Publications

Found 115 results
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F
Fosler-Lussier, E., & Williams G. (1999).  Not Just What, But Also When: Guided Automatic Pronunciation Modeling for Broadcast News. Proceedings of the DARPA Broadcast News Transcription and Understanding Workshop.
Floyd, S., & Henderson T. (1999).  The NewReno Modification to TCP's Fast Recovery Algorithm.
Floyd, S. (1996).  Ns Simulator Tests for Random Early Detection (RED) Gateways.
Fisher, J., Darrell T., Galup L., How J., Krause A., & Soatto S. (2015).  Nonparametric Representations for Integrated Inference, Control, and Sensing.
Fillmore, C. J. (1977).  The need for a frame semantics in linguistics. 12, 5-29.
Ferrari, D., Banerjea A., & Zhang H. (1992).  Network Support For Multimedia: A Discussion of the Tenet Approach.
Feldman, J. (1989).  Neural Representations of Conceptual Knowledge.
Feldman, J., Trott S., & Khayrallah H. (2015).  Natural Language For Human Robot Interaction.
Feldman, J. (1989).  Neural Representations of Conceptual Knowledge.
Feldman, J. (1990).  Neural Networks, Artificial Intelligence and Computational Reality. 14, 145-148.
Feldman, J., Dodge E., & Bryant J. (2009).  A Neural Theory of Language and Embodied Construction Grammar. 111-138.
Feldman, J. (2013).  The Neural Binding Problem(s). Cognitive Neurodynamics. 7(1), 1-11.
Feldman, J., & Shastri L. (1986).  Neural Nets, Routines and Semantic Networks.
Fabrikant, A., Luthra A., Maneva E., Papadimitriou C. H., & Shenker S. J. (2003).  On a Network Creation Game. Proceedings of the 22nd Annual Symposium on Principles of Distributed Computing (PODC '03). 347-351.
E
Estrin, D., Handley M., Heidemann J., McCanne S., Xu Y.., & Yu H. (2000).  Network Visualization with the Nam, VINT Network Animator.
Eskin, E., Halperin E., & Sharan R. (2006).  A Note on Optimally Phasing Long Genomic Regions Using Local Haplotype Predictions. Journal of Bioinformatics and Computational Biology. 4(3), 639-647.
N. Erichson, B., Taylor D., Wu Q., & Mahoney M. (2021).  Noise-Response Analysis of Deep Neural Networks Quantifies Robustness and Fingerprints Structural Malware. Proceedings of the 2021 SIAM International Conference on Data Mining (SDM). 100-108.
N. Erichson, B., Lim S. Hoe, Utrera F., Xu W., Cao Z., & Mahoney M. (2022).  NoisyMix: Boosting Robustness by Combining Data Augmentations, Stability Training, and Noise Injections. arXiv.
El-Yaniv, R., & Karp R. M. (1997).  Nearly Optimal Competitive Online Replacement Policies. 22,
Eisenhofer, T., Quiring E., Möller J., Riepel D., Holz T., & Rieck K. (In Press).  No more Reviewer #2: Subverting Automatic Paper-Reviewer Assignment using Adversarial Learning. Proceedings of 32nd USENIX Security Symposium.
Eddy, W. M., Ostermann S., & Allman M. (2004).  New Techniques for Making Transport Protocols Robust to Corruption-Based Loss. ACM SIGCOMM Computer Communication Review. 34(5), 75-88.
D
Duffield, N., Presti F. Lo, Paxson V., & Towsley D. (2006).  Network Loss Tomography Using Striped Unicast Probes. IEEE/ACM Transactions on Networking. 697-710.
Darken, C., & Moody J. (1991).  Note on Learning Rate Schedules for Stochastic Optimization. 3,
C
Conde, L., Bevan S.., Sitzer M.., Klopp N.., Illig T.., Thiery J.., et al. (2011).  Novel Associations for Coronary Artery Disease Derived from Genome Wide Association Studies Are Not Associated with Increased Carotid Intima-Media Thickness..... Atherosclerosis. 219(2), 684-689.
Cleve, R., & Luby M. (1990).  A Note on Self-Testing/Correcting Methods for Trigonometric Functions.

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