Missing data are frequently observed by practitioners and researchers in the building energy modeling community. In this regard, advanced data-driven solutions, such as Deep Learning methods, are typically required to reflect the non-linear behavior...
Advances in machine learning and increased computational power have driven progress in energy-related research. However, limited access to private energy data from buildings hinders traditional regression models relying on historical data. While gene...
Data processing for and with foundation models! ? ? ? ➡️ ➡️? ? ? (⭐ 6011)
Data-driven attribution is the default attribution model for most conversion actions. Follow the instructions below to update an existing conversion action's attribution model to "Data-driven": In …
Data attribution methods play a crucial role in understanding machine learning models, providing insight into which training data points are most responsible for model outputs during deployment. However, current state-of-the-art approaches require a...
In this paper, we present a data-driven controller design method for continuous-time nonlinear systems, using no model knowledge but only measured data affected by noise. While most existing approaches focus on systems with polynomial dynamics, our a...
A repo for the book 'Streamlit for Data Science' by Tyler Richards (⭐ 245)
Subscribe and bulk download collections of data at PO.DAAC (⭐ 114)
Recent research is revealing data-sonification as a promising complementary approach to vision, benefiting both data perception and interpretation. We present herakoi, a novel open-source software that uses machine learning to allow real-time image s...
Context: The first Gaia data release (DR1) delivered a catalogue of astrometry and photometry for over a billion astronomical sources. Within the panoply of methods used for data exploration, visualisation is often the starting point and even the gui...
that analyzes data about customer purchase history, and uses the results to recommend other purchases the customer might enjoy. Once data is analyzed, it
This paper presents a framework for generating adventure games from open data. Focusing on the murder mystery type of adventure games, the generator is able to transform open data from Wikipedia articles, OpenStreetMap and images from Wikimedia Commo...
This is a work on the data analysis of the Instagram pages of various Lok Sabha members in the 2024 elections. (⭐ 0)
Collaboration across institutional boundaries is widespread and increasing today. It depends on federations sharing data that often have governance rules or external regulations restricting their use. However, the handling of data governance rules (a...
The basic objective of data visualization is to provide an efficient graphical display for summarizing and reasoning about quantitative information. During the last decades, political science has accumulated a large corpus of various kinds of data su...
Federated embodied agent learning protects the data privacy of individual visual environments by keeping data locally at each client (the individual environment) during training. However, since the local data is inaccessible to the server under feder...
US H-1B Visa Lottery and Petition Data FY 2021 - FY 2024 (⭐ 233)
Source code for 'Pro Hadoop Data Analytics' by Kerry Koitzsch (⭐ 14)
Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more (⭐ 48073)
We present the R-package mgm for the estimation of k-order Mixed Graphical Models (MGMs) and mixed Vector Autoregressive (mVAR) models in high-dimensional data. These are a useful extensions of graphical models for only one variable type, since data...