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Score estimation is the backbone of score-based generative models (SGMs), especially denoising diffusion probabilistic models (DDPMs). A key result in this area shows that with accurate score estimates, SGMs can efficiently generate samples…

Machine Learning · Statistics 2025-04-08 Sinho Chewi , Alkis Kalavasis , Anay Mehrotra , Omar Montasser

Conditional density estimation (CDE) goes beyond regression by modeling the full conditional distribution, providing a richer understanding of the data than just the conditional mean in regression. This makes CDE particularly useful in…

Machine Learning · Computer Science 2024-10-16 Lincen Yang , Matthijs van Leeuwen

In this paper we investigate the behavior of iteratively decoded low-density parity-check codes over the binary erasure channel in the so-called ``waterfall region." We show that the performance curves in this region follow a very basic…

Information Theory · Computer Science 2007-07-13 Abdelaziz Amraoui , Andrea Montanari , Tom Richardson , Ruediger Urbanke

The deletion channel is the simplest point-to-point communication channel that models lack of synchronization. Input bits are deleted independently with probability d, and when they are not deleted, they are not affected by the channel.…

Information Theory · Computer Science 2011-05-02 Yashodhan Kanoria , Andrea Montanari

We consider a windowed decoding scheme for LDPC convolutional codes that is based on the belief-propagation (BP) algorithm. We discuss the advantages of this decoding scheme and identify certain characteristics of LDPC convolutional code…

Information Theory · Computer Science 2016-11-17 Aravind Iyengar , Marco Papaleo , Paul Siegel , Jack Wolf , Alessandro Vanelli-Coralli , Giovanni Corazza

The protograph low-density parity-check (LDPC) codes possess many attractive properties, such as the low encoding/decoding complexity and better error floor performance, and hence have been successfully applied to different types of…

Information Theory · Computer Science 2019-05-01 Xingwei Zhong , Kui Cai , Pingping Chen , Zhen Mei

The recent work of Sommer, Feder and Shalvi presented a new family of codes called low density lattice codes (LDLC) that can be decoded efficiently and approach the capacity of the AWGN channel. A linear time iterative decoding scheme which…

Information Theory · Computer Science 2010-03-23 Danny Bickson , Alexander T. Ihler , Danny Dolev

Assuming iterative decoding for binary erasure channels (BECs), a novel tree-based technique for upper bounding the bit error rates (BERs) of arbitrary, finite low-density parity-check (LDPC) codes is provided and the resulting bound can be…

Information Theory · Computer Science 2007-07-13 Chih-Chun Wang , Sanjeev R. Kulkarni , H. Vincent Poor

Convolutional rectifier networks, i.e. convolutional neural networks with rectified linear activation and max or average pooling, are the cornerstone of modern deep learning. However, despite their wide use and success, our theoretical…

Neural and Evolutionary Computing · Computer Science 2016-10-18 Nadav Cohen , Amnon Shashua

Differential Evolution (DE) is a renowned optimization stratagem that can easily solve nonlinear and comprehensive problems. DE is a well known and uncomplicated population based probabilistic approach for comprehensive optimization. It has…

Neural and Evolutionary Computing · Computer Science 2015-06-22 Sandeep Kumar , Vivek Kumar Sharma , Rajani Kumari

We consider a noisy Slepian-Wolf problem where two correlated sources are separately encoded (using codes of fixed rate) and transmitted over two independent binary memoryless symmetric channels. The capacity of each channel is…

Information Theory · Computer Science 2012-01-04 Arvind Yedla , Henry D. Pfister , Krishna R. Narayanan

While iterative quantizers based on low-density generator-matrix (LDGM) codes have been shown to be able to achieve near-ideal distortion performance with comparatively moderate block length and computational complexity requirements, their…

Information Theory · Computer Science 2013-09-12 Qingchuan Wang , Chen He , Lingge Jiang

Compact convolutional neural networks gain efficiency mainly through depthwise convolutions, expanded channels and complex topologies, which contrarily aggravate the training process. Besides, 3x3 kernels dominate the spatial representation…

Computer Vision and Pattern Recognition · Computer Science 2019-05-23 Shuang Wu , Guanrui Wang , Pei Tang , Feng Chen , Luping Shi

In this paper, we present a novel way for solving the main problem of designing the capacity approaching irregular low-density parity-check (LDPC) code ensemble over binary erasure channel (BEC). The proposed method is much simpler, faster,…

Information Theory · Computer Science 2021-03-02 H. Tavakoli , M. Ahmadian Attari , M. R. Peyghami

This paper considers the achievable rates and decoding complexity of low-density parity-check (LDPC) codes over statistically independent parallel channels. The paper starts with the derivation of bounds on the conditional entropy of the…

Information Theory · Computer Science 2007-07-13 Igal Sason , Gil Wiechman

This contribution is based on the contents of a talk delivered at the Next-SigmaPhi conference held in Crete in August 2005. It is adressed to an audience of physicists with diverse horizons and does not assume any background in…

Information Theory · Computer Science 2009-11-11 Nicolas Macris

We consider the effect of LLR saturation on belief propagation decoding of low-density parity-check codes. Saturation occurs universally in practice and is known to have a significant effect on error floor performance. Our focus is on…

Information Theory · Computer Science 2014-03-17 Shrinivas Kudekar , Tom Richardson , Aravind Iyengar

In this paper we analyze the performance of several bit-interleaving strategies applied to Non-Binary Low-Density Parity-Check (LDPC) codes over the Rayleigh fading channel. The technique of bit-interleaving used over fading channel…

Information Theory · Computer Science 2013-01-21 Valentin Savin , David Declercq

Block coordinate descent is an optimization paradigm that iteratively updates one block of variables at a time, making it quite amenable to big data applications due to its scalability and performance. Its convergence behavior has been…

Optimization and Control · Mathematics 2023-10-13 Liangzu Peng , René Vidal

We study distributed similarity estimation of quantum channels (DSEC), a primitive for cross-platform verification where two remote quantum devices are compared by estimating the inner product of their Choi states. We show that the optimal…

Quantum Physics · Physics 2026-01-19 Congcong Zheng , Kun Wang , Xutao Yu , Ping Xu , Zaichen Zhang