Data-Driven Neural Polar Codes for Unknown Channels With and Without Memory

no code implementations • 6 Sep 2023 • Ziv Aharoni, Bashar Huleihel, Henry D. Pfister, Haim H. Permuter

The proposed method leverages the structure of the successive cancellation (SC) decoder to devise a neural SC (NSC) decoder.

Data-Driven Optimization of Directed Information over Discrete Alphabets

1 code implementation • 2 Jan 2023 • Dor Tsur, Ziv Aharoni, Ziv Goldfeld, Haim Permuter

Directed information (DI) is a fundamental measure for the study and analysis of sequential stochastic models.

Capacity of Continuous Channels with Memory via Directed Information Neural Estimator

1 code implementation • 9 Mar 2020 • Ziv Aharoni, Dor Tsur, Ziv Goldfeld, Haim Henry Permuter

When no analytic solution is present or the channel model is unknown, there is no unified framework for calculating or even approximating capacity.

Capacity Estimation

Computing the Feedback Capacity of Finite State Channels using Reinforcement Learning

1 code implementation • 27 Jan 2020 • Ziv Aharoni, Oron Sabag, Haim Henry Permuter

In this paper, we propose a novel method to compute the feedback capacity of channels with memory using reinforcement learning (RL).

Computational Efficiency Decision Making +2

Gradual Learning of Recurrent Neural Networks

1 code implementation • 29 Aug 2017 • Ziv Aharoni, Gal Rattner, Haim Permuter

Recurrent Neural Networks (RNNs) achieve state-of-the-art results in many sequence-to-sequence modeling tasks.

Language Modelling