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Found 5 papers, 2 papers with code
Deep Augmentation: Self-Supervised Learning with Transformations in Activation Space
We use this observation to formulate a method for selecting which layer to target; in particular, our experimentation reveals that targeting deeper layers with Deep Augmentation outperforms augmenting the input data.
Relative Position Prediction as Pre-training for Text Encoders
Meaning is defined by the company it keeps.
Rewiring with Positional Encodings for Graph Neural Networks
As a conservative alternative, we use positional encodings to expand receptive fields to $r$-hop neighborhoods.
Universal Function Approximation on Graphs
In this work we produce a framework for constructing universal function approximators on graph isomorphism classes.
A Topology Layer for Machine Learning
Topology applied to real world data using persistent homology has started to find applications within machine learning, including deep learning.
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