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.

Input: Consider Input: A generally intelligent learner should generalize to more complex tasks than it has previously encountered, but the two common paradigms in machine learning -- either training a separate learner per task or training a single learner for all tasks -- both have difficulty with such generalization because they do not leverage  the compositional structure of the task distribution. This paper introduces the compositional problem graph as a broadly applicable formalism to relate tasks of different complexity in terms of problems with shared subproblems. We propose the compositional generalization problem for measuring how readily old knowledge can be reused and hence built upon. As a first step for tackling compositional generalization, we introduce the compositional recursive learner, a domain-general framework for learning algorithmic procedures for composing representation transformations, producing a learner that reasons about what computation to execute by making analogies to previously seen problems. We show on a symbolic and a high-dimensional domain that our compositional approach can generalize to more complex problems than the learner has previously encountered, whereas baselines that are not explicitly compositional do not.

Output: We explore the problem of compositional generalization and propose a means for endowing neural network architectures with the ability to compose themselves to solve these problems.


Input: Consider Input: Neural networks offer high-accuracy solutions to a range of problems, but are computationally costly to run in production systems. We propose a technique called Deep Learning Approximation to take an already-trained neural network model and build a faster (and almost equally accurate) network by manipulating the network structure and coefficients without requiring re-training or access to the training data. Speedup is achieved by applying a sequential series of independent optimizations that reduce the floating-point operations (FLOPs) required to perform a forward pass. An optimal lossy approximation is chosen for each layer by weighing the relative accuracy loss and FLOP reduction. On PASCAL VOC 2007 with the YOLO network, we show an end-to-end 2x speedup in a network forward pass with a $5$\% drop in mAP that can be re-gained by finetuning, enabling this network (and others like it) to be deployed in compute-constrained systems.

Output: Decompose weights to use fewer FLOPs with SVD


Input: Consider Input: Recurrent models for sequences have been recently successful at many tasks, especially for language modeling
 and machine translation. Nevertheless, it remains challenging to extract good representations from
 these models. For instance, even though language has a clear hierarchical structure going from characters
 through words to sentences, it is not apparent in current language models.
 We propose to improve the representation in sequence models by
 augmenting current approaches with an autoencoder that is forced to compress
 the sequence through an intermediate discrete latent space. In order to propagate gradients
 though this discrete representation we introduce an improved semantic hashing technique.
 We show that this technique performs well on a newly proposed quantitative efficiency measure.
 We also analyze latent codes produced by the model showing how they correspond to
 words and phrases. Finally, we present an application of the autoencoder-augmented
 model to generating diverse translations.


Output: Autoencoders for text with a new method for using discrete latent space.