Preface xiii List of abbreviations xv A guide to the book xxiii 1 Introduction 1 1.1 The vision of Ambient Intelligence 1 1.2 Application examples 3 1.3 Types of applications 6 1.4 Challenges for WSNs 7 1.4.1 Characteristic requirements 7 1.4.2 Requ
Bayesian network is an effective uncertain knowledge representation and reasoning method. Fuzzy sets can be used for expressing fuzzy events or fuzzy objectives in some special region. Combining these two theories, this paper discusses the probabili
Preface Chapter 1—Introduction 1.1 Neuroinformatics 1.1.1 Neural Memory: Neural Information Storage 1.1.2 Information-Traffic in the Neurocybernetic System 1.2 Information-Theoretic Framework of Neurocybernetics 1.3 Entropy, Thermodynamics and Infor
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 less the different explanatory factors of variation behind the data.
Existing deep convolutional neural networks (CNNs) require a fixed-size (e.g., 224224) input image. This requirement is “artificial” and may reduce the recognition accuracy for the images or sub-images of an arbitrary size/scale. In this work, we eq
This paper develops a model that addresses sentence embedding, a hot topic in current natural language processing research, using recurrent neural networks (RNN) with Long Short-Term Memory (LSTM) cells. The proposed LSTM-RNN model sequentially take
Machine learning stands as an important research area that aims at developing computational methods capable of improving their performances with previously acquired experiences. Although a large amount of machine learning techniques has been propose
High computational complexity hinders the widespread usage of Convolutional Neural Networks (CNNs), especially in mobile devices. Hardware accelerators are arguably the most promising approach for reducing both execution time and power consumption.
Graphs represent one of the most powerful and general forms of data representation, which can express diverse data, ranging from multi- dimensional entity-relation graphs, the web, social networks, commu- nication networks, and biological and chemic
Stanford 大学--Analysis of Networks课程13-19章
Handouts
Info Sheet
Lecture 01 - 09/25 Course Introduction and Structure of Graphs
Lecture 02 - 09/27 Measuring Networks, and Random Graph Model
Lecture 03 - 10/02 Link Analysis: PageRank
Lect
Recurrent neural networks (RNNs) are a powerful model for sequential data. End-to-end training methods such as Connectionist Temporal Classification make it possible to train RNNs for sequence labelling problems where the input-output alignment is u
This paper presents a speech recognition system that directly transcribes audio data with text, without requiring an intermediate phonetic representation. The system is based on a combination of the deep bidirectional LSTM recurrent neural network a
Graphs are useful data structures in complex real-life applications such as modeling physical systems, learning molecular fingerprints, controlling traffic networks, and recommending friends in social networks. However, these tasks require dealing wi
关于Noisy Networks for Exploration dqn的原始论文，适合初学者对深度强化学习Noisy Networks for Exploration dqn的认识和了解Published as a conference paper at ICLR 2018
T is assessed by the action-value function Q defined as
Q"(.a)=配
∑
rR(t, at)
(1)
where E is the expectation ove
