با عرض سلام
نفر سوم و چهارم از مقاله زیر رو جهت ثبت اسم نیاز داریم. این مقاله ۸ ماه داریم روش کار میکنیم.

Title: Gaussian Mixture latent for Recurrent Neural Networks Basic deficiencies


The problem of time series prediction analyzes patterns in past data to predict the future. Traditional machine learning algorithms, despite achieving impressive results, require manual feature selection. Automatic feature selection along with the addition of time concept in deep recurrent networks has led to the provision of more suitable solutions. The selection of feature order in deep recurrent networks leads to the provision of different results due to the use of Back-propagation. The problem of selecting feature order is an NP-complete problem. In this research, the aim is to provide a solution to improve this problem.......!

Jouranl: Expert system with application
هزینه نفر سوم ۵۰۰ دلار و هزینه نفر چهارم ۴۰۰ دلار می باشد. جهت ثبت اسم با ایدی بنده در ارتباط باشین.
@Raminmousa
@Machine_learn

نفر ۴ ام برداشته شد...!

با عرض سلام برای یکی از مقالاتمون نفر دوم رو لازم داریم زمان سابمیت امشب تا فردا شب

Time-series Forecasting of Bitcoin Prices and Illiquidity
using High-dimensional Features: XGBoostLSTM
Approach

Corresponding author: Ramin Mousa

Abstract Liquidity is the ease of converting an asset into cash or another asset
without loss, and is shown by the relationship between the time scale and the
price scale of an investment. This article examines the relationship between
Bitcoin’s price prediction and illiquidity. Bitcoin Hash Rate information was col-
lected in three different intervals, and three techniques of feature selection (FS)
Filter, Wrapper, and Embedded were used. Considering the regression nature of
illiquidity prediction, an approach based on LSTM network and XGBoost was
proposed. LSTM was used to extract time series features, and XGBoost was used
to learn these features. The proposed LSTMXGBoost approach was evaluated in
two modes: price prediction and illiquidity prediction. This approach achieved
MAE 1.60 in the next-day forecast and MAE 3.46 in the next-day illiquidity
forecast. In the cross-validation of the proposed approach on the FS approaches,
the best result was obtained in the prediction by the filter approach and in
the classification by the wrapper approach. These obtained results indicate that
the presented models outperform the existing models in the literature. Examin-
ing the confusion matrices indicates that the two tasks of price prediction and
illiquidity prediction have no correlation and harm each other.

Keywords: illiquidity prediction, Bitcoin hash rate, hybrid model, price pre-
diction, LSTMXGBoost
ژورنال سابمیت
Journal : Finanace innovation(springer)
If: 6.5
دوستانی که در سری زمانی کار می کنن می تونن در این مقاله شرکت کنن.

@Raminmousa

دوستانی که مایل به شرکت در این مقاله می باشند اخرین فرصت تا فردا شب...!

BashBook

📚 Book

@Machine_learn

DATA SCIENCE AND DATA
ANALYTICS OPPORTUNITIES AND CHALLENGES

Edited by
Amit Kumar Tyagi
#Book

@Machine_learn

📄 New methods for drug synergy prediction: A mini-review


📎 Study the paper


@Machine_learn

Assisting in Writing Wikipedia-like Articles From Scratch with Large Language Models

22 Feb 2024 · Yijia Shao, Yucheng Jiang, Theodore A. Kanell, Peter Xu, Omar Khattab, Monica S. Lam ·

We study how to apply large language models to write grounded and organized long-form articles from scratch, with comparable breadth and depth to Wikipedia pages. This underexplored problem poses new challenges at the pre-writing stage, including how to research the topic and prepare an outline prior to writing. We propose STORM, a writing system for the Synthesis of Topic Outlines through Retrieval and Multi-perspective Question Asking. STORM models the pre-writing stage by (1) discovering diverse perspectives in researching the given topic, (2) simulating conversations where writers carrying different perspectives pose questions to a topic expert grounded on trusted Internet sources, (3) curating the collected information to create an outline. For evaluation, we curate FreshWiki, a dataset of recent high-quality Wikipedia articles, and formulate outline assessments to evaluate the pre-writing stage. We further gather feedback from experienced Wikipedia editors. Compared to articles generated by an outline-driven retrieval-augmented baseline, more of STORM's articles are deemed to be organized (by a 25% absolute increase) and broad in coverage (by 10%). The expert feedback also helps identify new challenges for generating grounded long articles, such as source bias transfer and over-association of unrelated facts.

Paper: https://arxiv.org/pdf/2402.14207v2.pdf

Codes:
https://github.com/assafelovic/gpt-researcher
https://github.com/stanford-oval/storm



@Machine_learn

CNN Transfer Learning

@Machine_learn

یکی از ابزارهای خوبی که بنده تونستم توسعه بدم ابزار Stock Ai می باشد. در این ابزار از ۳۶۰ اندیکاتور استفاده کردم. گزارشات back test این ابزار در ویدیو های زیر موجود می باشد.

May 2024 :

https://youtu.be/aSS99lynMFQ?si=QSk8VVKhLqO_2Qi3

July 2014:

https://youtu.be/ThyZ0mZwsGk?si=FKPK7Hkz-mRx-752&t=209

از این رو سعی میکنیم مقاله ای این کار رو بنویسیم. شروع مقاله ی این کار ۲۰ اسفند خواهد بود.
دوستانی که می تونن به هر نحوی کمک کنند تا شروع مقاله می تونن نام نویسی کنند.
@Raminmousa

https://nowak.ece.wisc.edu/MFML.pdf

"Mathematical Foundations of Machine Learning"

PDF: https://nowak.ece.wisc.edu/MFML.pdf

@Machine_learn

LLM4Decompile: Decompiling Binary Code with Large Language Models

8 Mar 2024 · Hanzhuo Tan, Qi Luo, Jing Li, Yuqun Zhang ·

Decompilation aims to convert binary code to high-level source code, but traditional tools like Ghidra often produce results that are difficult to read and execute. Motivated by the advancements in Large Language Models (LLMs), we propose LLM4Decompile, the first and largest open-source #LLM series (1.3B to 33B) trained to decompile binary code. We optimize the LLM training process and introduce the LLM4Decompile-End models to decompile binary directly. The resulting models significantly outperform GPT-4o and Ghidra on the HumanEval and ExeBench benchmarks by over 100% in terms of re-executability rate. Additionally, we improve the standard refinement approach to fine-tune the LLM4Decompile-Ref models, enabling them to effectively refine the decompiled code from Ghidra and achieve a further 16.2% improvement over the LLM4Decompile-End. LLM4Decompile demonstrates the potential of LLMs to revolutionize binary code decompilation, delivering remarkable improvements in readability and executability while complementing conventional tools for optimal results.

Paper: https://arxiv.org/pdf/2403.05286v3.pdf

Code: https://github.com/albertan017/LLM4Decompile



@Machine_learn

نفرات ۳،۴ و ۵ این پروژه رو برای مشارکت در نظر گرفتیم. ژورنال مورد نظر برای ارسال
Finance innovation
If: 6.5
دوستانی که مایل به شرکت هستند با ایدی بنده در ارتباط باشند.

@Raminmousa

Mathematics for Machine Learning

📚 Book

@Machine_learn

FireRedASR: Open-Source Industrial-Grade Mandarin Speech Recognition Models from Encoder-Decoder to LLM Integration

24 Jan 2025 · Kai-Tuo Xu, Feng-Long Xie, Xu Tang, Yao Hu ·

We present FireRedASR, a family of large-scale automatic speech recognition (ASR) models for Mandarin, designed to meet diverse requirements in superior performance and optimal efficiency across various applications. FireRedASR comprises two variants: FireRedASR-LLM: Designed to achieve state-of-the-art (SOTA) performance and to enable seamless end-to-end speech interaction. It adopts an Encoder-Adapter-LLM framework leveraging large language model (LLM) capabilities. On public Mandarin benchmarks, FireRedASR-LLM (8.3B parameters) achieves an average Character Error Rate (CER) of 3.05%, surpassing the latest SOTA of 3.33% with an 8.4% relative CER reduction (CERR). It demonstrates superior generalization capability over industrial-grade baselines, achieving 24%-40% CERR in multi-source Mandarin ASR scenarios such as video, live, and intelligent assistant. FireRedASR-AED: Designed to balance high performance and computational efficiency and to serve as an effective speech representation module in LLM-based speech models. It utilizes an Attention-based Encoder-Decoder (AED) architecture. On public Mandarin benchmarks, FireRedASR-AED (1.1B parameters) achieves an average CER of 3.18%, slightly worse than FireRedASR-LLM but still outperforming the latest SOTA model with over 12B parameters. It offers a more compact size, making it suitable for resource-constrained applications. Moreover, both models exhibit competitive results on Chinese dialects and English speech benchmarks and excel in singing lyrics recognition.

Paper: https://arxiv.org/pdf/2501.14350v1.pdf

Code: https://github.com/fireredteam/fireredasr

Datasets: LibriSpeech - AISHELL-1 - AISHELL-2 - WenetSpeech

https://www.tg-me.com/deep_learning_proj

Applied Machine Learning with Python
#Book
@Machine_learn