Publications

Found 714 results
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2016
Zaharia, M., Xin R. S., Wendell P., Das T., Armbrust M., Dave A., et al. (2016).  Apache Spark: a unified engine for big data processing. Communications of the ACM. 59(11), 56-65.
Zaharia, M., Xin R. S., Wendell P., Das T., Armbrust M., Dave A., et al. (2016).  Apache Spark: a unified engine for big data processing. Communications of the ACM. 59(11), 56-65.
Gao, Y., Beijbom O., Zhang N., & Darrell T. (2016).  Compact Bilinear Pooling. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 317-326.
Hoffman, J., Gupta S., Leong J., Guadarrama S., & Darrell T. (2016).  Cross-modal adaptation for RGB-D detection. IEEE International Conference on Robotics and Automation (ICRA). 5032-5039.
Hoffman, J., Gupta S., Leong J., Guadarrama S., & Darrell T. (2016).  Cross-modal adaptation for RGB-D detection. IEEE International Conference on Robotics and Automation (ICRA). 5032-5039.
Gao, Y., Hendricks L. Anne, Kuchenbecker K. J., & Darrell T. (2016).  Deep learning for tactile understanding from visual and haptic data. IEEE International Conference on Robotics and Automation (ICRA). 536-543.
Gergen, S., Zeiler S., Abdelaziz A. Hussen, Nickel R., & Kolossa D. (2016).  Dynamic Stream Weighting for Turbo-Decoding-Based Audiovisual ASR. Proceedings of Interspeech 2016.
Zou, Y., Zetterberg P., Gustavsson U., Svensson T., Zaidi A., Kadur T., et al. (2016).  Impact of Major RF Impairments on mm-Wave Communications Using OFDM Waveforms. 2016 IEEE Globecom Workshops (GC Wkshps). 1-7.
Hoffman, J., Pathak D., Tzeng E., Long J., Guadarrama S., Darrell T., et al. (2016).  Large Scale Visual Recognition Through Adaptation Using Joint Representation and Multiple Instance Learning. J. Mach. Learn. Res.. 17, 4954–4984.
Song, H. Oh, Fritz M., Goehring D., & Darrell T. (2016).  Learning to Detect Visual Grasp Affordance. IEEE Transactions on Automation Science and Engineering. 13(2), 798-809.
Hoffman, J., Gupta S., & Darrell T. (2016).  Learning With Side Information Through Modality Hallucination. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 826-834.
Gittens, A., Devarakonda A., Racah E., Ringenburg M., Gerhardt L., Kottalam J., et al. (2016).  Matrix Factorization at Scale: a Comparison of Scientific Data Analytics in Spark and C+MPI Using Three Case Studies.
Gittens, A., Devarakonda A., Racah E., Ringenburg M., Gerhardt L., Kottalam J., et al. (2016).  Matrix Factorization at Scale: a Comparison of Scientific Data Analytics in Spark and C+MPI Using Three Case Studies.
Gleich, D., & Mahoney M. (2016).  Mining Large graphs. Handbook of Big Data. 191-220.
Gittens, A., Kottalam J., Yang J., Ringenburg M. F., Chhugani J., Racah E., et al. (2016).  A multi-platform evaluation of the randomized CX low-rank matrix factorization in Spark. Proceedings of the 5th International Workshop on Parallel and Distributed Computing for Large Scale Machine Learning and Big Data Analytics.
Gergen, S., Zeiler S., Abdelaziz A. Hussen, & Kolossa D. (2016).  New Insights into Turbo-Decoding-Based AVSR with Dynamic Stream Weights. Proceedings of ITG Conference on Speech Communication.
Fountoulakis, K., Gleich D., & Mahoney M. (2016).  An optimization approach to locally-biased graph algorithms.
Gallopoulos, E., Drineas P., Ipsen I., & Mahoney M. (2016).  RandNLA, Pythons, and the CUR for Your Data Problems: Reporting from G2S3 2015 in Delphi. SIAM News.
Girshick, R., Donahue J., Darrell T., & Malik J. (2016).  Region-Based Convolutional Networks for Accurate Object Detection and Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence. 38(1), 142-158.
Veldt, N., Gleich D., & Mahoney M. (2016).  A Simple and Strongly-Local Flow-Based Method for Cut Improvement. Proceedings of the 33rd ICML Conference.
Egelman, S., Bernd J., Friedland G., & Garcia D. D. (2016).  The Teaching Privacy Curriculum. Proceedings of the 47th ACM Technical Symposium on Computing Science Education (SIGCSE ’16), Memphis, TN. 591-596.
Vallina-Rodriguez, N., Sundaresan S., Razaghpanah A., Nithyanand R., Allman M., Kreibich C., et al. (2016).  Tracking the Trackers: Towards Understanding the Mobile Advertising and Tracking Ecosystem. Proceedings of Workshop on Data and Algorithmic Transparency.
Murali, A., Garg A., Krishnan S., Pokory F. T., Abbeel P., Darrell T., et al. (2016).  TSC-DL: Unsupervised trajectory segmentation of multi-modal surgical demonstrations with Deep Learning. IEEE International Conference on Robotics and Automation (ICRA). 4150-4157.
Murali, A., Garg A., Krishnan S., Pokory F. T., Abbeel P., Darrell T., et al. (2016).  TSC-DL: Unsupervised trajectory segmentation of multi-modal surgical demonstrations with Deep Learning. IEEE International Conference on Robotics and Automation (ICRA). 4150-4157.
2015
Thomas, K., Bursztein E., Jagpal N., Rajab M. Abu, Provos N., Pearce P., et al. (2015).  Ad Injection at Scale: Assessing Deceptive Advertisement Modifications.

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