6,436 research outputs found
Laplacian Mixture Modeling for Network Analysis and Unsupervised Learning on Graphs
Laplacian mixture models identify overlapping regions of influence in
unlabeled graph and network data in a scalable and computationally efficient
way, yielding useful low-dimensional representations. By combining Laplacian
eigenspace and finite mixture modeling methods, they provide probabilistic or
fuzzy dimensionality reductions or domain decompositions for a variety of input
data types, including mixture distributions, feature vectors, and graphs or
networks. Provable optimal recovery using the algorithm is analytically shown
for a nontrivial class of cluster graphs. Heuristic approximations for scalable
high-performance implementations are described and empirically tested.
Connections to PageRank and community detection in network analysis demonstrate
the wide applicability of this approach. The origins of fuzzy spectral methods,
beginning with generalized heat or diffusion equations in physics, are reviewed
and summarized. Comparisons to other dimensionality reduction and clustering
methods for challenging unsupervised machine learning problems are also
discussed.Comment: 13 figures, 35 reference
Is Neuro-Symbolic AI Meeting its Promise in Natural Language Processing? A Structured Review
Advocates for Neuro-Symbolic Artificial Intelligence (NeSy) assert that
combining deep learning with symbolic reasoning will lead to stronger AI than
either paradigm on its own. As successful as deep learning has been, it is
generally accepted that even our best deep learning systems are not very good
at abstract reasoning. And since reasoning is inextricably linked to language,
it makes intuitive sense that Natural Language Processing (NLP), would be a
particularly well-suited candidate for NeSy. We conduct a structured review of
studies implementing NeSy for NLP, with the aim of answering the question of
whether NeSy is indeed meeting its promises: reasoning, out-of-distribution
generalization, interpretability, learning and reasoning from small data, and
transferability to new domains. We examine the impact of knowledge
representation, such as rules and semantic networks, language structure and
relational structure, and whether implicit or explicit reasoning contributes to
higher promise scores. We find that systems where logic is compiled into the
neural network lead to the most NeSy goals being satisfied, while other factors
such as knowledge representation, or type of neural architecture do not exhibit
a clear correlation with goals being met. We find many discrepancies in how
reasoning is defined, specifically in relation to human level reasoning, which
impact decisions about model architectures and drive conclusions which are not
always consistent across studies. Hence we advocate for a more methodical
approach to the application of theories of human reasoning as well as the
development of appropriate benchmarks, which we hope can lead to a better
understanding of progress in the field. We make our data and code available on
github for further analysis.Comment: Surve
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