11 citations · 27 across the 16 of their papers we have counts for
16 papers
Enhancing Small Language Models for Power Outage Report Generation via Minimum Risk Training
Hung Phan, Waqwoya Abebe, Youssef Hussein +3
Minimum Risk Training (MRT) enables neural machine translation models to directly optimize sequence-level evaluation metrics instead of relying only on token- level maximum-likelih…
POTracker: Optimizing Large Language Models for Standard-Compliant Power Outage Report Generation
Hung Phan, Aniroop Naladala, Dubey Avanindra +3
Recent large language models (LLMs) are good at general text generation, but it is still hard to use them for domain-specific data generation because the output must follow strict…
Emerging Flexible Designs for Geospatial Multimodal Foundation Models
Philipe Dias, Waqwoya Abebe, Abhishek Potnis +4
Foundation models are rapidly transforming Earth observation by enabling scalable pretraining across diverse unlabeled geospatial modalities. However, their architectural diversity…
GeoGNN: Time Series Geo-Localization using Two-Tower Graph Neural Networks
Toan Tran, Waqwoya Abebe, Abhishek Potnis +4
This paper investigates a novel concept of time series geolocalization, where the goal is to infer the geographic origin of each raw time series. Successful geolocalization can pro…
SimLBR: Learning to Detect Fake Images by Learning to Detect Real Images
Aayush Dhakal, Subash Khanal, Srikumar Sastry +4
The rapid advancement of generative models has made the detection of AI-generated images a critical challenge for both research and society. Recent works have shown that most state…
OReole-FM: successes and challenges toward billion-parameter foundation models for high-resolution satellite imagery
Philipe Dias, Aristeidis Tsaris, Jordan Bowman +4
While the pretraining of Foundation Models (FMs) for remote sensing (RS) imagery is on the rise, models remain restricted to a few hundred million parameters. Scaling models to bil…