Randall Balestriero

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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...
10/27/2020 ∙ by Sina Alemohammad, et al. ∙ 0 ∙ share

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Provable Finite Data Generalization with Group Autoencoder

Deep Autoencoders (AEs) provide a versatile framework to learn a compres...
09/20/2020 ∙ by Romain Cosentino, et al. ∙ 0 ∙ share

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Ensembles of Generative Adversarial Networks for Disconnected Data

Most current computer vision datasets are composed of disconnected sets,...
06/25/2020 ∙ by Lorenzo Luzi, et al. ∙ 0 ∙ share

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The Recurrent Neural Tangent Kernel

The study of deep networks (DNs) in the infinite-width limit, via the so...
06/18/2020 ∙ by Sina Alemohammad, et al. ∙ 0 ∙ share

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Analytical Probability Distributions and EM-Learning for Deep Generative Networks

Deep Generative Networks (DGNs) with probabilistic modeling of their out...
06/17/2020 ∙ by Randall Balestriero, et al. ∙ 0 ∙ share

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Interpretable Super-Resolution via a Learned Time-Series Representation

We develop an interpretable and learnable Wigner-Ville distribution that...
06/13/2020 ∙ by Randall Balestriero, et al. ∙ 0 ∙ share

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SymJAX: symbolic CPU/GPU/TPU programming

SymJAX is a symbolic programming version of JAX simplifying graph input/...
05/21/2020 ∙ by Randall Balestriero, et al. ∙ 0 ∙ share

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Max-Affine Spline Insights into Deep Generative Networks

We connect a large class of Generative Deep Networks (GDNs) with spline ...
02/26/2020 ∙ by Randall Balestriero, et al. ∙ 9 ∙ share

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A Hessian Based Complexity Measure for Deep Networks

Deep (neural) networks have been applied productively in a wide range of...
05/28/2019 ∙ by Hamid Javadi, et al. ∙ 0 ∙ share

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The Geometry of Deep Networks: Power Diagram Subdivision

We study the geometry of deep (neural) networks (DNs) with piecewise aff...
05/21/2019 ∙ by Randall Balestriero, et al. ∙ 0 ∙ share

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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...
10/22/2018 ∙ by Randall Balestriero, et al. ∙ 16 ∙ share

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A Spline Theory of Deep Networks (Extended Version)

We build a rigorous bridge between deep networks (DNs) and approximation...
05/17/2018 ∙ by Randall Balestriero, et al. ∙ 6 ∙ share

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Semi-Supervised Learning Enabled by Multiscale Deep Neural Network Inversion

Deep Neural Networks (DNNs) provide state-of-the-art solutions in severa...
02/27/2018 ∙ by Randall Balestriero, et al. ∙ 0 ∙ share

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Overcomplete Frame Thresholding for Acoustic Scene Analysis

In this work, we derive a generic overcomplete frame thresholding scheme...
12/25/2017 ∙ by Romain Cosentino, et al. ∙ 0 ∙ share

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Semi-Supervised Learning via New Deep Network Inversion

We exploit a recently derived inversion scheme for arbitrary deep neural...
11/12/2017 ∙ by Randall Balestriero, et al. ∙ 0 ∙ share

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Deep Neural Networks

Deep Neural Networks (DNNs) are universal function approximators providi...
10/25/2017 ∙ by Randall Balestriero, et al. ∙ 0 ∙ share

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Linear Time Complexity Deep Fourier Scattering Network and Extension to Nonlinear Invariants

In this paper we propose a scalable version of a state-of-the-art determ...
07/18/2017 ∙ by Randall Balestriero, et al. ∙ 0 ∙ share

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Multiscale Residual Mixture of PCA: Dynamic Dictionaries for Optimal Basis Learning

In this paper we are interested in the problem of learning an over-compl...
07/18/2017 ∙ by Randall Balestriero, et al. ∙ 0 ∙ share

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Neural Decision Trees

In this paper we propose a synergistic melting of neural networks and de...
02/23/2017 ∙ by Randall Balestriero, et al. ∙ 0 ∙ share

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Randall Balestriero

Featured Co-authors

Richard G. Baraniuk

Richard Baraniuk

Herve Glotin

Ankit Patel

Ahmed Imtiaz Humayun

Hamid Javadi

Zichao Wang

Daniel LeJeune

Sebastien Paris

Behnaam Aazhang

Romain Cosentino

Wearing a MASK: Compressed Representations of Variable-Length Sequences Using Recurrent Neural Tangent Kernels

Provable Finite Data Generalization with Group Autoencoder

Ensembles of Generative Adversarial Networks for Disconnected Data

The Recurrent Neural Tangent Kernel

Analytical Probability Distributions and EM-Learning for Deep Generative Networks

Interpretable Super-Resolution via a Learned Time-Series Representation

SymJAX: symbolic CPU/GPU/TPU programming

Max-Affine Spline Insights into Deep Generative Networks

A Hessian Based Complexity Measure for Deep Networks

The Geometry of Deep Networks: Power Diagram Subdivision

From Hard to Soft: Understanding Deep Network Nonlinearities via Vector Quantization and Statistical Inference

A Spline Theory of Deep Networks (Extended Version)

Semi-Supervised Learning Enabled by Multiscale Deep Neural Network Inversion

Overcomplete Frame Thresholding for Acoustic Scene Analysis

Semi-Supervised Learning via New Deep Network Inversion

Deep Neural Networks

Linear Time Complexity Deep Fourier Scattering Network and Extension to Nonlinear Invariants

Multiscale Residual Mixture of PCA: Dynamic Dictionaries for Optimal Basis Learning

Neural Decision Trees