… It is our hypothesis that deep neural networks succeed in part by learning both what and
where things are. This paper tests this hypothesis, and provides convincing evidence that CNNs …

… simple prior information: Do … information, to train deep neural networks, which prevents
learning of a given bias. In other words, we regulate a network to minimize the mutual information …

… We first identify individual units that emerge as object detectors when training a network
on a scene classification task. The network we analyze is a convolutional neural network (CNN) …

… provide more useful information. Thus, in this paper, an improved CNN named multi-scale
cascade convolutional neural network (MC-CNN) is proposed for the classification information …

… Why deep neural networks (DNNs) capable of overfitting often generalize well in practice
is a mystery [24]. To find a potential mechanism, we focus on the study of implicit biases …

… It’s no wonder that neural networks require such numerous training examples and … division
of information between tasks is a fine line to walk: we want to allow information flow between …

… the first review of deep neural network concepts in background … deep neural networks
concepts, and also deep learned features. We then discuss the adequacy of deep neural networks …

… expected to extract only useful information and discard irrelevant details. There are a lot of
methods for the implementation of pooling operation in Deep Neural Networks. In this paper, …

… Consequently, there is a pressing need for tools and techniques for network analysis and …
deep neural networks. Marabou is an SMT-based tool that can answer queries about a network…

… of a deep network and LSTM network were modified through … and hyperparameters of
convolutional neural networks, and … cells that can integrate information over longer time scales (…