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Found 8 papers, 3 papers with code
Spectal Harmonics: Bridging Spectral Embedding and Matrix Completion in Self-Supervised Learning
Self-supervised methods received tremendous attention thanks to their seemingly heuristic approach to learning representations that respect the semantics of the data without any apparent supervision in the form of labels.
Unsupervised Embedding Quality Evaluation
Unsupervised learning has recently significantly gained in popularity, especially with deep learning-based approaches.
CC-Cert: A Probabilistic Approach to Certify General Robustness of Neural Networks
In safety-critical machine learning applications, it is crucial to defend models against adversarial attacks -- small modifications of the input that change the predictions.
GRASP: Graph Alignment through Spectral Signatures
In this paper, we transfer the shape-analysis concept of functional maps from the continuous to the discrete case, and treat the graph alignment problem as a special case of the problem of finding a mapping between functions on graphs.
FREDE: Anytime Graph Embeddings
Low-dimensional representations, or embeddings, of a graph's nodes facilitate several practical data science and data engineering tasks.
Just SLaQ When You Approximate: Accurate Spectral Distances for Web-Scale Graphs
Graph comparison is a fundamental operation in data mining and information retrieval.
The Shape of Data: Intrinsic Distance for Data Distributions
The ability to represent and compare machine learning models is crucial in order to quantify subtle model changes, evaluate generative models, and gather insights on neural network architectures.
Quadrature-based features for kernel approximation
We consider the problem of improving kernel approximation via randomized feature maps.
