Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Stable Variational Autoencoder for Text Modelling   Variational Autoencoder (VAE) is a powerful method for learning
representations of high-dimensional data. However, VAEs can suffer from an
issue known as latent variable collapse (or KL loss vanishing), where the
posterior collapses to the prior and the model will ignore the latent codes in
generative tasks. Such an issue is particularly prevalent when employing
VAE-RNN architectures for text modelling (Bowman et al., 2016). In this paper,
we present a simple architecture called holistic regularisation VAE (HR-VAE),
which can effectively avoid latent variable collapse. Compared to existing
VAE-RNN architectures, we show that our model can achieve much more stable
training process and can generate text with significantly better quality.

28
The categories of this paper are: alg-geom
33
The categories of this paper are: plasm-ph
15
The categories of this paper are: nucl-ex
6
The categories of this paper are: astro-ph
25
The categories of this paper are: patt-sol
3
The categories of this paper are: physics
12
The categories of this paper are: nucl-th
29
The categories of this paper are: funct-an
30
The categories of this paper are: q-alg
8
The categories of this paper are: hep-ex
24
The categories of this paper are: dg-ga
13
The categories of this paper are: quant-ph
20
The categories of this paper are: acc-phys
16
The categories of this paper are: stat
21
The categories of this paper are: adap-org
23
The categories of this paper are: solv-int