We provide a general framework for studying recurrent neural networks (RNNs) trained by injecting noise into hidden states. Specifically, we consider RNNs that can be viewed as discretizations of stochastic differential equations driven by input data. This framework allows us to study the implicit ...
A stochastic alternative would be to halt or continue according to binary samples drawn from the halting distribution—a technique that has recently been applied to scene understanding with recurrent networks [7]. However the mean-field approach has the advantage of using a smooth function of the ...
Fig. 1 illustrates the basic recurrent neural network prediction architecture used in this paper. An input vector sequence x = (x1, . . . , xT ) is passed through weighted connections to a stack of N recurrently connected hidden layers to compute first the hidden vector sequences h n = (...
We show how this technique is not exclusive to recurrent neural networks and can be applied more widely to train Bayesian neural networks. We also empirically demonstrate how Bayesian RNNs are superior to traditional RNNs on a language modelling benchmark and an image captioning task, as well ...
PredRNN: Recurrent Neural Networks for Predictive Learning using Spatiotemporal LSTMs,程序员大本营,技术文章内容聚合第一站。
近期由于研究方向的原因,第一次接触到Dilated RNN,于是找到这篇发表在NIPS2017的paper,精读了一下,本文主要是受到Dilated CNN的启发,但是Dialted CNN提取出的依赖长度受限于它的卷积核大小,而理论上说,RNN…
文章的题目是Multiresolution Recurrent Neural Networks: An Application to Dialogue Response Generation,作者是来自蒙特利尔大学的博士生Iulian Vlad Serban。 本文最大的贡献在于提出了一种多尺度循环神经网络(Multiresolution RNN,MrRNN),这里的多尺度是指描述文本序列的方式有多种尺度,不仅仅是传统的用一个又一个...
论文原文:Generating Sequences With Recurrent Neural Networks 作者: Alex Graves Department of Computer Science University of Toronto graves@cs.toronto.edu Abstract This paper shows how Long Short-term Memory recurrent neural net- works can be used to generate complex sequences with long-range struc-...
The neural network in the above example comprises an input layer composed of three input nodes, two hidden layers based on four nodes each, and an output layer consisting of two nodes. Structure of Feedforward Neural Networks In a feedforward network, signals can only move in one direction. ...
Recurrent Neural Networks (RNN) were proposed as an improvement over regular feed-forward neural networks to account for cyclic connections over time. Each successive time-step in the machining states is stored in the internal state of the network to provide a temporal memory. However, conventional...