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h5 指数:98
h5 中位数:162

h5 指数是指在过去整整 5 年中所发表文章的 h 指数。h 指在 2018-2022 年间发表的 h 篇文章每篇至少都被引用过 h 次的最大值。隐藏

出版物的 h5 中位数，是指出版物的 h5 指数所涵盖的所有文章获得的引用次数的中位值。隐藏

#14 Artificial Intelligence

标题/作者	引用次数	年份
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer C Raffel, N Shazeer, A Roberts, K Lee, S Narang, M Matena, Y Zhou, W Li, ... Journal of Machine Learning Research 21 (140), 1-67	3731	2020
Neural Architecture Search: A Survey T Elsken, JH Metzen, F Hutter Journal of Machine Learning Research 20 (55), 1-21	1607	2019
Automatic Differentiation in Machine Learning: a Survey AG Baydin, BA Pearlmutter, AA Radul, JM Siskind Journal of Machine Learning Research 18 (153), 1-43	1593	2018
Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning G LemaÃŽtre, F Nogueira, CK Aridas Journal of Machine Learning Research 18 (17), 1-5	1511	2017
Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations I Hubara, M Courbariaux, D Soudry, R El-Yaniv, Y Bengio Journal of Machine Learning Research 18 (187), 1-30	1507	2018
Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization L Li, K Jamieson, G DeSalvo, A Rostamizadeh, A Talwalkar Journal of Machine Learning Research 18 (185), 1-52	1346	2018
Auto-WEKA 2.0: Automatic model selection and hyperparameter optimization in WEKA L Kotthoff, C Thornton, HH Hoos, F Hutter, K Leyton-Brown Journal of Machine Learning Research 18 (25), 1-5	652	2017
Pyro: Deep Universal Probabilistic Programming E Bingham, JP Chen, M Jankowiak, F Obermeyer, N Pradhan, ... Journal of Machine Learning Research 20 (28), 1-6	593	2019
The Implicit Bias of Gradient Descent on Separable Data D Soudry, E Hoffer, MS Nacson, S Gunasekar, N Srebro Journal of Machine Learning Research 19 (70), 1-57	537	2018
Normalizing Flows for Probabilistic Modeling and Inference G Papamakarios, E Nalisnick, DJ Rezende, S Mohamed, ... Journal of Machine Learning Research 22 (57), 1-64	532	2021
Breaking the Curse of Dimensionality with Convex Neural Networks F Bach Journal of Machine Learning Research 18 (19), 1-53	501	2017
Katyusha: The First Direct Acceleration of Stochastic Gradient Methods Z Allen-Zhu Journal of Machine Learning Research 18 (221), 1-51	500	2018
Automatic Differentiation Variational Inference A Kucukelbir, D Tran, R Ranganath, A Gelman, DM Blei Journal of Machine Learning Research 18 (14), 1-45	496	2017
Deep Hidden Physics Models: Deep Learning of Nonlinear Partial Differential Equations M Raissi Journal of Machine Learning Research 19 (25), 1-24	491	2018
All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously A Fisher, C Rudin, F Dominici Journal of Machine Learning Research 20 (177), 1-81	468	2019
Persistence Images: A Stable Vector Representation of Persistent Homology H Adams, T Emerson, M Kirby, R Neville, C Peterson, P Shipman, ... Journal of Machine Learning Research 18 (8), 1-35	462	2017
Stochastic Gradient Descent as Approximate Bayesian Inference M Stephan, MD Hoffman, DM Blei Journal of Machine Learning Research 18 (134), 1-35	440	2017
Hinge-Loss Markov Random Fields and Probabilistic Soft Logic SH Bach, M Broecheler, B Huang, L Getoor Journal of Machine Learning Research 18 (109), 1-67	389	2017
PyOD: A Python Toolbox for Scalable Outlier Detection Y Zhao, Z Nasrullah, Z Li Journal of Machine Learning Research 20 (96), 1-7	388	2019
Why do deep convolutional networks generalize so poorly to small image transformations? A Azulay, Y Weiss Journal of Machine Learning Research 20 (184), 1-25	375	2019

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