Institution · Uber AI (United States)
Most cited papers from Uber AI (United States)
What are the most cited papers from Uber AI (United States)?
The most cited paper from Uber AI (United States) is "Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning" (Mohammad Sadegh Norouzzadeh, Anh‐Tu Nguyen, Margaret Kosmala et al., 2018), with 1,246 citations. Next come "A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting" (781) and "Multi-Task Multi-Sensor Fusion for 3D Object Detection" (777).
Showing 50 of 58 papers. Every paper with more than 45 citations is listed.
| # | Paper | ||
|---|---|---|---|
| 1 | Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning Mohammad Sadegh Norouzzadeh, Anh‐Tu Nguyen, Margaret Kosmala et al. · Proceedings of the National Academy of Sciences | 5 Jun 2018 | 1,246 |
| 2 | A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting Slawek Smyl · International Journal of Forecasting | 18 Jul 2019 | 781 |
| 3 | Multi-Task Multi-Sensor Fusion for 3D Object Detection Ming Liang, Bin Yang, Yun Chen et al. | Jun 2019 | 777 |
| 4 | Designing neural networks through neuroevolution Kenneth Owen Stanley, Jeff Clune, Joel Lehman et al. · Nature Machine Intelligence | 29 Dec 2018 | 706 |
| 5 | Learning Lane Graph Representations for Motion Forecasting Ming Xuan Liang, Bin Yang, Rui Sheng Hu et al. · Lecture notes in computer science | 2020 | 683 |
| 6 | Analysis of the Blockchain Protocol in Asynchronous Networks Rafael Pass, Lior Seeman, Abhi A. Shelat · Lecture notes in computer science | 2017 | 620 |
| 7 | Pyro: Deep Universal Probabilistic Programming Eli Bingham, Jonathan P. Chen, Martin Jankowiak et al. · arXiv (Cornell University) | 18 Oct 2018 | 589 |
| 8 | Plug & Play Generative Networks: Conditional Iterative Generation of Images in Latent Space Anh‐Tu Nguyen, Jeff Clune, Yoshua Bengio et al. | Jul 2017 | 513 |
| 9 | Introduction to Algorithms Josh Cutler, Matt Dickenson · Textbooks on political analysis | 2020 | 460 |
| 10 | Automated Identification of Northern Leaf Blight-Infected Maize Plants from Field Imagery Using Deep Learning Chad DeChant, Tyr Wiesner‐Hanks, Siyuan Chen et al. · Phytopathology | 27 Jun 2017 | 433 |
| 11 | UPSNet: A Unified Panoptic Segmentation Network Yuwen Xiong, Renjie Liao, Hengshuang Zhao et al. | Jun 2019 | 425 |
| 12 | Deep and Confident Prediction for Time Series at Uber Lingxue Zhu, Nikolay Laptev | Nov 2017 | 353 |
| 13 | Dynamic pricing and matching in ride‐hailing platforms Chiwei Yan, Helin Zhu, Nikita Korolko et al. · Naval Research Logistics (NRL) | 15 Nov 2019 | 335 |
| 14 | Handling incomplete heterogeneous data using VAEs Alfredo Nazábal, Pablo M. Olmos, Zoubin Ghahramani et al. · e-Archivo (Carlos III University of Madrid) | Nov 2020 | 329 |
| 15 | The Gender Earnings Gap in the Gig Economy: Evidence from over a Million Rideshare Drivers Cody Cook, Rebecca Diamond, Jonathan Hall et al. · The Review of Economic Studies | 13 Nov 2020 | 316 |
| 16 | Surge Pricing Solves the Wild Goose Chase Juan Camilo Castillo, Dan Knoepfle, Glen Weyl | 20 Jun 2017 | 281 |
| 17 | Maximum Relevance and Minimum Redundancy Feature Selection Methods for a Marketing Machine Learning Platform Zhenyu Zhao, Radhika Anand, Mallory Wang | Oct 2019 | 253 |
| 18 | A Hybrid Residual Dilated LSTM and Exponential Smoothing Model for Midterm Electric Load Forecasting Grzegorz Dudek, Paweł Pełka, Slawek Smyl · IEEE Transactions on Neural Networks and Learning Systems | 9 Jan 2021 | 233 |
| 19 | Manifold: A Model-Agnostic Framework for Interpretation and Diagnosis of Machine Learning Models Jiawei Zhang, Yang Wang, Piero Molino et al. · IEEE Transactions on Visualization and Computer Graphics | 20 Aug 2018 | 226 |
| 20 | First return, then explore Adrien Ecoffet, Joost Huizinga, Joel Lehman et al. · Nature | 24 Feb 2021 | 210 |
| 21 | The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communities Joel Lehman et al. · Artificial Life | 9 Apr 2020 | 185 |
| 22 | Learning Joint 2D-3D Representations for Depth Completion Yun Chen, Bin Yang, Ming Xuan Liang et al. | Oct 2019 | 185 |
| 23 | The future is big graphs Sherif Sakr et al. · Communications of the ACM | 24 Aug 2021 | 159 |
| 24 | SemanticAdv: Generating Adversarial Examples via Attribute-Conditioned Image Editing Haonan Qiu, Chaowei Xiao, Lei Yang et al. · Lecture notes in computer science | 2020 | 158 |
| 25 | Top Challenges from the first Practical Online Controlled Experiments Summit Somit Gupta et al. · ACM SIGKDD Explorations Newsletter | 13 May 2019 | 144 |
| 26 | EMP Xumiao Zhang, Anlan Zhang, Jiachen Sun et al. | 25 Oct 2021 | 135 |
| 27 | Deep Rigid Instance Scene Flow Wei-Chiu Ma, Shenlong Wang, Rui Sheng Hu et al. | Jun 2019 | 134 |
| 28 | Convolutional neural networks for fault interpretation in seismic images Xinming Wu, Yunzhi Shi, Sergey B. Fomel et al. | 27 Aug 2018 | 132 |
| 29 | Do Digital Platforms Reduce Moral Hazard? The Case of Uber and Taxis Meng Liu, Erik Brynjolfsson, Jason Dowlatabadi · Management Science | 26 Feb 2021 | 128 |
| 30 | Synthesizing entity matching rules by examples Rohit Kumar Singh, Venkata Vamsikrishna Meduri, Ahmed K. Elmagarmid et al. · Proceedings of the VLDB Endowment | Oct 2017 | 120 |
| 31 | Introduction to Machine Learning with Python Josh Cutler, Matt Dickenson · Textbooks on political analysis | 2020 | 113 |
| 32 | Revenue-Utility Tradeoff in Assortment Optimization Under the Multinomial Logit Model with Totally Unimodular Constraints Mika Sumida, Guillermo Gallego, Paat Rusmevichientong et al. · Management Science | 6 Oct 2020 | 111 |
| 33 | Artificial Intelligence in the Management of Glioma: Era of Personalized Medicine Houman Sotoudeh, Omid Shafaat, Joshua D. Bernstock et al. · Frontiers in Oncology | 14 Aug 2019 | 109 |
| 34 | Exploring the influence of built environment on Uber demand Sadegh Sabouri, Keunhyun Park, Amy Smith et al. · Transportation Research Part D Transport and Environment | 18 Mar 2020 | 107 |
| 35 | Prostate cancer diagnosis using deep learning with 3D multiparametric MRI Saifeng Liu, Huaixiu Zheng, Yesu Feng et al. · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE | 3 Mar 2017 | 93 |
| 36 | MCMLSD: A Dynamic Programming Approach to Line Segment Detection Emilio J. Almazán, Ron Tal, Yiming Qian et al. | Jul 2017 | 92 |
| 37 | DSDNet: Deep Structured Self-driving Network Wenyuan Zeng, Shenlong Wang, Renjie Liao et al. · Lecture notes in computer science | 2020 | 88 |
| 38 | Bayesian Social Learning from Consumer Reviews Bar Ifrach, Costis Maglaras, Marco Scarsini et al. · Operations Research | 23 Aug 2019 | 86 |
| 39 | Uber versus Taxi: A Driver’s Eye View Joshua D. Angrist, Sydnee Caldwell, Jonathan Hall · American Economic Journal Applied Economics | 28 Jun 2021 | 80 |
| 40 | 1.1-kV Vertical GaN p-n Diodes With p-GaN Regrown by Molecular Beam Epitaxy Zongyang Hu, Kazuki Nomoto, Meng Qi et al. · IEEE Electron Device Letters | 29 Jun 2017 | 75 |
| 41 | Tensor Voting Guided Mesh Denoising Mingqiang Wei, Luming Liang, Wai‐Man Pang et al. · IEEE Transactions on Automation Science and Engineering | 10 May 2016 | 73 |
| 42 | Using big data to enhance the bosch production line performance: A Kaggle challenge Ankita Mangal, Nishant Kumar | Dec 2016 | 67 |
| 43 | Dynamic Pricing and Matching in Ride-Hailing Platforms Nikita Korolko, Dawn B. Woodard, Chiwei Yan et al. · SSRN Electronic Journal | 2018 | 63 |
| 44 | POET Rui Wang, Joel Lehman, Jeff Clune et al. · Proceedings of the Genetic and Evolutionary Computation Conference | 3 Jul 2019 | 63 |
| 45 | Denotational validation of higher-order Bayesian inference Adam Ścibior, Ohad Kammar, Matthijs Vákár et al. · Proceedings of the ACM on Programming Languages | 27 Dec 2017 | 63 |
| 46 | Ensembles of localised models for time series forecasting Rakshitha Godahewa, Kasun Bandara, Geoffrey I. Webb et al. · Knowledge-Based Systems | 20 Sep 2021 | 56 |
| 47 | Scaling MAP-Elites to deep neuroevolution Cédric Colas, Vashisht Madhavan, Joost Huizinga et al. | 25 Jun 2020 | 53 |
| 48 | Matching and Pricing in Ride Hailing: Wild Goose Chases and How to Solve Them Juan Camilo Castillo, Dan Knoepfle, Eric Glen Weyl · Management Science | 11 Sep 2024 | 52 |
| 49 | Usage Patterns and the Economics of the Public Cloud Cinar Kilcioglu, Justin M. Rao, Aadharsh Kannan et al. | 3 Apr 2017 | 51 |
| 50 | Random sum-product networks: A simple and effective approach to probabilistic deep learning Robert Peharz, Antonio Vergari, Karl Stelzner et al. · Apollo (University of Cambridge) | 30 Oct 2019 | 50 |
Citations are OpenAlex's own counts, from its 2026-09-23 snapshot, so the ranking can differ from Google Scholar or Web of Science. A paper is listed for an institution when any of its authors gave it as an affiliation; retracted papers and papers whose citation count cannot be traced to citing works are left out, and a preprint and the article it became are shown once. Release dates are OpenAlex's: where a source gave only a month or a year, that is what is shown. How the lists are chosen.