23171
Admin: @Raminmousa ID: @Machine_learn link: https://t.me/Machine_learn
📃 Bioinformatics perspectives on transcriptomics: A comprehensive review of bulk and single-cell RNA sequencing analyses
📎 Study the paper
@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
🌟 🌟 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
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
/channel/+SP9l58Ta_zZmYmY0
Improving Deep Neural Networks: Hyperparameter tuning, Regularization and
Optimization
#Dl
@Machine_learn
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
/channel/+SP9l58Ta_zZmYmY0
Mathematical Foundations of Machine Learning
📓 book
@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
Towards System 2 Reasoning in LLMs
📕 Link
@Machine_learn
📃 Large language model to multimodal large language model: A journey to shape the biological macromolecules to biological sciences and medicine
📓 Journal: Molecular Therapy Nucleic Acids (I.F.=6.5)
📎 Study the paper
@Machine_learn
✨Data base normalization✨
@Machine_learn
Tensors in computations
📕Book
@Machine_learn
📽 Introduction to Network Analysis using NetworkX
🎞 Watch
@Machine_learn
هزینه نهایی برای این کار رو به ۲۵ میلیون کاهش دادیم برای نفر ۵ ...!🔥
Читать полностью…
⚡️ NeuZip
▶️# Install from PyPI
pip install neuzip
# Use Neuzip for Pytorch model
model: torch.nn.Module = # your model
+ manager = neuzip.Manager()
+ model = manager.convert(model)
🟡Arxiv
🖥GitHub
@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
/channel/+SP9l58Ta_zZmYmY0
🌟 LLaVA-CoT: VLM с
🟡Arxiv
🟡Demo
🖥GitHub
@Machine_learn
این فقط نفر ۴ امش باقی مونده
Читать полностью…
Recurrent Neural Networks(RNN)
#RNN
@Machine_learn
این مقاله جزو اولین سری مقالات ما در حوزه ی LLM هستش
Читать полностью…
PHY405 Relativity
📕 Lectures
@Machine_learn
🌟 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
با عرض سلام تمامي كار هاي مشترك تموم شدن و فقط اين كار باقي مونده....!
@Raminmousa
📑 Advances of the recent data-driven paradigm shift in medicine and healthcare: From machine learning to deep learning
📎 Study the paper
@Machine_learn
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
/channel/+SP9l58Ta_zZmYmY0
با عرض سلام دوستان كه مي خوان توي تيم هاي paper ما شركت كنن موضوعات زير رو مي خواهيم جلو ببريم.
1: survey on whole slide image
▫️
2: Proposed a new model for enrergy efficiency in deep image classification models
Authers: 2, 3, 4 🔺
3:BioPars: a pretrained biomedical large language
model for persian biomedical text mining
Authors: 5🔺
4: Air quality prediction by hybrid deep learning and machine learning models
Authors:4🔺
در تمامی این موارد نیاز به انجام تسک و پرداخت هزینه سرور ها می باشیم.
@Raminmousa
Automating the Search for Artificial Life with Foundation Models
paper: https://arxiv.org/pdf/2412.17799v1.pdf
Code: https://github.com/sakanaai/asal
@Machine_learn
📃A Survey of Graph Neural Networks for Social Recommender Systems
📎 Study paper
@Machine_learn
با عرض سلام پروژه Biopars رو شروع كرديم نفر ٥ ام از اين مقاله رو نياز داريم.
این کار تحت نظر استاد
Rex (Zhitao) Ying
انجام میشه.
link: https://scholar.google.com.au/citations?user=6fqNXooAAAAJ&hl=en
BioPars: a pre-trained biomedical large language model for persian biomedical text mining.
١- مراحل اوليه: جمع اوري متن هاي فارسي بيولوژيكي از منابع (...)
٢- پيش پردازش متن ها و تميز كردن متن ها
٣- اموزش ترنسفورمرها ي مورد نظر
٤- استفاده از بردارها ي اموزش داده شده در سه تسك (...)
هزينه سرور به ازاي هر ساعت ١.٢ دلار مي باشد. و حدود ٢ هزار ساعت براي اموزش مدل زباني نياز ميباشد.
دوستاني كه نياز دارن مي تونن به تيم ما اضافه بشن
🔸🔸🔸🔸🔸
@Raminmousa
🌟 🌟 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