Found 4169 results
Author Title [ Type(Desc)] Year
Conference Paper
Boulis, C., & Ostendorf M. (2004).  Combining Multiple Clustering Systems. Proceedings of the 15th European Conference on Machine Learning (ECML/PKDD 2004).
Morgan, N., & Fosler-Lussier E. (1998).  Combining Multiple Estimators of Speaking Rate. Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 1998). 729-732.
Ferber, M., Rauber T., & Hunold S. (2010).  Combining Object-Oriented Design and SOA with Remote Objects over Web Services. 83-90.
Yaman, S., Hakkani-Tür D., & Tur G. (2009).  Combining Semantic and Syntactic Information Sources for 5-W Question Answering. 2707-2710.
Müller, C., & Burkhardt F.. (2007).  Combining Short-term Cepstral and Long-term Pitch Features for Automatic Recognition of Speaker Age. 2277-2280.
Kotzias, P., Razaghpanah A., Amann J., Paterson K. G., Vallina-Rodriguez N., & Caballero J. (2018).  Coming of Age: A Longitudinal Study of TLS Deployment. Proceedings of IMC 18.
Allman, M. (2018).  Comments on DNS Robustness. ACM Internet Measurement Conference.
Breslau, L., Jamin S., & Shenker S. (2000).  Comments on the Performance of Measurement-Based Admission Control Algorithms. Proceedings of the Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies (INFOCOM 2000). 3, 1233-1242.
Schilling, M., & Narayanan S. (2013).  Communicating with Executable Action Representations.
Allman, M., Martin L., Rabinovich M., & Atchinson K. (2008).  On Community-Oriented Internet Measurement. 112-121.
Gao, Y., Beijbom O., Zhang N., & Darrell T. (2016).  Compact Bilinear Pooling. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 317-326.
Jia, Y., Vinyals O., & Darrell T. (2013).  On Compact Codes for Spatially Pooled Features.
Valente, F., Magimai-Doss M., Plahl C., Ravuri S., & Wang W. (2010).  A Comparative Large Scale Study of MLP Features for Mandarin ASR. 2630-2633.
Liu, Y., Stolcke A., Shriberg E., & Harper M. P. (2004).  Comparing and Combining Generative and Posterior Probability Models: Some Advances in Sentence Boundary Detection in Speech. Proceedings of Conference on Empirical Methods in Natural Language Processing.
Meyer, B. T., Ravuri S., Schädler M. René, & Morgan N. (2011).  Comparing Different Flavors of Spectro-Temporal Features for ASR. 1269-1272.
Liu, Y., & Shriberg E. (2007).  Comparing Evaluation Metrics for Sentence Boundary Detection. 4, 185-188.
Liu, Y., Shriberg E., Stolcke A., & Harper M. P. (2005).  Comparing HMM, Maximum Entropy, and Conditional Random Fields for Disfluency Detection. Proceedings of the 9th European Conference on Speech Communication and Technology (Interspeech 2005-Eurospeech 2005). 3313-3316.
Vinyals, O., & Ravuri S. (2011).  Comparing Multilayer Perceptron to Deep Belief Network Tandem Features for Robust ASR.
Laskowski, K., & Shriberg E. (2010).  Comparing the Contributions of Context and Prosody in Text-Independent Dialog Act Recognition. 5374-5377.
Radoslavov, P., Papadopoulos C., Govindan R., & Estrin D. (2001).  A Comparison of Application-Level and Router-Assisted Hierarchical Schemes for Reliable Multicast. Proceedings of the IEEE Infocom 2001.
Ferrer, L., Scheffer N., & Shriberg E. (2010).  A Comparison of Approaches for Modeling Prosodic Features in Speaker Recognition. 4414-4417.
Wester, M., & Fosler-Lussier E. (2000).  A Comparison of Data-Derived and Knowledge-Based Modeling of Pronunciation Variation. Proceedings of the 6th International Conference on Spoken Language Processing (ICSLP 2000).
Jamin, S., Shenker S., & Danzig P. B. (1997).  Comparison of Measurement-Based Admission Control Algorithms for Controlled-Load Service. Proceedings of the Sixteenth Annual Joint Conference of the IEEE Computer and Communications Societies (INFOCOM). 3, 973-980.
Kääriäinen, M., & Langford J. (2005).  A Comparison of Tight Generalization Error Bounds. Proceedings of the 22nd International Conference on Machine Learning (ICML 2005). 409-416.
Lei, H., & Mirghafori N. (2008).  Comparisons of Recent Speaker Recognition Approaches Based on Word Conditioning.