A deep generative model such as a GAN learns to model a rich set of semantic and physical rules about the target distribution, but up to now, it has been obscure how such rules are encoded in the network, or how a rule could be changed. In this paper, we introduce a new problem ...
Setting: manipulation of specific rules encoded by a deep generative model (操纵由深度生成模型编码的特定规则) 方法: 提出新方法,可以定位和更改模型中的特定语义关系 展示如何在保留现有规则的同时添加或修改特定规则,根据一个经典技术—”关联记忆”推导出一个简单的更新规则,通过直接测量和操纵模型的内部结构来...
A Deep Generative Model for Molecule Optimization via One Fragment Modification论文笔记 jimmy 2 人赞同了该文章 研究动机 (1)传统的化学方法的局限性,无法实现大规模的探索和自动化探索分子的优化结构。 (2)现有方法基于原子优化的生成,没有保留分子中具有重要作用的骨架,基于片段的方法能够在现有的结构上做更...
VAEM is a deep generative model that is trained in a two stage manner such that the first stage provides a more uniform representation of the data to the second stage, thereby sidestepping the problems caused by heterogeneous data. We provide extensions of VAEM to handle par...
Molecule optimization is a critical step in drug development to improve the desired properties of drug candidates through chemical modification. We have developed a novel deep generative model, Modof, over molecular graphs for molecule optimization. Modo
Here we present DarkNPS, a deep learning-enabled approach to automatically elucidate the structures of unidentified designer drugs using only mass spectrometric data. Our method employs a deep generative model to learn a statistical probability distribution over unobserved structures, which we term the ...
A deep generative model is developed for representation and analysis of images, based on a hierarchical convolutional dictionary-learning framework. Stochastic {\em unpooling} is employed to link consecutive layers in the model, yielding top-down image generation. A Bayesian support vector machine is...
Through comprehensive empirical evaluations using the open data of the Global Energy Forecasting Competition 2014, we demonstrate that this methodology is competitive with other state-of-the-art deep learning generative models: generative adversarial networks and variational autoencoders. The models ...
Rewriting a Deep Generative Model 摘要
Differential analysis of bulk RNA-seq data often suffers from lack of good controls. Here, we present a generative model that replaces controls, trained solely on healthy tissues. The unsupervised model learns a low-dimensional representation and can ide