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Graph algorithms play an important role in many computer science areas. In order to solve problems that can be modeled using graphs, it is necessary to use a data structure that can represent those graphs in an efficient manner. On top of…

General Matrix Multiplication (GEMM) is a critical kernel in high-performance computing and deep learning. While modern architectures like ARM's Scalable Matrix Extension (SME) introduce dedicated hardware for matrix operations, existing…

Multiobjective optimization problems (MOPs) are prevalent in machine learning, with applications in multi-task learning, learning under fairness or robustness constraints, etc. Instead of reducing multiple objective functions into a scalar…

Developing new ideas and algorithms in the fields of graph processing and relational learning requires public datasets. While Wikidata is the largest open source knowledge graph, involving more than fifty million entities, it is larger than…

The covariance matrix adaptation evolution strategy (CMA-ES) has been highly effective in black-box continuous optimization, as demonstrated by its success in both benchmark problems and various real-world applications. To address the need…

Urban spatio-temporal data present unique challenges for predictive analytics due to their dynamic and complex nature. We introduce STM-Graph, an open-source Python framework that transforms raw spatio-temporal urban event data into graph…

We introduce an open-source system called SIGMA (short for "Situated Interactive Guidance, Monitoring, and Assistance") as a platform for conducting research on task-assistive agents in mixed-reality scenarios. The system leverages the…

The $\sigma$ profile is presented as a tool to analyze the organization of systems at different scales, and how this organization changes in time. Describing structures at different scales as goal-oriented agents, one can define $\sigma \in…

Data exploration is an important step of every data science and machine learning project, including those involving textual data. We provide a novel language tool, in the form of a publicly available Python library for extracting patterns…

We present gradiend, an open-source Python package that operationalizes the GRADIEND method for learning feature directions from factual-counterfactual MLM and CLM gradients in language models. The package provides a unified workflow for…

Automated machine learning makes it easier for data scientists to develop pipelines by searching over possible choices for hyperparameters, algorithms, and even pipeline topologies. Unfortunately, the syntax for automated machine learning…

P2P systems are a great solution to the problem of distributing resources. The main issue of P2P networks is that searching and retrieving resources shared by peers is usually expensive and does not take into account similarities among…

In recent years, deep learning revolutionized machine learning and its applications, producing results comparable to human experts in several domains, including neuroscience. Each year, hundreds of scientific publications present…

Tuning hyperparameters for machine learning algorithms is a tedious task, one that is typically done manually. To enable automated hyperparameter tuning, recent works have started to use techniques based on Bayesian optimization. However,…

We introduce Geomstats, an open-source Python toolbox for computations and statistics on nonlinear manifolds, such as hyperbolic spaces, spaces of symmetric positive definite matrices, Lie groups of transformations, and many more. We…

We propose a variation of the self organizing map algorithm by considering the random placement of neurons on a two-dimensional manifold, following a blue noise distribution from which various topologies can be derived. These topologies…

Understanding how machine learning models respond to distributional shifts is a key research challenge. Mazes serve as an excellent testbed due to varied generation algorithms offering a nuanced platform to simulate both subtle and…

Web space is the huge repository of data. Everyday lots of new information get added to this web space. The more the information, more is demand for tools to access that information. Answering users' queries about the online information…

Self-Organizing Maps (SOM) are popular unsupervised artificial neural network used to reduce dimensions and visualize data. Visual interpretation from Self-Organizing Maps (SOM) has been limited due to grid approach of data representation,…

Background and Objective: Deep learning enables tremendous progress in medical image analysis. One driving force of this progress are open-source frameworks like TensorFlow and PyTorch. However, these frameworks rarely address issues…