Machine learning books and papers

📃 Bioinformatics perspectives on transcriptomics: A comprehensive review of bulk and single-cell RNA sequencing analyses 📎 Study the paper @Machine_learn

نفر ۴ از مقاله زیر قابل اضافه شدن میباشد. 🔸🔸🔸🔸🔸🔸🔸🔸🔸 Title: PKG-LLM: A Framework for Predicting GAD and MDD Using Knowledge Graphs and Large Language Models in Cognitive Neuroscience  🔸🔸🔸🔸🔸🔸🔸🔸🔸 Abstract: Purpose: The purpose of this study is to develop and evaluate PKG-LLM, a knowledge graph framework designed for cognitive neuroscience applications. The aim is to enhance the prediction of relationships between neurological entities and to improve named entity recognition (NER) and relation extraction (RE) from complex neurological datasets. By leveraging GPT-4 and expert review, we aim to demonstrate the framework’s ability to outperform traditional models in terms of precision, recall, and F1-score, and to provide valuable insights for clinical and research applications in neuroscience. Method: The evaluation of PKG-LLM involved two main tasks: relation extraction (RE) and named entity recognition (NER). In both tasks, we utilized GPT-4 to process the data and compute metrics such as precision, recall, and F1-score. Additionally, we integrated an expert review process, where neurologists and domain experts reviewed the extracted relationships and entities, improving the final performance metrics. The model's performance was compared against StrokeKG and Heart Failure KG. Moreover, PKG-LLM was assessed for link prediction using metrics like Mean Rank (MR), Mean Reciprocal Rank (MRR), and Precision at K (P@K). The model was benchmarked against other link prediction models, including TransE, RotatE, DistMult, ComplEx, ConvE, and HolmE. Findings: PKG-LLM demonstrated competitive performance in both relation extraction and named entity recognition tasks. In its traditional form, PKG-LLM achieved a precision of 75.45%, recall of 78.60%, and F1-score of 76.89% in relation extraction, which improved to 82.34%, 85.40%, and 83.85% after expert review. In named entity recognition, the traditional model scored 73.42% precision, 76.30% recall, and 74.84% F1-score, improving to 81.55%, 84.60%, and 82.99% after expert review. For link prediction, PKG-LLM achieved an MRR of 0.396, P@1 of 0.385, and P@10 of 0.531, placing it in a competitive range when compared to models like TransE, RotatE, and ConvE.   Journal: http://www.comsis.org/ http://www.math.md/en/publications/csjm/ https://journal.info.unlp.edu.ar https://bcn.iums.ac.ir/ @Raminmousa @Machine_learn https://t.me/+SP9l58Ta_zZmYmY0

Are They the Same? Exploring Visual Correspondence Shortcomings of Multimodal LLMs 🖥 Github: https://github.com/zhouyiks/CoLVA/tree/main 📕 Paper: https://arxiv.org/pdf/2501.04670v1.pdf ⭐️ Dataset: https://paperswithcode.com/dataset/bdd100k @Machine_learn

🌟 LLaVA-CoT: VLM с 🟡Arxiv 🟡Demo 🖥GitHub @Machine_learn

🌟 🌟 OuteTTS-0.2-500M # Install from PyPI pip install outetts # Interface Usage import outetts # Configure the model model_config = outetts.HFModelConfig_v1( model_path="OuteAI/OuteTTS-0.2-500M", language="en", # Supported languages in v0.2: en, zh, ja, ko ) # Initialize the interface interface = outetts.InterfaceHF(model_version="0.2", cfg=model_config) # Optional: Create a speaker profile (use a 10-15 second audio clip) speaker = interface.create_speaker( audio_path="path/to/audio/file", transcript="Transcription of the audio file." ) # Optional: Load speaker from default presets interface.print_default_speakers() speaker = interface.load_default_speaker(name="male_1") output = interface.generate( text="%Prompt Text%%.", temperature=0.1, repetition_penalty=1.1, max_length=4096, # Optional: Use a speaker profile speaker=speaker, ) # Save the synthesized speech to a file output.save("output.wav") 🟡Demo 🖥GitHub @Machine_learn

این فقط نفر ۴ امش باقی مونده

مقاله زیر نفرات ۲ تا ۴ قابل واگذاری می باشد 🔸🔸🔸🔸🔸🔸🔸🔸🔸 Title: PKG-LLM: A Framework for Predicting GAD and MDD Using Knowledge Graphs and Large Language Models in Cognitive Neuroscience  🔸🔸🔸🔸🔸🔸🔸🔸🔸 Abstract: Purpose: The purpose of this study is to develop and evaluate PKG-LLM, a knowledge graph framework designed for cognitive neuroscience applications. The aim is to enhance the prediction of relationships between neurological entities and to improve named entity recognition (NER) and relation extraction (RE) from complex neurological datasets. By leveraging GPT-4 and expert review, we aim to demonstrate the framework’s ability to outperform traditional models in terms of precision, recall, and F1-score, and to provide valuable insights for clinical and research applications in neuroscience. Method: The evaluation of PKG-LLM involved two main tasks: relation extraction (RE) and named entity recognition (NER). In both tasks, we utilized GPT-4 to process the data and compute metrics such as precision, recall, and F1-score. Additionally, we integrated an expert review process, where neurologists and domain experts reviewed the extracted relationships and entities, improving the final performance metrics. The model's performance was compared against StrokeKG and Heart Failure KG. Moreover, PKG-LLM was assessed for link prediction using metrics like Mean Rank (MR), Mean Reciprocal Rank (MRR), and Precision at K (P@K). The model was benchmarked against other link prediction models, including TransE, RotatE, DistMult, ComplEx, ConvE, and HolmE. Findings: PKG-LLM demonstrated competitive performance in both relation extraction and named entity recognition tasks. In its traditional form, PKG-LLM achieved a precision of 75.45%, recall of 78.60%, and F1-score of 76.89% in relation extraction, which improved to 82.34%, 85.40%, and 83.85% after expert review. In named entity recognition, the traditional model scored 73.42% precision, 76.30% recall, and 74.84% F1-score, improving to 81.55%, 84.60%, and 82.99% after expert review. For link prediction, PKG-LLM achieved an MRR of 0.396, P@1 of 0.385, and P@10 of 0.531, placing it in a competitive range when compared to models like TransE, RotatE, and ConvE.   Journal: http://www.comsis.org/ http://www.math.md/en/publications/csjm/ https://journal.info.unlp.edu.ar https://bcn.iums.ac.ir/ @Raminmousa @Machine_learn https://t.me/+SP9l58Ta_zZmYmY0

این مقاله جزو‌ اولین سری مقالات ما در حوزه ی LLM هستش

مقاله زیر نفرات ۲ تا ۴ قابل واگذاری می باشد 🔸🔸🔸🔸🔸🔸🔸🔸🔸 Title: PKG-LLM: A Framework for Predicting GAD and MDD Using Knowledge Graphs and Large Language Models in Cognitive Neuroscience  🔸🔸🔸🔸🔸🔸🔸🔸🔸 Abstract: Purpose: The purpose of this study is to develop and evaluate PKG-LLM, a knowledge graph framework designed for cognitive neuroscience applications. The aim is to enhance the prediction of relationships between neurological entities and to improve named entity recognition (NER) and relation extraction (RE) from complex neurological datasets. By leveraging GPT-4 and expert review, we aim to demonstrate the framework’s ability to outperform traditional models in terms of precision, recall, and F1-score, and to provide valuable insights for clinical and research applications in neuroscience. Method: The evaluation of PKG-LLM involved two main tasks: relation extraction (RE) and named entity recognition (NER). In both tasks, we utilized GPT-4 to process the data and compute metrics such as precision, recall, and F1-score. Additionally, we integrated an expert review process, where neurologists and domain experts reviewed the extracted relationships and entities, improving the final performance metrics. The model's performance was compared against StrokeKG and Heart Failure KG. Moreover, PKG-LLM was assessed for link prediction using metrics like Mean Rank (MR), Mean Reciprocal Rank (MRR), and Precision at K (P@K). The model was benchmarked against other link prediction models, including TransE, RotatE, DistMult, ComplEx, ConvE, and HolmE. Findings: PKG-LLM demonstrated competitive performance in both relation extraction and named entity recognition tasks. In its traditional form, PKG-LLM achieved a precision of 75.45%, recall of 78.60%, and F1-score of 76.89% in relation extraction, which improved to 82.34%, 85.40%, and 83.85% after expert review. In named entity recognition, the traditional model scored 73.42% precision, 76.30% recall, and 74.84% F1-score, improving to 81.55%, 84.60%, and 82.99% after expert review. For link prediction, PKG-LLM achieved an MRR of 0.396, P@1 of 0.385, and P@10 of 0.531, placing it in a competitive range when compared to models like TransE, RotatE, and ConvE.   Journal: http://www.comsis.org/ http://www.math.md/en/publications/csjm/ https://journal.info.unlp.edu.ar https://bcn.iums.ac.ir/ @Raminmousa @Machine_learn https://t.me/+SP9l58Ta_zZmYmY0

PHY405 Relativity 📕 Lectures @Machine_learn

Mathematical Foundations of Machine Learning 📓 book @Machine_learn

امکان ریکام دادن برای این مقاله هم هستش...!

با عرض سلام مقاله زیر در مرحله major revision می‌باشد. نفر ۴ ام از این مقاله قابل اضافه کردن. Abstract Breast cancer stands as a prevalent cause of fatality among females on a global scale, with prompt detection playing a pivotal role in diminishing mortality rates. The utilization of ultrasound scans in the BUSI dataset for medical imagery pertaining to breast cancer has exhibited commendable segmentation outcomes through the application of UNet and UNet++ networks. Nevertheless, a notable drawback of these models resides in their inattention towards the temporal aspects embedded within the images. This research endeavors to enrich the UNet++ architecture by integrating LSTM layers and self-attention mechanisms to exploit temporal characteristics for segmentation purposes. Furthermore, the incorporation of a Multiscale Feature Extraction Module aims to grasp varied scale features within the UNet++. Through the amalgamation of our proposed methodology with data augmentation on the BUSI with GT dataset, an accuracy rate of 98.88%, specificity of 99.53%, precision of 95.34%, sensitivity of 91.20%, F1-score of 93.74, and Dice coefficient of 92.74% are achieved. These findings demonstrate competitiveness with cutting-edge techniques outlined in existing literature. Keywords: Attention mechanisms, BUSI dataset, Deep Learning, Feature Extraction, Multi-Scale features دوستانی که نیاز دارن به ایدی بنده پیام بدن. @Raminmousa @Machine_learn https://t.me/+SP9l58Ta_zZmYmY0

🌟 DepthLab # Clone repo git clone https://github.com/Johanan528/DepthLab.git cd DepthLab # Create conda env conda env create -f environment.yaml conda activate DepthLab # Run inference cd scripts bash infer.sh 🟡Arxiv 🖥GitHub @Machine_learn

Are They the Same? Exploring Visual Correspondence Shortcomings of Multimodal LLMs 🖥 Github: https://github.com/zhouyiks/CoLVA/tree/main 📕 Paper: https://arxiv.org/pdf/2501.04670v1.pdf 🌟 Dataset: https://paperswithcode.com/dataset/bdd100k @Machine_learn

با عرض سلام تمامي كار هاي مشترك تموم شدن و فقط اين كار باقي مونده....! @Raminmousa

با عرض سلام از اين تيم ها فقط مورد ٢ و ٣ باقي مونده مابقي تيم ها كامل شدند....!

Towards System 2 Reasoning in LLMs 📕 Link @Machine_learn