_id,title
14176,Neighborhood contrastive learning applied to online patient monitoring
14177,On disentangled representations learned from correlated data
14178,On the transfer of disentangled representations in realistic settings
14179,Self-supervised learning with data augmentations provably isolates content from style
14180,Dynamic inference with neural interpreters
14181,Boosting variational inference with locally adaptive step-sizes
14182,Backward-compatible prediction updates: A probabilistic approach
14221,Enforcing and discovering structure in machine learning
14326,Object-centric learning with slot attention
14125,SCIM: Universal single-cell matching with unpaired feature sets
14186,A commentary on the unsupervised learning of disentangled representations
14187,Stochastic Frank-Wolfe for constrained finite-sum minimization
14188,Weakly-supervised disentanglement without compromises
14195,A sober look at the unsupervised learning of disentangled representations and their evaluation
14184,Disentangling factors of variation using few labels
14189,The incomplete Rosetta Stone problem: Identifiability results for multi-view nonlinear ICA
14190,On the transfer of inductive bias from simulation to the real world: a new disentanglement dataset
14191,Stochastic Frank-Wolfe for composite convex minimization
14193,Are disentangled representations helpful for abstract visual reasoning?
14197,On the fairness of disentangled representations