Found 565 results
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Chang, N. (2004).  A computational model of comprehension-based construction acquisition.
Ben-David, Y., Hasan S., Pal J., Vallentin M., Panjwani S.., Gutheim P.., et al. (2011).  Computing Security in the Developing World: A Case for Multidisciplinary Research. 39-44.
Paxson, V., Allman M., Chu J., & Sargent M. (2011).  Computing TCP's Retransmission Timer.
Krause, M., Hall M., Williams J. Jay, Paritosh P., Prip J., & Caton S. (2016).  Connecting Online Work and Online Education at Scale. Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems. 3536-3541.
Abrash, V., Cohen M., Franco H., Morgan N., & Konig Y. (1992).  Connectionist Gender Adaptation in a Hybrid Neural Network / Hidden Markov Model Speech Recognition System. Proceedings of the International Conference on Spoken Language Processing (ICSLP'92). 911-914.
Renals, S., Morgan N., Bourlard H., Cohen M., & Franco H. (1991).  Connectionist Optimisation of Tied Mixture Hidden Markov Models. IV, 167-174.
Renals, S., Morgan N., Cohen M., Bourlard H., & Franco H. (1992).  Connectionist Probability Estimation in the Decipher Speech Recognition System. Proceedings of the IEEE International Conference on Acoustics, Speech & Signal Processing (ICASSP 1992). I601-I604.
Renals, S., Morgan N., Bourlard H., Cohen M., & Franco H. (1993).  Connectionist Probability Estimators in HMM Speech Recognition. IEEE Transactions on Speech and Audio Processing.
Renals, S., Morgan N., Bourlard H., Cohen M., Franco H., Wooters C., et al. (1991).  Connectionist Speech Recognition: Status and Prospects.
Kannan, J. Kumar, Caesar M., Stoica I., & Shenker S. J. (2007).  On the Consistency of DHT-Based Routing.
Chang, N. (2009).  Constructing Grammar: A Computational Model of the Emergence of Early Constructions.
Cleve, R., & Dagum P. (1989).  A Constructive Omega(t(superscript 1.26)) Lower Bound for the Ramsey Number R (3,t).
Friedland, G., Janin A., Lei H., Choi J., & Sommer R. (2015).  Content-Based Privacy for Consumer-Produced Multimedia. 157-173.
Franco, H., Cohen M., Morgan N., Rumelhart D., & Abrash V. (1992).  Context-Dependent Connectionist Probability Estimation in a Hybrid HMM-Neural Net Speech Recognition System. Proceedings of the International Joint Conference on Neural Networks.
Cohen, M., Franco H., Morgan N., Rumelhart D., & Abrash V. (1993).  Context-Dependent Multiple Distribution Phonetic Modeling. V, 649-657.
Chang, N., & Gurevich O.. (2004).  Context-Driven Construction Learning. Proceedings of the 26th Annual Meeting of the Cognitive Science Society.
Mok, E., & Chang N. (2006).  Contextual Bootstrapping for Grammar Learning.
Wijesekera, P., Reardon J., Reyes I., Tsai L., Chen J-W., Good N., et al. (2018).  Contextualizing Privacy Decisions for Better Prediction (and Protection). Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI ’18).
Morgan, N., Wooters C., Bourlard H., & Cohen M. (1990).  Continuous Speech Recognition on the Resource Management Database Using Connectionist Probability Estimation.
Cleve, R. (1989).  Controlled Gradual Disclosure Schemes for Random Bits and Their Applications.
Tootoonchian, A., Gorbunov S., Ganjali Y., Casado M., & Sherwood R. (2012).  On Controller Performance in Software-Defined Networks.
Souiai, M., Nieuwenhuis C., Strekalovskiy E., & Cremers D. (2013).  Convex Optimization for Scene Understanding.
Zheng, X., Jiang J., Liang J., Duan H., Chen S.., Wan T., et al. (2015).  Cookies Lack Integrity: Real-World Implications. 707-721.
Guz, U., Cuendet S., Hakkani-Tür D., & Tur G. (2007).  Co-training Using Prosodic and Lexical Information for Sentence Segmentation. 2597-2600.
C. Christoudias, M., Urtasun R., Kapoor A., & Darrell T. (2009).  Co-Training with Noisy Perceptual Observations. 2844-2851.