Publications

Found 265 results
Author [ Title(Desc)] Type Year
Filters: First Letter Of Title is D  [Clear All Filters]
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z 
D
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.
Shastri, L., & Grannes D. Jeffrey (1995).  Dealing with negated knowledge and inconsistency in a neurally motivated model of memory and reflexive reasoning.
Shastri, L., & Grannes D. Jeffrey (1995).  Dealing with Negated Knowledge and Inconsistency in a Neurally Motivated Model of Memory and Reflexive Reasoning.
Weaver, N. (2022).  The Death of Cryptocurrency: The Case for Regulation. Yale Law School Information Society Project. Digital Future Whitepaper Series,
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.
Cantone, D., & Cutello V. (1991).  On the Decidability Problem for a Topological Syllogistic Involving the Notion of Topological Product.
Sander, T., & M. Shokrollahi A. (1997).  Deciding Properties of Polynomials without Factoring.
Halperin, E. (2010).  Deciphering the Genetic Components of Human Diseases.
Cantone, D., & Cutello V. (1992).  Decision Procedures for Flat Set-Theorectical Syllogistics.I. General Union, Powerset and Singleton Operators.
[Anonymous] (1998).  Decision Technologies for Computational Finance, Proceedings of the London Conference. (Refenes, A.., Burgess N.., & Moody J., Ed.).
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. 4, 207-223.
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. 55-60.
Tavakoli, A., Chu D., Hellerstein J. M., Levis P., & Shenker S. J. (2007).  A Declarative Sensornet Architecture. 4(3), 55-60.
M. Shokrollahi, A., & Wasserman H. (1998).  Decoding Algebraic-Geometric Codes Beyond the Error-Correction Bound.
Barker, J., Cooke M. P., & Ellis D. P. W. (2000).  Decoding Speech in the Presence of Other Sound Sources. Proceedings of the 6th International Conference on Spoken Language Processing (ICSLP 2000).
Fritz, M., & Schiele B. (2008).  Decomposition, Discovery, and Detection of Visual Categories Using Topic Models.
Alizadeh, M., Yang S., Katti S., McKeown N., Prabhakar B., & Shenker S. J. (2012).  Deconstructing Datacenter Packet Transport. 133-138.
Morgan, N. (2011).  Deep and Wide: Multiple Layers in Automatic Speech Recognition.
Morgan, N. (2012).  Deep and Wide: Multiple Layers in Automatic Speech Recognition. IEEE Transactions on Audio. 20(1), 7-13.
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).
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.
Yu, S. X., & Zipser K. (2016).  A Deep Neural Net Trained for Person Categorization Develops Both Detailed Local Features and Broad Contexual Specificities. Poster at Vision Sciences Society Annual Meeting.

Pages