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Image generated with MidjourneyOrganizations increasingly rely on data to make business decisions, develop strategies, or even make data or machine learning models their key product. As such, the quality of their data can make or break the success of the company. This article will guide you through the concept of  a data quality framework, its essential components, and how to implement it effectively within your organization.What is a data quality framework?A data quality framework refers to the principles, processes, and standards designed to manage and improve the quality of data within organizations. Such frameworks ensure that the data used for…

arXiv:2408.11974v1 Announce Type: new Abstract: We provide a unified analysis of two-timescale gradient descent ascent (TTGDA) for solving structured nonconvex minimax optimization problems in the form of $min_textbf{x} max_{textbf{y} in Y} f(textbf{x}, textbf{y})$, where the objective function $f(textbf{x}, textbf{y})$ is nonconvex in $textbf{x}$ and concave in $textbf{y}$, and the constraint set $Y subseteq mathbb{R}^n$ is convex and bounded. In the convex-concave setting, the single-timescale GDA achieves strong convergence guarantees and has been used for solving application problems arising from operations research and computer science. However, it can fail to converge in more general settings. Our contribution in this paper is…

arXiv:2408.11196v1 Announce Type: new Abstract: Advances in machine learning algorithms for sensor fusion have significantly improved the detection and prediction of other road users, thereby enhancing safety. However, even a small angular displacement in the sensor's placement can cause significant degradation in output, especially at long range. In this paper, we demonstrate a simple yet generic and efficient multi-task learning approach that not only detects misalignment between different sensor modalities but is also robust against them for long-range perception. Along with the amount of misalignment, our method also predicts calibrated uncertainty, which can be useful for filtering and fusing predicted…

arXiv:2408.11847v1 Announce Type: new Abstract: Recent surge in Large Language Model (LLM) availability has opened exciting avenues for research. However, efficiently interacting with these models presents a significant hurdle since LLMs often reside on proprietary or self-hosted API endpoints, each requiring custom code for interaction. Conducting comparative studies between different models can therefore be time-consuming and necessitate significant engineering effort, hindering research efficiency and reproducibility. To address these challenges, we present prompto, an open source Python library which facilitates asynchronous querying of LLM endpoints enabling researchers to interact with multiple LLMs concurrently, while maximising efficiency and utilising individual rate limits.…