Polynomial Codes Over Certain Finite Fields

By Firm J. Dennis Moore, W. J. Brady, Frederick J. Telecky. 2017

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Citations in the corpus

Honeycrisp: large-scale differentially private aggregation without a trusted core (2019, methodology): “We achieve this with Shamir Sharing [69], based on the ReedSolomon code [65], with parameter t = ⌊ 2 5 C⌋.”
Deep Learning for Joint Source-Channel Coding of Text (2018, methodology): “After source encoding using the above approaches, we use a Reed-Solomon code [29] that can correct up to the expected number of erasures.”
Improving Distributed Gradient Descent Using Reed-Solomon Codes (2017, methodology): “To that end, we propose a deterministic construction based on Reed–Solomon (RS) codes [9] accompanied with an efficient decoder, which is used to recover the full gradient update from a fixed number of returning machines.”
Computational Integrity with a Public Random String from Quasi-Linear PCPs (2017, methodology): “Proof generation The PCP proof π (P,x,y,T ) for τ (P,x,y,T ) is a concatenation of four sub-proofs: two codewords in a Reed-Solomon code [RS60] and two quasilinear size PCPs of Proximity (PCPP) for the RS-codewords [BS08].”
ViBand: High-Fidelity Bio-Acoustic Sensing Using Commodity Smartwatch Accelerometers (2016, methodology): “We use a Reed-Solomon code [39] with 5 bits per symbol, allowing us to have 31 symbols per message (a total of 155 bits).”
Deep Learning Enabled Semantic Communication Systems (2020, methodology): “Following the concept of semantic communications proposed in this paper, we have developed L-DeepSC [36] and DeepSCS [37] for text and speech transmission.”
Secure Distributed Computing With Straggling Servers Using Polynomial Codes (2019, unknown): “We reveal the upper bound on the recovery threshold by proposing an achievable secure distributed computing scheme for matrix multiplication using polynomial codes [17].”
SteganoGAN: High Capacity Image Steganography with GANs (2019, background): “Reed-Solomon error-correcting codes are a subset of linear block codes which offer the following guarantee: Given a message of length k , the code can generate a message of length n where n ≥ k such that it can recover from n − k 2 errors (Reed & Solomon, 1960).”
Understanding and Co-designing the Data Ingestion Pipeline for Industry-Scale RecSys Training (2021, methodology): “Tectonic subsequently splits files into filesystem blocks, which are Reed-Solomon [43] encoded into chunks, stored across Tectonic storage node disks.”