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.
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
20154.2k citationsJimmy Ba et al.arXiv (Cornell University)profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of Jimmy Ba'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 Jimmy Ba with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jimmy Ba more than expected).
This network shows the impact of papers produced by Jimmy Ba. 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 Jimmy Ba. The network helps show where Jimmy Ba may publish in the future.
Co-authorship network of co-authors of Jimmy Ba
This figure shows the co-authorship network connecting the top 25 collaborators of Jimmy Ba.
A scholar is included among the top collaborators of Jimmy Ba 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 Jimmy Ba. Jimmy Ba is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Wu, Yuhuai, et al.. (2021). INT: An Inequality Benchmark for Evaluating Generalization in Theorem Proving. arXiv (Cornell University).
4.
Wang, Tingwu & Jimmy Ba. (2020). Exploring Model-based Planning with Policy Networks. arXiv (Cornell University).3 indexed citations
5.
Stadie, Bradly C., et al.. (2020). Learning Intrinsic Rewards as a Bi-Level Optimization Problem. Uncertainty in Artificial Intelligence. 111–120.2 indexed citations
6.
Pérez, Juan Manuel, et al.. (2020). Improving Transformer Optimization Through Better Initialization. International Conference on Machine Learning. 1. 4475–4483.23 indexed citations
7.
Zhang, Guodong, et al.. (2020). On Solving Minimax Optimization Locally: A Follow-the-Ridge Approach. International Conference on Learning Representations.8 indexed citations
8.
Wen, Yeming, et al.. (2020). An Empirical Study of Stochastic Gradient Descent with Structured Covariance Noise. International Conference on Artificial Intelligence and Statistics. 3621–3631.1 indexed citations
9.
Ba, Jimmy, et al.. (2020). Generalization of Two-layer Neural Networks: An Asymptotic Viewpoint. International Conference on Learning Representations.10 indexed citations
10.
Zhang, Michael R., James Lucas, Jimmy Ba, & Geoffrey E. Hinton. (2019). Lookahead Optimizer: k steps forward, 1 step back. Neural Information Processing Systems. 32. 9593–9604.63 indexed citations
11.
Wang, Tingwu, Yuhao Zhou, Sanja Fidler, & Jimmy Ba. (2019). Neural Graph Evolution: Towards Efficient Automatic Robot Design. International Conference on Learning Representations.2 indexed citations
Wen, Yeming, et al.. (2019). Interplay Between Optimization and Generalization of Stochastic Gradient Descent with Covariance Noise.. arXiv (Cornell University).7 indexed citations
14.
Kiros, Jamie, et al.. (2019). DOM-Q-NET: Grounded RL on Structured Language. International Conference on Learning Representations.
15.
Martens, James, et al.. (2018). Kronecker-factored Curvature Approximations for Recurrent Neural Networks. International Conference on Learning Representations.13 indexed citations
16.
Wang, Tingwu, Renjie Liao, Jimmy Ba, & Sanja Fidler. (2018). NerveNet: Learning Structured Policy with Graph Neural Networks. International Conference on Learning Representations.66 indexed citations
17.
Wen, Yeming, Paul Vicol, Jimmy Ba, Dustin Tran, & Roger Grosse. (2018). Flipout: Efficient Pseudo-Independent Weight Perturbations on Mini-Batches. arXiv (Cornell University).6 indexed citations
18.
Ba, Jimmy, Roger Grosse, & James Martens. (2017). Distributed Second-Order Optimization using Kronecker-Factored Approximations. International Conference on Learning Representations.24 indexed citations
19.
Wu, Yuhuai, Elman Mansimov, Roger Grosse, S. Matthew Liao, & Jimmy Ba. (2017). Second-order Optimization for Deep Reinforcement Learning using Kronecker-factored Approximation. Neural Information Processing Systems. 5285–5294.3 indexed citations
20.
Ba, Jimmy & Brendan J. Frey. (2013). Adaptive dropout for training deep neural networks. neural information processing systems. 26. 3084–3092.156 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.