Publications

Found 265 results
Author Title [ Type(Desc)] Year
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Conference Paper
Friedman, E., Racz M. Z., & Shenker S. J. (2015).  Dynamic Budget-Constrained Pricing in the Cloud.
Kirchhoff, K., & Bilmes J. A. (1999).  Dynamic Classifier Combinations in Hybrid Speech Recognition Systems Using Utterance-Level Confidence Values. Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 1999).
Calle, E., Jové T., Vilà P., & Marzo J. L. (2001).  A Dynamic Multilevel MPLS Protection Domain. Proceedings of the Third International Workshop on Design of Reliable Communication Networks (DRCN 2001).
Suárez, A., Moody J., & Saffell M. (2009).  Dynamic Portfolio Management with Transaction Costs.
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.
Yeh, T., & Darrell T. (2008).  Dynamic Visual Category Learning.
Moody, J. (1990).  Dynamics of Lateral Interaction Networks. Proceedings of the IEEE International Joint Conference on Neural Networks (IJCNN 1990).
Conference Proceedings
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.
Journal Article
Shenker, S. J., Ratnasamy S., Karp B., Govindan R., & Estrin D. (2003).  Data-Centric Storage in Sensornets. ACM SIGCOMM Computer Communication Review. 33(1), 137-142.
Ratnasamy, S., Karp B., Shenker S. J., Estrin D., Govindan R., Yin L., et al. (2003).  Data-Centric Storage in Sensornets with GHT, a Geographic Hash Table. 8(4), 427-442.
Krahenbuhl, P., Doersch C., Donahue J., & Darrell T. (2015).  Data-dependent Initializations of Convolutional Neural Networks. CoRR. abs/1511.06856,
Koponen, T., Chawla M., Chun B-G., Ermolinskiy A., Kim K. Hyun, Shenker S. J., et al. (2007).  A Data-oriented (and Beyond) Network Architecture. Computer Communication Review. 37(4), 181-192.
Jing, L., Liu B., Choi J., Janin A., Bernd J., Mahoney M., et al. (2017).  DCAR: A Discriminative and Compact Audio Representation for Audio Processing. IEEE Transactions on Multimedia. PP(99), 
Walfish, M., Vutukuru M., Balakrishnan H., Karger D. R., & Shenker S. J. (2010).  DDoS Defense by Offense. ACM Transactions on Computer Systems. 28(1), 1-54.
Weaver, N. (2022).  The Death of Cryptocurrency: The Case for Regulation. Yale Law School Information Society Project. Digital Future Whitepaper Series,
Feldman, J., & Yakimovsky Y.. (1974).  Decision Theory and Artificial Intelligence: I. A Semantics-Based Region Analyzer. 5(4), 349-371.
Feldman, J., & Sproull R. F. (1977).  Decision Theory and Artificial Intelligence II: The Hungry Monkey. In Cognitive Science. 2, 158-192.
Tavakoli, A., Chu D., Hellerstein J. M., Levis P., & Shenker S. J. (2007).  A Declarative Sensornet Architecture. 4(3), 55-60.
Morgan, N. (2012).  Deep and Wide: Multiple Layers in Automatic Speech Recognition. IEEE Transactions on Audio. 20(1), 7-13.
Dodge, E. (2016).  A deep semantic corpus-based approach to metaphor analysis: A case study of metaphoric conceptualizations of poverty. MetaNet, Special Issue of Constructions and Frames. 8(2), 
Finn, C., Tan X. Yu, Duan Y., Darrell T., Levine S., & Abbeel P. (2016).  Deep spatial autoencoders for visuomotor learning. IEEE International Conference on Robotics and Automation (ICRA). 512-519.
Karp, R. M., Motwani R., & Raghaven P.. (1988).  Deferred Data Structuring. SIAM Journal on Computing. 17(5), 883-902.
Sherr, M., Gill H., Saeed T. Aquil, Mao A., Marczak B., Soundararajan S., et al. (2014).  The Design and Implementation of the A^3 Application-Aware Anonymity Platform. Computer Networks. 58, 206-227.
Anderson, T., Shenker S. J., Stoica I., & Wetherall D. (2003).  Design Guidelines for Robust Internet Protocols. 33(1), 125-130.
Preuss, M., König I. R., Thompson J. R., Erdmann J., Absher D., Assimes T. L., et al. (2010).  Design of the Coronary Artery Disease Genome-Wide Replication and Meta-Analysis (CARDIoGRAM) Study--A Genome-Wide Association Meta-Analysis Involving More than 22,000 Cases and 60,000 Controls. Circulation: Cardiovascular Genetics. 3, 475-483.

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