Kong Hao

Appointment: PhD Student

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391 to 400 of 460 Results

width_pruner_resnet50.yaml Dec 14, 2023 - Replication Data for: EMNAPE: Efficient Multi-Dimensional Neural Architecture Pruning for EdgeAI Unknown - 539 B - MD5: c1f5d09fde235ac1d4d46cacc1d8be4f
width_resnet50.tab Dec 14, 2023 - Replication Data for: EMNAPE: Efficient Multi-Dimensional Neural Architecture Pruning for EdgeAI Tabular Data - 3.3 KB - 11 Variables, 100 Observations - UNF:6:/yqxOdOsdrrzyEMQEXidNA==
width_resnet50.yaml Dec 14, 2023 - Replication Data for: EMNAPE: Efficient Multi-Dimensional Neural Architecture Pruning for EdgeAI Unknown - 296 B - MD5: fb8bf30e1a6b0c439c906e61e70f8562
wo-ines_ites.yaml Dec 14, 2023 - Replication Data for: EMNAPE: Efficient Multi-Dimensional Neural Architecture Pruning for EdgeAI Unknown - 604 B - MD5: d1aa5f61dd931e6c573e5919b1e3c719
wo-ines_wo-ites.yaml Dec 14, 2023 - Replication Data for: EMNAPE: Efficient Multi-Dimensional Neural Architecture Pruning for EdgeAI Unknown - 587 B - MD5: 1a185a80e4bc69ce37819b355b0996b6
Replication Data for: Towards Efficient Convolutional Neural Network for Embedded Hardware via Multi-Dimensional Pruning Dec 14, 2023 Kong, Hao; Liu, Weichen, 2023, "Replication Data for: Towards Efficient Convolutional Neural Network for Embedded Hardware via Multi-Dimensional Pruning", https://doi.org/10.21979/N9/BTNOJN, DR-NTU (Data), V1, UNF:6:YowCvL3Bn3yfomoWb2+WPg== [fileUNF] This dataset is created to store the related data of the following published paper: Hao Kong, Di Liu, Xiangzhong Luo, Shuo Huai, Ravi Subramaniam, Christian Makaya, Qian Lin, Weichen Liu*, “Towards Efficient Convolutional Neural Network for Embedded Hardware via Multi-Dimensional...
augment.py Dec 14, 2023 - Replication Data for: Towards Efficient Convolutional Neural Network for Embedded Hardware via Multi-Dimensional Pruning Python Source Code - 7.1 KB - MD5: d1ff82dff8c0e033758fd4c7686d1b31
benchmark.py Dec 14, 2023 - Replication Data for: Towards Efficient Convolutional Neural Network for Embedded Hardware via Multi-Dimensional Pruning Python Source Code - 7.4 KB - MD5: 43d7422a7337851ad25eb31b284e3ea9
benchmark_resnet50.csv Dec 14, 2023 - Replication Data for: Towards Efficient Convolutional Neural Network for Embedded Hardware via Multi-Dimensional Pruning Comma Separated Values - 485 B - MD5: b9c3f333d8f062b4e9544d0ef204e2fd
benchmark_resnet50.yaml Dec 14, 2023 - Replication Data for: Towards Efficient Convolutional Neural Network for Embedded Hardware via Multi-Dimensional Pruning Unknown - 288 B - MD5: 39416d452f5b4dd008c7f8d1b8a52150

width_pruner_resnet50.yaml

Dec 14, 2023 - Replication Data for: EMNAPE: Efficient Multi-Dimensional Neural Architecture Pruning for EdgeAI

Unknown - 539 B -

width_resnet50.tab

Dec 14, 2023 - Replication Data for: EMNAPE: Efficient Multi-Dimensional Neural Architecture Pruning for EdgeAI

Tabular Data - 3.3 KB - 11 Variables, 100 Observations -

width_resnet50.yaml

Dec 14, 2023 - Replication Data for: EMNAPE: Efficient Multi-Dimensional Neural Architecture Pruning for EdgeAI

Unknown - 296 B -

wo-ines_ites.yaml

Dec 14, 2023 - Replication Data for: EMNAPE: Efficient Multi-Dimensional Neural Architecture Pruning for EdgeAI

Unknown - 604 B -

wo-ines_wo-ites.yaml

Dec 14, 2023 - Replication Data for: EMNAPE: Efficient Multi-Dimensional Neural Architecture Pruning for EdgeAI

Unknown - 587 B -

Replication Data for: Towards Efficient Convolutional Neural Network for Embedded Hardware via Multi-Dimensional Pruning

Dec 14, 2023

Kong, Hao; Liu, Weichen, 2023, "Replication Data for: Towards Efficient Convolutional Neural Network for Embedded Hardware via Multi-Dimensional Pruning", https://doi.org/10.21979/N9/BTNOJN, DR-NTU (Data), V1, UNF:6:YowCvL3Bn3yfomoWb2+WPg== [fileUNF]

This dataset is created to store the related data of the following published paper: Hao Kong, Di Liu, Xiangzhong Luo, Shuo Huai, Ravi Subramaniam, Christian Makaya, Qian Lin, Weichen Liu*, “Towards Efficient Convolutional Neural Network for Embedded Hardware via Multi-Dimensional...

augment.py

Dec 14, 2023 - Replication Data for: Towards Efficient Convolutional Neural Network for Embedded Hardware via Multi-Dimensional Pruning

Python Source Code - 7.1 KB -

benchmark.py

Dec 14, 2023 - Replication Data for: Towards Efficient Convolutional Neural Network for Embedded Hardware via Multi-Dimensional Pruning

Python Source Code - 7.4 KB -

benchmark_resnet50.csv

Dec 14, 2023 - Replication Data for: Towards Efficient Convolutional Neural Network for Embedded Hardware via Multi-Dimensional Pruning

Comma Separated Values - 485 B -

benchmark_resnet50.yaml

Dec 14, 2023 - Replication Data for: Towards Efficient Convolutional Neural Network for Embedded Hardware via Multi-Dimensional Pruning

Unknown - 288 B -

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