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09/18/2023
Unsupervised Open-Vocabulary Object Localization in Videos
In this paper, we show that recent advances in video representation lear...
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09/01/2023
Object-Centric Multiple Object Tracking
Unsupervised object-centric learning methods allow the partitioning of s...
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07/18/2023
Self-Compatibility: Evaluating Causal Discovery without Ground Truth
As causal ground truth is incredibly rare, causal discovery algorithms a...
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07/18/2023
Unsupervised Conditional Slot Attention for Object Centric Learning
Extracting object-level representations for downstream reasoning tasks i...
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06/01/2023
Rotating Features for Object Discovery
The binding problem in human cognition, concerning how the brain represe...
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05/30/2023
Benign Overfitting in Deep Neural Networks under Lazy Training
This paper focuses on over-parameterized deep neural networks (DNNs) wit...
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04/20/2023
A data augmentation perspective on diffusion models and retrieval
Diffusion models excel at generating photorealistic images from text-que...
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04/17/2023
Leveraging sparse and shared feature activations for disentangled representation learning
Recovering the latent factors of variation of high dimensional data has ...
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04/04/2023
Divided Attention: Unsupervised Multi-Object Discovery with Contextually Separated Slots
We introduce a method to segment the visual field into independently mov...
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01/12/2023
Causal Triplet: An Open Challenge for Intervention-centric Causal Representation Learning
Recent years have seen a surge of interest in learning high-level causal...
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10/23/2022
Self-supervised Amodal Video Object Segmentation
Amodal perception requires inferring the full shape of an object that is...
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10/04/2022
ASIF: Coupled Data Turns Unimodal Models to Multimodal Without Training
Aligning the visual and language spaces requires to train deep neural ne...
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09/30/2022
Relative representations enable zero-shot latent space communication
Neural networks embed the geometric structure of a data manifold lying i...
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09/29/2022
Bridging the Gap to Real-World Object-Centric Learning
Humans naturally decompose their environment into entities at the approp...
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09/23/2022
TeST: Test-time Self-Training under Distribution Shift
Despite their recent success, deep neural networks continue to perform p...
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07/19/2022
Assaying Out-Of-Distribution Generalization in Transfer Learning
Since out-of-distribution generalization is a generally ill-posed proble...
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07/11/2022
Unsupervised Semantic Segmentation with Self-supervised Object-centric Representations
In this paper, we show that recent advances in self-supervised feature l...
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04/09/2022
Are Two Heads the Same as One? Identifying Disparate Treatment in Fair Neural Networks
We show that deep neural networks that satisfy demographic parity do so ...
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03/09/2022
Leveling Down in Computer Vision: Pareto Inefficiencies in Fair Deep Classifiers
Algorithmic fairness is frequently motivated in terms of a trade-off in ...
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03/08/2022
Score matching enables causal discovery of nonlinear additive noise models
This paper demonstrates how to recover causal graphs from the score of t...
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02/26/2022
Faster One-Sample Stochastic Conditional Gradient Method for Composite Convex Minimization
We propose a stochastic conditional gradient method (CGM) for minimizing...
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01/31/2022
Compositional Multi-Object Reinforcement Learning with Linear Relation Networks
Although reinforcement learning has seen remarkable progress over the la...
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11/26/2021
Enforcing and Discovering Structure in Machine Learning
The world is structured in countless ways. It may be prudent to enforce ...
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10/13/2021
Unsupervised Object Learning via Common Fate
Learning generative object models from unlabelled videos is a long stand...
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10/12/2021
Dynamic Inference with Neural Interpreters
Modern neural network architectures can leverage large amounts of data t...
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10/11/2021
You Mostly Walk Alone: Analyzing Feature Attribution in Trajectory Prediction
Predicting the future trajectory of a moving agent can be easy when the ...
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07/17/2021
Visual Representation Learning Does Not Generalize Strongly Within the Same Domain
An important component for generalization in machine learning is to unco...
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07/12/2021
Representation Learning for Out-Of-Distribution Generalization in Reinforcement Learning
Learning data representations that are useful for various downstream tas...
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07/02/2021
Backward-Compatible Prediction Updates: A Probabilistic Approach
When machine learning systems meet real world applications, accuracy is ...
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07/01/2021
Generalization and Robustness Implications in Object-Centric Learning
The idea behind object-centric representation learning is that natural s...
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06/09/2021
Neighborhood Contrastive Learning Applied to Online Patient Monitoring
Intensive care units (ICU) are increasingly looking towards machine lear...
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06/08/2021
Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style
Self-supervised representation learning has shown remarkable success in ...
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05/19/2021
Boosting Variational Inference With Locally Adaptive Step-Sizes
Variational Inference makes a trade-off between the capacity of the vari...
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02/22/2021
Towards Causal Representation Learning
The two fields of machine learning and graphical causality arose and dev...
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10/27/2020
On the Transfer of Disentangled Representations in Realistic Settings
Learning meaningful representations that disentangle the underlying stru...
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10/27/2020
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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07/28/2020
A Commentary on the Unsupervised Learning of Disentangled Representations
The goal of the unsupervised learning of disentangled representations is...
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06/14/2020
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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02/07/2020
Weakly-Supervised Disentanglement Without Compromises
Intelligent agents should be able to learn useful representations by obs...
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06/07/2019
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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05/31/2019
On the Fairness of Disentangled Representations
Recently there has been a significant interest in learning disentangled ...
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05/29/2019
Are Disentangled Representations Helpful for Abstract Visual Reasoning?
A disentangled representation encodes information about the salient fact...
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05/16/2019
The Incomplete Rosetta Stone Problem: Identifiability Results for Multi-View Nonlinear ICA
We consider the problem of recovering a common latent source with indepe...
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05/03/2019
Disentangling Factors of Variation Using Few Labels
Learning disentangled representations is considered a cornerstone proble...
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01/29/2019
Stochastic Conditional Gradient Method for Composite Convex Minimization
In this paper, we propose the first practical algorithm to minimize stoc...
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11/29/2018
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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06/06/2018
Deep Self-Organization: Interpretable Discrete Representation Learning on Time Series
Human professionals are often required to make decisions based on comple...
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06/06/2018
Boosting Black Box Variational Inference
Approximating a probability density in a tractable manner is a central t...
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04/30/2018
Clustering Meets Implicit Generative Models
Clustering is a cornerstone of unsupervised learning which can be though...
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03/26/2018