Parker Hill
Parker Hill
Verified email at umich.edu
Title
Cited by
Cited by
Year
Input responsiveness: using canary inputs to dynamically steer approximation
MA Laurenzano, P Hill, M Samadi, S Mahlke, J Mars, L Tang
Proceedings of the 37th ACM SIGPLAN Conference on Programming Language …, 2016
662016
Deftnn: Addressing bottlenecks for dnn execution on gpus via synapse vector elimination and near-compute data fission
P Hill, A Jain, M Hill, B Zamirai, CH Hsu, MA Laurenzano, S Mahlke, ...
Proceedings of the 50th Annual IEEE/ACM International Symposium on …, 2017
462017
Concise loads and stores: The case for an asymmetric compute-memory architecture for approximation
A Jain, P Hill, SC Lin, M Khan, ME Haque, MA Laurenzano, S Mahlke, ...
2016 49th Annual IEEE/ACM International Symposium on Microarchitecture …, 2016
462016
An evaluation dataset for intent classification and out-of-scope prediction
S Larson, A Mahendran, JJ Peper, C Clarke, A Lee, P Hill, JK Kummerfeld, ...
arXiv preprint arXiv:1909.02027, 2019
422019
Outlier detection for improved data quality and diversity in dialog systems
S Larson, A Mahendran, A Lee, JK Kummerfeld, P Hill, MA Laurenzano, ...
arXiv preprint arXiv:1904.03122, 2019
82019
System and method for implementing an artificially intelligent virtual assistant using machine learning
J Mars, L Tang, M Laurenzano, J Hauswald, P Hill
US Patent 10,572,801, 2020
62020
Rethinking numerical representations for deep neural networks
P Hill, B Zamirai, S Lu, YW Chao, M Laurenzano, M Samadi, ...
arXiv preprint arXiv:1808.02513, 2018
62018
Systems and methods for intelligently curating machine learning training data and improving machine learning model performance
Y Kang, Y Zhang, JK Kummerfeld, P Hill, J Hauswald, MA Laurenzano, ...
US Patent 10,303,978, 2019
52019
Cpsa: Compute precisely store approximately
A Jain, P Hill, MA Laurenzano, ME Haque, M Khan, S Mahlke, L Tang, ...
Workshop on Approximate Computing Across the Stack, 2016
32016
Systems and methods for intelligently curating machine learning training data and improving machine learning model performance
Y Kang, Y Zhang, JK Kummerfeld, P Hill, J Hauswald, MA Laurenzano, ...
US Patent 10,679,100, 2020
12020
Statistical error bounds for data parallel applications
P Hill, M Laurenzano, B Zamirai, M Samadi, S Mahlke, J Mars, L Tang
2016 Workshop on Approximate Computing Across the Stack, 2016
12016
Systems and methods for intelligently configuring and deploying a control structure of a machine learning-based dialogue system
P Hill, J Mars, L Tang, MA Laurenzano, J Hauswald, Y Kang, Y Zhang
US Patent 10,936,936, 2021
2021
Systems and methods for automatically detecting and repairing slot errors in machine learning training data for a machine learning-based dialogue system
S Larson, A Mahendran, P Hill, JK Kummerfeld, MA Laurenzano, L Tang, ...
US Patent 10,929,761, 2021
2021
Systems and methods for constructing an artificially diverse corpus of training data samples for training a contextually-biased model for a machine learning-based dialogue system
A Lee, S Larson, C Clarke, K Leach, JK Kummerfeld, P Hill, J Hauswald, ...
US Patent App. 17/009,695, 2021
2021
Systems and methods for intelligently configuring and deploying a machine learning-based dialogue system
J Mars, L Tang, MA Laurenzano, J Hauswald, P Hill, Y Kang, Y Zhang
US Patent App. 16/944,124, 2020
2020
Systems and methods for machine learning-based multi-intent segmentation and classification
J Peper, P Hill, K Leach, S Stapleton, JK Kummerfeld, J Hauswald, ...
US Patent 10,824,818, 2020
2020
Systems and methods for constructing an artificially diverse corpus of training data samples for training a contextually-biased model for a machine learning-based dialogue system
A Lee, S Larson, C Clarke, K Leach, JK Kummerfeld, P Hill, J Hauswald, ...
US Patent 10,796,104, 2020
2020
Systems and methods for intelligently configuring and deploying a machine learning-based dialogue system
J Mars, L Tang, MA Laurenzano, J Hauswald, P Hill, Y Kang, Y Zhang
US Patent 10,769,384, 2020
2020
Systems and methods for intelligently curating machine learning training data and improving machine learning model performance
Y Kang, Y Zhang, JK Kummerfeld, P Hill, J Hauswald, MA Laurenzano, ...
US Patent App. 16/864,120, 2020
2020
Systems and methods for automatically configuring training data for training machine learning models of a machine learning-based dialogue system
S Larson, A Mahendran, A Lee, JK Kummerfeld, P Hill, MA Laurenzano, ...
US Patent App. 16/864,140, 2020
2020
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