user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learning undirected models via query training

Paper content:
Typical amortized inference in variational autoencoders is specialized for a single probabilistic query.
Here we propose an inference network architecture that generalizes to unseen probabilistic queries.
Instead of an encoder-decoder pair, we can train a single inference network directly from data, using a cost function that is stochastic not only over samples, but also over queries.
We can use this network to perform the same inference tasks as we would in an undirected graphical model with hidden variables, without having to deal with the intractable partition function.
The results can be mapped to the learning of an actual undirected model, which is a notoriously hard problem.
Our network also marginalizes nuisance variables as required.
We show that our approach generalizes to unseen probabilistic queries on also unseen test data, providing fast and flexible inference.
Experiments show that this approach outperforms or matches PCD and AdVIL on 9 benchmark datasets.
Learning the parameters of an undirected probabilistic graphical model (PGM) with hidden variables using maximum likelihood (ML) is a notably difficult problem (Welling and Sutton, 2005; Kuleshov and Ermon, 2017; Li et al., 2019) .
When all variables are observed, the range of applicable techniques is broadened (Sutton and McCallum, 2005; Sutton and Minka, 2006; Sutton and McCallum, 2007; Bradley, 2013) , but the problem remains intractable in general.
When hidden variables are present, the intractability is twofold:
(a) integrating out the hidden variables (also a challenge in directed models) and
(b) computing the partition function.
The second problem is generally deemed to be harder (Welling and Sutton, 2005) .
After learning, the probabilistic queries are in most cases not tractable either, so one has to resort to approximations such as belief propagation or variational inference.
These approximations operate in the same way regardless of whether the model is directed, and do not need to compute the partition function.
In general, ML learning is harder than inference both in directed and undirected models, but even more so in the latter case.
Approximate inference via belief propagation (BP) or variational inference (VI) can be cast as an optimization problem.
As such, it rarely has a closed-form solution and is instead solved iteratively, which is computationally intensive.
To address this problem, one can use amortized inference.
A prime example of this are variational autoencoders (Kingma and Welling, 2013) : a learned function (typically a neural network) is combined with the reparameterization trick (Rezende et al., 2014; Titsias and Lázaro-Gredilla, 2014) to compute the posterior over the hidden variables given the visible ones.
Although a variational autoencoder (VAE) performs inference much faster than actual VI optimization, this is not without limitations: they are specialized to answer a single predefined query.
In contrast, BP and VI answer arbitrary queries, albeit usually need more computation time.
The end goal of learning the parameters of a PGM is to obtain a model that can answer arbitrary probabilistic queries.
A probabilistic query requests the distribution of a subset of the variables of the model given some (possibly soft) evidence about another subset of variables.
This allows, for instance, to train a model on full images and then perform inpainting in a test image in an arbitrary region that was not known at training time.
Since the end goal is to be able to perform arbitrary inference, in this work we suggest to learn a system that is able to answer arbitrary probabilistic queries and avoid ML learning altogether, which completely sidesteps the difficulties associated to the partition function.
This puts directed and undirected models on equal footing in terms of usability.
To this end, we first unroll inference (we will use BP, but other options are possible) over iterations into a neural network (NN) that outputs the result of an arbitrary query, and then we train said NN to increase its prediction accuracy.
At training time we randomize the queries, looking for a consistent parameterization of the NN that generalizes to new queries.
The hope for existence of such a parameterization comes from BP actually working for arbitrary queries in a graphical model with a single parameterization.
We call this approach query training (QT).
Query training is a general approach to learn to infer when the inference target is unknown at training time.
It offers the following advantages:
1) no need to estimate the partition function or its gradient (the "sleep" phase of other common algorithms);
2) produces an inference network, which can be faster and more accurate than iterative VI or BP because its weights are trained to compensate for the imperfections of approximate inference run for a small number of iterations;
3) arbitrary queries can be solved.
In contrast, a VAE is only trained to infer the posterior over the hidden variables, or some other constant query.
Why would QT-NNs generalize to new queries or scale well?
The worry is that only a small fraction of the exponential number of potential queries is seen during training.
The existence of a single inference network that works reasonably well for many different queries follows from the existence of a single PGM in which BP can approximate inference.
The discoverability of such a network from limited training data is not guaranteed.
However, there is hope for it, since the amount of training data required to adjust the model parameters should scale with the number of these, and not with the number of potential queries.
Just like training data should come from the same distribution as test data, the training queries must come from the same distribution the test queries to avoid "query overfitting".
In future work we will show how QT can be used in more complex undirected models, such as grid MRFs.
Other interesting research avenues are modifications to allow sample generation and unroll other inference mechanisms, such as VI.

assistant: text: Instead of learning the parameters of a graphical model from data, learn an inference network that can answer the same probabilistic queries.