OpenAI o3-mini System Card

📚 Reed

@Machine_learn

DeepSeek LLM: Scaling Open-Source Language Models with Longtermism

Paper: https://arxiv.org/pdf/2401.02954v1.pdf

Code: https://github.com/deepseek-ai/deepseek-llm

Dataset: AlignBench


@Machine_learn

📃Can social network analysis contribute to supply chain
management? A systematic literature review and
bibliometric analysis


📎 Study paper


@Machine_learn

Machine Learning for High-Risk Applications

@Machine_learn

20% از کارهای این مقاله انجام شده و این اخرین فرصت برای جوین شدن به پروژست.

@Raminmousa

A Little Bit of Reinforcement Learning
from Human Feedback

📓 Book


@Machine_learn

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@Machine_learn

RIGNO: A Graph-based framework for robust and accurate operator learning for PDEs on arbitrary domains


Paper: https://arxiv.org/pdf/2501.19205v1.pdf

Code: https://github.com/camlab-ethz/rigno



@Machine_learn

نفر ۵ از این پروژه همچنان خالی هست...!
@Raminmousa

CoSTI: Consistency Models for (a faster) Spatio-Temporal Imputation

31 Jan 2025 · Javier Solís-García, Belén Vega-Márquez, Juan A. Nepomuceno, Isabel A. Nepomuceno-Chamorro ·

Multivariate Time Series Imputation (MTSI) is crucial for many applications, such as healthcare monitoring and traffic management, where incomplete data can compromise decision-making. Existing state-of-the-art methods, like Denoising Diffusion Probabilistic Models (DDPMs), achieve high imputation accuracy; however, they suffer from significant computational costs and are notably time-consuming due to their iterative nature. In this work, we propose CoSTI, an innovative adaptation of Consistency Models (CMs) for the MTSI domain. CoSTI employs Consistency Training to achieve comparable imputation quality to DDPMs while drastically reducing inference times, making it more suitable for real-time applications. We evaluate CoSTI across multiple datasets and missing data scenarios, demonstrating up to a 98% reduction in imputation time with performance on par with diffusion-based models. This work bridges the gap between efficiency and accuracy in generative imputation tasks, providing a scalable solution for handling missing data in critical spatio-temporal systems.

Paper: https://arxiv.org/pdf/2501.19364v1.pdf

Code: https://github.com/javiersgjavi/costi



@Machine_learn

Introduction to Python for Computational Science and Engineering

📚 book


@Machine_learn

Practical Statistics for Data Scientists
50+ Essential Concepts Using R and Python
#Python #Book

@Machine_learn