23171
Admin: @Raminmousa ID: @Machine_learn link: https://t.me/Machine_learn
با عرض سلام نيازمند co-author براي مقاله زیر هستيم.
Target Journal: International Journal of Media and Networks | Opast Publishing Group (opastpublishers.com)
if: 1.2
Paper link: A Survey of Generative Adversarial Network on Next Generation Network[v1] | Preprints.org
تغييرات كامل نسخه نهايي تا يك هفته اينده اعمال ميشه كسي از دوستان تمايل به همكاري داشت به ايدي بنده پيام بدن.
@Raminmousa
@Machine_learn
/channel/+SP9l58Ta_zZmYmY0
An Infinite Descent into Pure Mathematics
📚 Book
@Machine_learn
NotebookLlama: An Open Source version of NotebookLM
📚 Book
@Machine_learn
با عرض سلام
در حال نوشتن مقاله اي تحت عنوان
title:A Comparative Survey on Large Language Models for Biological Data and Knowledge Graph systems
هستيم كه ژورنال هدف Nature ميباشد. ٢ نفر از دوستان به دليل مشغله كاري نتونستن همكاري كنن. نفر ٤ و نفر ٦ از اين ليست رو تصمیم به جايگذيني كرديم. دوستاني كه توانايي كار دارن لطفا به بنده پيام بدن. تسك ها كامل مشخص شده و هزينه هر شخص هم تعيين شده.
@Raminmousa
@Machine_learn
/channel/+SP9l58Ta_zZmYmY0
💡 SAM2Long, a training-free enhancement to SAM 2 for long-term video segmentation
🟡Technical Report: https://huggingface.co/papers/2410.16268
🟡Github: https://github.com/Mark12Ding/SAM2Long
🟡Homepage: https://mark12ding.github.io/project/SAM2Long/
@Machine_learn
private link:
/channel/+SP9l58Ta_zZmYmY0
LLM Engineer's Handbook: Master the art of engineering Large Language Models from concept to production.
🖥 Github
@Machine_learn
Linear Algebra Done Right
📓 Book
@Machine_learn
يكي از بهترين موضوعات در طبقه بندي متن؛ تحليل احساس چند دامنه اي مي باشد. براي اين منظور مدلي تحت عنوان
Title: TRCAPS: The Transformer-based Capsule Approach for Persian Multi-
Domain Sentiment Analysis
طراحي كرديم كه نتايج خيلي بهتري نسبت به IndCaps داشته است.
دوستاني كه نياز به مقاله تو حوزه NLP دارن مي تونن تا اخر اين هفته داخل اين مقاله شركت كنند.
ژورنال هدف Array elsevier مي باشد.
شركت كنندگان داخل اين مقاله نياز به انجام تسك هايي نيز مي باشند.
@Raminmousa
@Machine_learn
@Paper4money
🌟 Zamba2-Instruct
🟢Zamba2-1.2B-instruct;
🟠Zamba2-2.7B-instruct.# Clone repo
git clone https://github.com/Zyphra/transformers_zamba2.git
cd transformers_zamba2
# Install the repository & accelerate:
pip install -e .
pip install accelerate
# Inference:
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
tokenizer = AutoTokenizer.from_pretrained("Zyphra/Zamba2-2.7B-instruct")
model = AutoModelForCausalLM.from_pretrained("Zyphra/Zamba2-2.7B-instruct", device_map="cuda", torch_dtype=torch.bfloat16)
user_turn_1 = "user_prompt1."
assistant_turn_1 = "assistant_prompt."
user_turn_2 = "user_prompt2."
sample = [{'role': 'user', 'content': user_turn_1}, {'role': 'assistant', 'content': assistant_turn_1}, {'role': 'user', 'content': user_turn_2}]
chat_sample = tokenizer.apply_chat_template(sample, tokenize=False)
input_ids = tokenizer(chat_sample, return_tensors='pt', add_special_tokens=False).to("cuda")
outputs = model.generate(**input_ids, max_new_tokens=150, return_dict_in_generate=False, output_scores=False, use_cache=True, num_beams=1, do_sample=False)
print((tokenizer.decode(outputs[0])))
🖥GitHub
@Machine_learn
Prompt Engineering Techniques: Comprehensive Repository for Development and Implementation 🖋️
📓 Github
@Machine_learn
تا اخر امشب این وقت مونده...!
Читать полностью…
Algebraic topology for physicists
📓 Book
@Machine_learn
📑 Nine quick tips for open meta-analyses
📎 Study the paper
✅@Machine_learn
پروژه های بیشتر شبیه این ریپورت داخل این پک قرار داره. دوستانی که نیاز دارن می تونن به ایدی بنده مراجعه کنن.
@Raminmousa
🌟 Zamba2-Instruct
В семействе 2 модели:
🟢Zamba2-1.2B-instruct;
🟠Zamba2-2.7B-instruct.# Clone repo
git clone https://github.com/Zyphra/transformers_zamba2.git
cd transformers_zamba2
# Install the repository & accelerate:
pip install -e .
pip install accelerate
# Inference:
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
tokenizer = AutoTokenizer.from_pretrained("Zyphra/Zamba2-2.7B-instruct")
model = AutoModelForCausalLM.from_pretrained("Zyphra/Zamba2-2.7B-instruct", device_map="cuda", torch_dtype=torch.bfloat16)
user_turn_1 = "user_prompt1."
assistant_turn_1 = "assistant_prompt."
user_turn_2 = "user_prompt2."
sample = [{'role': 'user', 'content': user_turn_1}, {'role': 'assistant', 'content': assistant_turn_1}, {'role': 'user', 'content': user_turn_2}]
chat_sample = tokenizer.apply_chat_template(sample, tokenize=False)
input_ids = tokenizer(chat_sample, return_tensors='pt', add_special_tokens=False).to("cuda")
outputs = model.generate(**input_ids, max_new_tokens=150, return_dict_in_generate=False, output_scores=False, use_cache=True, num_beams=1, do_sample=False)
print((tokenizer.decode(outputs[0])))
🖥GitHub
/channel/deep_learning_proj
Tutorial on Diffusion Models for Imaging and Vision
📚 Book
@Machine_learn
The State of AI Report
📚 Report
@Machine_learn
📑 A guide to RNA sequencing and functional analysis
📎 Study the paper
@Machine_learn
فقط نفر ۲ و ۴ از این باقی مونده ....!
Читать полностью…
Title: BERTCaps: BERT Capsule for persian Multi-domain Sentiment Analysis.
Abstract:
Sentiment classification is widely known as a domain-dependent problem. In order to learn an accurate domain-specific sentiment classifier, a large number of labeled samples are needed, which are expensive and time-consuming to annotate. Multi-domain sentiment analysis based on multi-task learning can leverage labeled samples in each single domain, which can alleviate the need for large amount of labeled data in all domains. In this article, the purpose is BERTCaps to provide a multi-domain classifier. In this model, BERT was used for Instance Representation and Capsule was used for instance learning. In the evaluation dataset, the model was able to achieve an accuracy of 0.9712 in polarity classification and an accuracy of 0.8509 in domain classification.
journal: https://www.sciencedirect.com/journal/array
If: 2.3
نفرات ٢ تا ٤ اين مقاله رو نياز داريم.
دوستاني كه مايل به شركت هستن مي تونن به ايدي بنده پيام بدن.
@Raminmousa
@Paper4money
@Machine_learn
💡 Ultimate Guide to Fine-Tuning LLMs
📚 link
@Machine_learn
فقط نفر دوم از این مقاله مونده...!
Читать полностью…
📄 Advances of Artificial Intelligence in Anti-Cancer Drug Design: A Review of the Past Decade
📎 Study the paper
@Machine_learn
estimating body and hand motion from a pair of glasses 🤓
website: http://egoallo.github.io
code: http://github.com/brentyi/egoallo
@Machine_learn
🔥 NVIDIA silently release a Llama 3.1 70B fine-tune that outperforms
GPT-4o and Claude Sonnet 3.5
Llama 3.1 Nemotron 70B Instruct a further RLHFed model on
huggingface
https://huggingface.co/collections/nvidia/llama-31-nemotron-70b-670e93cd366feea16abc13d8
✅/channel/deep_learning_proj
✔️ LVD-2M: A Long-take Video Dataset with Temporally Dense Captions
New pipeline for selecting high-quality long-take videos and generating temporally dense captions.
Dataset with four key features essential for training long video generation models: (1) long videos covering at least 10 seconds, (2) long-take videos without cuts, (3) large motion and diverse contents, and (4) temporally dense captions.
🖥 Github: https://github.com/silentview/lvd-2m
📕 Paper: https://arxiv.org/abs/2410.10816v1
🖥 Dataset: https://paperswithcode.com/dataset/howto100m
🔸@Machine_learn
با عرض سلام در يكي از مقالاتمون با موضوع
multimodal capsule fusion with self-attention approach for alzheimer disease classification
نياز به نفر دوم هستيم. تسك ها به صورت مشخص شده براي نفر دوم در نظر گرفته شده است. دوستاني كه ميخوان مشاركت كنن به بنده
پيام بدن با تشكر.
@Raminmousa
@Machine_learn
@Paper4money
📃Network Modeling and Control of Dynamic Disease Pathways, Review and Perspectives
📎 Study the paper
@Machine_learn
Thesis: Yolo object detection
این پروژه سال ۲۰۲۰ با یکی از دوستان انجام دادیم که هدف تشخیص وزن پل با استفاده از Yolo بود. جزئیات مدل یولو رو داخل این بررسی کردیم . برای دوستانی که می خوان بیشتر این مدل رو بررسی کنن می تونه مفید باشه.
@Machine_learn