Tom Young

5.3k total citations · 2 hit papers
9 papers, 2.7k citations indexed

About

Tom Young is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Signal Processing. According to data from OpenAlex, Tom Young has authored 9 papers receiving a total of 2.7k indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Artificial Intelligence, 1 paper in Computer Vision and Pattern Recognition and 1 paper in Signal Processing. Recurrent topics in Tom Young's work include Topic Modeling (9 papers), Natural Language Processing Techniques (7 papers) and Speech and dialogue systems (6 papers). Tom Young is often cited by papers focused on Topic Modeling (9 papers), Natural Language Processing Techniques (7 papers) and Speech and dialogue systems (6 papers). Tom Young collaborates with scholars based in Singapore, China and Italy. Tom Young's co-authors include Erik Cambria, Soujanya Poria, Devamanyu Hazarika, Hao Zhou, Minlie Huang, Jingfang Xu, Xiaoyan Zhu, Haizhou Zhao, Vlad Pandelea and Jinjie Ni and has published in prestigious journals such as Neurocomputing, Neural Computing and Applications and Artificial Intelligence Review.

In The Last Decade

Tom Young

9 papers receiving 2.6k citations

Hit Papers

align trajectories

What are hit papers?

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.

Recent Trends in Deep Learning Based Natural Language Processing [Review Article]

2018 2.0k citations Tom Young, Devamanyu Hazarika et al. IEEE Computational Intelligence Magazine profile →
Commonsense Knowledge Aware Conversation Generation with Graph Attention

2018 317 citations Hao Zhou, Tom Young et al. profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Tom Young Singapore	7	1.7k	467	242	236	151	9	2.7k
Zachary C. Lipton United States	20	1.4k 0.8×	571 1.2×	269 1.1×	213 0.9×	332 2.2×	62	2.8k
Afshin Rostamizadeh United States	21	1.5k 0.8×	800 1.7×	179 0.7×	133 0.6×	144 1.0×	34	2.6k
Jun Tian China	13	1.1k 0.6×	358 0.8×	147 0.6×	212 0.9×	99 0.7×	75	2.0k
Richard Maclin United States	13	1.6k 0.9×	519 1.1×	163 0.7×	252 1.1×	184 1.2×	29	2.7k
Carlos Soares Portugal	24	1.6k 0.9×	260 0.6×	197 0.8×	368 1.6×	200 1.3×	126	2.9k
Aruna Tiwari India	17	1.1k 0.6×	503 1.1×	142 0.6×	204 0.9×	194 1.3×	117	2.0k
Gavin Brown United Kingdom	21	1.5k 0.8×	733 1.6×	252 1.0×	271 1.1×	228 1.5×	65	2.7k
Hong Yu China	24	1.0k 0.6×	443 0.9×	271 1.1×	563 2.4×	226 1.5×	251	2.7k
Kun Zhang United States	23	1.6k 0.9×	652 1.4×	117 0.5×	179 0.8×	233 1.5×	155	2.8k

Countries citing papers authored by Tom Young

Since Specialization

Citations

This map shows the geographic impact of Tom Young'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 Tom Young with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tom Young more than expected).

Fields of papers citing papers by Tom Young

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Tom Young. 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 Tom Young. The network helps show where Tom Young may publish in the future.

Co-authorship network of co-authors of Tom Young

This figure shows the co-authorship network connecting the top 25 collaborators of Tom Young. A scholar is included among the top collaborators of Tom Young 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 Tom Young. Tom Young is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

9 of 9 papers shown

1.

Young, Tom, Frank Xing, Vlad Pandelea, Jinjie Ni, & Erik Cambria. (2022). Fusing Task-Oriented and Open-Domain Dialogues in Conversational Agents. Proceedings of the AAAI Conference on Artificial Intelligence. 36(10). 11622–11629. 34 indexed citations

2.

Ni, Jinjie, et al.. (2022). HiTKG: Towards Goal-Oriented Conversations via Multi-Hierarchy Learning. Proceedings of the AAAI Conference on Artificial Intelligence. 36(10). 11112–11120. 22 indexed citations

3.

Ni, Jinjie, Tom Young, Vlad Pandelea, Fuzhao Xue, & Erik Cambria. (2022). Recent advances in deep learning based dialogue systems: a systematic survey. Artificial Intelligence Review. 56(4). 3055–3155. 124 indexed citations

4.

Pandelea, Vlad, Edoardo Ragusa, Tom Young, Paolo Gastaldo, & Erik Cambria. (2021). Toward hardware-aware deep-learning-based dialogue systems. Neural Computing and Applications. 34(13). 10397–10408. 6 indexed citations

5.

Ni, Jinjie, et al.. (2021). Recent Advances in Deep Learning-based Dialogue Systems. 3 indexed citations

6.

Young, Tom, Vlad Pandelea, Soujanya Poria, & Erik Cambria. (2020). Dialogue systems with audio context. Neurocomputing. 388. 102–109. 29 indexed citations

7.

Zhou, Hao, Tom Young, Minlie Huang, et al.. (2018). Commonsense Knowledge Aware Conversation Generation with Graph Attention. 4623–4629. 317 indexed citations breakdown →

8.

Young, Tom, et al.. (2018). Augmenting End-to-End Dialogue Systems With Commonsense Knowledge. Proceedings of the AAAI Conference on Artificial Intelligence. 32(1). 166 indexed citations

9.

Young, Tom, Devamanyu Hazarika, Soujanya Poria, & Erik Cambria. (2018). Recent Trends in Deep Learning Based Natural Language Processing [Review Article]. IEEE Computational Intelligence Magazine. 13(3). 55–75. 2026 indexed citations breakdown →

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.

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