instruction:
In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
question:
This paper presents a Mutual Information Neural Estimator (MINE) that is linearly scalable in dimensionality as well as in sample size. MINE is  back-propable and we prove that it is strongly consistent. We illustrate a handful of applications in which MINE is succesfully applied  to enhance the property of generative models in both unsupervised and supervised settings. We apply our framework to estimate the information bottleneck, and apply it in tasks related to supervised classification problems. Our results  demonstrate substantial added flexibility and improvement in these settings.

answer:
A scalable in sample size and dimensions mutual information estimator.


question:
Oversubscription planning (OSP) is the problem of finding plans that maximize the utility value of their end state while staying within a specified cost bound. Recently, it has been shown that OSP problems can be reformulated as classical planning problems with multiple cost functions but no utilities.   Here we take advantage of this reformulation to show that OSP problems can be solved optimally using the A* search algorithm, in contrast to previous approaches that have used variations on branch-and-bound search. This allows many powerful techniques developed for classical planning to be applied to OSP problems. We also introduce novel bound-sensitive heuristics, which are able to reason about the primary cost of a solution while taking into account secondary cost functions and bounds, to provide superior guidance compared to heuristics that do not take these bounds into account. We implement two such bound-sensitive variants of existing classical planning heuristics, and show experimentally that the resulting search is significantly more informed than comparable heuristics that do not consider bounds.
answer:
We show that oversubscription planning tasks can be solved using A* and introduce novel bound-sensitive heuristics for oversubscription planning tasks.


question:
The unconditional generation of high fidelity images is a longstanding benchmark
 for testing the performance of image decoders. Autoregressive image models
 have been able to generate small images unconditionally, but the extension of
 these methods to large images where fidelity can be more readily assessed has
 remained an open problem. Among the major challenges are the capacity to encode
 the vast previous context and the sheer difficulty of learning a distribution that
 preserves both global semantic coherence and exactness of detail. To address the
 former challenge, we propose the Subscale Pixel Network (SPN), a conditional
 decoder architecture that generates an image as a sequence of image slices of equal
 size. The SPN compactly captures image-wide spatial dependencies and requires a
 fraction of the memory and the computation. To address the latter challenge, we
 propose to use multidimensional upscaling to grow an image in both size and depth
 via intermediate stages corresponding to distinct SPNs. We evaluate SPNs on the
 unconditional generation of CelebAHQ of size 256 and of ImageNet from size 32
 to 128. We achieve state-of-the-art likelihood results in multiple settings, set up
 new benchmark results in previously unexplored settings and are able to generate
 very high fidelity large scale samples on the basis of both datasets.
answer:

We show that autoregressive models can generate high fidelity images.