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.

[EX Q]: Models of code can learn distributed representations of a program's syntax and semantics to predict many non-trivial properties of a program. Recent state-of-the-art models leverage highly structured representations of programs, such as trees, graphs and paths therein (e.g. data-flow relations), which are precise and abundantly available for code. This provides a strong inductive bias towards semantically meaningful relations, yielding more generalizable representations than classical sequence-based models. Unfortunately, these models primarily rely on graph-based message passing to represent relations in code, which makes them de facto local due to the high cost of message-passing steps, quite in contrast to modern, global sequence-based models, such as the Transformer. In this work, we bridge this divide between global and structured models by introducing two new hybrid model families that are both global and incorporate structural bias: Graph Sandwiches, which wrap traditional (gated) graph message-passing layers in sequential message-passing layers; and Graph Relational Embedding Attention Transformers (GREAT for short), which bias traditional Transformers with relational information from graph edge types. By studying a popular, non-trivial program repair task, variable-misuse identification, we explore the relative merits of traditional and hybrid model families for code representation. Starting with a graph-based model that already improves upon the prior state-of-the-art for this task by 20%, we show that our proposed hybrid models improve an additional 10-15%, while training both faster and using fewer parameters.
[EX A]: Models of source code that combine global and structural features learn more powerful representations of programs.

[EX Q]: Decades of research on the neural code underlying spatial navigation have revealed a diverse set of neural response properties. The Entorhinal Cortex (EC) of the mammalian brain contains a rich set of spatial correlates, including grid cells which encode space using tessellating patterns. However, the mechanisms and functional significance of these spatial representations remain largely mysterious. As a new way to understand these neural representations, we trained recurrent neural networks (RNNs) to perform navigation tasks in 2D arenas based on velocity inputs. Surprisingly, we find that grid-like spatial response patterns emerge in trained networks, along with units that exhibit other spatial correlates, including border cells and band-like cells. All these different functional types of neurons have been observed experimentally. The order of the emergence of grid-like and border cells is also consistent with observations from developmental studies. Together, our results suggest that grid cells, border cells and others as observed in EC may be a natural solution for representing space efficiently given the predominant recurrent connections in the neural circuits.

[EX A]: To our knowledge, this is the first study to show how neural representations of space, including grid-like cells and border cells as observed in the brain, could emerge from training a recurrent neural network to perform navigation tasks.

[EX Q]: We consider the learning of algorithmic tasks by mere observation of input-output
pairs. Rather than studying this as a black-box discrete regression problem with
no assumption whatsoever on the input-output mapping, we concentrate on tasks
that are amenable to the principle of divide and conquer, and study what are its
implications in terms of learning.
This principle creates a powerful inductive bias that we leverage with neural
architectures that are defined recursively and dynamically, by learning two scale-
invariant atomic operations: how to split a given input into smaller sets, and how
to merge two partially solved tasks into a larger partial solution. Our model can be
trained in weakly supervised environments, namely by just observing input-output
pairs, and in even weaker environments, using a non-differentiable reward signal.
Moreover, thanks to the dynamic aspect of our architecture, we can incorporate
the computational complexity as a regularization term that can be optimized by
backpropagation. We demonstrate the flexibility and efficiency of the Divide-
and-Conquer Network on several combinatorial and geometric tasks: convex hull,
clustering, knapsack and euclidean TSP. Thanks to the dynamic programming
nature of our model, we show significant improvements in terms of generalization
error and computational complexity.
[EX A]:
Dynamic model that learns divide and conquer strategies by weak supervision.