Machine learning books and papers

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InterpreTabNet: Distilling Predictive Signals from Tabular Data by Salient Feature Interpretation 🖥 Github: https://github.com/jacobyhsi/InterpreTabNet 📕Paper: https://arxiv.org/abs/2406.00426v1 @Machine_learn

با عرض سلام این مقاله رو می خواییم برای Nature بفرستیم جایگاه های ۱ تا ۴ اش خالیه از دوستان کسی نیاز داشت در خدمتیم Title: Detection of brain tumors from images using the UNet architecture, with a comparative analysis of transfer learning methods and CNNs. ——————————————————————-- Abstract: Health is crucial for human life, especially brain health, which is vital for all executive functions. Diagnosing brain health issues is often done using magnetic resonance imaging (MRI) devices, which provide critical data for health decision-makers. Images from these devices serve as a significant source of big data for artificial intelligence applications. This big data facilitates high performance in image processing classification problems, a subfield of artificial intelligence. In this study, we aim to classify brain tumors such as glioma, meningioma, and pituitary tumors from brain MRI images using the UNet architecture. To compare the results and gain a better understanding, we also employed Convolutional Neural Networks (CNN) and CNN-based models like Inception-V3, EfficientNetB4, VGG19, along with transfer learning methods for classification tasks. The models were evaluated using F-score, recall, precision, and accuracy metrics. The best accuracy result was achieved with CNN-VGG16, reaching 97%. The same transfer learning model also showed an F-score of 96%, an Area Under the Curve (AUC) value of 98%, a recall value of 98%, and a precision value of 97%. The UNet architecture and CNN-based transfer learning models play a significant role in the early diagnosis and rapid treatment of brain tumors, which is vital for improving patient outcomes. —————————————————————— Keywords: Brain tumor detection, UNet, CNN, Transfer Learning. —————————————————————— Journal: Scientific Reports @Raminmousa @Machine_learn @paper4money

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Images that Sound: Composing Images and Sounds on a Single Canvas abs: https://arxiv.org/abs/2405.12221 project page: https://ificl.github.io/images-that-sound/ code: https://github.com/IFICL/images-that-sound This paper introduces an inference-time procedure that generates images that are also spectrograms corresponding to the prompt. It uses a latent image and audio diffusion model with same latent space (Stable Diffusion v1.5 and Auffusion) and denoise the same latent with both. @Machine_learn

Large Brain Model for Learning Generic Representations with Tremendous EEG Data in BCI 🖥 Github: https://github.com/935963004/labram 📕Paper: https://arxiv.org/abs/2405.18765v1 @Machine_learn

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⚡️ 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