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08/08/2023
PUG: Photorealistic and Semantically Controllable Synthetic Data for Representation Learning
Synthetic image datasets offer unmatched advantages for designing and ev...
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07/31/2023
Predicting masked tokens in stochastic locations improves masked image modeling
Self-supervised learning is a promising paradigm in deep learning that e...
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06/28/2023
Identifiability of Discretized Latent Coordinate Systems via Density Landmarks Detection
Disentanglement aims to recover meaningful latent ground-truth factors f...
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04/26/2023
Do SSL Models Have Déjà Vu? A Case of Unintended Memorization in Self-supervised Learning
Self-supervised learning (SSL) algorithms can produce useful image repre...
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04/25/2023
Objectives Matter: Understanding the Impact of Self-Supervised Objectives on Vision Transformer Representations
Joint-embedding based learning (e.g., SimCLR, MoCo, DINO) and reconstruc...
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04/11/2023
A surprisingly simple technique to control the pretraining bias for better transfer: Expand or Narrow your representation
Self-Supervised Learning (SSL) models rely on a pretext task to learn re...
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03/16/2023
Instance-Conditioned GAN Data Augmentation for Representation Learning
Data augmentation has become a crucial component to train state-of-the-a...
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03/03/2023
Towards Democratizing Joint-Embedding Self-Supervised Learning
Joint Embedding Self-Supervised Learning (JE-SSL) has seen rapid develop...
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01/19/2023
Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture
This paper demonstrates an approach for learning highly semantic image r...
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11/03/2022
ImageNet-X: Understanding Model Mistakes with Factor of Variation Annotations
Deep learning vision systems are widely deployed across applications whe...
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10/13/2022
Disentanglement of Correlated Factors via Hausdorff Factorized Support
A grand goal in deep learning research is to learn representations capab...
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10/13/2022
The Hidden Uniform Cluster Prior in Self-Supervised Learning
A successful paradigm in representation learning is to perform self-supe...
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06/27/2022
Guillotine Regularization: Improving Deep Networks Generalization by Removing their Head
One unexpected technique that emerged in recent years consists in traini...
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04/14/2022
Masked Siamese Networks for Label-Efficient Learning
We propose Masked Siamese Networks (MSN), a self-supervised learning fra...
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12/16/2021
High Fidelity Visualization of What Your Self-Supervised Representation Knows About
Discovering what is learned by neural networks remains a challenge. In s...
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03/02/2021
Online Adversarial Attacks
Adversarial attacks expose important vulnerabilities of deep learning mo...
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03/01/2021
Accounting for Variance in Machine Learning Benchmarks
Strong empirical evidence that one machine-learning algorithm A outperfo...
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10/24/2020
Efficient Learning in Non-Stationary Linear Markov Decision Processes
We study episodic reinforcement learning in non-stationary linear (a.k.a...
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08/03/2020
Implicit Regularization in Deep Learning: A View from Function Space
We approach the problem of implicit regularization in deep learning from...
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07/08/2020
Stochastic Hamiltonian Gradient Methods for Smooth Games
The success of adversarial formulations in machine learning has brought ...
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07/07/2020
Sharp Analysis of Smoothed Bellman Error Embedding
The Smoothed Bellman Error Embedding algorithm <cit.>, known as SBEED, w...
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07/01/2020
Adversarial Example Games
The existence of adversarial examples capable of fooling trained neural ...
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06/22/2020
Revisiting Loss Modelling for Unstructured Pruning
By removing parameters from deep neural networks, unstructured pruning m...
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04/16/2020
Do sequence-to-sequence VAEs learn global features of sentences?
A longstanding goal in NLP is to compute global sentence representations...
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03/09/2020
Stable Policy Optimization via Off-Policy Divergence Regularization
Trust Region Policy Optimization (TRPO) and Proximal Policy Optimization...
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07/31/2019
An Empirical Study of Batch Normalization and Group Normalization in Conditional Computation
Batch normalization has been widely used to improve optimization in deep...
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06/11/2019
A Closer Look at the Optimization Landscapes of Generative Adversarial Networks
Generative adversarial networks have been very successful in generative ...
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06/10/2019
Stochastic Neural Network with Kronecker Flow
Recent advances in variational inference enable the modelling of highly ...
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06/09/2019
SVRG for Policy Evaluation with Fewer Gradient Evaluations
Stochastic variance-reduced gradient (SVRG) is an optimization method or...
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02/08/2019
Reducing Uncertainty in Undersampled MRI Reconstruction with Active Acquisition
The goal of MRI reconstruction is to restore a high fidelity image from ...
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11/21/2018
fastMRI: An Open Dataset and Benchmarks for Accelerated MRI
Accelerating Magnetic Resonance Imaging (MRI) by taking fewer measuremen...
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06/11/2018
Fast Approximate Natural Gradient Descent in a Kronecker-factored Eigenbasis
Optimization algorithms that leverage gradient covariance information, s...
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06/06/2018
Randomized Value Functions via Multiplicative Normalizing Flows
Randomized value functions offer a promising approach towards the challe...
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02/28/2018
A Variational Inequality Perspective on Generative Adversarial Nets
Stability has been a recurrent issue in training generative adversarial ...
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09/05/2017
Improving Landmark Localization with Semi-Supervised Learning
We present two techniques to improve landmark localization from partiall...
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08/08/2017
Parametric Adversarial Divergences are Good Task Losses for Generative Modeling
Generative modeling of high dimensional data like images is a notoriousl...
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06/01/2017
Learning to Compute Word Embeddings On the Fly
Words in natural language follow a Zipfian distribution whereby some wor...
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05/25/2017
Convergent Tree-Backup and Retrace with Function Approximation
Off-policy learning is key to scaling up reinforcement learning as it al...
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03/20/2017
Learning to Generate Samples from Noise through Infusion Training
In this work, we investigate a novel training procedure to learn a gener...
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09/19/2016
A Cheap Linear Attention Mechanism with Fast Lookups and Fixed-Size Representations
The softmax content-based attention mechanism has proven to be very bene...
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06/26/2016
Exact gradient updates in time independent of output size for the spherical loss family
An important class of problems involves training deep neural networks wi...
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05/24/2016
Hierarchical Memory Networks
Memory networks are neural networks with an explicit memory component th...
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05/09/2016
Theano: A Python framework for fast computation of mathematical expressions
Theano is a Python library that allows to define, optimize, and evaluate...
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04/29/2016
The Z-loss: a shift and scale invariant classification loss belonging to the Spherical Family
Despite being the standard loss function to train multi-class neural net...
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11/23/2015
Recombinator Networks: Learning Coarse-to-Fine Feature Aggregation
Deep neural networks with alternating convolutional, max-pooling and dec...
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11/16/2015
An Exploration of Softmax Alternatives Belonging to the Spherical Loss Family
In a multi-class classification problem, it is standard to model the out...
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07/31/2015
Artificial Neural Networks Applied to Taxi Destination Prediction
We describe our first-place solution to the ECML/PKDD discovery challeng...
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07/21/2015
Clustering is Efficient for Approximate Maximum Inner Product Search
Efficient Maximum Inner Product Search (MIPS) is an important task that ...
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06/29/2015
Dropout as data augmentation
Dropout is typically interpreted as bagging a large number of models sha...
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03/18/2015
