Andrew L. Maas
- Artificial Intelligence top 0.5%
- Computer Vision and Pattern Recognition top 2%
- Signal Processing top 2%
- Control and Systems Engineering top 5%
- Automotive Engineering top 5%
- Co-authors
- Andrew Y. NgChristopher PottsDan HuangAnind K. DeyJ. Andrew BagnellBrian D. ZiebartPatrick NguyenOriol Vinyals
- Topics
- Music and Audio Processing (3 papers)Speech Recognition and Synthesis (3 papers)Bayesian Modeling and Causal Inference (2 papers)
- Journals
- arXiv (Cornell University)FigshareMeeting of the Association for Computational Linguistics
- Partner nations
- United States
In The Last Decade
Andrew L. Maas
12 papers receiving 3.0k citations
Hit Papers
Peers
Comparison fields: 5 of 131
- Artificial Intelligence 2.4k
- Computer Vision and Pattern Recognition 510
- Signal Processing 410
- Control and Systems Engineering 331
- Automotive Engineering 263
Countries citing papers authored by Andrew L. Maas
This map shows the geographic impact of Andrew L. Maas'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 Andrew L. Maas with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Andrew L. Maas more than expected).
Fields of papers citing papers by Andrew L. Maas
This network shows the impact of papers produced by Andrew L. Maas. 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 Andrew L. Maas. The network helps show where Andrew L. Maas may publish in the future.
Co-authorship network of co-authors of Andrew L. Maas
This figure shows the co-authorship network connecting the top 25 collaborators of Andrew L. Maas. A scholar is included among the top collaborators of Andrew L. Maas 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 Andrew L. Maas. Andrew L. Maas is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | Retrofitting Distributional Embeddings to Knowledge Graphs with Functional Relations | 4 |
| 2 | Maximum Entropy Inverse Reinforcement Learningbreakdown → | 795 |
| 3 | 4 | |
| 4 | 68 | |
| 5 | Increasing Deep Neural Network Acoustic Model Size for Large Vocabulary Continuous Speech Recognition | 11 |
| 6 | 25 | |
| 7 | 13 | |
| 8 | 225 | |
| 9 | Spectral Chinese Restaurant Processes: Nonparametric Clustering Based on Similarities | 19 |
| 10 | Learning Word Vectors for Sentiment Analysisbreakdown → | 1805 |
| 11 | Human Behavior Modeling with Maximum Entropy Inverse Optimal Control | 31 |
| 12 | 196 |
About Andrew L. Maas
Andrew L. Maas is a scholar working on Signal Processing, Artificial Intelligence and Management Science and Operations Research, having authored 12 papers that have together received 3.2k indexed citations. Recurring topics across this work include Music and Audio Processing (3 papers), Speech Recognition and Synthesis (3 papers) and Bayesian Modeling and Causal Inference (2 papers). The work is most often cited by research in Artificial Intelligence (2.4k citations), Signal Processing (410 citations) and Transportation (178 citations). Andrew L. Maas has collaborated with scholars based in United States. Frequent co-authors include Andrew Y. Ng, Christopher Potts, Dan Huang, Anind K. Dey, J. Andrew Bagnell, Brian D. Ziebart, Patrick Nguyen, Oriol Vinyals, Quoc V. Le and Ziang Xie. Their work appears in journals such as arXiv (Cornell University), Figshare and Meeting of the Association for Computational Linguistics.
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.