摘要作为一个十余年来快速发展的崭新领域, 深度学**受到了越来越多研究者的关注,
它在特征提取和模型拟合上都有着相较于浅层模型显然的优势. 深度学**善于从原始输入数据中 …

The success of machine learning algorithms generally depends on data representation, and
we hypothesize that this is because different representations can entangle and hide more or …

Since the proposal of a fast learning algorithm for deep belief networks in 2006, the deep
learning techniques have drawn ever-increasing research interests because of their …

We introduce a guide to help deep learning practitioners understand and manipulate
convolutional neural network architectures. The guide clarifies the relationship between …

Data in vision domain often exhibit highly-skewed class distribution, ie, most data belong to
a few majority classes, while the minority classes only contain a scarce amount of instances …

An introduction to a broad range of topics in deep learning, covering mathematical and
conceptual background, deep learning techniques used in industry, and research …

Convolutional Neural Networks (CNNs) have recently achieved remarkably strong
performance on the practically important task of sentence classification (kim 2014 …

We seek to improve deep neural networks by generalizing the pooling operations that play a
central role in current architectures. We pursue a careful exploration of approaches to allow …

Current methods for training convolutional neural networks depend on large amounts of
labeled samples for supervised training. In this paper we present an approach for training a …

Inventors have long dreamed of creating machines that think. Ancient Greek myths tell of
intelligent objects, such as animated statues of human beings and tables that arrive full of …