arXiv research

FinGPT is an open-source financial LLM for democratizing financial data.

Study uses open data to improve traffic emissions estimation.

Fine-tuned open-source LLMs match or exceed closed-source models in social science research.

SAGDA generates synthetic agricultural datasets to improve ML in African farming.

FinRobot opens-source AI for financial tasks, breaking down complex problems.

We provide complete source code for building a fundamental industry classification based on publically available and freely downloadable data. We compare various fundamental industry classifications by running a horserace of short-horizon trading signals (alphas) utilizing open source heterotic risk models (https://ssr…

Scoping review and benchmarking of synthetic EHR data generation methods.

SMART is an open source web application designed to help data scientists and research teams efficiently build labeled training data sets for supervised machine learning tasks. SMART provides users with an intuitive interface for creating labeled data sets, supports active learning to help reduce the required amount of …

UniFeat is an open-source Java tool for feature selection.

Apache Spark is a popular open-source platform for large-scale data processing that is well-suited for iterative machine learning tasks. In this paper we present MLlib, Spark's open-source distributed machine learning library. MLlib provides efficient functionality for a wide range of learning settings and includes sev…

EmTract extracts emotions from financial social media text.

Deep learning methods have shown extraordinary potential for analyzing very diverse biomedical data, but their dissemination beyond developers is hindered by important computational hurdles. We introduce ImJoy (https://imjoy.io/), a flexible and open-source browser-based platform designed to facilitate widespread reuse…

Survey examines challenges and tools for integrating multiple types of omics data.

SurvSet offers a repository of 76 T2E datasets for ML benchmarking.

LeanDojo removes barriers to theorem proving with open-source tools and data.

CoinTossX is a low-latency, open-source matching engine for financial trading.

This study examines representation bias in open-source Qwen models for investment decisions.

AHN package simplifies supervised learning with hydrocarbon networks.

This paper presents an open-source enforcement learning toolkit named CytonRL (https://github.com/arthurxlw/cytonRL). The toolkit implements four recent advanced deep Q-learning algorithms from scratch using C++ and NVIDIA's GPU-accelerated libraries. The code is simple and elegant, owing to an open-source general-purp…

3D CNNs interpret brain MRI differences between men and women.

Deep Learning has established itself to be a common occurrence in the business lexicon. The unprecedented success of deep learning in recent years can be attributed to: abundance of data, availability of gargantuan compute capabilities offered by GPUs, and adoption of open-source philosophy by the researchers and indus…

AutoML serves as the bridge between varying levels of expertise when designing machine learning systems and expedites the data science process. A wide range of techniques is taken to address this, however there does not exist an objective comparison of these techniques. We present a benchmark of current open source Aut…

Uncertainty Toolbox aids in assessing and improving uncertainty quantification in machine learning.

This paper benchmarks FinGPT for financial datasets using open-source large language models.

Python library for causal discovery from observational data.

NoLimits.jl: Flexible and Composable Nonlinear Mixed-Effects Modeling in Julia

Recent advances in machine learning have made significant contributions to drug discovery. Deep neural networks in particular have been demonstrated to provide significant boosts in predictive power when inferring the properties and activities of small-molecule compounds. However, the applicability of these techniques …

ML4Chem offers a user-friendly platform for developing and deploying machine learning models in chemistry.

auton-survival simplifies survival analysis for healthcare data.

Today, artificial intelligence systems driven by machine learning algorithms can be in a position to take important, and sometimes legally binding, decisions about our everyday lives. In many cases, however, these systems and their actions are neither regulated nor certified. To help counter the potential harm that suc…

In this paper we present Horizon, Facebook's open source applied reinforcement learning (RL) platform. Horizon is an end-to-end platform designed to solve industry applied RL problems where datasets are large (millions to billions of observations), the feedback loop is slow (vs. a simulator), and experiments must be do…

PyPOTS simplifies machine learning on time series with missing data.

This paper documents the release of the ELKI data mining framework, version 0.7.5. ELKI is an open source (AGPLv3) data mining software written in Java. The focus of ELKI is research in algorithms, with an emphasis on unsupervised methods in cluster analysis and outlier detection. In order to achieve high performance a…

HealthSyn generates synthetic user behavior data for health interventions.

mcanalysis quantifies menstrual cycle effects in health data.

GPI uses GenAI models to infer causal and predictive effects from unstructured data.

Quantum CNNs improve on multi-channel data processing.

PyHHMM is a Python library for HHMMs with advanced features.

Framework for few-shot relation classification with minimal training data.

The AlphaGo, AlphaGo Zero, and AlphaZero series of algorithms are remarkable demonstrations of deep reinforcement learning's capabilities, achieving superhuman performance in the complex game of Go with progressively increasing autonomy. However, many obstacles remain in the understanding of and usability of these prom…

Recently, a lot of papers proposed to use neural networks to approximately solve partial differential equations (PDEs). Yet, there has been a lack of flexible framework for convenient experimentation. In an attempt to fill the gap, we introduce a PyDEns-module open-sourced on GitHub. Coupled with capabilities of BatchF…

Software helps teach latent variable methods in multivariate data analytics.

In recent years, an active field of research has developed around automated machine learning (AutoML). Unfortunately, comparing different AutoML systems is hard and often done incorrectly. We introduce an open, ongoing, and extensible benchmark framework which follows best practices and avoids common mistakes. The fram…

Preference learning (PL) is a core area of machine learning that handles datasets with ordinal relations. As the number of generated data of ordinal nature is increasing, the importance and role of the PL field becomes central within machine learning research and practice. This paper introduces an open source, scalable…

Tourism is one of the most important economic activities in the world: for many countries it represents the single largest product in their export basket. However, it is a product difficult to chart: "exporters" of tourism do not ship it abroad, but they welcome importers inside the country. Current research uses socia…

MAPIE provides uncertainty quantification for ML models.

This paper presents the philosophy, design and feature-set of Neural Network Distiller, an open-source Python package for DNN compression research. Distiller is a library of DNN compression algorithms implementations, with tools, tutorials and sample applications for various learning tasks. Its target users are both en…

We introduce Graph-Sparse Logistic Regression, a new algorithm for classification for the case in which the support should be sparse but connected on a graph. We val- idate this algorithm against synthetic data and benchmark it against L1-regularized Logistic Regression. We then explore our technique in the bioinformat…