1,337 research outputs found
Structural Agnostic Modeling: Adversarial Learning of Causal Graphs
A new causal discovery method, Structural Agnostic Modeling (SAM), is
presented in this paper. Leveraging both conditional independencies and
distributional asymmetries in the data, SAM aims at recovering full causal
models from continuous observational data along a multivariate non-parametric
setting. The approach is based on a game between players estimating each
variable distribution conditionally to the others as a neural net, and an
adversary aimed at discriminating the overall joint conditional distribution,
and that of the original data. An original learning criterion combining
distribution estimation, sparsity and acyclicity constraints is used to enforce
the end-to-end optimization of the graph structure and parameters through
stochastic gradient descent. Besides the theoretical analysis of the approach
in the large sample limit, SAM is extensively experimentally validated on
synthetic and real data
ClusterGAN : Latent Space Clustering in Generative Adversarial Networks
Generative Adversarial networks (GANs) have obtained remarkable success in
many unsupervised learning tasks and unarguably, clustering is an important
unsupervised learning problem. While one can potentially exploit the
latent-space back-projection in GANs to cluster, we demonstrate that the
cluster structure is not retained in the GAN latent space.
In this paper, we propose ClusterGAN as a new mechanism for clustering using
GANs. By sampling latent variables from a mixture of one-hot encoded variables
and continuous latent variables, coupled with an inverse network (which
projects the data to the latent space) trained jointly with a clustering
specific loss, we are able to achieve clustering in the latent space. Our
results show a remarkable phenomenon that GANs can preserve latent space
interpolation across categories, even though the discriminator is never exposed
to such vectors. We compare our results with various clustering baselines and
demonstrate superior performance on both synthetic and real datasets.Comment: GANs, Clustering, Latent Space, Interpolation (v2 : Typos fixed, some
new experiments added, reported metrics on best validated model.
Learn to Generate Time Series Conditioned Graphs with Generative Adversarial Nets
Deep learning based approaches have been utilized to model and generate
graphs subjected to different distributions recently. However, they are
typically unsupervised learning based and unconditioned generative models or
simply conditioned on the graph-level contexts, which are not associated with
rich semantic node-level contexts. Differently, in this paper, we are
interested in a novel problem named Time Series Conditioned Graph Generation:
given an input multivariate time series, we aim to infer a target relation
graph modeling the underlying interrelationships between time series with each
node corresponding to each time series. For example, we can study the
interrelationships between genes in a gene regulatory network of a certain
disease conditioned on their gene expression data recorded as time series. To
achieve this, we propose a novel Time Series conditioned Graph
Generation-Generative Adversarial Networks (TSGG-GAN) to handle challenges of
rich node-level context structures conditioning and measuring similarities
directly between graphs and time series. Extensive experiments on synthetic and
real-word gene regulatory networks datasets demonstrate the effectiveness and
generalizability of the proposed TSGG-GAN
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