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Admin: @Raminmousa Watsapp: +989333900804 ID: @Machine_learn link: https://t.me/Machine_learn
New research papers and github codes
🟢Motivo
🟡Paper 🟡Demo 🟡Github
🟢Video Seal
🟡Paper 🟡Demo 🟡Github
🟢Flow Matching
🟡Paper 🟡Github
🟢Explore Theory-of-Mind
🟡Paper 🟡Github 🟡Dataset
🟢Large Concept Model (LCM)
🟡Paper 🟡Github
🟢Dynamic Byte Latent Transformer
🟡Paper 🟡Github
🟢Memory Layers.
🟡Paper 🟡Github
🟢EvalGym
🟡Paper 🟡Github
🟢CLIP 1.2
🟡Paper 🟡Github 🟡Dataset 🟡Model
@Machine_learn
با عرض سلام در راستاي ادامه تحقيقات مشترك سعي داريم روي حوزه ي LLM مدل ها كار كنيم.
این کار تحت نظر استاد
Rex (Zhitao) Ying
انجام میشه.
link: https://scholar.google.com.au/citations?user=6fqNXooAAAAJ&hl=en
۲نفر براي همکاری نياز داريم.
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 (...)
دوستاني كه مايل به مشاركت هستن مي تونين بهم اطلاع بدن.
هزينه سرور به ازاي هر ساعت ١.٢ دلار مي باشد. و حدود ٢ هزار ساعت براي اموزش مدل زباني نياز ميباشد. هزينه به ترتيب براي نفرات علاوه بر انجام تسك ها به صورت زير مي باشد.
🔹نفر سوم 500 دلار
🔺نفر چهارم 400 دلار
@Raminmousa
@Machine_learn
/channel/+SP9l58Ta_zZmYmY0
🌟 AlphaFold 3
🟡Paper
🟡Demo
🖥GitHub
@Machine_learn
📑 Application of graph theory in liver research: A review
📎 Study paper
@Machine_learn
Probability, Random Processes, and Statistical Analysis Applications to Communications, Signal Processing, Queueing Theory and Mathematical Finance
📕 Book
@Machine_learn
Time Series Visualization from Raw Data to Insights
🔹 #Code
@Machine_learn
با عرض سلام در راستاي ادامه تحقيقات مشترك سعي داريم از ١ ام دي ماه روي حوزه ي LLM مدل ها كار كنيم.
این کار تحت نظر استاد
Rex (Zhitao) Ying
انجام میشه.
link: https://scholar.google.com.au/citations?user=6fqNXooAAAAJ&hl=en
1نفر براي همکاری نياز داريم.
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 (...)
دوستاني كه مايل به مشاركت هستن مي تونين تا ١ دي بهم اطلاع بدن.
هزينه سرور به ازاي هر ساعت ١.٢ دلار مي باشد. و حدود ٢ هزار ساعت براي اموزش مدل زباني نياز ميباشد. هزينه به ترتيب براي نفرات علاوه بر انجام تسك ها به صورت زير مي باشد.
🔹نفر چهارم 500 دلار
@Raminmousa
@Machine_learn
/channel/+SP9l58Ta_zZmYmY0
🔹🔹شروع کار از امشب🔹🔹
Perfect Roadmap To Learn Data Science In 2024
📖 Book
@Machine_learn
🌟 SmolLM2
⏩SmolLM2-1.7B🟢SmolLM2-1.7B-Instruct🟢Instruct GGUF
⏩SmolLM2-360M🟠SmolLM2-360M-Instruct 🟠Instruct GGUF
⏩SmolLM2-135M 🟠SmolLM2-135M-Instruct 🟠Instruct GGUF от комьюнити
▶️SmolLM2-1.7B :from transformers import AutoModelForCausalLM, AutoTokenizer
checkpoint = "HuggingFaceTB/SmolLM2-1.7B"
device = "cuda" # for GPU usage or "cpu" for CPU usage
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForCausalLM.from_pretrained(checkpoint).to(device)
inputs = tokenizer.encode("Gravity is", return_tensors="pt").to(device)
outputs = model.generate(inputs)
print(tokenizer.decode(outputs[0]))
📌Apache 2.0 License.
🟡Demo SmolLM2 1.7B
@Machine_learn
تنها نفر ۴ ام از این کار مشترک باقی مونده
شروع کار ۱ دی ماه هستش. جهت همکاری به ایدی بنده پیام بدین.
@Raminmousa
🀄 GuoFeng Webnovel: A Discourse-Level and Multilingual Corpus of Web Fiction
🖥 Github: https://github.com/longyuewangdcu/guofeng-webnovel
📕 Paper: https://arxiv.org/abs/2412.11732v1
🌟 Dataset: www2.statmt.org/wmt24/literary-trans
@Machine_learn
با عرض سلام نفر سوم براي مقاله زير رو خالي داريم.
Title: Alzheimer’s disease (AD) classification
using swin transformer wavelet
and Improved Gray Wolf
Optimization (IGWO)
Abstract: Alzheimer’s disease (AD) is a slow neurological disorder that destroys the thought process, and consciousness, of a human. It directly affects the development of mental ability and neurocognitive functionality. The number of patients with Alzheimer’s disease is increasing day by day, especially in old aged people, who are above 60 years of age, and, gradually, it becomes cause of their death. In this research, our goal is to present ALzSwinTNet for Alzheimer’s classification based on FMRI images. The proposed approach uses wavelet fusion in the swin transformer network to extract features. The igwo and fox optimization approaches were used to find the hyperparameters of the model. ALzSwinTNet was able to achieve an accuracy of 0.98 in 4-class classification and 1 in 2-class classification.
journal: https://www.sciencedirect.com/journal/expert-systems-with-applications
if:7.5
هزینه مشارکت برای نفر سوم ۲۰ تومن می باشد. این هزینه صرف تسویه سرورها خواهد شد.
@Raminmousa
@Machine_learn
/channel/+SP9l58Ta_zZmYmY0
PDF Math Translate
DF scientific paper translation with preserved formats
Creator: Byaidu
Stars ⭐️: 5.1k
Forked By: 375
https://github.com/Byaidu/PDFMathTranslate
@Machine_learn
با عرض سلام نفر سوم براي مقاله زير رو خالي داريم.
Title: Alzheimer’s disease (AD) classification
using swin transformer wavelet
and Improved Gray Wolf
Optimization (IGWO)
Abstract: Alzheimer’s disease (AD) is a slow neurological disorder that destroys the thought process, and consciousness, of a human. It directly affects the development of mental ability and neurocognitive functionality. The number of patients with Alzheimer’s disease is increasing day by day, especially in old aged people, who are above 60 years of age, and, gradually, it becomes cause of their death. In this research, our goal is to present ALzSwinTNet for Alzheimer’s classification based on FMRI images. The proposed approach uses wavelet fusion in the swin transformer network to extract features. The igwo and fox optimization approaches were used to find the hyperparameters of the model. ALzSwinTNet was able to achieve an accuracy of 0.98 in 4-class classification and 1 in 2-class classification.
journal: https://www.sciencedirect.com/journal/expert-systems-with-applications
if:7.5
هزینه مشارکت برای نفر سوم ۲۰ تومن می باشد. این هزینه صرف تسویه سرورها خواهد شد.
@Raminmousa
@Machine_learn
/channel/+SP9l58Ta_zZmYmY0
📃 Large language models and their applications in bioinformatics
📎 Study the paper
@Machine_learn
دوستان خروجي اين كار ٣ تا مقاله خواهد بود...!
Читать полностью…
🌟 LLaMA-Mesh:
🟡Arxiv
🖥GitHub
/channel/deep_learning_proj
Building Blocks for Theoretical Computer Science
🎓 Link
@Machine_learn
با عرض سلام مقاله زیر در مرحله major revision می‌باشد. نفر ۴ ام از این مقاله قابل اضافه کردن است.
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.
#Unet++
#Segmentation
@Raminmousa
@Machine_learn
/channel/+SP9l58Ta_zZmYmY0
Book: The Art of Data Science
Authors: Roger D. Peng & Elizabeth Matsui
@Machine_learn
New o3 OpenAI model is changing the game!
For a long time, ARC was seen as proof that AI models “can’t think.” The argument went: if they truly could, why do they perform so poorly on this benchmark?
Well, those days are over. The o3 model demonstrates not only the ability to think but also the capability to tackle tasks once considered out of reach.
👀 Check out the full breakdown of this breakthrough: https://arcprize.org/blog/oai-o3-pub-breakthrough
It might be time to rethink what AI can achieve. Looking forward to the release!
@Machine_learn
Gemini API Cookbook
📚 Github
@Machine_learn
با عرض سلام نفر سوم براي مقاله زير رو خالي داريم.
Title: Alzheimer’s disease (AD) classification
using swin transformer wavelet
and Improved Gray Wolf
Optimization (IGWO)
Abstract: Alzheimer’s disease (AD) is a slow neurological disorder that destroys the thought process, and consciousness, of a human. It directly affects the development of mental ability and neurocognitive functionality. The number of patients with Alzheimer’s disease is increasing day by day, especially in old aged people, who are above 60 years of age, and, gradually, it becomes cause of their death. In this research, our goal is to present ALzSwinTNet for Alzheimer’s classification based on FMRI images. The proposed approach uses wavelet fusion in the swin transformer network to extract features. The igwo and fox optimization approaches were used to find the hyperparameters of the model. ALzSwinTNet was able to achieve an accuracy of 0.98 in 4-class classification and 1 in 2-class classification.
💠journal: https://www.sciencedirect.com/journal/expert-systems-with-applications
🔺if:7.5
هزینه مشارکت برای نفر سوم ۲۰ تومن می باشد. این هزینه صرف تسویه سرورها خواهد شد.
امکان co-author نیز برای این کار هستش.
@Raminmousa
@Machine_learn
/channel/+SP9l58Ta_zZmYmY0
Practitioner Guide for Creating Effective Prompts in Large Language Models
🔗 Paper
@Machine_learn
Introduction to Data Science – Lecture Material
🔗 Github
@Machine_learn
در اين پروژه ابتدا BioparsData ارائه ميشود كه فرايند جمع اوري سنگيني خواهد داشت. پس از ان BioparsQ ارائه ميشود كه ١٠ هزار سوال بيولوژكي براي ارزيابي مدل ارائه خواهد شد. در انتها Biopars را ارائه خواهيم داد. تمامي اين فرايند پس از نهايي شدن در دسترس عموم قرار ميدهيم.
Читать полностью…
📃A Comprehensive Survey on Automatic Knowledge Graph Construction
📎 Study paper
@Machine_learn
امكان واگذاري co-author هم داره.
Читать полностью…
⚡️ Byte Latent Transformer: Patches Scale Better Than Tokens
Byte Latent Transformer architecture (BLTs), a new byte-level LLM architecture that for the first time, matches tokenization-based LLM performance at scale, with significant improvements in inference efficiency and robustness.
🖥 Github: https://github.com/facebookresearch/blt
📕 Paper: https://arxiv.org/abs/2412.09871v1
🌟 Dataset: https://paperswithcode.com/dataset/mmlu
@Machine_learn
٣ روز براي شروع اين پروژه مونده...!
Читать полностью…