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THANAPOL Panissara

Main Referenced Co-authors
BOUVRY, Pascal  (2)
LEPREVOST, Franck  (2)
LAVANGNANANDA, Kittichai  (1)
Lavangnananda, Kittichai (1)
PINEL, Frédéric  (1)
Main Referenced Keywords
batch normalisation (1); Computer Science (all) (1); data augmentation (1); Deep learning (1); deep learning (1);
Main Referenced Unit & Research Centers
ULHPC - University of Luxembourg: High Performance Computing (1)
Main Referenced Disciplines
Computer science (3)

Publications (total 3)

The most downloaded
483 downloads
THANAPOL, P., Lavangnananda, K., BOUVRY, P., PINEL, F., & LEPREVOST, F. (2020). Reducing overfitting and improving generalization in training convolutional neural network under limited sample sizes in image recognition. In 5th International Conference on Information Technology, Bangsaen 21-22 October 2020 (pp. 300-305). https://hdl.handle.net/10993/45107

The most cited

58 citations (Scopus®)

THANAPOL, P., Lavangnananda, K., BOUVRY, P., PINEL, F., & LEPREVOST, F. (2020). Reducing overfitting and improving generalization in training convolutional neural network under limited sample sizes in image recognition. In 5th International Conference on Information Technology, Bangsaen 21-22 October 2020 (pp. 300-305). https://hdl.handle.net/10993/45107

THANAPOL, P. (2024). A Model-Similarity-Based Scheduling Policy for Deep Learning Training Workload in a GPU Cluster [Doctoral thesis, Unilu - University of Luxembourg]. ORBilu-University of Luxembourg. https://orbilu.uni.lu/handle/10993/61272

THANAPOL, P., LAVANGNANANDA, K., LEPREVOST, F., SCHLEICH, J., & BOUVRY, P. (2023). Scheduling Deep Learning Training in GPU Cluster Using the Model-Similarity-Based Policy. In N. T. Nguyen & B. Hnatkowska (Eds.), Intelligent Information and Database Systems - 15th Asian Conference, ACIIDS 2023, Proceedings. Springer Science and Business Media Deutschland GmbH. doi:10.1007/978-981-99-5837-5_30
Peer reviewed

THANAPOL, P., Lavangnananda, K., BOUVRY, P., PINEL, F., & LEPREVOST, F. (2020). Reducing overfitting and improving generalization in training convolutional neural network under limited sample sizes in image recognition. In 5th International Conference on Information Technology, Bangsaen 21-22 October 2020 (pp. 300-305).
Peer reviewed

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