1 citations · 1 across the 5 of their papers we have counts for
5 papers
Node Embeddings via Neighbor Embeddings
Jan Niklas Böhm, Marius Keute, Alica Guzmán +3
Node embeddings are a paradigm in non-parametric graph representation learning, where graph nodes are embedded into a given vector space to enable downstream processing. State-of-t…
On the Importance of Embedding Norms in Self-Supervised Learning
Andrew Draganov, Sharvaree Vadgama, Sebastian Damrich +4
Self-supervised learning (SSL) allows training data representations without a supervised signal and has become an important paradigm in machine learning. Most SSL methods employ th…
A Tight VC-Dimension Analysis of Clustering Coresets with Applications
Vincent Cohen-Addad, Andrew Draganov, Matteo Russo +2
We consider coresets for -clustering problems, where the goal is to assign points to centers minimizing powers of distances. A popular example is the -median objective $\sum_…
The Hidden Pitfalls of the Cosine Similarity Loss
Andrew Draganov, Sharvaree Vadgama, Erik J. Bekkers
We show that the gradient of the cosine similarity between two points goes to zero in two under-explored settings: (1) if a point has large magnitude or (2) if the points are on op…
Settling Time vs. Accuracy Tradeoffs for Clustering Big Data
Andrew Draganov, David Saulpic, Chris Schwiegelshohn
We study the theoretical and practical runtime limits of k-means and k-median clustering on large datasets. Since effectively all clustering methods are slower than the time it tak…