Results 11 to 20 of about 483,886 (315)
Stationary quantum source coding [PDF]
Journal of Mathematical Physics, 2001 In this article the quantum version of the source coding theorem is obtained for a completely ergodic source. This result extends Schumacher’s quantum noiseless coding theorem for memoryless sources. The control of the memory effects requires some earlier results of Hiai and Petz on high probability subspaces. Our result is equivalently considered as a
Dėnes Petz, Milán Mosonyi
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PAC Codes for Source and Joint Source-Channel Coding
2023 IEEE Globecom Workshops (GC Wkshps), 2023 Polarization-adjusted convolutional (PAC) codes, as a concatenated coding scheme based on polar codes, is able to approach the finite-length bound of binary-input AWGN channel at short blocklengths. In this paper, we extend PAC codes to the fields of source coding and joint source-channel coding and show that they can also approach the corresponding ...
Zheng, Mengfan, Ling, Cong
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Efficient and Compact Representations of Deep Neural Networks via Entropy Coding
IEEE Access, 2023 Matrix operations are nowadays central in many Machine Learning techniques, including in particular Deep Neural Networks (DNNs), whose core of any inference is represented by a sequence of dot product operations.
Giosue Cataldo Marino +3 more
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Gaussian source coding with spherical codes [PDF]
IEEE Transactions on Information Theory, 2002 Summary: A fixed-rate shape-gain quantizer for the memoryless Gaussian source is proposed. The shape quantizer is constructed from wrapped spherical codes that map a sphere packing in \(\mathbb R^{k-1}\) onto a sphere in \(\mathbb R^k\), and the gain codebook is a globally optimal scalar quantizer.
Jon Hamkins, Kenneth Zeger
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Entropy, 2018 In this paper, we give upper bounds for the rate-distortion function (RDF) of any Gaussian vector, and we propose coding strategies to achieve such bounds.
Jesús Gutiérrez-Gutiérrez +3 more
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Robust 2-bit Quantization of Weights in Neural Network Modeled by Laplacian Distribution
Advances in Electrical and Computer Engineering, 2021 Significant efforts are constantly involved in finding manners to decrease the number of bits required for quantization of neural network parameters. Although in addition to compression, in neural networks, the application of quantizer models that are ...
PERIC, Z. +3 more
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Medienimpulse, 2011 „They say ev’rything can be replaced Yet ev’ry distance is not near “ Bob ...
Thomas Ballhausen, Lisa Leitenmüller
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On Linear Coding over Finite Rings and Applications to Computing
Entropy, 2017 This paper presents a coding theorem for linear coding over finite rings, in the setting of the Slepian–Wolf source coding problem. This theorem covers corresponding achievability theorems of Elias (IRE Conv. Rec. 1955, 3, 37–46) and Csiszár (IEEE Trans.
Sheng Huang, Mikael Skoglund
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Source Coding with a Causal Helper
Information, 2020 A multi-terminal network, in which an encoder is assisted by a side-information-aided helper, describes a memoryless identically distributed source to a receiver, is considered. The encoder provides a non-causal one-shot description of the source to both
Shraga I. Bross
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LDGM Codes for Channel Coding and Joint Source-Channel Coding of Correlated Sources
EURASIP Journal on Advances in Signal Processing, 2005 Summary: We propose a coding scheme based on the use of systematic linear codes with low-density generator matrix (LDGM codes) for channel coding and joint source-channel coding of multiterminal correlated binary sources. In both cases, the structures of the LDGM encoder and decoder are shown, and a concatenated scheme aimed at reducing the error floor
Wei Zhong, Javier Garcia-Frias
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