“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

비만아 부모의 돌봄 경험: q 방법론

Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems. In this entry, we introduce ANN using familiar econometric terminology and provide an overview of ANN modeling approach and its implementation methods. † Correspondence: Chung-Ming Kuan, Institute of Economics, Academia Sinica, 128 Academia Road, Sec. 2, Taipei 115, Taiwan; ckuan@econ.sinica.edu.tw. †† I would like to express my sincere gratitude to the editor, Professor Steven Durlauf, for his patience and constructive comments on early drafts of this entry. I also thank Shih-Hsun Hsu and Yu-Lieh Huang for very helpful suggestions. The remaining errors are all mine.

Artificial neural networks

Ensemble methods are considered the state‐of‐the art solution for many machine learning challenges. Such methods improve the predictive performance of a single model by training multiple models and combining their predictions. This paper introduce the concept of ensemble learning, reviews traditional, novel and state‐of‐the‐art ensemble methods and discusses current challenges and trends in the field.

Ensemble learning: A survey

The need for appropriate ways to measure the distance or similarity between data is ubiquitous in machine learning, pattern recognition and data mining, but handcrafting such good metrics for specific problems is generally difficult. This has led to the emergence of metric learning, which aims at automatically learning a metric from data and has attracted a lot of interest in machine learning and related fields for the past ten years. This survey paper proposes a systematic review of the metric learning literature, highlighting the pros and cons of each approach. We pay particular attention to Mahalanobis distance metric learning, a well-studied and successful framework, but additionally present a wide range of methods that have recently emerged as powerful alternatives, including nonlinear metric learning, similarity learning and local metric learning. Recent trends and extensions, such as semi-supervised metric learning, metric learning for histogram data and the derivation of generalization guarantees, are also covered. Finally, this survey addresses metric learning for structured data, in particular edit distance learning, and attempts to give an overview of the remaining challenges in metric learning for the years to come.

A Survey on Metric Learning for Feature Vectors and Structured Data

A review on the attention mechanism of deep learning

This letter adopts long short-term memory (LSTM) to predict sea surface temperature (SST), and makes short-term prediction, including one day and three days, and long-term prediction, including weekly mean and monthly mean The SST prediction problem is formulated as a time series regression problem The proposed network architecture is composed of two kinds of layers: an LSTM layer and a full-connected dense layer The LSTM layer is utilized to model the time series relationship The full-connected layer is utilized to map the output of the LSTM layer to a final prediction The optimal setting of this architecture is explored by experiments and the accuracy of coastal seas of China is reported to confirm the effectiveness of the proposed method The prediction accuracy is also tested on the SST anomaly data In addition, the model’s online updated characteristics are presented

Prediction of Sea Surface Temperature Using Long Short-Term Memory

An overview on data representation learning: From traditional feature learning to recent deep learning

Stock Market Prediction Based on Generative Adversarial Network

Deep learning has recently achieved great success in many areas due to its strong capacity in data process. For instance, it has been widely used in financial areas such as stock market prediction, portfolio optimization, financial information processing and trade execution strategies. Stock market prediction is one of the most popular and valuable area in finance. In this paper, we propose a novel architecture of Generative Adversarial Network (GAN) with the Multi-Layer Perceptron (MLP) as the discriminator and the Long Short-Term Memory (LSTM) as the generator for forecasting the closing price of stocks. The generator is built by LSTM to mine the data distributions of stocks from given data in stock market and generate data in the same distributions, whereas the discriminator designed by MLP aims to discriminate the real stock data and generated data. We choose the daily data on S&P 500 Index and several stocks in a wide range of trading days and try to predict the daily closing price. Experimental results show that our novel GAN can get a promising performance in the closing price prediction on the real data compared with other models in machine learning and deep learning.

Guoqiang Zhong

Papers

A review on the attention mechanism of deep learning

Prediction of Sea Surface Temperature Using Long Short-Term Memory

An overview on data representation learning: From traditional feature learning to recent deep learning

Stock Market Prediction Based on Generative Adversarial Network

Stock Market Prediction Based on Generative Adversarial Network.