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Found 12 papers, 2 papers with code
Compressive Estimation of a Stochastic Process with Unknown Autocorrelation Function
To design such a blind predictor, we use the random spectral representation of a stationary Gaussian process.
Deep Learning for Channel Coding via Neural Mutual Information Estimation
However, one of the drawbacks of current learning approaches is that a differentiable channel model is needed for the training of the underlying neural networks.
Using AoI Forecasts in Communicating and Robust Distributed Model-Predictive Control
By contrast, the present approachestablishes a robust distributed model-predictive controlscheme, in which the local subsystem controllers oper-ate under the assumption of a variable communicationschedule that is predicted by a network controller.
Systems and Control Systems and Control
ComPass: Proximity Aware Common Passphrase Agreement Protocol for Wi-Fi devices Using Physical Layer Security
Secure and scalable device provisioning is a notorious challenge in Wi-Fi.
Cryptography and Security
Reinforce Security: A Model-Free Approach Towards Secure Wiretap Coding
The use of deep learning-based techniques for approximating secure encoding functions has attracted considerable interest in wireless communications due to impressive results obtained for general coding and decoding tasks for wireless communication systems.
Explicit CSI Feedback Compression via Learned Approximate Message Passing
Explicit channel state information at the transmitter side is helpful to improve downlink precoding performance for multi-user MIMO systems.
A Reverse Jensen Inequality Result with Application to Mutual Information Estimation
The Jensen inequality is a widely used tool in a multitude of fields, such as for example information theory and machine learning.
An Algorithm for Exact Numerical Age-of-Information Evaluation in Multi-Agent Systems
We present an algorithm for the numerical evaluation of the state-space distribution of an Age-of-Information network.
Power of Explanations: Towards automatic debiasing in hate speech detection
Hate speech detection is a common downstream application of natural language processing (NLP) in the real world.
Explaining text classifiers through progressive neighborhood approximation with realistic samples
The importance of neighborhood construction in local explanation methods has been already highlighted in the literature.
Differentially Private Synthetic Data Generation via Lipschitz-Regularised Variational Autoencoders
It is often overlooked though that generative models are prone to memorising many details of individual training records and often generate synthetic data that too closely resembles the underlying sensitive training data, hence violating strong privacy regulations as, e. g., encountered in health care.
On Gradient-like Explanation under a Black-box Setting: When Black-box Explanations Become as Good as White-box
Attribution methods shed light on the explainability of data-driven approaches such as deep learning models by revealing the most contributing features to decisions that have been made.
