Found 245 results
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Gildea, D., & Hofmann T. (1999).  Topic-Based Language Models Using EM. Proceedings of the 6th European Conference on Speech Communication and Technology (Eurospeech '99).
Gildea, D., & Jurafsky D. (2000).  Automatic Labeling of Semantic Roles. The 38th Annual Meeting of the Association for Computational Linguistics (ACL-2000). 512-520.
Gildea, D. (2001).  Corpus Variation and Parser Performance. Proceedings of the 2001 Conference on Empirical Methods in Natural Language Processing (EMNLP 2001).
Gildea, D., & Jurafsky D. (2002).  Automatic Labeling of Semantic Roles. Computational Linguistics. 28(3), 245-288.
Gilge, M., & Gusella R. (1991).  Motion Video Coding for Packet-Switching Networks -- An Integrated Approach.
Gilge, M. (1991).  Distortion Accumulation in Image Transform Coding/Decoding Cascades.
Gillick, D., Riedhammer K., Favre B., & Hakkani-Tür D. (2009).  A Global Optimization Framework for Meeting Summarization. 4769-4772.
Gillick, D., Favre B., & Hakkani-Tür D. (2008).  The ICSI Summarization System at TAC 2008.
Gillick, D., & Favre B. (2009).  A Scalable Global Model for Summarization. 10-18.
Gillick, D., Hakkani-Tür D., & Levit M. (2008).  Unsupervised Learning of Edit Parameters for Matching Name Variants. 467-470.
Gillick, D., Wegmann S., & Gillick L. (2012).  Discriminative Training for Speech Recognition is Compensating for Statistical Dependence on the HMM Framework. 4745-4748.
Gillick, D., Stafford S., & Peskin B. (2005).  Speaker Detection Without Models. Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2005). 757-760.
Gillick, D., Gillick L., & Wegmann S. (2011).  Don't Multiply Lightly: Quantifying Problems with the Acoustic Model Assumptions in Speech Recognition.
Gillick, D. (2010).  Can Conversational Word Usage Be Used to Predict Speaker Demographics?.
Girshick, R., Iandola F., Darrell T., & Malik J. (2015).  Deformable Part Models are Convolutional Neural Networks. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 437-446.
Girshick, R., Donahue J., Darrell T., & Malik J. (2014).  Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation.
Girshick, R., Song H. Oh, & Darrell T. (2013).  Discriminatively Activated Sparselets.
Girshick, R., Donahue J., Darrell T., & Malik J. (2016).  Region-Based Convolutional Networks for Accurate Object Detection and Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence. 38(1), 142-158.
Gittens, A., Devarakonda A., Racah E., Ringenburg M., Gerhardt L., Kottalam J., et al. (2016).  Matrix Factorization at Scale: a Comparison of Scientific Data Analytics in Spark and C+MPI Using Three Case Studies.
Gittens, A.., Rothauge K.., Mahoney M.. W., Wang S.., Gerhardt L.., Prabhat, et al. (2018).  Alchemist: An Apache Spark <=> MPI Interface. Concurrency and Computation: Practice and Experience (Special Issue of the Cray User Group, CUG 2018), e5026.
Gittens, A., Kottalam J., Yang J., Ringenburg M. F., Chhugani J., Racah E., et al. (2016).  A multi-platform evaluation of the randomized CX low-rank matrix factorization in Spark. Proceedings of the 5th International Workshop on Parallel and Distributed Computing for Large Scale Machine Learning and Big Data Analytics.
Gittens, A.., Rothauge K.., Wang S.., Mahoney M.. W., Gerhardt L.., Prabhat, et al. (2018).  Accelerating Large-Scale Data Analysis by Offloading to High-Performance Computing Libraries using Alchemist. Proceedings of the 24th Annual SIGKDD. 293-301.
Gleich, D., & Mahoney M. W. (2015).  Using Local Spectral Methods to Robustify Graph-Based Learning Algorithms. Proceedings of the 21st Annual SIGKDD.
Gleich, D., & Mahoney M. W. (2016).  Mining Large graphs. Handbook of Big Data. 191-220.
Gleich, D., & Mahoney M. W. (2014).  Anti-Differentiating Approximation Algorithms: A Case Study with Min-Cuts, Spectral, and Flow.