A U-Net GAN reconstructs CMB T and E maps from Planck-like simulations with foregrounds and systematics, achieving under 1% error outside the Galactic region and demonstrating first-time correction for non-circular beams and asymmetric scans.
Adversarial Feature Matching for Text Generation
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
abstract
The Generative Adversarial Network (GAN) has achieved great success in generating realistic (real-valued) synthetic data. However, convergence issues and difficulties dealing with discrete data hinder the applicability of GAN to text. We propose a framework for generating realistic text via adversarial training. We employ a long short-term memory network as generator, and a convolutional network as discriminator. Instead of using the standard objective of GAN, we propose matching the high-dimensional latent feature distributions of real and synthetic sentences, via a kernelized discrepancy metric. This eases adversarial training by alleviating the mode-collapsing problem. Our experiments show superior performance in quantitative evaluation, and demonstrate that our model can generate realistic-looking sentences.
verdicts
UNVERDICTED 3representative citing papers
VACS is a two-level hierarchical VAE that generates diverse code-switched sentences, and augmenting monolingual data with its output reduces language model perplexity by 33.06%.
Only gated RNN language models reproduce the long-range correlation scaling of natural language among tested models, with Taylor's law exponent serving as a quality indicator.
citing papers explorer
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Deep Learning for CMB Foreground Removal and Beam Deconvolution: A U-Net GAN Approach
A U-Net GAN reconstructs CMB T and E maps from Planck-like simulations with foregrounds and systematics, achieving under 1% error outside the Galactic region and demonstrating first-time correction for non-circular beams and asymmetric scans.
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A Deep Generative Model for Code-Switched Text
VACS is a two-level hierarchical VAE that generates diverse code-switched sentences, and augmenting monolingual data with its output reduces language model perplexity by 33.06%.
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Evaluating Computational Language Models with Scaling Properties of Natural Language
Only gated RNN language models reproduce the long-range correlation scaling of natural language among tested models, with Taylor's law exponent serving as a quality indicator.