
Fast JacobianVector Product for Deep Networks
Jacobianvector products (JVPs) form the backbone of many recent develop...
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MaxAffine Spline Insights Into Deep Network Pruning
In this paper, we study the importance of pruning in Deep Networks (DNs)...
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Interpretable Image Clustering via DiffeomorphismAware KMeans
We design an interpretable clustering algorithm aware of the nonlinear s...
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Sparse MultiFamily Deep Scattering Network
In this work, we propose the Sparse MultiFamily Deep Scattering Network...
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Scalable Neural Tangent Kernel of Recurrent Architectures
Kernels derived from deep neural networks (DNNs) in the infinitewidth p...
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Wearing a MASK: Compressed Representations of VariableLength Sequences Using Recurrent Neural Tangent Kernels
High dimensionality poses many challenges to the use of data, from visua...
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Provable Finite Data Generalization with Group Autoencoder
Deep Autoencoders (AEs) provide a versatile framework to learn a compres...
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Ensembles of Generative Adversarial Networks for Disconnected Data
Most current computer vision datasets are composed of disconnected sets,...
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The Recurrent Neural Tangent Kernel
The study of deep networks (DNs) in the infinitewidth limit, via the so...
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Analytical Probability Distributions and EMLearning for Deep Generative Networks
Deep Generative Networks (DGNs) with probabilistic modeling of their out...
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Interpretable SuperResolution via a Learned TimeSeries Representation
We develop an interpretable and learnable WignerVille distribution that...
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SymJAX: symbolic CPU/GPU/TPU programming
SymJAX is a symbolic programming version of JAX simplifying graph input/...
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MaxAffine Spline Insights into Deep Generative Networks
We connect a large class of Generative Deep Networks (GDNs) with spline ...
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A Hessian Based Complexity Measure for Deep Networks
Deep (neural) networks have been applied productively in a wide range of...
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The Geometry of Deep Networks: Power Diagram Subdivision
We study the geometry of deep (neural) networks (DNs) with piecewise aff...
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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...
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A Spline Theory of Deep Networks (Extended Version)
We build a rigorous bridge between deep networks (DNs) and approximation...
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SemiSupervised Learning Enabled by Multiscale Deep Neural Network Inversion
Deep Neural Networks (DNNs) provide stateoftheart solutions in severa...
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Overcomplete Frame Thresholding for Acoustic Scene Analysis
In this work, we derive a generic overcomplete frame thresholding scheme...
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SemiSupervised Learning via New Deep Network Inversion
We exploit a recently derived inversion scheme for arbitrary deep neural...
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Deep Neural Networks
Deep Neural Networks (DNNs) are universal function approximators providi...
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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 stateoftheart determ...
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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 overcompl...
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Neural Decision Trees
In this paper we propose a synergistic melting of neural networks and de...
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Randall Balestriero
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