Problem: time series classification shallow RNNs: the first layer splits the input sequence and runs several independent RNNs.  The second layer consumes the output of the first layer to capture long dependencies. We improve inference time over stand…

Problem: time series classification shapelet-based method: two issues 1. for multi-class imbalanced classification tasks, these methods will ignore the shapelets that can distinguish minority class from other classes. 2. the shapelets are fixed after…

From: Stanford University; Jure Leskovec, citation 6w+; Problem: subsequence clustering. Challenging: discover patterns is challenging because it requires simultaneous segmentation and clustering of the time series + interpreting the cluster results…

Problem: TSC, time series classification; Traditional TSC: find global similarities or local patterns/subsequence(shapelet). We extract statistical features from VG to facilitate TSC Introduction: Global similarity: the difference between TSC and oth…

Problem Forecasting time series. Other methods' drawback: even though existing methods (exponential smoothing, auto-regression and moving average-MA, ARIMA, maximum entropy method, modified grey model) have a good performance, they are not accurate e…

Problem: the important frequency information is lack of effective modelling. ?? what is frequency information in time series? and why other models don't model this kind of frequency information? frequency learning we propose two deep learning models:…

Shallow copy and Deep copy 第一部分: 一.来自wikipidia的解释: Shallow copy One method of copying an object is the shallow copy. In that case a new object B is created, and the fields values of A are copied over to B. This is also known as a field-by-field copy,…

A processor of a plurality of processors includes a processor core and a message manager. The message manager is in communication with the processor core. The message manager to receive a message from a second processor of the plurality of processors…

ICLR 2014 International Conference on Learning Representations Apr 14 - 16, 2014, Banff, Canada Workshop Track Submitted Papers Stochastic Gradient Estimate Variance in Contrastive Divergence and Persistent Contrastive Divergence Mathias Berglund, Ta…

Research Code A rational methodology for lossy compression - REWIC is a software-based implementation of a a rational system for progressive transmission which, in absence of a priori knowledge about regions of interest, choose at any truncation time…