Zobrazeno 1 - 10
of 515
pro vyhledávání: '"Çetin, A. Enis"'
In this article, we introduce the concept of controllability and observability to the M amba architecture in our Sparse-Mamba (S-Mamba) for natural language processing (NLP) applications. The structured state space model (SSM) development in recent s
Externí odkaz:
http://arxiv.org/abs/2409.00563
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:
Zhu, Xin, Cetin, Ahmet Enis
The lack of an efficient preamble detection algorithm remains a challenge for solving preamble collision problems in intelligent massive random access (RA) in practical communication scenarios. To solve this problem, we present a novel early preamble
Externí odkaz:
http://arxiv.org/abs/2403.18846
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
This paper surveys different publicly available neural network models used for detecting wildfires using regular visible-range cameras which are placed on hilltops or forest lookout towers. The neural network models are pre-trained on ImageNet-1K and
Externí odkaz:
http://arxiv.org/abs/2306.12276
In this paper, we propose a novel Hadamard Transform (HT)-based neural network layer for hybrid quantum-classical computing. It implements the regular convolutional layers in the Hadamard transform domain. The idea is based on the HT convolution theo
Externí odkaz:
http://arxiv.org/abs/2305.17510