Zobrazeno 1 - 10
of 113
pro vyhledávání: '"Wong, Ken C. L."'
Autor:
Kashyap, Satyananda, D'Souza, Niharika S., Shi, Luyao, Wong, Ken C. L., Wang, Hongzhi, Syeda-Mahmood, Tanveer
Content-addressable memories such as Modern Hopfield Networks (MHN) have been studied as mathematical models of auto-association and storage/retrieval in the human declarative memory, yet their practical use for large-scale content storage faces chal
Externí odkaz:
http://arxiv.org/abs/2409.16408
Autor:
Wong, Ken C. L., Klein, Levente, da Silva, Ademir Ferreira, Wang, Hongzhi, Singh, Jitendra, Syeda-Mahmood, Tanveer
Soil organic carbon (SOC) sequestration is the transfer and storage of atmospheric carbon dioxide in soils, which plays an important role in climate change mitigation. SOC concentration can be improved by proper land use, thus it is beneficial if SOC
Externí odkaz:
http://arxiv.org/abs/2311.13016
With the introduction of Transformers, different attention-based models have been proposed for image segmentation with promising results. Although self-attention allows capturing of long-range dependencies, it suffers from a quadratic complexity in t
Externí odkaz:
http://arxiv.org/abs/2310.04466
Due to the computational complexity of 3D medical image segmentation, training with downsampled images is a common remedy for out-of-memory errors in deep learning. Nevertheless, as standard spatial convolution is sensitive to variations in image res
Externí odkaz:
http://arxiv.org/abs/2310.03872
Autor:
Wong, Ken C. L., Moradi, Mehdi
The convolutional layer and loss function are two fundamental components in deep learning. Because of the success of conventional deep learning kernels, the less versatile Gabor kernels become less popular despite the fact that they can provide abund
Externí odkaz:
http://arxiv.org/abs/2201.03644
Autor:
Wong, Ken C. L., Wang, Hongzhi, Vos, Etienne E., Zadrozny, Bianca, Watson, Campbell D., Syeda-Mahmood, Tanveer
Global warming leads to the increase in frequency and intensity of climate extremes that cause tremendous loss of lives and property. Accurate long-range climate prediction allows more time for preparation and disaster risk management for such extrem
Externí odkaz:
http://arxiv.org/abs/2112.05254
Network-based transfer learning allows the reuse of deep learning features with limited data, but the resulting models can be unnecessarily large. Although network pruning can improve inference efficiency, existing algorithms usually require fine-tun
Externí odkaz:
http://arxiv.org/abs/2108.02893
Congenital heart disease (CHD) is the most common congenital abnormality associated with birth defects in the United States. Despite training efforts and substantial advancement in ultrasound technology over the past years, CHD remains an abnormality
Externí odkaz:
http://arxiv.org/abs/2103.12245
Transfer learning with pre-trained neural networks is a common strategy for training classifiers in medical image analysis. Without proper channel selections, this often results in unnecessarily large models that hinder deployment and explainability.
Externí odkaz:
http://arxiv.org/abs/2103.12228
Autor:
Kashyap, Satyananda, Karargyris, Alexandros, Wu, Joy, Gur, Yaniv, Sharma, Arjun, Wong, Ken C. L., Moradi, Mehdi, Syeda-Mahmood, Tanveer
Publikováno v:
2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI)
Deep learning has now become the de facto approach to the recognition of anomalies in medical imaging. Their 'black box' way of classifying medical images into anomaly labels poses problems for their acceptance, particularly with clinicians. Current
Externí odkaz:
http://arxiv.org/abs/2008.00363