Automatically characterizing large scale program behavior T Sherwood, E Perelman, G Hamerly, B Calder ACM SIGPLAN Notices 37 (10), 45-57, 2002 | 2258 | 2002 |
Learning the k in k-means G Hamerly, C Elkan Advances in neural information processing systems 16: proceedings of the …, 2004 | 1406 | 2004 |
Alternatives to the k-means algorithm that find better clusterings G Hamerly, C Elkan Proceedings of the eleventh international conference on Information and …, 2002 | 788 | 2002 |
Simpoint 3.0: Faster and more flexible program phase analysis G Hamerly, E Perelman, J Lau, B Calder Journal of Instruction Level Parallelism 7 (4), 1-28, 2005 | 492 | 2005 |
Using simpoint for accurate and efficient simulation E Perelman, G Hamerly, M Van Biesbrouck, T Sherwood, B Calder ACM SIGMETRICS Performance Evaluation Review 31 (1), 318-319, 2003 | 456 | 2003 |
Discovering and exploiting program phases T Sherwood, E Perelman, G Hamerly, S Sair, B Calder IEEE micro 23 (6), 84-93, 2003 | 378 | 2003 |
Picking statistically valid and early simulation points E Perelman, G Hamerly, B Calder 2003 12th International Conference on Parallel Architectures and Compilation …, 2003 | 321 | 2003 |
Bayesian approaches to failure prediction for disk drives G Hamerly, C Elkan ICML 1 (2001), 202-209, 2001 | 305 | 2001 |
Making k-means even faster G Hamerly Proceedings of the 2010 SIAM international conference on data mining, 130-140, 2010 | 269 | 2010 |
Accelerating Lloyd’s Algorithm for k-Means Clustering G Hamerly, J Drake Partitional clustering algorithms, 41-78, 2015 | 168 | 2015 |
PG-means: learning the number of clusters in data Y Feng, G Hamerly Advances in Neural Information Processing Systems 19: Proceedings of the …, 2007 | 136 | 2007 |
The strong correlation between code signatures and performance J Lau, J Sampson, E Perelman, G Hamerly, B Calder IEEE International Symposium on Performance Analysis of Systems and Software …, 2005 | 125 | 2005 |
Accelerated k-means with adaptive distance bounds J Drake, G Hamerly 5th NIPS workshop on optimization for machine learning 8, 1-4, 2012 | 122 | 2012 |
Motivation for variable length intervals and hierarchical phase behavior J Lau, E Perelman, G Hamerly, T Sherwood, B Calder IEEE International Symposium on Performance Analysis of Systems and Software …, 2005 | 103 | 2005 |
How to use simpoint to pick simulation points G Hamerly, E Perelman, B Calder ACM SIGMETRICS Performance Evaluation Review 31 (4), 25-30, 2004 | 99 | 2004 |
Using machine learning to guide architecture simulation. G Hamerly, E Perelman, J Lau, B Calder, T Sherwood, H Hirsh Journal of Machine Learning Research 7 (2), 2006 | 40 | 2006 |
Autonomous early detection of eye disease in childhood photographs MC Munson, DL Plewman, KM Baumer, R Henning, CT Zahler, ... Science advances 5 (10), eaax6363, 2019 | 37 | 2019 |
Detection of leukocoria using a soft fusion of expert classifiers under non-clinical settings P Rivas-Perea, E Baker, G Hamerly, BF Shaw BMC ophthalmology 14, 1-15, 2014 | 31 | 2014 |
Cross binary simulation points E Perelman, J Lau, H Patil, A Jaleel, G Hamerly, B Calder 2007 IEEE International Symposium on Performance Analysis of Systems …, 2007 | 23 | 2007 |
A convolutional neural network approach for classifying leukocoria R Henning, P Rivas-Perea, B Shaw, G Hamerly 2014 southwest symposium on image analysis and interpretation, 9-12, 2014 | 20 | 2014 |