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.

[Q]: Model distillation aims to distill the knowledge of a complex model into a simpler one. In this paper, we consider an alternative formulation called dataset distillation: we keep the model fixed and instead attempt to distill the knowledge from a large training dataset into a small one. The idea is to synthesize a small number of data points that do not need to come from the correct data distribution, but will, when given to the learning algorithm as training data, approximate the model trained on the original data. For example, we show that it is possible to compress 60,000 MNIST training images into just 10 synthetic distilled images (one per class) and achieve close to the original performance, given a fixed network initialization. We evaluate our method in various initialization settings.   Experiments on multiple datasets, MNIST, CIFAR10, PASCAL-VOC, and CUB-200, demonstrate the ad-vantage of our approach compared to alternative methods.   Finally, we include a real-world application of dataset distillation to the continual learning setting: we show that storing distilled images as episodic memory of previous tasks can alleviate forgetting more effectively than real images.
[A]: We propose to distill a large dataset into a small set of synthetic data that can train networks close to original performance. 


[Q]: We present Spectral Inference Networks, a framework for learning eigenfunctions of linear operators by stochastic optimization. Spectral Inference Networks generalize Slow Feature Analysis to generic symmetric operators, and are closely related to Variational Monte Carlo methods from computational physics. As such, they can be a powerful tool for unsupervised representation learning from video or graph-structured data. We cast training Spectral Inference Networks as a bilevel optimization problem, which allows for online learning of multiple eigenfunctions. We show results of training Spectral Inference Networks on problems in quantum mechanics and feature learning for videos on synthetic datasets. Our results demonstrate that Spectral Inference Networks accurately recover eigenfunctions of linear operators and can discover interpretable representations from video in a fully unsupervised manner.
[A]: We show how to learn spectral decompositions of linear operators with deep learning, and use it for unsupervised learning without a generative model.


[Q]: Many tasks in natural language processing and related domains require high precision output that obeys dataset-specific constraints. This level of fine-grained control can be difficult to obtain in large-scale neural network models. In this work, we propose a structured latent-variable approach that adds discrete control states within a standard autoregressive neural paradigm. Under this formulation, we can include a range of rich, posterior constraints to enforce task-specific knowledge that is effectively trained into the neural model. This approach allows us to provide arbitrary grounding of internal model decisions, without sacrificing any representational power of neural models. Experiments consider applications of this approach for text generation and part-of-speech induction. For natural language generation, we find that this method improves over standard benchmarks, while also providing fine-grained control.
[A]:
A structured latent-variable approach that adds discrete control states within a standard autoregressive neural paradigm to provide arbitrary grounding of internal model decisions, without sacrificing any representational power of neural models.