Publications

Found 175 results
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Liu, Z., Miao Z., Zhan X., Wang J., Gong B., & Yu S. X. (2019).  Large-Scale Long-Tailed Recognition in An Open World. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.
Narihira, T., Maire M., & Yu S. X. (2015).  Learning Lightness from Human Judgement on Relative Reflectance.
Shi, J., Dong Y., Su H., & Yu S. X. (2016).  Learning Non-Lambertian Object Intrinsics across ShapeNet Categories.
Feng, J., Jegelka S., Yan S., & Darrell T. (2014).  Learning Scalable Discriminative Dictionary with Sample Relatedness.
Jiang, H., Yu S. X., & Martin D. R. (2011).  Linear Scale and Rotation Invariant Matching. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE. 33(7), 1339-1355.
Wang, X., Lian L., Miao Z., Liu Z., & Yu S. X. (2021).  Long-tailed Recognition by Routing Diverse Distribution-Aware Experts. Proceedings of International Conference on Learning Representations.
M
Livescu, K., Bezman A., Borges N., Yung L., etin Ö. Ç., Frankel J., et al. (2007).  Manual Transcription of Conversational Speech at the Articulatory Feature Level. 4, 953-956.
Shalf, J., Asanović K., Patterson D., Keutzer K., Mattson T.., & Yelick K. (2009).  The Manycore Revolution: Will HPC Lead or Follow?. 40-49.
Narihira, T., Borth D., Yu S. X., Ni K., & Darrell T. (2015).  Mapping Images to Sentiment Adjective Noun Pairs with Factorized Neural Nets.
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.
Yeh, C-H., Yu S. X., & Lin M. C. (2021).  Meibography Phenotyping and Classification From Unsupervised Discriminative Feature Learning. Translational Vision Science and Technology. 10(4), 
Yeh, C-H., Yu S. X., & Lin M. C. (2021).  Meibography Phenotyping and Classification From Unsupervised Discriminative Feature Learning. Translational Vision Science and Technology. 10(4), 
Wang, K., Kellman M., Sandino C. M., Zhang K., Vasanawala S. S., Tamir J. I., et al. (2021).  Memory-efficient Learning for High-Dimensional MRI Reconstruction. Proceedings of International Society for Magnetic Resonance in Medicine.
Yago, C.. M., Ruiz P. M., & Gómez-Skarmeta A. (2005).  Metodo de Eliminacion de Reenvios Basado en Contador y Adaptable a la Movilidad para Reducir la Sobrecarga de Datos al Encaminar. Proceedings of the V Jornadas de Ingenieria Telematica.
Yang, W-Y., Novembre J., Eskin E., & Halperin E. (2012).  A Model-Based Approach for Analysis of Spatial Structure in Genetic Data.
Yang, W-Y., Novembre J., Eskin E., & Halperin E. (2012).  A Model-Based Approach for Analysis of Spatial Structure in Genetic Data. Nature Genetics. 44, 725-731.
Yang, W-Y., Novembre J., Eskin E., & Halperin E. (2012).  A Model-Based Approach for Analysis of Spatial Structure in Genetic Data. Nature Genetics. 44, 725-731.
Ke, T-W., Yu S. X., & Whitney D. (2017).  Mooney Face Classification and Prediction by Learning across Tone. Proceedings of the International Conference on Image Processing 2017.
Ke, T-W., Yu S. X., & Whitney D. (2017).  Mooney Faces from Photos. Proceedings of the Vision Sciences Society Annual Meeting.
Rejaie, R., Yu H., Handley M., & Estrin D. (2000).  Multimedia Proxy Caching for Quality Adaptive Streaming Applications in the Internet. Proceedings of Infocom 2000.
Fukui, A., Park D. Huk, Yang D., Rohrbach A., Darrell T., & Rohrbach M. (2016).  Multimodal Compact Bilinear Pooling for Visual Question Answering and Visual Grounding. CoRR. abs/1606.01847,
Friedland, G., Hung H., & Yeo C. (2009).  Multi-Modal Speaker Diarization of Real-World Meeting Using Compressed-Domain Video Features. 4069-4072.
Friedland, G., Hung H., & Yeo C. (2008).  Multi-Modal Speaker Diarization of Real-World Meetings Using Compressed-Domain Video Features.
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., 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.

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