scholarly journals Structured variational inference for simulating populations of radio galaxies

2021 ◽  
Vol 503 (3) ◽  
pp. 3351-3370
Author(s):  
David J Bastien ◽  
Anna M M Scaife ◽  
Hongming Tang ◽  
Micah Bowles ◽  
Fiona Porter
Keyword(s):  
Data Augmentation ◽  
Radio Galaxies ◽  
Variational Inference ◽  
Postage Stamp ◽  
Synthetic Populations ◽  
Latent Space ◽  
Variational Autoencoder ◽  
Decoder Architecture ◽  
Fully Connected

ABSTRACT We present a model for generating postage stamp images of synthetic Fanaroff–Riley Class I and Class II radio galaxies suitable for use in simulations of future radio surveys such as those being developed for the Square Kilometre Array. This model uses a fully connected neural network to implement structured variational inference through a variational autoencoder and decoder architecture. In order to optimize the dimensionality of the latent space for the autoencoder, we introduce the radio morphology inception score (RAMIS), a quantitative method for assessing the quality of generated images, and discuss in detail how data pre-processing choices can affect the value of this measure. We examine the 2D latent space of the VAEs and discuss how this can be used to control the generation of synthetic populations, whilst also cautioning how it may lead to biases when used for data augmentation.

Penalized Variational Autoencoder for Molecular Design

2019 ◽  
Author(s):  
Sadegh Mohammadi ◽  
Bing O'Dowd ◽  
Christian Paulitz-Erdmann ◽  
Linus Goerlitz
Keyword(s):  
Molecular Design ◽  
Biological Properties ◽  
Biological Information ◽  
Penalty Term ◽  
Cross Domain ◽  
Latent Space ◽  
Variational Autoencoder ◽  
Linear String ◽  
Weight Penalty

Variational autoencoders have emerged as one of the most common approaches for automating molecular generation. We seek to learn a cross-domain latent space capturing chemical and biological information, simultaneously. To do so, we introduce the Penalized Variational Autoencoder which directly operates on SMILES, a linear string representation of molecules, with a weight penalty term in the decoder to address the imbalance in the character distribution of SMILES strings. We find that this greatly improves upon previous variational autoencoder approaches in the quality of the latent space and the generalization ability of the latent space to new chemistry. Next, we organize the latent space according to chemical and biological properties by jointly training the Penalized Variational Autoencoder with linear units. Extensive experiments on a range of tasks, including reconstruction, validity, and transferability demonstrates that the proposed methods here substantially outperform previous SMILES and graph-based methods, as well as introduces a new way to generate molecules from a set of desired properties, without prior knowledge of a chemical structure.

scholarly journals Cada-Fvae-Gan: Adversarial Training for Few-Shot Event Detection

2020 ◽  
Author(s):  
Xiaoxiang Zhu ◽  
Mengshu Hou ◽  
Xiaoyang Zeng ◽  
Hao Zhu
Keyword(s):  
Event Detection ◽  
Semantic Features ◽  
Learning Framework ◽  
Latent Space ◽  
Variational Autoencoder ◽  
Adversarial Training ◽  
Human Effort ◽  
Latent Representations ◽  
Typical Solution

Most supervised systems of event detection (ED) task reply heavily on manual annotations and suffer from high-cost human effort when applied to new event types. To tackle this general problem, we turn our attention to few-shot learning (FSL). As a typical solution to FSL, cross-modal feature generation based frameworks achieve promising performance on images classification, which inspires us to advance this approach to ED task. In this work, we propose a model which extracts latent semantic features from event mentions, type structures and type names, then these three modalities are mapped into a shared low-dimension latent space by modality-specific aligned variational autoencoder enhanced by adversarial training. We evaluate the quality of our latent representations by training a CNN classifier to perform ED task. Experiments conducted on ACE2005 dataset show an improvement with 12.67% on F1-score when introducing adversarial training to VAE model, and our method is comparable with existing transfer learning framework for ED.

scholarly journals OptAGAN: Entropy-based Finetuning on Text VAE-GAN

2021 ◽  
Author(s):  
Paolo Tirotta ◽  
Stefano Lodi
Keyword(s):  
Language Processing ◽  
Generative Adversarial Networks ◽  
Word Generation ◽  
Adversarial Networks ◽  
Current State ◽  
Latent Space ◽  
Variational Autoencoder ◽  
Maximum Likelihood Methods ◽  
High Level

Transfer learning through large pre-trained models has changed the landscape of current applications in natural language processing (NLP). Recently Optimus, a variational autoencoder (VAE) which combines two pre-trained models, BERT and GPT-2, has been released, and its combination with generative adversarial networks (GANs) has been shown to produce novel, yet very human-looking text. The Optimus and GANs combination avoids the troublesome application of GANs to the discrete domain of text, and prevents the exposure bias of standard maximum likelihood methods. We combine the training of GANs in the latent space, with the finetuning of the decoder of Optimus for single word generation. This approach lets us model both the high-level features of the sentences, and the low-level word-by-word generation. We finetune using reinforcement learning (RL) by exploiting the structure of GPT-2 and by adding entropy-based intrinsically motivated rewards to balance between quality and diversity. We benchmark the results of the VAE-GAN model, and show the improvements brought by our RL finetuning on three widely used datasets for text generation, with results that greatly surpass the current state-of-the-art for the quality of the generated texts.

Penalized Variational Autoencoder for Molecular Design

2019 ◽  
Author(s):  
Sadegh Mohammadi ◽  
Bing O'Dowd ◽  
Christian Paulitz-Erdmann ◽  
Linus Goerlitz
Keyword(s):  
Molecular Design ◽  
Biological Properties ◽  
Biological Information ◽  
Penalty Term ◽  
Cross Domain ◽  
Latent Space ◽  
Variational Autoencoder ◽  
Linear String ◽  
Weight Penalty

Variational autoencoders have emerged as one of the most common approaches for automating molecular generation. We seek to learn a cross-domain latent space capturing chemical and biological information, simultaneously. To do so, we introduce the Penalized Variational Autoencoder which directly operates on SMILES, a linear string representation of molecules, with a weight penalty term in the decoder to address the imbalance in the character distribution of SMILES strings. We find that this greatly improves upon previous variational autoencoder approaches in the quality of the latent space and the generalization ability of the latent space to new chemistry. Next, we organize the latent space according to chemical and biological properties by jointly training the Penalized Variational Autoencoder with linear units. Extensive experiments on a range of tasks, including reconstruction, validity, and transferability demonstrates that the proposed methods here substantially outperform previous SMILES and graph-based methods, as well as introduces a new way to generate molecules from a set of desired properties, without prior knowledge of a chemical structure.

Penalized Variational Autoencoder for Molecular Design

2019 ◽  
Author(s):  
Sadegh Mohammadi ◽  
Bing O'Dowd ◽  
Christian Paulitz-Erdmann ◽  
Linus Goerlitz
Keyword(s):  
Molecular Design ◽  
Biological Properties ◽  
Biological Information ◽  
Penalty Term ◽  
Cross Domain ◽  
Latent Space ◽  
Variational Autoencoder ◽  
Linear String ◽  
Weight Penalty

Variational autoencoders have emerged as one of the most common approaches for automating molecular generation. We seek to learn a cross-domain latent space capturing chemical and biological information, simultaneously. To do so, we introduce the Penalized Variational Autoencoder which directly operates on SMILES, a linear string representation of molecules, with a weight penalty term in the decoder to address the imbalance in the character distribution of SMILES strings. We find that this greatly improves upon previous variational autoencoder approaches in the quality of the latent space and the generalization ability of the latent space to new chemistry. Next, we organize the latent space according to chemical and biological properties by jointly training the Penalized Variational Autoencoder with linear units. Extensive experiments on a range of tasks, including reconstruction, validity, and transferability demonstrates that the proposed methods here substantially outperform previous SMILES and graph-based methods, as well as introduces a new way to generate molecules from a set of desired properties, without prior knowledge of a chemical structure.

Data Augmentation via Latent Space Interpolation for Image Classification

2018 ◽  
Author(s):  
Xiaofeng Liu ◽  
Yang Zou ◽  
Lingsheng Kong ◽  
Zhihui Diao ◽  
Junliang Yan ◽  
...  
Keyword(s):  
Image Classification ◽  
Data Augmentation ◽  
Latent Space

scholarly journals Predicting drug polypharmacology from cell morphology readouts using variational autoencoder latent space arithmetic

2021 ◽  
Author(s):  
Yuen Ler Chow ◽  
Shantanu Singh ◽  
Anne E Carpenter ◽  
Gregory P. Way
Keyword(s):  
Gene Expression ◽  
Cell Morphology ◽  
Learning Algorithm ◽  
Simulated Data ◽  
Biomedical Data ◽  
Data Types ◽  
Generative Capacity ◽  
Latent Space ◽  
Variational Autoencoder ◽  
Target Effects

A variational autoencoder (VAE) is a machine learning algorithm, useful for generating a compressed and interpretable latent space. These representations have been generated from various biomedical data types and can be used to produce realistic-looking simulated data. However, standard vanilla VAEs suffer from entangled and uninformative latent spaces, which can be mitigated using other types of VAEs such as β-VAE and MMD-VAE. In this project, we evaluated the ability of VAEs to learn cell morphology characteristics derived from cell images. We trained and evaluated these three VAE variants-Vanilla VAE, β-VAE, and MMD-VAE-on cell morphology readouts and explored the generative capacity of each model to predict compound polypharmacology (the interactions of a drug with more than one target) using an approach called latent space arithmetic (LSA). To test the generalizability of the strategy, we also trained these VAEs using gene expression data of the same compound perturbations and found that gene expression provides complementary information. We found that the β-VAE and MMD-VAE disentangle morphology signals and reveal a more interpretable latent space. We reliably simulated morphology and gene expression readouts from certain compounds thereby predicting cell states perturbed with compounds of known polypharmacology. Inferring cell state for specific drug mechanisms could aid researchers in developing and identifying targeted therapeutics and categorizing off-target effects in the future.

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