7 citations · 8 across the 5 of their papers we have counts for
6 papers
Communication-Aware Model Distributed Inference via Latent Representation Compression
Peyman Gholami, Theodoros-Thirimachos Davarakis, Teng Li +9
We study optimization of distributed model inference over resource-constrained edge resources. We propose a framework that optimizes the trade-off between model accuracy and commun…
MUDAS: Mote-scale Unsupervised Domain Adaptation in Multi-label Sound Classification
Jihoon Yun, Chengzhang Li, Dhrubojyoti Roy +1
Unsupervised Domain Adaptation (UDA) is essential for adapting machine learning models to new, unlabeled environments where data distribution shifts can degrade performance. Existi…
PAMLR: A Passive-Active Multi-Armed Bandit-Based Solution for LoRa Channel Allocation
Jihoon Yun, Chengzhang Li, Anish Arora
Achieving low duty cycle operation in low-power wireless networks in urban environments is complicated by the complex and variable dynamics of external interference and fading. We…
Infrastructure-free, Deep Learned Urban Noise Monitoring at 100mW
Jihoon Yun, Sangeeta Srivastava, Dhrubojyoti Roy +6
The Sounds of New York City (SONYC) wireless sensor network (WSN) has been fielded in Manhattan and Brooklyn over the past five years, as part of a larger human-in-the-loop cyber-p…
A Study on Robustness to Perturbations for Representations of Environmental Sound
Sangeeta Srivastava, Ho-Hsiang Wu, Joao Rulff +5
Audio applications involving environmental sound analysis increasingly use general-purpose audio representations, also known as embeddings, for transfer learning. Recently, Holisti…
Physics-Guided Problem Decomposition for Scaling Deep Learning of High-dimensional Eigen-Solvers: The Case of Schrödinger's Equation
Sangeeta Srivastava, Samuel Olin, Viktor Podolskiy +3
Given their ability to effectively learn non-linear mappings and perform fast inference, deep neural networks (NNs) have been proposed as a viable alternative to traditional simula…