Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data.

Qingfeng Xia

dblp:91/10821 · DBLP profile ↗
← Back
5ranked-venue papers
1as first author
5since 2021 · last 2026
—ORCID · conflict

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 3 · 3 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021Theory of computation · 1 · 1 first-author · 1 since 2021Applied, interdisciplinary, general and emerging computing · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Theoretical computer science
1 paper
Coding theory · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Coding theory
error-correcting codes
0.812024
Function-Correcting Codes for Symbol-Pair Read Channels · IEEE Trans. Inf. Theory 2024
Coding theory › error-correcting codes
function-correcting codes
0.812024
Function-Correcting Codes for Symbol-Pair Read Channels · IEEE Trans. Inf. Theory 2024
Coding theory › source coding › universal coding
redundancy bounds
0.812024
Function-Correcting Codes for Symbol-Pair Read Channels · IEEE Trans. Inf. Theory 2024
Coding theory › error-correcting codes
symbol-pair read channels
0.812024
Function-Correcting Codes for Symbol-Pair Read Channels · IEEE Trans. Inf. Theory 2024

Methods — techniques the papers use, named apart from their topics

combinatorial construction · 0.8
YearPublicationVenuePosition
2026 Legendre-KAN: High Accuracy KA Network Based on Legendre Polynomials
Yanyi Liu, Qingfeng Xia
ICPR (6)3
2025 Time Efficiency: Legendre Polynomials in Kolmogorov-Arnold Network
Qingfeng Xia
ICIC (22)3
2024 An overview: Attention mechanisms in multi-agent reinforcement learning
Kai Hu 0006, Keer Xu, Qingfeng Xia, Mingyang Li 0006, Zhiqiang Song
Neurocomputing3
2024 A review of research on reinforcement learning algorithms for multi-agents
Kai Hu 0006, Mingyang Li 0006, Zhiqiang Song, Keer Xu, Qingfeng Xia, Peng Zhou 0026, Min Xia 0002
Neurocomputing5
2024 Function-Correcting Codes for Symbol-Pair Read Channels
abstract
Function-correcting codes (FCCs) are a class of codes designed to protect the function evaluation of a message against errors whose key advantage is the reduced redundancy. In this paper, we develop the theory of FCCs over symbol-pair read channels. We introduce the notion of function-correcting symbol-pair codes (FCSPCs) and aim to find their optimal redundancy. To this end, we introduce the notion of irregular-pair-distance codes and derive upper and lower bounds on the optimal redundancy in terms of the shortest length of the irregular-pair-distance codes. We then simplify these bounds and employ these general results to specific functions including pair-locally binary functions, pair weight functions and pair weight distribution functions. In addition, we provide some general constructions for FCSPCs. Lastly, by comparison with classical symbol-pair codes, we find that the theory of FCSPCs developed in our paper really reduces the redundancy under the condition that the receiver can recover certain attribute of the message.
Qingfeng Xia, Hongwei Liu 0003, Bocong Chen
IEEE Trans. Inf. Theory1