most citedEnergy Use of AI Inference, Efficiency Pathways, and Test-Time Scaling

5 citations · 6 across the 5 of their papers we have counts for

collaborators

5 papers

eess.SP2026

Project SPARROW and the Future of Conservation Technology

Juan M. Lavista Ferres, Carl Chalmers, Bruno Demuro Segundo +14

Global biodiversity is declining at unprecedented rates, yet the tools available to monitor and protect ecosystems remain limited by constraints in power, connectivity, and accessi…

cs.CV2026

WATCH: Wide-Area Archaeological Site Tracking for Change Detection

Girmaw Abebe Tadesse, Titien Bartette, Andrew Hassanali +7

Monitoring archaeological sites at scale is vital for protecting cultural heritage, yet pinpointing when disturbances occur remains difficult because visual cues are subtle and gro…

cs.CV2026

Satellite-Based Detection of Looted Archaeological Sites Using Machine Learning

Girmaw Abebe Tadesse, Titien Bartette, Andrew Hassanali +7

Looting at archaeological sites poses a severe risk to cultural heritage, yet monitoring thousands of remote locations remains operationally difficult. We present a scalable and sa…

cs.LG2025★ 5 cited

Energy Use of AI Inference, Efficiency Pathways, and Test-Time Scaling

Felipe Oviedo, Fiodar Kazhamiaka, Esha Choukse +5

As AI inference scales to billions of queries, estimates of per-query energy use are increasingly important for capacity planning, efficiency interventions, and policy. Yet many pu…

cs.LG2025★ 1 cited

Global Renewables Watch: A Temporal Dataset of Solar and Wind Energy Derived from Satellite Imagery

Caleb Robinson, Anthony Ortiz, Allen Kim +6

We present a comprehensive global temporal dataset of commercial solar photovoltaic (PV) farms and onshore wind turbines, derived from high-resolution satellite imagery analyzed qu…