这就是混合密度网络(Mixture Density Network, MDN)的思想。 混合密度网络(Mixture Density Network) 混合密度网络(Mixture Density Networks,MDNs)是由Christopher Bishop在上世纪90年代提出的,通过这个方法神经网络的预测输出不再是单一值,而是一堆概率分布。这是一个非常Powerful的思路,并已经广泛应用于机器学习的各个...
Mixture Density Network (MDN) Structure 1. Combining Neural Networks and Mixture Models: An MDN uses a neural network to determine the parameters of a mixture density model. The neural network takes the input vector and outputs parameters for the mixture model, which then represents the conditional...
In this work, the training of a Mixture Density Network (MDN) type of Neural Network (NN) is presented. This network is used to forecast the power generated by wind and photovoltaic farms in Uruguay in a one week time frame. With the MDN model, not only the expected value of the ...
例如,我们可以使用神经网络来表示门控函数和专家模型。这将产生一个被称为混合密度网络(mixture density network)的结果。 什么是混合密度网络?MDN的核心思想是使用一个神经网络来预测输出的条件概率分布的参数,而不仅仅是单个确定性的输出值。 假设我们正在构建一个情感分析模型,目标是分析一段文本的情感,例如是积极的...
A generic Mixture Density Networks (MDN) implementation for distribution and uncertainty estimation by using Keras (TensorFlow) deep-neural-networks deep-learning neural-network master-thesis tensorflow mdn keras tensorflow-experiments mixture-density-networks uncertainty-estimation mixture-density-model mixture...
The goal of Siri's TTS system is to train a unified model based on deep learning that can automatically and accurately predict both target and concatenation costs for the units in the database. Thus, instead of HMMs, the approach uses a deep mixture density network (MDN)[7][8]to predict...
本文设计的系统如图1所示,其使用MDN网络来预测GMM分布。图中prosody extractor是从音素对应的mel-spec来抽取prosody embedding,主要在训练阶段使用。在推理阶段则使用prosody prediector来预测GMM,并获取prosody embedding。 3 实验 本文先对比使用global和phone级别合成的语音效果,table1展示PL的语音更加接近原始音频。图2...
The hierarchical mixture density is topped upon the CNN output layer, and the whole network is trained end-to-end with the differentiable density functions. See Fig. 2. The distribution of the proposed method HMDN captures diverse joint locations in a compact manner, compared to the network ...
The hierarchical mixture density is topped upon the CNN out- put layer, and the whole network is trained end-to-end with the differentiable density functions. See Fig. 2. The distribution of the proposed method HMDN captures diverse joint locations in a compact manner, compared to the network...
MDN. The IOR-ROI LSTM plays a critical role in modeling the IOR dynamics and attention shift behavior. Instead of predicting a unique ROI in a deterministic manner for each step, the MDN is used to generate the distribution of ROIs as GMs. The next fixation is sampled from the mixture ...