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Found 3 papers, 1 papers with code
URLOST: Unsupervised Representation Learning without Stationarity or Topology
Unsupervised representation learning has seen tremendous progress but is constrained by its reliance on data modality-specific stationarity and topology, a limitation not found in biological intelligence systems.
Minimalistic Unsupervised Learning with the Sparse Manifold Transform
Though there remains a small performance gap between our simple constructive model and SOTA methods, the evidence points to this as a promising direction for achieving a principled and white-box approach to unsupervised learning.
Ranked #1 on
Unsupervised MNIST
on MNIST
Self-Supervised Learning
Sparse Representation-based Classification
+3
Transformer visualization via dictionary learning: contextualized embedding as a linear superposition of transformer factors
Transformer networks have revolutionized NLP representation learning since they were introduced.
