23171
Admin: @Raminmousa ID: @Machine_learn link: https://t.me/Machine_learn
Evolutionary Computation in the Era of Large Language Model: Survey and Roadmap
Large language models (LLMs) have not only revolutionized natural language processing but also extended their prowess to various domains, marking a significant stride towards artificial general intelligence. The interplay between LLMs and evolutionary algorithms (EAs), despite differing in objectives and methodologies, share a common pursuit of applicability in complex problems. Meanwhile, EA can provide an optimization framework for LLM's further enhancement under black-box settings, empowering LLM with flexible global search capacities. On the other hand, the abundant domain knowledge inherent in LLMs could enable EA to conduct more intelligent searches. Furthermore, the text processing and generative capabilities of LLMs would aid in deploying EAs across a wide range of tasks. Based on these complementary advantages, this paper provides a thorough review and a forward-looking roadmap, categorizing the reciprocal inspiration into two main avenues: LLM-enhanced EA and EA-enhanced #LLM. Some integrated synergy methods are further introduced to exemplify the complementarity between LLMs and EAs in diverse scenarios, including code generation, software engineering, neural architecture search, and various generation tasks. As the first comprehensive review focused on the EA research in the era of #LLMs, this paper provides a foundational stepping stone for understanding the collaborative potential of LLMs and EAs. The identified challenges and future directions offer guidance for researchers and practitioners to unlock the full potential of this innovative collaboration in propelling advancements in optimization and artificial intelligence.
Paper: https://arxiv.org/pdf/2401.10034v3.pdf
Code: https://github.com/wuxingyu-ai/llm4ec
/channel/deep_learning_proj
Foundations of Large Language Models
📝 Table of Contents:
● Pre-training
● Generative Models
● Prompting
● Alignment
Tong Xiao and Jingbo Zhu
January 17, 2025
📃 Download from arXiv.
@Machine_learn
Mathematics of Backpropagation Through Time.
📕 Paper
@Machine_learn
از این پروژه نفرات ۲ ، ۳، ۶ و ۷ رزرو شدن...!🔸🔹🔺
Читать полностью…
Continual Forgetting for Pre-trained Vision Models (CVPR2024)
🖥 Github: https://github.com/bjzhb666/GS-LoRA
📕 Paper: https://arxiv.org/abs/2501.09705v1
🧠 Dataset: https://paperswithcode.com/dataset/coco
@Machine_learn
با عرض سلام پروژه جدیدمون شروع شد.
هدف اصلی این پروژه اموزش یک مدل پیشنهاد دهنده ی مدل برای مسائله طبقه بندی تصاویر پزشکی میباشد که از اموزش مجدد مدل ها جلوگیری میکند. این مسائله با جنبه جلوگیری از مصرف انرژی اموزشی و زمان اموزش مدل ها ارائه می شود. برای این منظور ۵۰۰۰ مقاله در این زمینه جمع اوری شده است. جزئیات بیشتر در لینک گیت قرار دارد.
Project Title: MedRec: Medical recommender system for image classification without retraining
Github: https://github.com/Ramin1Mousa/MedicalRec
Journal: IEEE Transactions on Pattern Analysis and Machine Intelligence
Impact factor: 20.8
۷ نفر دیگر امکان اضافه شدن به این پروژه رو دارند. هر شخص نیاز هست که حدودا داده های ۴۰۰ مقاله رو بررسی کند. زمان تقریبی هر مقاله ۵-۱۰ دقیقه می باشد. هزینه مشارکت در مقاله:
🔹 2- 600$
🔺 3- 500$
💠 4- 400$
🔺 5- 300$
🔹 6- 200$
🔸 7- 200$
جهت مشارکت می تونید به ایدی بنده پیام بدین.
@Raminmousa
Titans: Transformer v.2?
📃🖋Read the paper.
@Machine_learn
📄 Application of Artificial Intelligence In Drug-target Interactions Prediction: A Review
📗 Journal: npj Biomedical Innovations
🗓Publish year: 2025
📎 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
/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
تنها دو روز تا شروع اين پروژه باقي مونده دوستاني كه مايل به همكاري هستن به ايدي بنده پيام بدن
@Raminmousa
با عرض سلام پروژه جدیدمون شروع شد.
هدف اصلی این پروژه اموزش یک مدل پیشنهاد دهنده ی مدل برای مسائله طبقه بندی تصاویر پزشکی میباشد که از اموزش مجدد مدل ها جلوگیری میکند. این مسائله با جنبه جلوگیری از مصرف انرژی اموزشی و زمان اموزش مدل ها ارائه می شود. برای این منظور ۵۰۰۰ مقاله در این زمینه جمع اوری شده است. جزئیات بیشتر در لینک گیت قرار دارد.
Project Title: MedRec: Medical recommender system for image classification without retraining
Github: https://github.com/Ramin1Mousa/MedicalRec
Journal: IEEE Transactions on Pattern Analysis and Machine Intelligence
Impact factor: 20.8
۷ نفر دیگر امکان اضافه شدن به این پروژه رو دارند. هر شخص نیاز هست که حدودا داده های ۴۰۰ مقاله رو بررسی کند. زمان تقریبی هر مقاله ۵-۱۰ دقیقه می باشد. هزینه مشارکت در مقاله:
🔹 2- 600$❌
🔺 3- 500$❌
💠 4- 400$✅
🔺 5- 300$✅
🔹 6- 200$❌
🔸 7- 200$❌
جهت مشارکت می تونید به ایدی بنده پیام بدین.
تنها نفرات ۴ و ۵ باقی مانده....!
@Raminmousa
Top 10 Machine Learning Algorithms
@Machine_learn
LatentSync: Audio Conditioned Latent Diffusion Models for Lip Sync
Paper: https://arxiv.org/pdf/2412.09262v1.pdf
Code: https://github.com/bytedance/LatentSync
@Machine_learn
دوستان این مقاله هم If اش بالاست و هم یک کار بنیادیه. جزئیات کار داخل گیت هست.
Читать полностью…
📃Understanding When and Why Graph Attention Mechanisms Work via Node Classification
📎 Study the paper
@Machine_learn
Mathematicians and physicists alike will jump on this Representation theory primer by Etingof, Hensel, Golberg++
📕 Paper
@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