activity
20192026
most citedExplainable Knowledge Graph Embedding: Inference Reconciliation for Knowledge Inferences Supporting Robot Actions

2 citations · 3 across the 9 of their papers we have counts for

collaborators

12 papers

cs.CV2026

Ground-to-Satellite Localization in Unconstrained Image Collections for 3D Scene Reconstruction

Angel Daruna, Ben Southall, Niluthpol Chowdhury Mithun +6

Ground image localization with respect to satellite imagery is a key enabler for metrically-accurate, geo-localized 3D scene reconstruction from unconstrained image collections. Ex…

cs.CV2025

GeoSURGE: Geo-localization using Semantic Fusion with Hierarchy of Geographic Embeddings

Angel Daruna, Nicholas Meegan, Han-Pang Chiu +2

Worldwide visual geo-localization aims to determine the geographic location of an image anywhere on Earth using only its visual content. Despite recent progress, learning expressiv…

cs.LG2024

GFM4MPM: Towards Geospatial Foundation Models for Mineral Prospectivity Mapping

Angel Daruna, Vasily Zadorozhnyy, Georgina Lukoczki +1

Machine Learning (ML) for Mineral Prospectivity Mapping (MPM) remains a challenging problem as it requires the analysis of associations between large-scale multi-modal geospatial d…

cs.LG2023

Uncertainty Propagation through Trained Deep Neural Networks Using Factor Graphs

Angel Daruna, Yunye Gong, Abhinav Rajvanshi +2

Predictive uncertainty estimation remains a challenging problem precluding the use of deep neural networks as subsystems within safety-critical applications. Aleatoric uncertainty…

cs.AI2022★ 2 cited

Explainable Knowledge Graph Embedding: Inference Reconciliation for Knowledge Inferences Supporting Robot Actions

Angel Daruna, Devleena Das, Sonia Chernova

Learned knowledge graph representations supporting robots contain a wealth of domain knowledge that drives robot behavior. However, there does not exist an inference reconciliation…

cs.RO2021

Towards Robust One-shot Task Execution using Knowledge Graph Embeddings

Angel Daruna, Lakshmi Nair, Weiyu Liu +1

Requiring multiple demonstrations of a task plan presents a burden to end-users of robots. However, robustly executing tasks plans from a single end-user demonstration is an ongoin…