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Found 5 papers, 3 papers with code
Settling Time vs. Accuracy Tradeoffs for Clustering Big Data
We study the theoretical and practical runtime limits of k-means and k-median clustering on large datasets.
Unexplainable Explanations: Towards Interpreting tSNE and UMAP Embeddings
It has become standard to explain neural network latent spaces with attraction/repulsion dimensionality reduction (ARDR) methods like tSNE and UMAP.
ActUp: Analyzing and Consolidating tSNE and UMAP
tSNE and UMAP are popular dimensionality reduction algorithms due to their speed and interpretable low-dimensional embeddings.
GiDR-DUN; Gradient Dimensionality Reduction -- Differences and Unification
In this work, we show that this is indeed possible by combining the two approaches into a single method.
ChaRRNets: Channel Robust Representation Networks for RF Fingerprinting
We introduce the Lie group of transformations that a signal experiences under the multipath propagation model and define operations that are equivariant and invariant to the frequency response of a Finite Impulse Response (FIR) filter to build a ChaRRNet.
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