Given a motif M, the framework aims to find a cluster of the set of nodes S such that nodes of S participate in many instances of M and avoid cutting instances of M (that is only a subset of nodes in instances of M appears in S).
Mathematically, the aim is to minimise the motif conductance metric given as cutM(S, S’) / min[volM(S), volM(S’)] where S’ is complement of S, cutM(S, S’)= number...
Given a graph G = (V, E), compute a series of successively smaller (coarse) graphs G0, …, GL. Learn the node representations in GL and successively refine the embeddings for larger graphs in the series.
The architecture is independent of the algorithms used to embed the nodes or to refine the node representations.
Graph coarsening technique that preserves global structure
Collapse edges and stars to preserve first and second order proximity.
The paper presents a new activation function called Swish with formulation f(x) = x.sigmod(x) and its parameterised version called Swish-β where f(x, β) = 2x.sigmoid(β.x) and β is a training parameter.
The paper shows that Swish is consistently able to outperform RELU and other activations functions over a variety of datasets (CIFAR, ImageNet, WMT2014) though by small margins only in some cases.
Existing machine comprehension (MC) datasets are either too small or synthetic (with a distribution different from that or real-questions posted by humans). MARCO questions are sampled from real, anonymized user queries.
Most datasets would provide a comparatively small and clean context to answer the question. In MARCO, the context documents (which may or may not contain the answ...