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Variational autoencoders (VAEs) are one class of generative probabilistic latent-variable models designed for inference based on known data. We develop three variations on VAEs by introducing a second parameterized encoder/decoder pair and,…

Machine Learning · Computer Science 2023-04-06 R. I. Cukier

We present a probabilistic autoencoder (PAE) framework for galaxy spectral energy distribution (SED) modeling and redshift estimation, applied to synthetic SPHEREx 102-band spectrophotometry. Our PAE learns a compact latent representation…

Instrumentation and Methods for Astrophysics · Physics 2026-03-27 Richard M. Feder , Liam Parker , Uroš Seljak

A new coded modulation scheme is proposed. At the transmitter, the concatenation of a distribution matcher and a systematic binary encoder performs probabilistic signal shaping and channel coding. At the receiver, the output of a bitwise…

Information Theory · Computer Science 2015-04-23 Georg Böcherer , Patrick Schulte , Fabian Steiner

Estimating probabilistic deformable template models is a new approach in the fields of computer vision and probabilistic atlases in computational anatomy. A first coherent statistical framework modelling the variability as a hidden random…

Computation · Statistics 2009-01-16 Stéphanie Allassonnière , Estelle Kuhn

One of the most popular methods of the machine learning prediction explanation is the SHapley Additive exPlanations method (SHAP). An imprecise SHAP as a modification of the original SHAP is proposed for cases when the class probability…

Machine Learning · Computer Science 2021-06-18 Lev V. Utkin , Andrei V. Konstantinov , Kirill A. Vishniakov

Probabilistic constellation shaping has been used in long-haul optically amplified coherent systems for its capability to approach the Shannon limit and realize fine rate granularity. The availability of high-bandwidth optical-electronic…

Signal Processing · Electrical Eng. & Systems 2023-03-31 Md Sabbir-Bin Hossain , Georg Bocherer , Talha Rahman , Tom Wettlin , Nebojsa Stojanovic , Stefano Calabro , Stephan Pachnicke

The error exponent of the typical random code is defined as the asymptotic normalized expectation of the logarithm of the probability of error, as opposed to the traditional definition of the random coding exponent as the normalized…

Information Theory · Computer Science 2018-12-18 Neri Merhav

A new universal coding/decoding scheme for random access with collision detection is given in the case of two senders. The result is used to give an achievable joint source-channel coding error exponent for multiple access channels in the…

Information Theory · Computer Science 2013-09-19 Lóránt Farkas , Tamás Kói

Concatenated forward error correction is studied using an outer KP4 Reed-Solomon code with hard-decision decoding and inner single parity check (SPC) codes with Chase/Wagner soft-decision decoding. Analytical expressions are derived for the…

Information Theory · Computer Science 2023-05-18 Diego Lentner , Emna Ben Yacoub , Stefano Calabrò , Georg Böcherer , Nebojša Stojanović , Gerhard Kramer

This paper applies probabilistic amplitude shaping (PAS) to a cyclic redundancy check (CRC) aided trellis coded modulation (TCM) to achieve the short-blocklength random coding union (RCU) bound. In the transmitter, the equally likely…

Information Theory · Computer Science 2021-11-18 Linfang Wang , Dan Song , Felipe Areces , Richard D. Wesel

Formants are the spectral maxima that result from acoustic resonances of the human vocal tract, and their accurate estimation is among the most fundamental speech processing problems. Recent work has been shown that those frequencies can…

Sound · Computer Science 2022-06-24 Yosi Shrem , Felix Kreuk , Joseph Keshet

Despite the extreme error-correction performance, the amount of computation of sequential decoding of the polarization-adjusted convolutional (PAC) codes is random. In sequential decoding of convolutional codes, the computational cutoff…

Information Theory · Computer Science 2022-11-30 Mohsen Moradi

In this paper, probabilistic shaping is numerically and experimentally investigated for increasing the transmission reach of wavelength division multiplexed (WDM) optical communication system employing quadrature amplitude modulation (QAM).…

Variational autoencoders employ an encoding neural network to generate a probabilistic representation of a data set within a low-dimensional space of latent variables followed by a decoding stage that maps the latent variables back to the…

Statistical Mechanics · Physics 2022-04-13 David Yevick

Polynomial chaos expansion (PCE) is a classical and widely used surrogate modeling technique in physical simulation and uncertainty quantification. By taking a linear combination of a set of basis polynomials - orthonormal with respect to…

Machine Learning · Computer Science 2026-04-01 Johannes Exenberger , Sascha Ranftl , Robert Peharz

A variation of Gallager error-correcting codes is investigated using statistical mechanics. In codes of this type, a given message is encoded into a codeword which comprises Boolean sums of message bits selected by two randomly constructed…

Disordered Systems and Neural Networks · Physics 2009-10-31 Tatsuto Murayama , Yoshiyuki Kabashima , David Saad , Renato Vicente

A new geometric shaping technique, referred to as coset shaping, is proposed and analyzed for coded QAM and PAM signaling. This method can be applied to both information and parity bits without introducing additional complexity. It is shown…

Information Theory · Computer Science 2026-03-30 Irina Bocharova , Maiara F. Bollauf , Boris Kudryashov

Powerful Forward Error Correction (FEC) schemes are used in optical communications to achieve bit-error rates below $10^{-15}$. These FECs follow one of two approaches: concatenation of simpler hard-decision codes or usage of inherently…

Hardware Architecture · Computer Science 2017-09-01 Carlo Condo , Pascal Giard , François Leduc-Primeau , Gabi Sarkis , Warren J. Gross

This paper studies a large random matrix system (LRMS) model involving an arbitrary signal distribution and forward error control (FEC) coding. We establish an area property based on the approximate message passing (AMP) algorithm. Under…

Information Theory · Computer Science 2021-06-07 Lei Liu , Chulong Liang , Junjie Ma , Li Ping

Sparse Regression Codes (SPARCs) are capacity-achieving codes introduced for communication over the Additive White Gaussian Noise (AWGN) channels and were later extended to general memoryless channels. In particular it was shown via…

Information Theory · Computer Science 2024-09-10 Yuhao Liu , Yizhou Xu , Tianqi Hou
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