IEEE transactions on pattern analysis and machine intelligence
Jul 1, 2022
Remarkable achievements have been obtained by deep neural networks in the last several years. However, the breakthrough in neural networks accuracy is always accompanied by explosive growth of computation and parameters, which leads to a severe limit...
IEEE transactions on pattern analysis and machine intelligence
Jul 1, 2022
While most classical approaches to Granger causality detection assume linear dynamics, many interactions in real-world applications, like neuroscience and genomics, are inherently nonlinear. In these cases, using linear models may lead to inconsisten...
IEEE transactions on pattern analysis and machine intelligence
Jul 1, 2022
Lung cancer is the most common cause of cancer death worldwide. A timely diagnosis of the pulmonary nodules makes it possible to detect lung cancer in the early stage, and thoracic computed tomography (CT) provides a convenient way to diagnose nodule...
IEEE transactions on pattern analysis and machine intelligence
Jul 1, 2022
We propose a new STAckable Recurrent cell (STAR) for recurrent neural networks (RNNs), which has fewer parameters than widely used LSTM [16] and GRU [10] while being more robust against vanishing or exploding gradients. Stacking recurrent units into ...
IEEE transactions on pattern analysis and machine intelligence
Jul 1, 2022
Deblurring images captured in dynamic scenes is challenging as the motion blurs are spatially varying caused by camera shakes and object movements. In this paper, we propose a spatially varying neural network to deblur dynamic scenes. The proposed mo...
IEEE transactions on pattern analysis and machine intelligence
Jul 1, 2022
Deep convolutional neural networks (DCNNs) are currently the method of choice both for generative, as well as for discriminative learning in computer vision and machine learning. The success of DCNNs can be attributed to the careful selection of thei...
IEEE transactions on pattern analysis and machine intelligence
Jun 3, 2022
A sum-product network (SPN) is a probabilistic model, based on a rooted acyclic directed graph, in which terminal nodes represent probability distributions and non-terminal nodes represent convex sums (weighted averages) and products of probability d...
IEEE transactions on pattern analysis and machine intelligence
Jun 3, 2022
In neural networks, developing regularization algorithms to settle overfitting is one of the major study areas. We propose a new approach for the regularization of neural networks by the local Rademacher complexity called LocalDrop. A new regularizat...
IEEE transactions on pattern analysis and machine intelligence
Jun 3, 2022
Domain Adaptation aims at adapting the knowledge learned from a domain (source-domain) to another (target-domain). Existing approaches typically require a portion of task-relevant target-domain data a priori. We propose an approach, zero-shot deep do...
IEEE transactions on pattern analysis and machine intelligence
Jun 3, 2022
Image segmentation is a key task in computer vision and image processing with important applications such as scene understanding, medical image analysis, robotic perception, video surveillance, augmented reality, and image compression, among others, ...