Noah A. Smith

412 total citations
11 papers, 119 citations indexed

About

Noah A. Smith is a scholar working on Artificial Intelligence, Computer Networks and Communications and Molecular Biology. According to data from OpenAlex, Noah A. Smith has authored 11 papers receiving a total of 119 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Artificial Intelligence, 4 papers in Computer Networks and Communications and 2 papers in Molecular Biology. Recurrent topics in Noah A. Smith's work include Error Correcting Code Techniques (3 papers), Natural Language Processing Techniques (3 papers) and Bayesian Modeling and Causal Inference (2 papers). Noah A. Smith is often cited by papers focused on Error Correcting Code Techniques (3 papers), Natural Language Processing Techniques (3 papers) and Bayesian Modeling and Causal Inference (2 papers). Noah A. Smith collaborates with scholars based in United States, Portugal and Czechia. Noah A. Smith's co-authors include Eric P. Xing, Pedro M. Q. Aguiar, Mário A. T. Figueiredo, Brendan O’Connor, Jacob Eisenstein, Hakob Avetisyan, André F. T. Martins, Jan Holub, Suchin Gururangan and Brendan O’Connor and has published in prestigious journals such as Journal of Machine Learning Research, IEEE Wireless Communications Letters and arXiv (Cornell University).

In The Last Decade

Noah A. Smith

8 papers receiving 109 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Noah A. Smith United States	5	69	37	27	14	8	11	119
Rati Gelashvili United States	6	26 0.4×	50 1.4×	16 0.6×	13 0.9×	7 0.9×	15	98
Ali Vakilian United States	5	38 0.6×	22 0.6×	9 0.3×	5 0.4×	9 1.1×	15	67
Dingmin Wang China	8	177 2.6×	53 1.4×	62 2.3×	10 0.7×	9 1.1×	21	235
Vincent Zhao United States	3	122 1.8×	49 1.3×	49 1.8×	12 0.9×	4 0.5×	3	194
Prajit Ramachandran United States	5	118 1.7×	20 0.5×	50 1.9×	12 0.9×	5 0.6×	6	154
Abhishek Singh India	5	93 1.3×	21 0.6×	10 0.4×	9 0.6×	9 1.1×	16	140
Vladimir Nikulin Australia	7	50 0.7×	12 0.3×	16 0.6×	9 0.6×	9 1.1×	30	114
Marek Klonowski Poland	7	49 0.7×	69 1.9×	22 0.8×	21 1.5×	26 3.3×	39	130
Toru Akishita Denmark	4	96 1.4×	15 0.4×	70 2.6×	10 0.7×	12 1.5×	7	114

Countries citing papers authored by Noah A. Smith

Since Specialization

Citations

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

Fields of papers citing papers by Noah A. Smith

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Noah A. Smith. 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 Noah A. Smith. The network helps show where Noah A. Smith may publish in the future.

Co-authorship network of co-authors of Noah A. Smith

This figure shows the co-authorship network connecting the top 25 collaborators of Noah A. Smith. A scholar is included among the top collaborators of Noah A. Smith 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 Noah A. Smith. Noah A. Smith 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:

11 of 11 papers shown

1.

Liu, Jiacheng, et al.. (2025). Infini-gram mini: Exact n-gram Search at the Internet Scale with FM-Index. 24955–24980.

2.

Lambert, Nathan, Bill Yuchen Lin, Khyathi Raghavi Chandu, et al.. (2025). RewardBench: Evaluating Reward Models for Language Modeling. 1755–1797. 1 indexed citations

3.

Gonen, Hila, et al.. (2025). Does Liking Yellow Imply Driving a School Bus? Semantic Leakage in Language Models. 785–798. 1 indexed citations

4.

Gururangan, Suchin, et al.. (2024). Time is Encoded in the Weights of Finetuned Language Models. 2571–2587. 2 indexed citations

5.

Henderson, Peter, Yangsibo Huang, Kai Li, et al.. (2024). Evaluating Copyright Takedown Methods for Language Models. 139114–139150.

6.

Holub, Jan, et al.. (2018). Analysis of the Dependency of Call Duration on the Quality of VoIP Calls. IEEE Wireless Communications Letters. 7(4). 638–641. 13 indexed citations

7.

Martins, André F. T., Noah A. Smith, Eric P. Xing, Pedro M. Q. Aguiar, & Mário A. T. Figueiredo. (2018). Augmenting Dual Decomposition for MAP Inference. Figshare.

8.

Martins, André F. T., Mário A. T. Figueiredo, Pedro M. Q. Aguiar, Noah A. Smith, & Eric P. Xing. (2018). AD3 : Alternating Directions Dual Decomposition for MAP Inference in Graphical Models. Journal of Machine Learning Research. 16(1). 495–545. 16 indexed citations

9.

Schneider, Nathan, Brendan O’Connor, Naomi Saphra, et al.. (2013). A Framework for (Under)specifying Dependency Syntax without Overloading Annotators. arXiv (Cornell University). 51–60. 7 indexed citations

10.

Eisenstein, Jacob, Brendan O’Connor, Noah A. Smith, & Eric P. Xing. (2012). Mapping the geographical diffusion of new words. arXiv (Cornell University). 23 indexed citations

11.

Aguiar, Pedro M. Q., et al.. (2011). An Augmented Lagrangian Approach to Constrained MAP Inference. International Conference on Machine Learning. 169–176. 56 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.

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