Below are the abstract, introduction, and conclusion of a computer science research paper. Please summarize the main contribution of the work in a single sentence. Your response should include the summary and no additional text.

Paper text:

Deep neural networks can learn meaningful representations of data.
However, these representations are hard to interpret.
For example, visualizing a latent layer is generally only possible for at most three dimensions.
Neural networks are able to learn and benefit from much higher dimensional representations but these are not visually interpretable because nodes have arbitrary ordering within a layer.
Here, we utilize the ability of the human observer to identify patterns in structured representations to visualize higher dimensions.
To do so, we propose a class of regularizations we call \textit{Graph Spectral Regularizations} that impose graph-structure on latent layers.
This is achieved by treating activations as signals on a predefined graph and constraining those activations using graph filters, such as low pass and wavelet-like filters.
This framework allows for any kind of graph as well as filter to achieve a wide range of structured regularizations depending on the inference needs of the data.
First, we show a synthetic example that the graph-structured layer can reveal topological features of the data.
Next, we show that a smoothing regularization can impose semantically consistent ordering of nodes when applied to capsule nets.
Further, we show that the graph-structured layer, using wavelet-like spatially localized filters, can form localized receptive fields for improved image and biomedical data interpretation.
In other words, the mapping between latent layer, neurons and the output space becomes clear due to the localization of the activations.
Finally, we show that when structured as a grid, the representations create coherent images that allow for image-processing techniques such as convolutions.
Neural networks have revolutionized many areas of machine learning including image and natural language processing.
However, one of the major challenges for neural networks is that they are still black boxes to the user.
It is not quite clear how network internals map from inputs to outputs or how to interpret the features learned by the nodes.
This is mainly because the features are not constrained to have specific structure or characteristics.
Existing regularizations constrain the learned code to have certain properties.
However, they are not designed to specifically aid in interpretation of the latent encoding.
For example, L 1 regularization induces sparsity in the activations but does not impose specific structure between dimensions.Here, we introduce a new class of regularizations called Graph Spectral Regularizations that result in activations that are filtered on a predefined graph.
We define specific members of this class for applications.
First, we introduce a (graph) Laplacian smoothing regularization which enforces smoothly varying activations while at the same time reconstructing the data.
This regularization is useful for learning features with specific topologies.
For instance, we show that on a clusterstructured topology where features correspond to hierarchical cluster structure in the data it reflects the abstract grouping of features.
We also show it is useful for inducing feature consistency between nodes of capsule networks BID11 .
The graph regularization semantically aligns the features such that they appear in the same order in each capsule.
When trained on MNIST digits, we find that each of our 10 capsules consisting of 16 nodes encodes the same transformation (rotation, scale, skew, etc) of a particular digit in the same node.While the Laplacian smoothing regularizations is useful in the context where the features of the data have a recognizable topology, often we don't know the explicit structure of the data.
Instead, we would like to extract the topology of the data itself.
Thus, we design a filter that encourages the graph structure layer to learn data-shape features.
We achieve this by using a spatially localized, Gaussian filter to localize the activations for any particular data point.
We ensure that only one of a dictionary of localized filters is chosen as the activation via a spectral bottleneck layer preceding the graph-structured layer.
We show that spatially-localized filter regularizations are useful for detecting circular and linear topologies of data that are not immediately reflected by the observed features.
We also explore a biological system -a single-cell protein expression dataset depicting T cell development in the thymus -that has continuous progression structure.
The graph structured layer (with a ring graph) reveals the data to have a Y-shaped topology reflecting the bifurcation into CD4 (regulatory) and CD8 (cytotoxic) T cells, confirming known T cell biology.Finally, we show that the graph-structured layer, when imposing a 2D grid, creates a "pseudo" image that can be analyzed by convolution layers.
We show that such re-encoded images of MNIST digits have localized receptive fields that can be used for classification and visual interpretability.
Interestingly, we find that the convolution obviates the need for a spectral bottleneck as the convolution and max pooling themselves may provide that function.Our contributions are as follows:• A framework for imposing graph structure on latent layers using graph spectral regularizations.•
A Laplacian graph smoothing regularization and its applications in learning feature smoothness and consistency.•
Spatially localized graph regularizations using a spectral bottleneck based on a dictionary of Gaussian Kernels and its application in recognizing data topology.•
Applications of graph spectral regularizations, natural and biological datasets to demonstrate feature interpretability and data topology.The rest of this paper is organized as follows. We
first define graph structured layers and two techniques utilizing this layer in Section 2.
Then we present experiments demonstrating improved interpretability in Section 3.
Finally, we wrap up with conclusions in Section 4.
Imposing graph structure on neural network layers for improved visual interpretability.