12 citations · 37 across the 17 of their papers we have counts for
6 papers · 1 filter
Extremum Encoding for Joint Baseband Signal Compression and Time-Delay Estimation for Distributed Systems
Amir Weiss, Yuval Kochman, Gregory W. Wornell
The ubiquitous time-delay estimation (TDE) problem becomes nontrivial when sensors are non-co-located and communication between them is limited. Building on the recently proposed "…
Estimating the Number and Locations of Boundaries in Reverberant Environments with Deep Learning
Toros Arikan, Luca M. Chackalackal, Fatima Ahsan +4
Underwater acoustic environment estimation is a challenging but important task for remote sensing scenarios. Current estimation methods require high signal strength and a solution…
RF Challenge: The Data-Driven Radio Frequency Signal Separation Challenge
Alejandro Lancho, Amir Weiss, Gary C. F. Lee +4
We address the critical problem of interference rejection in radio-frequency (RF) signals using a data-driven approach that leverages deep-learning methods. A primary contribution…
A Joint Data Compression and Time-Delay Estimation Method For Distributed Systems via Extremum Encoding
Amir Weiss, Yuval Kochman, Gregory W. Wornell
Motivated by the proliferation of mobile devices, we consider a basic form of the ubiquitous problem of time-delay estimation (TDE), but with communication constraints between two…
Thermometer: Towards Universal Calibration for Large Language Models
Maohao Shen, Subhro Das, Kristjan Greenewald +3
We consider the issue of calibration in large language models (LLM). Recent studies have found that common interventions such as instruction tuning often result in poorly calibrate…
Are Uncertainty Quantification Capabilities of Evidential Deep Learning a Mirage?
Maohao Shen, J. Jon Ryu, Soumya Ghosh +4
This paper questions the effectiveness of a modern predictive uncertainty quantification approach, called \emph{evidential deep learning} (EDL), in which a single neural network mo…