Traditional deep learning like stacked autoencoder (SAE) only captures the feature representations by minimizing the global reconstruction errors, which causes a loss of the intrinsic geometric structure embedde
Fig. 1: Overview of the structure field and crystal structure autoencoder using the NeSF. a The structure field consists of two vector fields, namely, position field fp and species field fs, which are defined in 3D space. Given a 3D point as a query, the position field is trained to ...
Figure 2. Overview of the methods reviewed and benchmark results sections of the paper. CGNN: Causal Generative Neural Networks; DAG-GNN: Directed Acyclic Graph-Graph Neural Network; FCI: Fast Causal Interface; GAE: graph autoencoder; GES: Greedy Equivalence Search; GRAN-DAG: Gradient-based neu...
这就是Autoencoder。 Introduction of Autoencoder:在Autoencoder的结构中,有Encoder 结构,将输入样本通过卷积层,线性层 映射为一个一个向量,这个向量表示输入数据的特征,这些特征是隐藏的,所以称为latent variable。 这样我们吧一个样本编码成一个特征。
Deep neural networks are good at extracting low-dimensional subspaces (latent spaces) that represent the essential features inside a high-dimensional dataset. Deep generative models represented by variational autoencoders (VAEs) can generate and infer hi
In this paper, we explore the challenge of radar signal deinterleaving in open-set environments and innovatively incorporate open-set recognition techniques to construct a variational autoencoder (LVAEGRU) based on a gated recurrent unit with a probabilistic ladder structure. The model fully integrates...
Increasingly, techniques from machine learning (ML) and data science are being used to solve problems in materials science9,10,11. In particular, generative modeling approaches based on autoencoder architectures and generative adversarial networks12have been used to generate crystal structures13,14,15,...
Adversarial Defense based on Structure-to-Signal Autoencoders Joachim Folz∗ Sebastian Palacio⋆ Joern Hees German Research Center for Artificial Intelligence (DFKI) TU Kaiserslautern first.last@dfki.de Andreas Dengel Abstract Adversarial attacks have exposed the intricacies...
使用聚类损失函数指导代表特征空间的 points 分布; 采用under-complete autoencoder 维护数据的局部结构; 联合聚类损失 和 AE 损失 来训练。IDEC 既可以很好的实现聚类任务,还可以学到能保持局部结构的表示(Representation)。2 Related Work2.1 Deep Clustering
In this process, 3D structural models are usually built to satisfy the functional constraints derived from design objectives, and accurate energy models are needed to guide the movements of the atoms in the simulated system [1]. With the advent of deep learning (DL) algorithms, new approaches ...