detectors, whereas existing backdoor sample detection methods fail. Our experiments also reveal that an unintentional backdoor already exists in the original CC3M dataset and has been trained into a popular open-source model released by OpenCLIP. Based on our detector, one can clean up a million-scale web dataset (e.g., CC3M) efficiently within 15 minutes using 4 Nvidia A100 GPUs. Paper: https://arxiv.org/pdf/2502.01385v1.pdf Code: https://github.com/HanxunH/Detect-CLIP-Backdoor-Samples Datasets: Conceptual Captions CC12M RedCaps @Machine_learn

📚 Linear Algebra for Computer Vision, Robotics, and Machine Learning 👉 Book @Machine_learn

با عرض سلام بسیاری از دوستان که می خواهند مقاله شروع کنند نیاز به نقش راهی برای شروع دارند. از این رو سعی داریم جهت مشاوره در موضوعات زیر همکاری داشته باشیم. انتخاب موضوع، انتخاب ایده، بررسی ساختار کلی مقاله و انتخاب ژورنال با بنده خواهد بود و هر هفته یک جلسه جهت بررسی کارهای انجام شده خواهیم داشت. هزینه مشاوره در هر موضوع ۵ تومن می باشد. 💠Medical Image 1-alzheimer disease classification 2-Wound image classification 3- skin cancer classification 4- breast cancer segmentation 💠Time series: 1- crypro market price prediction: High dimensional features 2-crypto market illiquidity prediction: High dimensional features 3-Air quality prediction 4-Network traffic prediction 5-Malware detection 💠Text mining 1-Large languge model: systmatic survey 2-multi-domain sentiment analysis 3- Extracting psychiatric stressors for suicide from twitter جهت مشارکت می تونین با ایدی بنده در ارتباط باشین. @Raminmousa

Mathematical Foundations of Reinforcement Learning 📚 Link @Machine_learn

CycleGuardian: A Framework for Automatic RespiratorySound classification Based on Improved Deep clustering and Contrastive Learning 🖥 Github: https://github.com/chumingqian/CycleGuardian 📕 Paper: https://arxiv.org/abs/2502.00734v1 🌟 Dataset: https://paperswithcode.com/dataset/icbhi-respiratory-sound-database @Machine_learn

با عرض سلام نفر سوم از مقاله زیر رو جهت همکاری نیاز داریم. این مقاله ۸ ماه داریم روش کار میکنیم. Title: Gaussian Mixture latent for Recurrent Neural Networks Basic deficiencies The problem of time series prediction analyzes patterns in past data to predict the future. Traditional machine learning algorithms, despite achieving impressive results, require manual feature selection. Automatic feature selection along with the addition of time concept in deep recurrent networks has led to the provision of more suitable solutions. The selection of feature order in deep recurrent networks leads to the provision of different results due to the use of Back-propagation. The problem of selecting feature order is an NP-complete problem. In this research, the aim is to provide a solution to improve this problem.......! هزینه مشارکت نفر سوم این مقاله ۱۰۰۰$ و ژورنال هدف Expert system میباشد. @Raminmousa @Machine_learn

DeepSeek-VL2: Mixture-of-Experts Vision-Language Models for Advanced Multimodal Understanding 13 Dec 2024 · Zhiyu Wu, Xiaokang Chen, Zizheng Pan, Xingchao Liu, Wen Liu, Damai Dai, Huazuo Gao, Yiyang Ma, Chengyue Wu, Bingxuan Wang, Zhenda Xie, Yu Wu, Kai Hu, Jiawei Wang, Yaofeng Sun, Yukun Li, Yishi Piao, Kang Guan, Aixin Liu, Xin Xie, Yuxiang You, Kai Dong, Xingkai Yu, Haowei Zhang, Liang Zhao, Yisong Wang, Chong Ruan · We present DeepSeek-VL2, an advanced series of large Mixture-of-Experts (MoE) Vision-Language Models that significantly improves upon its predecessor, DeepSeek-VL, through two key major upgrades. For the vision component, we incorporate a dynamic tiling vision encoding strategy designed for processing high-resolution images with different aspect ratios. For the language component, we leverage #DeepSeekMoE models with the Multi-head Latent Attention mechanism, which compresses Key-Value cache into latent vectors, to enable efficient inference and high throughput. Trained on an improved vision-language dataset, DeepSeek-VL2 demonstrates superior capabilities across various tasks, including but not limited to visual question answering, optical character recognition, document/table/chart understanding, and visual grounding. Our model series is composed of three variants: DeepSeek-VL2-Tiny, #DeepSeek-VL2-Small and DeepSeek-VL2, with 1.0B, 2.8B and 4.5B activated parameters respectively. DeepSeek-VL2 achieves competitive or state-of-the-art performance with similar or fewer activated parameters compared to existing open-source dense and MoE-based models. Codes and pre-trained models are publicly accessible at https://github.com/deepseek-ai/DeepSeek-VL2. Paper: https://arxiv.org/pdf/2412.10302v1.pdf Code: https://github.com/deepseek-ai/deepseek-vl2 Datasets: RefCOCO TextVQA MMBench DocVQA 💠 https://t.me/deep_learning_proj

Microsoft just updated their blog with 300 examples of real-world AI use cases. 📕 Article @Machine_learn

با عرض سلام تخفیف ۵۰٪ دو پکیچ یادگیری ماشین و یادگیری عمیق که شامل ۳۶ پروژه عملی در بحث پردازش تصویر و پردازش متن می باشند رو در نظر گرفتیم. دوستانی که نیاز به این دو پک دارند می تونن به بنده پیام بدن. ۱ ماه مشاوره ریکان راجع به این پروژه ها هم خواهیم داشت. 🟥🟥🟥🟥🟥🟥 @Raminmousa

Introduction to Python for Computational Science and Engineering 📚 book @Machine_learn