15 citations · 34 across the 16 of their papers we have counts for
21 papers
Scaling Cheap Synthetic Pretraining Improves Indoor Radio-Map Prediction
Khoren Petrosyan, Artashes Mkrtchyan, Rafayel Mkrtchyan +2
Real measurements are too limited to train an accurate indoor pathloss predictor that works across a wide range of buildings. Large-scale synthetic pretraining offers a way forward…
Data Density Scaling Laws for Image Self-Distillation
Alvard Barseghyan, Ani Vanyan, Hakob Tamazyan +1
Current scaling law formulations suggest that increasing the number of unique samples in a dataset improves model performance, while repeated exposure to the same samples provides…
PMO-Dock: Benchmarking Docking, Specificity, and Generalization in Molecular Optimization
Gor Simonyan, Tatevik Abrahamyan, Narek Abelyan +2
The Practical Molecular Optimization (PMO) benchmark standardized evaluation in molecular optimization, but it is built on simple property-based oracles that do not capture structu…
On the Physical Plausibility and Distribution Alignment for Sim-to-Real RF Positioning
Ararat Saribekyan, Armen Manukyan, Hrant Khachatrian +1
Reliable radio frequency (RF) positioning from cellular measurements is limited by the high cost and limited coverage of real drive-test data, especially when models must work on s…
GeoCrossBench: Cross-Band Generalization for Remote Sensing
Hakob Tamazyan, Ani Vanyan, Alvard Barseghyan +3
The data for remote sensing is constantly acquired, and new data comes from a growing number and diversity of satellites, while the vast majority of labeled data comes from older s…
Do Satellite Tasks Need Special Pretraining?
Ani Vanyan, Alvard Barseghyan, Hakob Tamazyan +4
Foundation models have advanced machine learning across various modalities, including images. Recently multiple teams trained foundation models specialized for remote sensing appli…