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

✨Data base normalization✨

@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
https://www.tg-me.com/+SP9l58Ta_zZmYmY0

📃 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

📑 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 تمامي كار هاي مشترك تموم شدن و فقط اين كار باقي مونده....!
@Raminmousa

Mathematical Foundations of Machine Learning

📓 book

@Machine_learn

PHY405 Relativity

📕 Lectures

@Machine_learn

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

Improving Deep Neural Networks: Hyperparameter tuning, Regularization and
Optimization

#Dl
@Machine_learn

Recurrent Neural Networks(RNN)
#RNN

@Machine_learn