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.
Long-term recurrent convolutional networks for visual recognition and description
20153.1k citationsJeff Donahue, Sergio Guadarrama et al.profile →
Adversarial Discriminative Domain Adaptation
20173.0k citationsEric Tzeng, Judy Hoffman et al.profile →
This map shows the geographic impact of Kate Saenko'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 Kate Saenko with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kate Saenko more than expected).
This network shows the impact of papers produced by Kate Saenko. 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 Kate Saenko. The network helps show where Kate Saenko may publish in the future.
Co-authorship network of co-authors of Kate Saenko
This figure shows the co-authorship network connecting the top 25 collaborators of Kate Saenko.
A scholar is included among the top collaborators of Kate Saenko 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 Kate Saenko. Kate Saenko is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
All Works
20 of 20 papers shown
1.
Saito, Kuniaki, Donghyun Kim, & Kate Saenko. (2021). OpenMatch: Open-Set Semi-supervised Learning with Open-set Consistency Regularization. Neural Information Processing Systems. 34.18 indexed citations
2.
Plummer, Bryan A., et al.. (2020). Shapeshifter Networks: Decoupling Layers from Parameters for Scalable and Effective Deep Learning. arXiv (Cornell University).1 indexed citations
3.
Saito, Kuniaki, Donghyun Kim, Stan Sclaroff, & Kate Saenko. (2020). Universal Domain Adaptation through Self Supervision. neural information processing systems. 33. 16282–16292.2 indexed citations
Sun, Ximeng, Rameswar Panda, Rogério Feris, & Kate Saenko. (2019). AdaShare: Learning What To Share For Efficient Deep Multi-Task Learning. arXiv (Cornell University).9 indexed citations
6.
Peng, Xingchao, et al.. (2019). Generalized Domain Adaptation with Covariate and Label Shift CO-ALignment.. arXiv (Cornell University).7 indexed citations
7.
Usman, Ben, et al.. (2019). Adversarial Self-Defense for Cycle-Consistent GANs. Neural Information Processing Systems. 32. 635–645.6 indexed citations
8.
Bargal, Sarah Adel, et al.. (2019). Are CNN Predictions based on Reasonable Evidence. Computer Vision and Pattern Recognition. 67–70.1 indexed citations
9.
Fried, Daniel, Ronghang Hu, Volkan Cirik, et al.. (2018). Speaker-Follower Models for Vision-and-Language Navigation. Neural Information Processing Systems. 31. 3314–3325.61 indexed citations
10.
Xu, Huijuan, Kun He, Leonid Sigal, Stan Sclaroff, & Kate Saenko. (2018). Text-to-Clip Video Retrieval with Early Fusion and Re-Captioning.. arXiv (Cornell University).8 indexed citations
11.
Usman, Ben, Kate Saenko, & Brian Kulis. (2018). Stable Distribution Alignment Using the Dual of the Adversarial Distance. International Conference on Learning Representations.1 indexed citations
12.
Sun, Ximeng, Huijuan Xu, & Kate Saenko. (2018). A Two-Stream Variational Adversarial Network for Video Generation.. arXiv (Cornell University).4 indexed citations
13.
Tzeng, Eric, Judy Hoffman, Kate Saenko, & Trevor Darrell. (2017). Adversarial Discriminative Domain Adaptation (workshop extended abstract).. International Conference on Learning Representations.2 indexed citations
14.
Pas, Andreas ten, et al.. (2017). Learning a visuomotor controller for real world robotic grasping using simulated depth images. 291–300.50 indexed citations
15.
Levy, Andrew, Robert W. Platt, & Kate Saenko. (2017). Hierarchical Actor-Critic.. arXiv (Cornell University).18 indexed citations
Thomason, Jesse, Subhashini Venugopalan, Sergio Guadarrama, Kate Saenko, & Raymond J. Mooney. (2014). Integrating Language and Vision to Generate Natural Language Descriptions of Videos in the Wild. International Conference on Computational Linguistics. 1218–1227.107 indexed citations
18.
Hoffman, Judy, Eric Tzeng, Jeff Donahue, et al.. (2013). One-Shot Adaptation of Supervised Deep Convolutional Models. arXiv (Cornell University).10 indexed citations
Saenko, Kate & Trevor Darrell. (2009). Filtering Abstract Senses From Image Search Results. Neural Information Processing Systems. 22. 1589–1597.5 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.