Machine learning books and papers

⚡️ Deblur-GS: 3D Gaussian Splatting from Camera Motion Blurred Images ▪Code: https://github.com/Chaphlagical/Deblur-GS ▪Paper: https://chaphlagical.icu/Deblur-GS/static/paper/Deblur_GS_author_version.pdf ▪Project: https://chaphlagical.icu/Deblur-GS/ @Machine_learn

Awesome-Text-to-Video-Generation Awesome 🖥 Github: https://github.com/soraw-ai/awesome-text-to-video-generation 📕 Paper: https://arxiv.org/abs/2405.10674v1 @Machine_learn

💡 Lumina-T2X: Transforming Text into Any Modality, Resolution, and Duration via Flow-based Large Diffusion Transformers ▪Github: https://github.com/alpha-vllm/lumina-t2x ▪Paper: https://arxiv.org/abs/2405.05945 ▪Demo: https://lumina.sylin.host/ @Machine_learn

Enhancing Semantics in Multimodal Chain of Thought via Soft Negative Sampling 🖥 Github: https://github.com/zgmin/snse-cot 📕 Paper: https://paperswithcode.com/dataset/scienceqa @Machine_learn

🔥 Say Goodbye to LoRA, Hello to DoRA 🤩🤩 DoRA consistently outperforms LoRA with various tasks (LLM, LVLM, etc.) and backbones (LLaMA, LLaVA, etc.) [Paper] https://arxiv.org/abs/2402.09353 [Code] https://github.com/NVlabs/DoRA 😄@Machine_learn

✅Title: Automatic Image Annotation (AIA) of AlmondNet-20 Method for Almond Detection by Improved CNN-based Model ✅Short title Machine Learning, Convolutional Neural Networks (CNNs), Image Annotation, Food Industry, Almond, Nuts Detection Abstract: In response to the global demand for high-quality agricultural products, especially in the competitive nut market, we present an innovative approach to enhance the grading of almonds and their shells. Leveraging Deep Convolutional Neural Networks (AlmondNet-20), we achieved over 99% accuracy through 20 layers of CNN, employing data augmentation for robust almond-shell differentiation. Our model, trained over 1000 epochs, demonstrated a remarkable accuracy of 99%, with a low loss function of 0.0567. Test evaluations revealed perfect precision, recall, and F1-score for almond detection. This advanced classification system not only boosts grading accuracy but also ensures reliability in distinguishing almonds from shells globally, benefiting both experts and non-experts. The application of deep learning algorithms opens avenues for product patents, contributing to the economic value of our country. Field Food Industry, Agricultural Engineering, Industrial Engineering, Computer Engineering. 1. Agronomy (3.7 CiteScore, 5.2 Impact Factor) 2. Biosystems Engineering (10.1 CiteScore, 5.1 Impact Factor) 3. Precision Agriculture (9.9 CiteScore, 6.2 Impact Factor) @Raminmousa @Machine_learn @Paper4money

Dynamic Prompt Learning: Addressing Cross-Attention Leakage for Text-Based Image Editing 🖥 Github: https://github.com/wangkai930418/DPL 📕 Paper: https://arxiv.org/abs/2405.01496v1 🔥Dataset: https://neurips.cc/virtual/2023/poster/72801 @Machine_learn

نفرات ۱ تا ۴ مقاله ی زیر خالی می باشد از دوستان اگر کسی خواست در خدمتیم Title Solar Energy Production Forecasting: A Comparative Study of LSTM, Bi-LSTM, and XGBoost Models with Activation Function Analysis Abstract This research focuses on the integration of Machine Learning (ML) methodologies and climatic parameters to predict solar panel energy generation, with a specific emphasis on addressing consumption-production imbalances. Leveraging a dataset sourced from the Kaggle platform, the study is conducted in the context of Estonia, aiming to optimize solar energy utilization in this geographic region. The dataset, obtained from Kaggle, encompasses comprehensive information on climatic variables, including sunlight intensity, temperature, and humidity, alongside corresponding solar panel energy output. Through the utilization of machine learning algorithms, such as XGBoost regression and neural networks, our predictive model endeavors to discern intricate patterns and correlations within these datasets. By tailoring the model to Estonia's climatic nuances, we seek to enhance the accuracy of energy production forecasts and, consequently, better manage the challenges associated with consumption-production imbalances. Furthermore, the research investigates the adaptability of the proposed model to diverse climatic conditions, ensuring its applicability for similar endeavors in other geographical locations. By utilizing Kaggle's rich dataset and employing advanced machine learning techniques, this study aims to contribute valuable insights that can inform sustainable energy policies and practices, ultimately promoting a more efficient and reliable renewable energy infrastructure. Related Fields Business, Marketing, Industrial Engineering, Computer Engineering. Candidate Journals 1. Sustainability (5.8 CiteScore, 3.9 Impact Factor) 2. Archives of Computational Methods in Engineering (14.1 CiteScore, 9.7 Impact Factor) 3. Journal of Building Engineering (8.3 CiteScore, 6.4 Impact Factor) @Raminmousa @paper4money @Machine_learn