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Disentanglement

Monotonicity Regularization: Improved Penalties and Novel Applications to Disentangled Representation Learning and Robust Classification
Monotonicity Regularization: Improved Penalties and Novel Applications to Disentangled Representation Learning and Robust Classification
We study settings where gradient penalties are used alongside risk minimization with the goal of obtaining predictors satisfying …
Conference on Uncertainty in Artificial Intelligence (UAI),  2022.
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Disentangling the independently controllable factors of variation by interacting with the world
Disentangling the independently controllable factors of variation by interacting with the world
It has been postulated that a good representation is one that disentangles the underlying explanatory factors of variation. However, it …
Workshop at the Neural Information Processing Systems (NeurIPS),  2017.
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