Publications

Found 273 results
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I
Galley, M., & McKeown K. R. (2003).  Improving Word Sense Disambiguation in Lexical Chaining. Proceedings of the 18th International Joint Conference on Artificial Intelligence (IJCAI 03). 1486-1488.
Pasaniuc, B., Kennedy J.., & Mandoiu I.. I. (2009).  Imputation-Based Local Ancestry Inference in Admixed Populations. 221-233.
Strayer, T., Allman M., Armitage G., Bellovin S. M., Jin S., & Moore A. W. (2008).  IMRG Workshop on Application Classification and Identification Report. 38(3), 87-90.
P. Godfrey, B., Schapira M., Zohar A.., & Shenker S. J. (2010).  Incentive Compatibility and Dynamics of Congestion Control. ACM SIGMETRICS Performance Evaluation Review. 36(1), 95-106.
Feigenbaum, J., Ramachandran V., & Schapira M. (2006).  Incentive-Compatible Interdomain Routing. Proceedings of the 7th ACM Conference on Electronic Commerce (EC'06). 130-139.
Feigenbaum, J., & Shenker S. J. (2002).  Incentives and Internet Computation. 33(4), 37-54.
Pe'er, I., Pupko T., Shamir R., & Sharan R. (2004).  Incomplete Directed Perfect Phylogeny. SIAM Journal on Computing. 33(3), 590-607.
Bernini, C.., Codenotti B., Leoncini M., & Resta G. (1991).  Incomplete Factorizations for Certain Toeplitz Matrices.
Fosler-Lussier, E., Greenberg S., & Morgan N. (1999).  Incorporating Contextual Phonetics Into Automatic Speech Recognition. Proceedings of the International Congress of Phonetic Sciences. 1, 611-614.
Wu, S-L. (1998).  Incorporating Information From Syllable-Length Time Scales into Automatic Speech Recognition.
Wu, S-L. (1998).  Incorporating Information from Syllable-length Time Scales into Automatic Speech Recognition.
Wu, S-L., Kingsbury B., Morgan N., & Greenberg S. (1998).  Incorporating Information from Syllable-length Time Scales into Automatic Speech Recognition. Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 1998). 721-724.
Zhu, Q., Stolcke A., Chen B. Y., & Morgan N. (2004).  Incorporating Tandem/HATs MLP Features into SRI's Conversational Speech Recognition System. Proceedings of the EARS RT-04F Workshop.
Davidovich, O.., Kimmel G., Halperin E., & Shamir R. (2009).  Increasing the Power of Association Studies by Imputation-Based Sparse Tag SNP Selection. Communications in Information and Systems. 9(3), 269-282.
Mańdziuk, J., & Shastri L. (1998).  Incremental Class Learning Approach and Its Application to Handwritten Digit Recognition.
He, X., Papadopoulos C., & Radoslavov P. (2006).  Incremental Deployment Strategies for Router-Assisted Reliable Multicast. IEEE/ACM Transactions on Networking. 14(4), 779-792.
Foraker, S., Regier T., Khetarpal N., Perfors A., & Tenenbaum J. (2009).  Indirect Evidence and the Poverty of the Stimulus: The Case of Anaphoric One. Cognitive Science. 33(2), 287-300.
Webster, M. A., & Kay P. (2006).  Individual and Population Differences in Focal Colors. 29-54.
Koch, T. (1992).  Inductive Learning of Compact Rule Sets by Using Effcient Hypotheses Reduction.
Jain, P., Kulis B., & Dhillon I. (2010).  Inductive Regularized Learning of Kernel Functions. 946-954.
Schmid, U., & Kitzelmann E. (2011).  Inductive Rule Learning on the Knowledge Level. Cognitive Systems Research. 12(3-4), 237-248.
Ma, L.., Montague G.., Ye J.., Yao Z.., Gholami A.., Keutzer K.., et al. (2020).  Inefficiency of K-FAC for Large Batch Size Training. Proceedings of the AAAI-20 Conference.
Shastri, L. (1999).  Infants learning algebraic rules. Science. 285(5434), 
Zaitlen, N., Kang H. Min, Feolo M. L., Sherry S. T., Halperin E., & Eskin E. (2005).  Inference and Analysis of Haplotypes from Combined Genotyping Studies Deposited in dbSNP. Genome Research. 15(11), 1594-1600.
Sankararaman, S., Kimmel G., Halperin E., & Jordan M. I. (2008).  On the Inference of Ancestries in Admixed Populations. 668-675.

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