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Found 13 papers, 3 papers with code
Approaching Rate-Distortion Limits in Neural Compression with Lattice Transform Coding
On general vector sources, LTC improves upon standard neural compressors in one-shot coding performance.
Score-Based Methods for Discrete Optimization in Deep Learning
One class of these problems involve objective functions which depend on neural networks, but optimization variables which are discrete.
WrappingNet: Mesh Autoencoder via Deep Sphere Deformation
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.
Text + Sketch: Image Compression at Ultra Low Rates
Recent advances in text-to-image generative models provide the ability to generate high-quality images from short text descriptions.
On a Relation Between the Rate-Distortion Function and Optimal Transport
We discuss a relationship between rate-distortion and optimal transport (OT) theory, even though they seem to be unrelated at first glance.
Federated Neural Compression Under Heterogeneous Data
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.
Neural Estimation of the Rate-Distortion Function With Applications to Operational Source Coding
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.
Robust Graph Neural Networks via Probabilistic Lipschitz Constraints
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
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
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.
Siamese Neural Networks for Wireless Positioning and Channel Charting
In this paper, we propose a unified architecture based on Siamese networks that can be used for supervised UE positioning and unsupervised channel charting.
Novel Prediction Techniques Based on Clusterwise Linear Regression
In this paper we propose two novel approaches on how to solve this problem.
Characterization of Hemodynamic Signal by Learning Multi-View Relationships
We aim to investigate the usefulness of nonlinear multi-view relations to characterize multi-view data in an explainable manner.
