| Evolvegcn: Evolving graph convolutional networks for dynamic graphs A Pareja, G Domeniconi, J Chen, T Ma, T Suzumura, H Kanezashi, ... Proceedings of the AAAI Conference on Artificial Intelligence 34 (04), 5363-5370, 2020 | 1042 | 2020 |
| Ordering heuristics for parallel graph coloring W Hasenplaugh, T Kaler, TB Schardl, CE Leiserson Proceedings of the 26th ACM symposium on Parallelism in algorithms and …, 2014 | 118 | 2014 |
| Scalable Graph Learning for Anti-Money Laundering: A First Look M Weber, J Chen, T Suzumura, A Pareja, T Ma, H Kanezashi, T Kaler, ... arXiv preprint arXiv:1812.00076, 2018 | 113 | 2018 |
| Accelerating training and inference of graph neural networks with fast sampling and pipelining T Kaler, N Stathas, A Ouyang, AS Iliopoulos, T Schardl, CE Leiserson, ... Proceedings of Machine Learning and Systems 4, 172-189, 2022 | 49 | 2022 |
| Executing dynamic data-graph computations deterministically using chromatic scheduling T Kaler, W Hasenplaugh, TB Schardl, CE Leiserson ACM Transactions on Parallel Computing (TOPC) 3 (1), 1-31, 2016 | 40 | 2016 |
| Executing dynamic data-graph computations deterministically using chromatic scheduling T Kaler, W Hasenplaugh, TB Schardl, CE Leiserson Proceedings of the 26th ACM symposium on Parallelism in algorithms and …, 2014 | 40 | 2014 |
| A multicore path to connectomics-on-demand A Matveev, Y Meirovitch, H Saribekyan, W Jakubiuk, T Kaler, G Odor, ... Proceedings of the 22nd ACM SIGPLAN Symposium on Principles and Practice of …, 2017 | 24 | 2017 |
| Code in the air: simplifying sensing on smartphones T Kaler, JP Lynch, T Peng, L Ravindranath, A Thiagarajan, ... Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems …, 2010 | 12 | 2010 |
| Communication-Efficient Graph Neural Networks with Probabilistic Neighborhood Expansion Analysis and Caching T Kaler, A Iliopoulos, P Murzynowski, T Schardl, CE Leiserson, J Chen Proceedings of Machine Learning and Systems 5, 2023 | 11 | 2023 |
| Evolving graph convolutional networks for dynamic graphs J Chen, A Pareja, G Domeniconi, T Ma, T Suzumura, T Kaler, TB Schardl, ... US Patent 11,537,852, 2022 | 11 | 2022 |
| PARAD: A Work-Efficient Parallel Algorithm for Reverse-Mode Automatic Differentiation T Kaler, TB Schardl, B Xie, CE Leiserson, J Chen, A Pareja, G Kollias Symposium on Algorithmic Principles of Computer Systems (APOCS), 144-158, 2021 | 11 | 2021 |
| Optimal Reissue Policies for Reducing Tail Latency T Kaler, Y He, S Elnikety Proceedings of the 29th ACM Symposium on Parallelism in Algorithms and …, 2017 | 10 | 2017 |
| Polylogarithmic fully retroactive priority queues via hierarchical checkpointing ED Demaine, T Kaler, Q Liu, A Sidford, A Yedidia Algorithms and Data Structures: 14th International Symposium, WADS 2015 …, 2015 | 10 | 2015 |
| Cache efficient bloom filters for shared memory machines T Kaler Paper. Implementierungen im Ordner../Beispiele, 2013 | 7 | 2013 |
| Cilkmem: Algorithms for analyzing the memory high-water mark of fork-join parallel programs T Kaler, W Kuszmaul, TB Schardl, D Vettorel Symposium on Algorithmic Principles of Computer Systems, 162-176, 2020 | 6 | 2020 |
| Programming technologies for engineering quality multicore software TTFS Kaler Massachusetts Institute of Technology, 2020 | 3 | 2020 |
| High-throughput image alignment for connectomics using frugal snap judgments: poster T Kaler, B Wheatman, S Wooders Proceedings of the 24th Symposium on Principles and Practice of Parallel …, 2019 | 3 | 2019 |
| High-Throughput Image Alignment for Connectomics using Frugal Snap Judgments T Kaler, B Wheatman, S Wooders 2020 IEEE High Performance Extreme Computing Conference (HPEC), 1-9, 2020 | 2 | 2020 |
| Chromatic scheduling of dynamic data-graph computations TTFS Kaler Massachusetts Institute of Technology, 2013 | 2 | 2013 |
| Spatial Data Structures-Performance Comparision T Kaler, O Moll O. Moll (Ed.), 2012 | 1 | 2012 |
Tao B. SchardlResearch Scientist in computer science, MIT CSAIL在 mit.edu 的电子邮件经过验证
