Firas Abuzaid
Firas Abuzaid
Verified email at cs.stanford.edu - Homepage
Title
Cited by
Cited by
Year
Noscope: optimizing neural network queries over video at scale
D Kang, J Emmons, F Abuzaid, P Bailis, M Zaharia
Proceedings of the VLDB Endowment 10 (11), 1586-1597, 2017
1482017
Caffe con troll: Shallow ideas to speed up deep learning
S Hadjis, F Abuzaid, C Zhang, C Ré
Proceedings of the Fourth Workshop on Data analytics in the Cloud, 2, 2015
442015
Filter before you parse: faster analytics on raw data with sparser
S Palkar, F Abuzaid, P Bailis, M Zaharia
Proceedings of the VLDB Endowment 11 (11), 1576-1589, 2018
232018
Yggdrasil: An optimized system for training deep decision trees at scale
F Abuzaid, JK Bradley, FT Liang, A Feng, L Yang, M Zaharia, ...
Advances in Neural Information Processing Systems, 3817-3825, 2016
192016
NoScope: Optimizing Deep CNN-Based Queries over Video Streams at Scale.
D Kang, J Emmons, F Abuzaid, P Bailis, M Zaharia
PVLDB 10 (11), 1586-1597, 2017
162017
Optimizing deep cnn-based queries over video streams at scale
D Kang, J Emmons, F Abuzaid, P Bailis, M Zaharia
CoRR, abs/1703.02529, 2017
152017
Caffe con Troll: Shallow ideas to speed up deep learning
F Abuzaid, S Hadjis, C Zhang, C Ré
CoRR abs/1504.04343, 2015
92015
DIFF: a relational interface for large-scale data explanation
F Abuzaid, P Kraft, S Suri, E Gan, E Xu, A Shenoy, A Ananthanarayan, ...
Proceedings of the VLDB Endowment 12 (4), 419-432, 2018
52018
Offshore Wind Energy
F Abuzaid
PH240, Stanford University, 2010
42010
To Index or Not to Index: Optimizing Exact Maximum Inner Product Search
F Abuzaid, G Sethi, P Bailis, M Zaharia
2019 IEEE 35th International Conference on Data Engineering (ICDE), 1250-1261, 2019
32019
MacroBase: Prioritizing Attention in Fast Data
F Abuzaid, P Bailis, J Ding, E Gan, S Madden, D Narayanan, K Rong, ...
ACM Transactions on Database Systems (TODS) 43 (4), 15, 2018
22018
Optimizing CPU Performance for Convolutional Neural Networks
F Abuzaid
URL: http://cs231n. stanford. edu/reports/fabuzaid_final_report. pdf [As of …, 2015
22015
Developing Convolutional Neural Networks for Deep Learning of Ventricular Action Potentials to Predict Risk for Ventricular Arrhythmias
A Selvalingam, M Alhusseini, AJ Rogers, DE Krummen, FM Abuzaid, ...
Circulation 140 (Suppl_1), A14675-A14675, 2019
12019
MACHINE LEARNING IDENTIFIES SITES WHERE ABLATION TERMINATES PERSISTENT ATRIAL FIBRILLATION
M Alhusseini, F Abuzaid, P Clopton, A Rogers, M Rodrigo, T Baykaner, ...
Journal of the American College of Cardiology 73 (9 Supplement 1), 301, 2019
12019
Sites Where Ablation Terminated Atrial Fibrillation Identified by Machine Learning Models
M Alhusseini, F Abuzaid, M Swerdlow, G Meckler, P Clopton, A Rogers, ...
Circulation 138 (Suppl_1), A13161-A13161, 2018
12018
Abstract 14493
M Alhusseini, F Abuzaid, M Swerdlow, P Clopton, G Meckler, N Maniar, ...
Circulation 138 (Suppl_1), 2018
1*2018
Machine Learning of Remodeled Ventricular Action Potentials and Long-Term Follow-Up of Ventricular Arrhythmias
AJ Rogers, M Alhusseini, A Selvalingam, DE Krummen, F Abuzaid, ...
Circulation 140 (Suppl_1), A16231-A16231, 2019
2019
Machine Learning Reveals That Drivers for Persistent Atrial Fibrillation at Termination Sites Show Irregular Rotational Cycles and Domain Size
M Alhusseini, F Abuzaid, M Swerdlow, P Clopton, GL Meckler, NM Maniar, ...
Circulation 138 (Suppl_1), A14493-A14493, 2018
2018
Predicting Academy Award Winners
F Abuzaid, E Cheng, O Odetunde
2014
CS224W Project Final Report
A Quach, F Abuzaid, J Chen
2012
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Articles 1–20