… setup for all methods and only vary the kernel function. Concretely, we compare the following:
an RBF kernel, our improper deep learning kernel (IDK), and the kernel of [4]. For the latter…

… such kernels, the RKHS contains indeed deep convolutional … In contrast to traditional kernel
methods, the RKHS norm … are tightly linked in our kernel framework. Specifically, some lower …

… Indeed, in nearly all real world data where deep learning is applied to, the input … We
show without any distributional assumption, a three-layer residual network (ResNet) can (improperly…

… class of deep recurrent neural operations and formally characterize their associated kernel
spaces… filters in CNN) via the kernels. Similar to traditional neural operations, these reference …

… kernel method for learning multi-layer neural networks, under the framework of improper …
We extend this method to deeper networks. In particular, we assume that the neural network …

… kernel functions to capture diverse data characteristics. Thus, selecting an appropriate kernel
function is crucial because an improper … , thus constructing deep Gaussian processes. In …

… superficially and did not strike deep into the kernel. Subsequently Aspergillus fivus and A.
fumigatus predominated on the exposed surface of individual rice kernel and penetrated into …

… Using the local deep kernel function with tree structure can promote the computational
efficiency. Taking the global features into account maintains the recognition accuracy. Automatic …

… This study presented a deep convolutional neural network (… : (1) several convolution kernels
with multiple sizes, and (2) … were designed for multi-size kernels, dilation rates, and input …

… kernel detection were constructed based on image processing, machine learning, and deep
… Hough circle detection to identify kernels. For the machine learning (cascade detector) and …