Publications

Found 468 results
Author Title [ Type(Desc)] Year
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Conference Paper
Gao, Y., Beijbom O., Zhang N., & Darrell T. (2016).  Compact Bilinear Pooling. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 317-326.
Jia, Y., Vinyals O., & Darrell T. (2013).  On Compact Codes for Spatially Pooled Features.
Jamin, S., Shenker S. J., & Danzig P. B. (1997).  Comparison of Measurement-Based Admission Control Algorithms for Controlled-Load Service. Proceedings of the Sixteenth Annual Joint Conference of the IEEE Computer and Communications Societies (INFOCOM). 3, 973-980.
Baswana, S., Biswas S., Doerr B., Friedrich T., Kurur P., & Neumann F. (2009).  Computing Single Source Shortest Paths Using Single-Objective Fitness Functions. 59-66.
Pauls, A., DeNero J., & Klein D. (2009).  Consensus Training for Consensus Decoding in Machine Translation. 1418-1427.
Zhang, Y., Duffield N., Paxson V., & Shenker S. J. (2001).  On the Constancy of Internet Path Properties. Proceedings of the 1st ACM SIGCOMM Internet Measurement Workshop (IMW '01). 197-211.
Pathak, D., Krahenbuhl P., & Darrell T. (2015).  Constrained Convolutional Neural Networks for Weakly Supervised Segmentation. The IEEE International Conference on Computer Vision (ICCV). 1796-1804.
Dunietz, J., Levin L., & Petruck M. R. L. (2017).  Construction Detection in a Conventional NLP Pipeline. Proceedings of the AAAI 2017 Spring Symposium on Computational Construction Grammar and Natural Language Understanding. 178-184.
Pathak, D., Krahenbuhl P., Donahue J., Darrell T., & Efros A. A. (2016).  Context Encoders: Feature Learning by Inpainting. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2536-2544.
Pathak, D., Krahenbuhl P., Donahue J., Darrell T., & Efros A. A. (2016).  Context Encoders: Feature Learning by Inpainting. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2536-2544.
Hoffman, J., Darrell T., & Saenko K. (2014).  Continuous Manifold Based Adaptation for Evolving Visual Domains.
Kulis, B., Sra S., & Dhillon I. (2009).  Convex Perturbations for Scalable Semidefinite Programming. 296-303.
Zheng, X., Jiang J., Liang J., Duan H., Chen S.., Wan T., et al. (2015).  Cookies Lack Integrity: Real-World Implications. 707-721.
C. Christoudias, M., Urtasun R., Kapoor A., & Darrell T. (2009).  Co-Training with Noisy Perceptual Observations. 2844-2851.
Kramer, O., & Danielsiek H.. (2010).  DBSCAN-Based Multi-Objective Niching to Approximate Equivalent Pareto-Subsets. 503-510.
Donahue, J., Jia Y., Vinyals O., Hoffman J., Zhang N., Tzeng E., et al. (2014).  DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition.
Donahue, J., Jia Y., Vinyals O., Hoffman J., Zhang N., Tzeng E., et al. (2014).  DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition.
Hendricks, L. Anne, Venugopalan S., Rohrbach M., Mooney R., Saenko K., & Darrell T. (2016).  Deep Compositional Captioning: Describing Novel Object Categories Without Paired Training Data. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 1-10.
Andreas, J., Rohrbach M., Darrell T., & Klein D. (2016).  Deep compositional question answering with neural module networks. IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
Zhang, N., Farrell R., Iandola F., & Darrell T. (2013).  Deformable Part Descriptors for Fine-Grained Recognition and Attribute Prediction.
Girshick, R., Iandola F., Darrell T., & Malik J. (2015).  Deformable Part Models are Convolutional Neural Networks. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 437-446.
Althoff, T., Song H. Oh, & Darrell T. (2012).  Detection Bank: An Object Detection Based Video Representation for Multimedia Event Recognition.
Hoffman, J., Pathak D., Darrell T., & Saenko K. (2015).  Detector Discovery in the Wild: Joint Multiple Instance and Representation Learning. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2883-2891.
Hoffman, J., Saenko K., Kulis B., & Darrell T. (2012).  Discovering Latent Domains for Multisource Domain Adaptation. 702-715.
Zaharia, M., Das T., Li H., Shenker S. J., & Stoica I. (2012).  Discretized Streams: An Efficient and Fault-Tolerant Model for Stream Processing on Large Clusters. 1-6.

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