Deep Neural Networks for High Dimension, Low …

Deep Neural Networks for High Dimension, Low Sample Size Data Bo Liu, Ying Wei, Yu Zhang, Qiang Yang Hong Kong University of Science and Technology, Hong Kong

  High, Network, Dimensions, Deep, Neural, Deep neural networks for high dimension

Training Feedforward Neural Networks Using …

Training Feedforward Neural Networks Using Genetic Algorithms David J. Montana and Lawrence Davis BBN Systems and Technologies Corp. 10 Mouiton St.

  Genetic, Algorithm, Neural, Genetic algorithms, Feedforward neural, Feedforward

Learning Feature Engineering for Classification

Given a set of features and class labels, the classiÞer ... c!T of arityr, an ordered list of features[f i,...,f i+r ! 1] and a usefulness score. ... paradigm for each combination and selects top-useful ones.k In the following section, we describe how LFE learns and

  Feature, Class

Spatio-Temporal Graph Convolutional Networks: A Deep ...

3.2 Graph CNNs for Extracting Spatial Features The trafÞc network generally organizes as a graph structure. It is natural and reasonable to formulate road networks as graphs mathematically. However, previous studies neglect spatial attributes of trafÞc networks: the connectivity and globality of the networks are overlooked, since they are split

  Network, Graph, Spatial, Convolutional, Temporal, Positas, Spatio temporal graph convolutional networks

Imaging Time-Series to Improve Classification and Imputation

iis the time stamp and Nis a con-stant factor to regularize the span of the polar coordinate sys-tem. This polar coordinate based representation is a novel way to understand time series. As time increases, correspond-ing values warp among different angular points on the span-ning circles, like water rippling. The encoding map of equa-

  Series, Time, Improves, Imaging, Imaging time series to improve

Deep Matrix Factorization Models for Recommender Systems

Deep Matrix Factorization Models for Recommender Systems Hong-Jian Xue, Xin-Yu Dai, Jianbing Zhang, Shujian Huang, Jiajun Chen National Key Laboratory for Novel Software Technology; Nanjing University, Nanjing 210023, China

L2,1-Norm Regularized Discriminative Feature Selection …

rithms, e.g., Fisher score [Duda et al., 2001] , robust regres-sion [Nie et al., 2010], sparse multi-output regression [Zhao et al., 2010] and trace ratio [Nie et al., 2008], usually select featuresaccordingto labels of the training data. Because dis-criminative informationis enclosed in labels, supervised fea-

  Feature, Selection, Norm, Discriminative, Dudas, Norm regularized discriminative feature selection, Regularized

DeepFM: A Factorization-Machine based Neural Network …

DeepFM: A Factorization-Machine based Neural Network for CTR Prediction Huifeng Guo 1, Ruiming Tang2, Yunming Yey1, Zhenguo Li2, Xiuqiang He2 1Shenzhen Graduate School, Harbin Institute of Technology, China 2Noah's Ark Research Lab, Huawei, China 1huifengguo@yeah.net, yeyunming@hit.edu.cn,2ftangruiming, li.zhenguo, hexiuqiangg@huawei.com Abstract Learning …

  Based, Network, Machine, Neural, Factorization, Deepfm, A factorization machine based neural network

Detecting Rumors from Microblogs with Recurrent Neural ...

Detecting Rumors from Microblogs with Recurrent Neural Networks Jing Ma,1 Wei Gao,2 Prasenjit Mitra,2 Sejeong Kwon,3 Bernard J. Jansen,2 Kam-Fai Wong,1 Meeyoung Cha3 1The Chinese University of Hong Kong, Hong Kong SAR 2Qatar Computing Research Institute, Hamad Bin Khalifa University, Qatar 3Graduate School of Culture Technology, Korea Advanced …

  Detecting

Time-Aware Multi-Scale RNNs for Time Series Modeling

of genres requires modeling the emotional changes in music, which are controlled by note duration. Therefore, different scales are also needed at different time steps as the notes have different durations at different times [Hu et al., 2019]. Recently, some methods have been proposed to select ap-propriate scales corresponding to each sample ...

  Green, Music

Social LSTM: Human Trajectory Prediction in Crowded Spaces

Long Short-Term Memory (LSTM) networks have been shown to successfully learn and generalize the properties of isolated sequences like handwriting [20] and speech [21]. Inspired by this, we develop a LSTM based model for our trajectory prediction problem as well. In particular, we have one LSTM for each person in a scene. This LSTM learns

  Memory, Terms, Short, Long, Stlm, Long short term memory

Using LSTM in Stock prediction and Quantitative Trading

Long-Short Term Memory (LSTM) model is an updated version of RNN. It can overcome the drawback of RNN in capturing long term influences. LSTM introduces the memory cell that enables long-term dependency between time lags. The

  Memory, Terms, Short, Long, Stlm, Long short term memory

Sequence to Sequence Learning with Neural Networks

However, th e Long Short-Term Memory (LSTM) [16] is known to learn problems with long range temporal dependencies, so an LSTM may succeed in this setting. The goal of the LSTM is to estimate the conditional probability p(y

  Memory, Terms, Learning, Short, Long, Sequence, Stlm, Sequence to sequence learning, Long short term memory

Convolutional LSTM Network: A Machine Learning Approach ...

2.2 Long Short-Term Memory for Sequence Modeling For general-purpose sequence modeling, LSTM as a special RNN structure has proven stable and powerful for modeling long-range dependencies in various previous studies [12, 11, 17, 23]. The major innovation of LSTM is its memory cell ct which essentially acts as an accumulator of the state ...

  Memory, Terms, Short, Long, Stlm, Long short term memory

Generating Sequences With Recurrent Neural Networks - …

Long Short-term Memory (LSTM) [16] is an RNN architecture designed to be better at storing and accessing information than standard RNNs. LSTM has recently given state-of-the-art results in a variety of sequence processing tasks, including speech and handwriting recognition [10, 12]. The main goal of this

  Memory, With, Terms, Short, Long, Sequence, Generating, Neural, Recurrent, Stlm, Generating sequences with recurrent neural, Long short term memory

Abstract - arXiv

2.2 Long Short-Term Memory for Sequence Modeling For general-purpose sequence modeling, LSTM as a special RNN structure has proven stable and powerful for modeling long-range dependencies in various previous studies [12, 11, 17, 23]. The major innovation of LSTM is its memory cell ct which essentially acts as an accumulator of the state ...

  Memory, Terms, Short, Long, Stlm, Long short term memory

Stock Market Prediction using CNN and LSTM

and Long-Short Term Memory. The difference in performance was attributed to the fact that CNN does not rely on historical data as is the case with time sequence based models. On the other hand, Sutskever et al. [13] argues for the use of …

  Memory, Using, Terms, Market, Short, Long, Prediction, Stocks, Stlm, Stock market prediction using cnn and lstm, Long short term memory