Information Theory Workshop
About: Information Theory Workshop is an academic conference. The conference publishes majorly in the area(s): Decoding methods & Linear code. Over the lifetime, 2766 publication(s) have been published by the conference receiving 36078 citation(s).
Topics: Decoding methods, Linear code, Block code, Concatenated error correction code, Low-density parity-check code
Papers published on a yearly basis
01 Jul 2007
Abstract: A Fountain code is a code of fixed dimension and a limitless block-length. This is a class of codes with many interesting properties and applications. In this talk I will introduce several classes of probabilistic Fountain codes, including LT-and Raptor codes, show tools for their design and analysis, and discuss how they are used today to solve various data transmission problems on heterogenous unreliable networks. I will also talk about the theory of these codes when transmission takes place over non-erasure channels, and low-complexity algorithms are used for their decoding.
••25 Jun 2015
TL;DR: It is argued that both the optimal architecture, number of layers and features/connections at each layer, are related to the bifurcation points of the information bottleneck tradeoff, namely, relevant compression of the input layer with respect to the output layer.
Abstract: Deep Neural Networks (DNNs) are analyzed via the theoretical framework of the information bottleneck (IB) principle. We first show that any DNN can be quantified by the mutual information between the layers and the input and output variables. Using this representation we can calculate the optimal information theoretic limits of the DNN and obtain finite sample generalization bounds. The advantage of getting closer to the theoretical limit is quantifiable both by the generalization bound and by the network's simplicity. We argue that both the optimal architecture, number of layers and features/connections at each layer, are related to the bifurcation points of the information bottleneck tradeoff, namely, relevant compression of the input layer with respect to the output layer. The hierarchical representations at the layered network naturally correspond to the structural phase transitions along the information curve. We believe that this new insight can lead to new optimality bounds and deep learning algorithms.
••22 Jun 1998
TL;DR: The results of Monte Carlo simulations of the decoding of infinite LDPC codes which can be used to obtain good constructions for finite codes and empirical results for the Gaussian channel are presented.
Abstract: Binary low density parity check (LDPC) codes have been shown to have near Shannon limit performance when decoded using a probabilistic decoding algorithm. The analogous codes defined over finite fields GF(q) of order q>2 show significantly improved performance. We present the results of Monte Carlo simulations of the decoding of infinite LDPC codes which can be used to obtain good constructions for finite codes. We also present empirical results for the Gaussian channel including a rate 1/4 code with bit error probability of 10/sup -4/ at E/sub b//N/sub 0/=-0.05 dB.
••24 Oct 2004
TL;DR: This paper shows that under a simplified layered model of wireless networks, the minimum-energy multicast problem in mobile ad hoc networks is solvable as a linear program, assuming network coding.
Abstract: The minimum energy required to transmit one bit of information through a network characterizes the most economical way to communicate in a network. In this paper, we show that, under a layered model of wireless networks, the minimum energy-per-bit for multicasting in a mobile ad hoc network can be found by a linear program; the minimum energy-per-bit can be attained by performing network coding. Compared with conventional routing solutions, network coding not only allows a potentially lower energy-per-bit to be achieved, but also enables the optimal solution to be found in polynomial time, in sharp contrast with the NP-hardness of constructing the minimum-energy multicast tree as the optimal routing solution. We further show that the minimum energy multicast formulation is equivalent to a cost minimization with linear edge-based pricing, where the edge prices are the energy-per-bits of the corresponding physical broadcast links. This paper also investigates minimum energy multicasting with routing. Due to the linearity of the pricing scheme, the minimum energy-per-bit for routing is achievable by using a single distribution tree. A characterization of the admissible rate region for routing with a single tree is presented. The minimum energy-per-bit for multicasting with routing is found by an integer linear program. We show that the relaxation of this integer linear program, studied earlier in the Steiner tree literature, can now be interpreted as the optimization for minimum energy multicasting with network coding. In short, this paper presents a unifying study of minimum energy multicasting with network coding and routing.
••01 Jan 2010
TL;DR: The present paper outlines the derivation of AMP from standard sum-product belief propagation, and its extension in several directions, and discusses relations with formal calculations based on statistical mechanics methods.
Abstract: In a recent paper, the authors proposed a new class of low-complexity iterative thresholding algorithms for reconstructing sparse signals from a small set of linear measurements . The new algorithms are broadly referred to as AMP, for approximate message passing. This is the first of two conference papers describing the derivation of these algorithms, connection with the related literature, extensions of the original framework, and new empirical evidence. In particular, the present paper outlines the derivation of AMP from standard sum-product belief propagation, and its extension in several directions. We also discuss relations with formal calculations based on statistical mechanics methods.
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