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Found 6 papers, 3 papers with code
Post-Hoc Reversal: Are We Selecting Models Prematurely?
This phenomenon is especially prominent in high-noise settings.
Relational Deep Learning: Graph Representation Learning on Relational Databases
The core idea is to view relational databases as a temporal, heterogeneous graph, with a node for each row in each table, and edges specified by primary-foreign key links.
SelfVC: Voice Conversion With Iterative Refinement using Self Transformations
In this work, instead of explicitly disentangling attributes with loss terms, we present a framework to train a controllable voice conversion model on entangled speech representations derived from self-supervised learning and speaker verification models.
A Solver-Free Framework for Scalable Learning in Neural ILP Architectures
Experiments on several problems, both perceptual as well as symbolic, which require learning the constraints of an ILP, show that our approach has superior performance and scales much better compared to purely neural baselines and other state-of-the-art models that require solver-based training.
GREED: A Neural Framework for Learning Graph Distance Functions
To elaborate, although GED is a metric, its neural approximations do not provide such a guarantee.
NeuroSED: Learning Subgraph Similarity via Graph Neural Networks
Subgraph edit distance (SED) is one of the most expressive measures of subgraph similarity.
