Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Very Deep Convolutional Neural Networks for Noise Robust Speech Recognition
2016252 citationsYanmin Qian, Kai Yu et al.IEEE/ACM Transactions on Audio Speech and Language Processingprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of Kai Yu's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Kai Yu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kai Yu more than expected).
This network shows the impact of papers produced by Kai Yu. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Kai Yu. The network helps show where Kai Yu may publish in the future.
Co-authorship network of co-authors of Kai Yu
This figure shows the co-authorship network connecting the top 25 collaborators of Kai Yu.
A scholar is included among the top collaborators of Kai Yu based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Kai Yu. Kai Yu is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Dinkel, Heinrich, Mengyue Wu, & Kai Yu. (2019). Text-based Depression Detection: What Triggers An Alert. arXiv (Cornell University).10 indexed citations
12.
Zhang, Yiming, Jon Crowcroft, Dongsheng Li, et al.. (2018). KylinX: A Dynamic Library Operating System for Simplified and Efficient Cloud Virtualization.. Cambridge University Engineering Department Publications Database. 173–186.10 indexed citations
Yu, Kai. (2012). GPR signal processing under low SNR based on empirical mode decomposition. Journal of Central South University(Science and Technology).2 indexed citations
15.
Tsiakoulis, Pirros, et al.. (2012). The Effect of Cognitive Load on a Statistical Dialogue System. Annual Meeting of the Special Interest Group on Discourse and Dialogue. 74–78.6 indexed citations
16.
Gašić, Milica, Filip Jurčíček, Simon Keizer, et al.. (2010). Gaussian Processes for Fast Policy Optimisation of POMDP-based Dialogue Managers. Cambridge University Engineering Department Publications Database. 201–204.38 indexed citations
17.
Mairesse, François, Milica Gašić, Filip Jurčíček, et al.. (2010). Phrase-Based Statistical Language Generation Using Graphical Models and Active Learning. Cambridge University Engineering Department Publications Database. 1552–1561.73 indexed citations
18.
Yu, Kai, Blaise Thomson, & Steve Young. (2010). From discontinuous to continuous F0 modelling in HMM-based speech synthesis.. SSW. 94–99.7 indexed citations
19.
Yu, Kai, et al.. (2010). Chemical Constituents with α-Glycosidase Inhibiting Activity from the Bark of Broussonetia papyrifera. Tianran chanwu yanjiu yu kaifa. 23(6). 934.1 indexed citations
20.
Keizer, Simon, Milica Gašić, Filip Jurčíček, et al.. (2010). Parameter estimation for agenda-based user simulation. Cambridge University Engineering Department Publications Database. 116–123.24 indexed citations
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.