Zobrazeno 1 - 10
of 62
pro vyhledávání: '"Bidokhti, Shirin Saeedi"'
We address the challenge of sampling and remote estimation for autoregressive Markovian processes in a multi-hop wireless network with statistically-identical agents. Agents cache the most recent samples from others and communicate over wireless coll
Externí odkaz:
http://arxiv.org/abs/2404.03227
Neural compression has brought tremendous progress in designing lossy compressors with good rate-distortion (RD) performance at low complexity. Thus far, neural compression design involves transforming the source to a latent vector, which is then rou
Externí odkaz:
http://arxiv.org/abs/2403.07320
Recent advances in text-to-image generative models provide the ability to generate high-quality images from short text descriptions. These foundation models, when pre-trained on billion-scale datasets, are effective for various downstream tasks with
Externí odkaz:
http://arxiv.org/abs/2307.01944
We discuss a relationship between rate-distortion and optimal transport (OT) theory, even though they seem to be unrelated at first glance. In particular, we show that a function defined via an extremal entropic OT distance is equivalent to the rate-
Externí odkaz:
http://arxiv.org/abs/2307.00246
We discuss a federated learned compression problem, where the goal is to learn a compressor from real-world data which is scattered across clients and may be statistically heterogeneous, yet share a common underlying representation. We propose a dist
Externí odkaz:
http://arxiv.org/abs/2305.16416
A diamond network is considered in which the central processor is connected, via backhaul noiseless links, to multiple conferencing base stations, which communicate with a single user over a multiple access channel. We propose coding techniques along
Externí odkaz:
http://arxiv.org/abs/2205.01748
A fundamental question in designing lossy data compression schemes is how well one can do in comparison with the rate-distortion function, which describes the known theoretical limits of lossy compression. Motivated by the empirical success of deep n
Externí odkaz:
http://arxiv.org/abs/2204.01612
In applications of group testing in networks, e.g. identifying individuals who are infected by a disease spread over a network, exploiting correlation among network nodes provides fundamental opportunities in reducing the number of tests needed. We m
Externí odkaz:
http://arxiv.org/abs/2202.02467
Graph neural networks (GNNs) have recently been demonstrated to perform well on a variety of network-based tasks such as decentralized control and resource allocation, and provide computationally efficient methods for these tasks which have tradition
Externí odkaz:
http://arxiv.org/abs/2112.07575
In recent years, deep neural network (DNN) compression systems have proved to be highly effective for designing source codes for many natural sources. However, like many other machine learning systems, these compressors suffer from vulnerabilities to
Externí odkaz:
http://arxiv.org/abs/2110.07007