activity
20182025
most citedA framework for fostering transparency in shared artificial intelligence models by increasing visibility of contributions

18 citations · 27 across the 9 of their papers we have counts for

collaborators

9 papers

cs.CV2025

GEO-Bench-2: From Performance to Capability, Rethinking Evaluation in Geospatial AI

Naomi Simumba, Nils Lehmann, Paolo Fraccaro +9

Geospatial Foundation Models (GeoFMs) are transforming Earth Observation (EO), but evaluation lacks standardized protocols. GEO-Bench-2 addresses this with a comprehensive framewor…

cs.CL2025

Zero-Shot Open-Schema Entity Structure Discovery

Xueqiang Xu, Jinfeng Xiao, James Barry +7

Entity structure extraction, which aims to extract entities and their associated attribute-value structures from text, is an essential task for text understanding and knowledge gra…

cs.CL2025★ 5 cited

QGen Studio: An Adaptive Question-Answer Generation, Training and Evaluation Platform

Movina Moses, Mohab Elkaref, James Barry +5

We present QGen Studio: an adaptive question-answer generation, training, and evaluation platform. QGen Studio enables users to leverage large language models (LLMs) to create cust…

cs.IR2024

KnowledgeHub: An end-to-end Tool for Assisted Scientific Discovery

Shinnosuke Tanaka, James Barry, Vishnudev Kuruvanthodi +5

This paper describes the KnowledgeHub tool, a scientific literature Information Extraction (IE) and Question Answering (QA) pipeline. This is achieved by supporting the ingestion o…

cs.LG2023

Encoding Seasonal Climate Predictions for Demand Forecasting with Modular Neural Network

Smit Marvaniya, Jitendra Singh, Nicolas Galichet +3

Current time-series forecasting problems use short-term weather attributes as exogenous inputs. However, in specific time-series forecasting solutions (e.g., demand prediction in t…

cs.AI2021★ 18 cited

A framework for fostering transparency in shared artificial intelligence models by increasing visibility of contributions

Iain Barclay, Harrison Taylor, Alun Preece +3

Increased adoption of artificial intelligence (AI) systems into scientific workflows will result in an increasing technical debt as the distance between the data scientists and eng…