Zobrazeno 1 - 10
of 69
pro vyhledávání: '"Lei, Eric"'
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
Discrete optimization problems often arise in deep learning tasks, despite the fact that neural networks typically operate on continuous data. One class of these problems involve objective functions which depend on neural networks, but optimization v
Externí odkaz:
http://arxiv.org/abs/2310.09890
There have been recent efforts to learn more meaningful representations via fixed length codewords from mesh data, since a mesh serves as a complete model of underlying 3D shape compared to a point cloud. However, the mesh connectivity presents new d
Externí odkaz:
http://arxiv.org/abs/2308.15413
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 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
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
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. In this paper, we propos
Externí odkaz:
http://arxiv.org/abs/2009.02798