Adaptive learning is an important neural network characteristic; this means that they learn how to take care of difficult tasks by learning through illustrative samples of the problem to solve. Since neural networks can learn to tell the difference among many patterns by samples and training, there is no need to elaborate an a priori model, neither to develop specific probability distribution f...

Latest results indicate that features learned via convolutional neural networks outperform previous descriptors on classification tasks by a large margin. It has been shown that these networks still work well when they are applied to datasets or recognition tasks different from those they were trained on. However, descriptors like SIFT are not only used in recognition but also for many correspo...

Convolutional Neural Networks (CNNs) have achieved great success due to the powerful feature learning ability of convolution layers. Specifically, standard traverses input images/features using a sliding window scheme extract features. However, not all windows contribute equally prediction results CNNs. In practice, convolutional operation on some (e.g., smooth that contain very similar pixels)...

Current convolutional neural networks algorithms for video object tracking spend the same amount of computation for each object and video frame. However, it is harder to track an object in some frames than others, due to the varying amount of clutter, scene complexity, amount of motion, and object’s distinctiveness against its background. We propose a depth-adaptive convolutional Siamese networ...

Convolutional kernels are basic and vital components of deep Convolutional Neural Networks (CNN). In this paper, we equip convolutional kernels with shape attributes to generate the deep Irregular Convolutional Neural Networks (ICNN). Compared to traditional CNN applying regular convolutional kernels like 3× 3, our approach trains irregular kernel shapes to better fit the geometric variations o...

Multiword expressions (MWEs) are lexical items that can be decomposed into multiple component words, but have properties that are unpredictable with respect to their component words. In this paper we propose the first deep learning models for token-level identification of MWEs. Specifically, we consider a layered feedforward network, a recurrent neural network, and convolutional neural networks...