Eric Nalisnick
Impact in
- Artificial Intelligence top 10%
- Topic Modeling
- Natural Language Processing Techniques
- Anomaly Detection Techniques and Applications
- Advanced Text Analysis Techniques
- Adversarial Robustness in Machine Learning
- Sentiment Analysis and Opinion Mining
- Text and Document Classification Technologies
- Information Systems top 10%
- Information Retrieval and Search Behavior
Papers in
-
- Gaussian Processes and Bayesian Inference 5
- Topic Modeling 4
- Anomaly Detection Techniques and Applications 4
- Machine Learning and Data Classification 3
- Machine Learning and Algorithms 3
- Advanced Text Analysis Techniques 2
- Neural Networks and Applications 2
-
- Time Series Analysis and Forecasting 2
- Co-authors
- Nick Craswell (1 shared paper)Rich Caruana (1 shared paper)Bhaskar Mitra (1 shared paper)Henry S. Baird (2 shared papers)Padhraic Smyth (5 shared papers)Balaji Lakshminarayanan (2 shared papers)Yee Whye Teh (2 shared papers)Akihiro Matsukawa (2 shared papers)
- Journals
- Annual Review of Statistics and Its Application (1 paper)International Conference on Learning Representations (1 paper)eScholarship (California Digital Library) (1 paper)UvA-DARE (University of Amsterdam) (2 papers)Cambridge University Engineering Department Publications Database (1 paper)
- Partner nations
- United StatesNetherlandsUnited Kingdom
In The Last Decade
Eric Nalisnick
19 papers receiving 195 citations
Peers
Comparison fields: 5 of 54
- Artificial Intelligence 173
- Information Systems 47
- Computer Vision and Pattern Recognition 42
- Signal Processing 7
- Statistical and Nonlinear Physics 8
Countries citing papers authored by Eric Nalisnick
This map shows the geographic impact of Eric Nalisnick'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 Eric Nalisnick with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Eric Nalisnick more than expected).
Fields of papers citing papers by Eric Nalisnick
This network shows the impact of papers produced by Eric Nalisnick. 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 Eric Nalisnick. The network helps show where Eric Nalisnick may publish in the future.
Co-authors
The 25 scholars most cited alongside Eric Nalisnick, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2016 | 87 | |
| 2 | Character-to-Character Sentiment Analysis in Shakespeare's Plays | 2013 | 21 |
| 3 | Detecting Out-of-Distribution Inputs to Deep Generative Models Using a Test for Typicality. | 2019 | 21 |
| 4 | 2013 | 19 | |
| 5 | Bayesian Batch Active Learning as Sparse Subset Approximation | 2019 | 12 |
| 6 | On Priors for Bayesian Neural Networks | 2018 | 9 |
| 7 | 2022 | 7 | |
| 8 | 2022 | 6 | |
| 9 | Expressive yet Tractable Bayesian Deep Learning via Subnetwork Inference | 2021 | 6 |
| 10 | Dropout as a Structured Shrinkage Prior | 2019 | 4 |
| 11 | Learning Priors for Invariance. | 2018 | 4 |
| 12 | 2019 | 4 | |
| 13 | 2023 | 4 | |
| 14 | 2021 | 3 | |
| 15 | Analyzing NIH Funding Patterns over Time with Statistical Text Analysis. | 2016 | 2 |
| 16 | THE EFFECTIVENESS OF A TWO-LAYER NEURAL NETWORK FOR RECOMMENDATIONS | 2018 | 2 |
| 17 | Variational Reference Priors | 2017 | 1 |
| 18 | Automatic Methods for Tracking Sentiment Dynamics in Plays | 2013 | 1 |
| 19 | 2024 | 1 |
About Eric Nalisnick
Eric Nalisnick is a scholar working on Artificial Intelligence, Signal Processing, Molecular Biology, Social Psychology and Information Systems, having authored 19 papers that have together received 214 indexed citations. Recurring topics across this work include Gaussian Processes and Bayesian Inference (5 papers), Topic Modeling (4 papers), Anomaly Detection Techniques and Applications (4 papers), Machine Learning and Data Classification (3 papers), Machine Learning and Algorithms (3 papers), Advanced Text Analysis Techniques (2 papers), Time Series Analysis and Forecasting (2 papers) and Neural Networks and Applications (2 papers). The work is most often cited by research in Artificial Intelligence (173 citations), Information Systems (47 citations), Computer Vision and Pattern Recognition (42 citations), Signal Processing (7 citations) and Statistical and Nonlinear Physics (8 citations). Eric Nalisnick has collaborated with scholars based in United States, Netherlands and United Kingdom. Frequent co-authors include Nick Craswell, Rich Caruana, Bhaskar Mitra, Henry S. Baird, Padhraic Smyth, Balaji Lakshminarayanan, Yee Whye Teh, Akihiro Matsukawa, José Miguel Hernández-Lobato and Jonathan Gordon. Their work appears in journals such as Annual Review of Statistics and Its Application, International Conference on Learning Representations, eScholarship (California Digital Library), UvA-DARE (University of Amsterdam) and Cambridge University Engineering Department Publications Database.
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.