Top 6 Python neurip Projects

resp

4 317 4.8 Python

Fetch Academic Research Papers from different sources
monitors4codegen

1 105 7.5 Python

Code and Data artifact for NeurIPS 2023 paper - "Monitor-Guided Decoding of Code LMs with Static Analysis of Repository Context". `multispy` is a lsp client library in Python intended to be used to build applications around language servers.

Project mention: Show HN: Multilspy – A library to easily use language servers to analyze code | news.ycombinator.com | 2023-11-28

InfluxDB

www.influxdata.com sponsored

Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
OSDT

1 94 4.4 Python

Optimal Sparse Decision Trees
Revisiting-Contrastive-SSL

1 86 0.0 Python

Revisiting Contrastive Methods for Unsupervised Learning of Visual Representations. [NeurIPS 2021]
arxivbox

1 10 1.8 Python

Web interface for browsing arXiv papers
MTR

1 9 4.9 Python

The official implementation of the paper "Rethinking Data Augmentation for Tabular Data in Deep Learning" (by somaonishi)

Project mention: Rethinking Data Augmentation for Tabular Data in Deep Learning | /r/BotNewsPreprints | 2023-05-18

Tabular data is the most widely used data format in machine learning (ML). While tree-based methods outperform DL-based methods in supervised learning, recent literature reports that self-supervised learning with Transformer-based models outperforms tree-based methods. In the existing literature on self-supervised learning for tabular data, contrastive learning is the predominant method. In contrastive learning, data augmentation is important to generate different views. However, data augmentation for tabular data has been difficult due to the unique structure and high complexity of tabular data. In addition, three main components are proposed together in existing methods: model structure, self-supervised learning methods, and data augmentation. Therefore, previous works have compared the performance without comprehensively considering these components, and it is not clear how each component affects the actual performance. In this study, we focus on data augmentation to address these issues. We propose a novel data augmentation method, $\textbf{M}$ask $\textbf{T}$oken $\textbf{R}$eplacement ($\texttt{MTR}$), which replaces the mask token with a portion of each tokenized column; $\texttt{MTR}$ takes advantage of the properties of Transformer, which is becoming the predominant DL-based architecture for tabular data, to perform data augmentation for each column embedding. Through experiments with 13 diverse public datasets in both supervised and self-supervised learning scenarios, we show that $\texttt{MTR}$ achieves competitive performance against existing data augmentation methods and improves model performance. In addition, we discuss specific scenarios in which $\texttt{MTR}$ is most effective and identify the scope of its application. The code is available at https://github.com/somaonishi/MTR/.

NOTE: The open source projects on this list are ordered by number of github stars. The number of mentions indicates repo mentiontions in the last 12 Months or since we started tracking (Dec 2020).