Publications

Found 4070 results
Author [ Title(Desc)] Type Year
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A
Moody, J., Shapere A. D., & Wilczek F. (1989).  Adiabatic Effective Lagrangian.
Jamin, S., Shenker S., Zhang L., & Clark D. D. (1992).  An Admission Control Algorithm for Predictive Real-Time Service (Extended Abstract). Proceedings of the Third International Workshop on Network and Operating System Support for Digital Audio and Video (NOSSDAV). 349-356.
Fàbrega, L., Jové T., Bueno A., & Marzo J. L. (2001).  An Admission Control Approach for Elastic Flows in the Internet. Proceedings of the 9th IFIP Working Conference on Performance Modeling and Evaluation of ATM and IP Networks (IFIP ATM and IP 2001).
Liebeherr, J., Wrege D. E., & Ferrari D. (1994).  Admission Control in Networks with Bounded Delay Services.
Goebel, C., Tribowski C., & Günther O. (2009).  Adoption of Cross-Company RFID: An Empirical Analysis of Perceived Influence Factors.
Renals, S., Morgan N., Bourlard H., Cohen M., Franco H., Wooters C., et al. (1991).  Advances in Connectionist Speech Recognition.
Petruck, M. R. L. (2011).  Advances in Frame Semantics. Constructions and Frames. 3(1), 1-8.
Petruck, M. R. L. (2013).  Advances in Frame Semantics. 1-12.
Breslau, L. (1999).  Advances in Network Simulation.
Breslau, L. (2000).  Advances in Network Simulation. IEEE Computer. 59-67.
[Anonymous] (1991).  Advances in Neural Information Processing Systems 3. (Lippmann, R.., Moody J., & Touretzky D.., Ed.).
[Anonymous] (1992).  Advances in Neural Information Processing Systems 4. (Moody, J., Hanson S.., & Lippmann R.., Ed.).
Shastri, L. (1998).  Advances in SHRUTI: A neurally motivated model of relational knowledge representation and rapid inference using temporal synchrony..
Shastri, L. (1999).  Advances in Shruti - A Neurally Motivated Model of Relational Knowledge Representation and Rapid Inference Using Temporal Synchrony. Applied Intelligence. 11(1), 79-108.
Feldman, J. (2011).  Advancing Embodied Theories of Language.
Eddy, W. M., & Allman M. (2000).  Advantages of Parallel Processing and the Effects of Communications Time.
Miller, B., Kantchelian A., Afroz S., Bachwani R., Dauber E., Huang L., et al. (2014).  Adversarial Active Learning. Proceedings of the 2014 Workshop on Artificial Intelligent and Security Workshop (AISec '14). 3–14.
Donahue, J., Krahenbuhl P., & Darrell T. (2016).  Adversarial Feature Learning. CoRR. abs/1605.09782,
Maire, M., Narihira T., & Yu S. X. (2016).  Affinity CNN: Learning Pixel-Centric Pairwise Relations for Figure/Ground Embedding. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.
Feldman, J., & Narayanan S. (2014).  Affordances, Actionability, and Simulation.
Feigenbaum, J., Nisan N., Ramachandran V., Sami R., & Shenker S. (2002).  Agents' Privacy In Distributed Algorithmic Mechanisms (Position Paper).
Thaler, D., & Handley M. (2000).  On the Aggregatability of Multicast Forwarding State. Proceedings of Infocom 2000.
Mahajan, R., Bellovin S. M., Floyd S., Ioannidis J., Paxson V., & Shenker S. (2002).  Aggregate Congestion Control. ACM SIGCOMM Computer Communication Review. 32(1), 69.
Rosenfeld, A.., Feldman J., Kanal L.. N., & Winston P.. H. (1977).  AI and Pattern Recognition.
Metere, A. (2019).  AI will never conquer humanity. It’s too rational. Cosmos Magazine.

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