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

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

collaborators

5 papers

cs.LG20263 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…

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

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…