除了被视为自动编码器神经网络架构( autoencoder neural network architecture)之外,变分自动编码器(variational autoencoders)还可以在变分贝叶斯方法的数学公式( variational Bayesian methods)中进行研究,通过对应于变分分布参数( probabilistic latent space)的概率隐空间(例如,多元高斯分布)将神经编码器网络连接到其解码器...
Different types of autoencoders make adaptations to this structure to better suit different tasks and data types. In addition to selecting the appropriate type of neural network—for example, a CNN-based architecture, an RNN-based architecture likelong short-term memory, a transformer architecture o...
自动编码器(Auto-Encoder, AE)是一种无监督学习的人工神经网络,被广泛应用于维数约减、特征学习和生成...
they all have drawbacks when it comes to high-dimensional and small sample size data, such as high value of variance gradients and over-fitting.To address these issues, we proposed a dynamic variational autoencoder based deep neural network architecture, based on a mathematical foundation for unsup...
To revisit our graphical model, we can useqqto infer the possible hidden variables (ie. latent state) which was used to generate an observation. We can further construct this model into a neural network architecture where the encoder model learns a mapping from xx to zz and the decoder model...
The proposed deep stacked sparse autoencoder neural network architecture exhibits excellent results, with an overall accuracy of 98.7% for advanced gastric cancer classification and 97.3% for early gastric cancer detection using breath analysis. Moreover, the developed model produces an excellent result ...
and chromatin images using an autoencoder neural network architecture. Separate decoders are used to reconstruct the three modalities from the joint latent space. UMAP is used to visualize the joint latent representation of all cells in the tissue samples; the cells are colored by cluster membership...
The presence of a bottleneck layer is the key feature of this architecture. If all the layers in the network had the same number of neurons, the network could easily learn to memorize the input data values by passing them all along the network. ...
Therefore, this study presents a state-of-the-art DNN-centric architecture for MIMO signal detection called variational autoencoder-enhanced DNN-based detection (VAE-DNN-Det), which harnesses the power of variational autoencoders (VAEs) to efficiently capture underlying data distributions, thereby ...
To optimize the deep architecture of NCAE, we develop a three-stage pre-training mechanism that combines supervised and unsupervised feature learning. Moreover, to prevent overfitting on the implicit setting, we propose an error reweighting module and a sparsity-aware data-augmentation strategy. ...