Representation Learning Comparison Hub
4 papers - avg viability 3.0
Top Papers
- Statistical and structural identifiability in representation learning(5.0)
This paper proposes a new framework for understanding and improving identifiability in representation learning models.
- Disentangled Representation Learning through Unsupervised Symmetry Group Discovery(3.0)
A method for unsupervised symmetry group discovery to enhance representation learning.
- Hypersolid: Emergent Vision Representations via Short-Range Repulsion(2.0)
A novel self-supervised learning method that uses short-range repulsion for representation learning to prevent collapse.
- Revisiting the Platonic Representation Hypothesis: An Aristotelian View(2.0)
Introducing a new framework to calibrate representational similarity metrics in neural networks for clearer insights into converging representations.