Zobrazeno 1 - 10
of 208
pro vyhledávání: '"Pan, Hongyi"'
Central to the Transformer architectures' effectiveness is the self-attention mechanism, a function that maps queries, keys, and values into a high-dimensional vector space. However, training the attention weights of queries, keys, and values is non-
Externí odkaz:
http://arxiv.org/abs/2405.13901
Autor:
Zhang, Zheyuan, Keles, Elif, Durak, Gorkem, Taktak, Yavuz, Susladkar, Onkar, Gorade, Vandan, Jha, Debesh, Ormeci, Asli C., Medetalibeyoglu, Alpay, Yao, Lanhong, Wang, Bin, Isler, Ilkin Sevgi, Peng, Linkai, Pan, Hongyi, Vendrami, Camila Lopes, Bourhani, Amir, Velichko, Yury, Gong, Boqing, Spampinato, Concetto, Pyrros, Ayis, Tiwari, Pallavi, Klatte, Derk C. F., Engels, Megan, Hoogenboom, Sanne, Bolan, Candice W., Agarunov, Emil, Harfouch, Nassier, Huang, Chenchan, Bruno, Marco J., Schoots, Ivo, Keswani, Rajesh N., Miller, Frank H., Gonda, Tamas, Yazici, Cemal, Tirkes, Temel, Turkbey, Baris, Wallace, Michael B., Bagci, Ulas
Automated volumetric segmentation of the pancreas on cross-sectional imaging is needed for diagnosis and follow-up of pancreatic diseases. While CT-based pancreatic segmentation is more established, MRI-based segmentation methods are understudied, la
Externí odkaz:
http://arxiv.org/abs/2405.12367
Autor:
Jha, Debesh, Tomar, Nikhil Kumar, Biswas, Koushik, Durak, Gorkem, Antalek, Matthew, Zhang, Zheyuan, Wang, Bin, Rahman, Md Mostafijur, Pan, Hongyi, Medetalibeyoglu, Alpay, Velichko, Yury, Ladner, Daniela, Borhani, Amir, Bagci, Ulas
Accurate segmentation of organs from abdominal CT scans is essential for clinical applications such as diagnosis, treatment planning, and patient monitoring. To handle challenges of heterogeneity in organ shapes, sizes, and complex anatomical relatio
Externí odkaz:
http://arxiv.org/abs/2405.06166
Autor:
Das, Abhijit, Jha, Debesh, Gorade, Vandan, Biswas, Koushik, Pan, Hongyi, Zhang, Zheyuan, Ladner, Daniela P., Velichko, Yury, Borhani, Amir, Bagci, Ulas
Computer-aided segmentation methods can assist medical personnel in improving diagnostic outcomes. While recent advancements like UNet and its variants have shown promise, they face a critical challenge: balancing accuracy with computational efficien
Externí odkaz:
http://arxiv.org/abs/2405.01503
Autor:
Zhu, Xin, Pan, Hongyi, Velichko, Yury, Murphy, Adam B., Ross, Ashley, Turkbey, Baris, Cetin, Ahmet Enis, Bagci, Ulas
Magnetic field inhomogeneity correction remains a challenging task in MRI analysis. Most established techniques are designed for brain MRI by supposing that image intensities in the identical tissue follow a uniform distribution. Such an assumption c
Externí odkaz:
http://arxiv.org/abs/2403.05024
The lack of an efficient compression model remains a challenge for the wireless transmission of gearbox data in non-contact gear fault diagnosis problems. In this paper, we present a signal-adaptive asymmetrical autoencoder with a transform domain la
Externí odkaz:
http://arxiv.org/abs/2310.02862
Autor:
Pan, Hongyi, Wang, Bin, Zhang, Zheyuan, Zhu, Xin, Jha, Debesh, Cetin, Ahmet Enis, Spampinato, Concetto, Bagci, Ulas
Domain generalization aims to train models on multiple source domains so that they can generalize well to unseen target domains. Among many domain generalization methods, Fourier-transform-based domain generalization methods have gained popularity pr
Externí odkaz:
http://arxiv.org/abs/2309.09866
Electroencephalogram (EEG) data compression is necessary for wireless recording applications to reduce the amount of data that needs to be transmitted. In this paper, an asymmetrical sparse autoencoder with a discrete cosine transform (DCT) layer is
Externí odkaz:
http://arxiv.org/abs/2309.12201
Traditional preamble detection algorithms have low accuracy in the grant-based random access scheme in massive machine-type communication (mMTC). We present a novel preamble detection algorithm based on Stein variational gradient descent (SVGD) at th
Externí odkaz:
http://arxiv.org/abs/2309.08782
Autor:
Darabi, Nastaran, Hashem, Maeesha Binte, Pan, Hongyi, Cetin, Ahmet, Gomes, Wilfred, Trivedi, Amit Ranjan
The edge processing of deep neural networks (DNNs) is becoming increasingly important due to its ability to extract valuable information directly at the data source to minimize latency and energy consumption. Frequency-domain model compression, such
Externí odkaz:
http://arxiv.org/abs/2309.01771