Search Results for author: Eric Lei

Found 7 papers, 2 papers with code

Neural Estimation of the Rate-Distortion Function With Applications to Operational Source Coding

1 code implementation4 Apr 2022 Eric Lei, Hamed Hassani, Shirin Saeedi Bidokhti

Motivated by the empirical success of deep neural network (DNN) compressors on large, real-world data, we investigate methods to estimate the rate-distortion function on such data, which would allow comparison of DNN compressors with optimality.

Data Compression

Robust Graph Neural Networks via Probabilistic Lipschitz Constraints

no code implementations14 Dec 2021 Raghu Arghal, Eric Lei, Shirin Saeedi Bidokhti

This allows for the use of the same computationally efficient algorithm on sampled constraints, which provides PAC-style guarantees on the stability of the GNN using results in scenario optimization.

Out-of-Distribution Robustness in Deep Learning Compression

no code implementations13 Oct 2021 Eric Lei, Hamed Hassani, Shirin Saeedi Bidokhti

In recent years, deep neural network (DNN) compression systems have proved to be highly effective for designing source codes for many natural sources.

CSI-Based Multi-Antenna and Multi-Point Indoor Positioning Using Probability Fusion

no code implementations6 Sep 2020 Emre Gönültaş, Eric Lei, Jack Langerman, Howard Huang, Christoph Studer

Channel state information (CSI)-based fingerprinting via neural networks (NNs) is a promising approach to enable accurate indoor and outdoor positioning of user equipments (UEs), even under challenging propagation conditions.

Outdoor Positioning

Siamese Neural Networks for Wireless Positioning and Channel Charting

no code implementations29 Sep 2019 Eric Lei, Oscar Castañeda, Olav Tirkkonen, Tom Goldstein, Christoph Studer

In this paper, we propose a unified architecture based on Siamese networks that can be used for supervised UE positioning and unsupervised channel charting.

Dimensionality Reduction

Characterization of Hemodynamic Signal by Learning Multi-View Relationships

no code implementations17 Sep 2017 Eric Lei, Kyle Miller, Michael R. Pinsky, Artur Dubrawski

We aim to investigate the usefulness of nonlinear multi-view relations to characterize multi-view data in an explainable manner.

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