
Neighborhood Contrastive Learning Applied to Online Patient Monitoring
Intensive care units (ICU) are increasingly looking towards machine lear...
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SelfSupervised Learning with Data Augmentations Provably Isolates Content from Style
Selfsupervised representation learning has shown remarkable success in ...
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Boosting Variational Inference With Locally Adaptive StepSizes
Variational Inference makes a tradeoff between the capacity of the vari...
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Towards Causal Representation Learning
The two fields of machine learning and graphical causality arose and dev...
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On the Transfer of Disentangled Representations in Realistic Settings
Learning meaningful representations that disentangle the underlying stru...
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A Sober Look at the Unsupervised Learning of Disentangled Representations and their Evaluation
The idea behind the unsupervised learning of disentangled representation...
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A Commentary on the Unsupervised Learning of Disentangled Representations
The goal of the unsupervised learning of disentangled representations is...
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Is Independence all you need? On the Generalization of Representations Learned from Correlated Data
Despite impressive progress in the last decade, it still remains an open...
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WeaklySupervised Disentanglement Without Compromises
Intelligent agents should be able to learn useful representations by obs...
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On the Transfer of Inductive Bias from Simulation to the Real World: a New Disentanglement Dataset
Learning meaningful and compact representations with structurally disent...
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On the Fairness of Disentangled Representations
Recently there has been a significant interest in learning disentangled ...
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Are Disentangled Representations Helpful for Abstract Visual Reasoning?
A disentangled representation encodes information about the salient fact...
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The Incomplete Rosetta Stone Problem: Identifiability Results for MultiView Nonlinear ICA
We consider the problem of recovering a common latent source with indepe...
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Disentangling Factors of Variation Using Few Labels
Learning disentangled representations is considered a cornerstone proble...
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Stochastic Conditional Gradient Method for Composite Convex Minimization
In this paper, we propose the first practical algorithm to minimize stoc...
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Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations
In recent years, the interest in unsupervised learning of disentangled r...
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Deep SelfOrganization: Interpretable Discrete Representation Learning on Time Series
Human professionals are often required to make decisions based on comple...
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Boosting Black Box Variational Inference
Approximating a probability density in a tractable manner is a central t...
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Clustering Meets Implicit Generative Models
Clustering is a cornerstone of unsupervised learning which can be though...
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Revisiting FirstOrder Convex Optimization Over Linear Spaces
Two popular examples of firstorder optimization methods over linear spa...
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Boosting Variational Inference: an Optimization Perspective
Variational Inference is a popular technique to approximate a possibly i...
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Greedy Algorithms for Cone Constrained Optimization with Convergence Guarantees
Greedy optimization methods such as Matching Pursuit (MP) and FrankWolf...
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A Unified Optimization View on Generalized Matching Pursuit and FrankWolfe
Two of the most fundamental prototypes of greedy optimization are the ma...
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Francesco Locatello
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