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05/25/2023
Martian time-series unraveled: A multi-scale nested approach with factorial variational autoencoders
Unsupervised source separation involves unraveling an unknown set of sou...
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04/24/2023
A Cookbook of Self-Supervised Learning
Self-supervised learning, dubbed the dark matter of intelligence, is a p...
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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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03/27/2023
Active Self-Supervised Learning: A Few Low-Cost Relationships Are All You Need
Self-Supervised Learning (SSL) has emerged as the solution of choice to ...
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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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03/01/2023
An Information-Theoretic Perspective on Variance-Invariance-Covariance Regularization
In this paper, we provide an information-theoretic perspective on Varian...
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03/01/2023
FAIR-Ensemble: When Fairness Naturally Emerges From Deep Ensembling
Ensembling independent deep neural networks (DNNs) is a simple and effec...
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02/20/2023
Unsupervised Learning on a DIET: Datum IndEx as Target Free of Self-Supervision, Reconstruction, Projector Head
Costly, noisy, and over-specialized, labels are to be set aside in favor...
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02/06/2023
The SSL Interplay: Augmentations, Inductive Bias, and Generalization
Self-supervised learning (SSL) has emerged as a powerful framework to le...
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11/07/2022
On minimal variations for unsupervised representation learning
Unsupervised representation learning aims at describing raw data efficie...
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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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11/02/2022
POLICE: Provably Optimal Linear Constraint Enforcement for Deep Neural Networks
Deep Neural Networks (DNNs) outshine alternative function approximators ...
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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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10/05/2022
RankMe: Assessing the downstream performance of pretrained self-supervised representations by their rank
Joint-Embedding Self Supervised Learning (JE-SSL) has seen a rapid devel...
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09/29/2022
Variance Covariance Regularization Enforces Pairwise Independence in Self-Supervised Representations
Self-Supervised Learning (SSL) methods such as VICReg, Barlow Twins or W...
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09/29/2022
Joint Embedding Self-Supervised Learning in the Kernel Regime
The fundamental goal of self-supervised learning (SSL) is to produce use...
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09/29/2022
Batch Normalization Explained
A critically important, ubiquitous, and yet poorly understood ingredient...
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07/20/2022
What Do We Maximize in Self-Supervised Learning?
In this paper, we examine self-supervised learning methods, particularly...
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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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05/23/2022
Contrastive and Non-Contrastive Self-Supervised Learning Recover Global and Local Spectral Embedding Methods
Self-Supervised Learning (SSL) surmises that inputs and pairwise positiv...
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04/07/2022
The Effects of Regularization and Data Augmentation are Class Dependent
Regularization is a fundamental technique to prevent over-fitting and to...
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04/07/2022
DeepTensor: Low-Rank Tensor Decomposition with Deep Network Priors
DeepTensor is a computationally efficient framework for low-rank decompo...
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03/10/2022
projUNN: efficient method for training deep networks with unitary matrices
In learning with recurrent or very deep feed-forward networks, employing...
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03/07/2022
Singular Value Perturbation and Deep Network Optimization
We develop new theoretical results on matrix perturbation to shed light ...
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02/23/2022
NeuroView-RNN: It's About Time
Recurrent Neural Networks (RNNs) are important tools for processing sequ...
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02/16/2022
A Data-Augmentation Is Worth A Thousand Samples: Exact Quantification From Analytical Augmented Sample Moments
Data-Augmentation (DA) is known to improve performance across tasks and ...
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02/16/2022
Spatial Transformer K-Means
K-means defines one of the most employed centroid-based clustering algor...
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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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10/18/2021
Learning in High Dimension Always Amounts to Extrapolation
The notion of interpolation and extrapolation is fundamental in various ...
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10/15/2021
MaGNET: Uniform Sampling from Deep Generative Network Manifolds Without Retraining
Deep Generative Networks (DGNs) are extensively employed in Generative A...
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10/15/2021
NeuroView: Explainable Deep Network Decision Making
Deep neural networks (DNs) provide superhuman performance in numerous co...
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04/01/2021
Fast Jacobian-Vector Product for Deep Networks
Jacobian-vector products (JVPs) form the backbone of many recent develop...
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01/07/2021
Max-Affine Spline Insights Into Deep Network Pruning
In this paper, we study the importance of pruning in Deep Networks (DNs)...
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12/16/2020
Interpretable Image Clustering via Diffeomorphism-Aware K-Means
We design an interpretable clustering algorithm aware of the nonlinear s...
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12/14/2020
Sparse Multi-Family Deep Scattering Network
In this work, we propose the Sparse Multi-Family Deep Scattering Network...
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12/09/2020
Scalable Neural Tangent Kernel of Recurrent Architectures
Kernels derived from deep neural networks (DNNs) in the infinite-width p...
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10/27/2020
Wearing a MASK: Compressed Representations of Variable-Length Sequences Using Recurrent Neural Tangent Kernels
High dimensionality poses many challenges to the use of data, from visua...
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09/20/2020
Provable Finite Data Generalization with Group Autoencoder
Deep Autoencoders (AEs) provide a versatile framework to learn a compres...
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06/25/2020
Ensembles of Generative Adversarial Networks for Disconnected Data
Most current computer vision datasets are composed of disconnected sets,...
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06/18/2020
The Recurrent Neural Tangent Kernel
The study of deep networks (DNs) in the infinite-width limit, via the so...
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06/17/2020
Analytical Probability Distributions and EM-Learning for Deep Generative Networks
Deep Generative Networks (DGNs) with probabilistic modeling of their out...
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06/13/2020
Interpretable Super-Resolution via a Learned Time-Series Representation
We develop an interpretable and learnable Wigner-Ville distribution that...
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05/21/2020
SymJAX: symbolic CPU/GPU/TPU programming
SymJAX is a symbolic programming version of JAX simplifying graph input/...
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02/26/2020
Max-Affine Spline Insights into Deep Generative Networks
We connect a large class of Generative Deep Networks (GDNs) with spline ...
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05/28/2019
A Hessian Based Complexity Measure for Deep Networks
Deep (neural) networks have been applied productively in a wide range of...
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05/21/2019
The Geometry of Deep Networks: Power Diagram Subdivision
We study the geometry of deep (neural) networks (DNs) with piecewise aff...
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10/22/2018
From Hard to Soft: Understanding Deep Network Nonlinearities via Vector Quantization and Statistical Inference
Nonlinearity is crucial to the performance of a deep (neural) network (D...
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05/17/2018
A Spline Theory of Deep Networks (Extended Version)
We build a rigorous bridge between deep networks (DNs) and approximation...
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02/27/2018
Semi-Supervised Learning Enabled by Multiscale Deep Neural Network Inversion
Deep Neural Networks (DNNs) provide state-of-the-art solutions in severa...
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12/25/2017