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.
Time series classification from scratch with deep neural networks: A strong baseline
20171.2k citationsZhiguang Wang, Tim Oates et al.profile →
This map shows the geographic impact of Tim Oates'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 Tim Oates with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tim Oates more than expected).
This network shows the impact of papers produced by Tim Oates. 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 Tim Oates. The network helps show where Tim Oates may publish in the future.
Co-authorship network of co-authors of Tim Oates
This figure shows the co-authorship network connecting the top 25 collaborators of Tim Oates.
A scholar is included among the top collaborators of Tim Oates 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 Tim Oates. Tim Oates is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
All Works
20 of 20 papers shown
1.
Oates, Tim, et al.. (2016). A Gold Standard for Scalar Adjectives. Language Resources and Evaluation. 2669–2675.5 indexed citations
Page, Adam, et al.. (2014). Comparing Raw Data and Feature Extraction for Seizure Detection with Deep Learning Methods. The Florida AI Research Society.17 indexed citations
4.
Oates, Tim, et al.. (2010). Mining Script-Like Structures from the Web. North American Chapter of the Association for Computational Linguistics. 34–42.11 indexed citations
5.
Dinalankara, Wikum, Shomir Wilson, Donald Perlis, et al.. (2010). The Metacognitive Loop: An Architecture for Building Robust Intelligent Systems. National Conference on Artificial Intelligence. 33–39.6 indexed citations
6.
Anderson, Michael L. & Tim Oates. (2010). A critique of multi-voxel pattern analysis. eScholarship (California Digital Library). 32(32).26 indexed citations
7.
Nirenburg, Sergei & Tim Oates. (2009). Learning by reading and learning to read : papers from the AAAI Spring Symposium.2 indexed citations
8.
Oates, Tim, et al.. (2008). The role of metacognition in robust AI systems. National Conference on Artificial Intelligence.5 indexed citations
9.
Pickett, Marc, et al.. (2007). Models of Strategic Deficiency and Poker. Clinical Genetics. 80 Suppl 1. 1–74.
10.
Oates, Tim, et al.. (2007). UNDERTOW: multi-level segmentation of real-valued time series. National Conference on Artificial Intelligence. 1842–1843.2 indexed citations
11.
Gupta, Aarti & Tim Oates. (2007). Using ontologies and the web to learn lexical semantics. International Joint Conference on Artificial Intelligence. 1618–1623.5 indexed citations
Oates, Tim, et al.. (2004). On the relationship between lexical semantics and syntax for the inference of context-free grammars. National Conference on Artificial Intelligence. 431–436.4 indexed citations
14.
Cañamero, Lola, Zachary Dodds, Lloyd Greenwald, et al.. (2004). The 2004 AAAI Spring Symposium Series. AI Magazine. 25(4). 95–95.5 indexed citations
15.
Oates, Tim, et al.. (2002). Learning k-Reversible Context-Free Grammars from Positive Structural Examples. International Conference on Machine Learning. 459–465.11 indexed citations
16.
Oates, Tim, et al.. (2000). A Method for Clustering the Experiences of a Mobile Robot that Accords with Human Judgments. National Conference on Artificial Intelligence. 846–851.50 indexed citations
17.
Oates, Tim, et al.. (1999). Learned models for continuous planning.. International Conference on Artificial Intelligence and Statistics. 89(S1). 609–615.34 indexed citations
18.
Oates, Tim, et al.. (1999). Efficient Mining of Statistical Dependencies. International Joint Conference on Artificial Intelligence. 2. 794–799.4 indexed citations
19.
Oates, Tim & Paul R. Cohen. (1996). Searching for Structure in Multiple Streams of Data.. International Conference on Machine Learning. 346–354.49 indexed citations
20.
Oates, Tim & Paul R. Cohen. (1994). Toward a plan steering agent: experiments with schedule maintenance. 134–139.3 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.