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Found 3 papers, 3 papers with code
Matryoshka Representation Learning
The flexibility within the learned Matryoshka Representations offer: (a) up to 14x smaller embedding size for ImageNet-1K classification at the same level of accuracy; (b) up to 14x real-world speed-ups for large-scale retrieval on ImageNet-1K and 4K; and (c) up to 2% accuracy improvements for long-tail few-shot classification, all while being as robust as the original representations.
Ranked #25 on
Image Classification
on ObjectNet
(using extra training data)
Representing and extending ensembles of parsimonious evolutionary histories with a directed acyclic graph
In many situations, it would be useful to know not just the best phylogenetic tree for a given data set, but the collection of high-quality trees.
Densely sampled phylogenies frequently deviate from maximum parsimony in simple and local ways
Why do phylogenetic algorithms fail when they return incorrect answers?
