Our method uses a multilayered Long Short-Term Memory (LSTM) to map the input sequence to a vector of a fixed dimensionality, and then another deep LSTM to decode the target sequence from the vector.
#5 best model for Traffic Prediction on PeMS-M
The ever-increasing size of modern data sets combined with the difficulty of obtaining label information has made semi-supervised learning one of the problems of significant practical importance in modern data analysis.
We introduce a method to learn a hierarchy of successively more abstract representations of complex data based on optimizing an information-theoretic objective.
It is well-known that neural networks are computationally hard to train.
Predicting depth is an essential component in understanding the 3D geometry of a scene.
Our architecture is trained and evaluated on the standard video actions benchmarks of UCF-101 and HMDB-51, where it is competitive with the state of the art.
#4 best model for Action Recognition In Videos on VIVA Hand Gestures Dataset
We develop collaborative topic Poisson factorization (CTPF), a generative model of articles and reader preferences.
In this paper we explore how machine learning techniques can be applied to the discovery of efficient mathematical identities.