
On the Empirical Neural Tangent Kernel of Standard FiniteWidth Convolutional Neural Network Architectures
The Neural Tangent Kernel (NTK) is an important milestone in the ongoing...
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Inverse Learning of Symmetry Transformations
Symmetry transformations induce invariances and are a crucial building b...
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Learning Extremal Representations with Deep Archetypal Analysis
Archetypes are typical population representatives in an extremal sense, ...
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On the Difference Between the Information Bottleneck and the Deep Information Bottleneck
Combining the Information Bottleneck model with deep learning by replaci...
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Optimizing for Interpretability in Deep Neural Networks with Tree Regularization
Deep models have advanced prediction in many domains, but their lack of ...
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Regional Tree Regularization for Interpretability in Black Box Models
The lack of interpretability remains a barrier to the adoption of deep n...
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Tensor BSpline Numerical Methods for PDEs: a HighPerformance Alternative to FEM
Tensor Bspline methods are a highperformance alternative to solve part...
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Deep Archetypal Analysis
"Deep Archetypal Analysis" generates latent representations of highdime...
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Estimating Causal Effects With Partial Covariates For Clinical Interpretability
Estimating the causal effects of an intervention in the presence of conf...
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Informed MCMC with Bayesian Neural Networks for Facial Image Analysis
Computer vision tasks are difficult because of the large variability in ...
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Causal Deep Information Bottleneck
Estimating causal effects in the presence of latent confounding is a fre...
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Learning Sparse Latent Representations with the Deep Copula Information Bottleneck
Deep latent variable models are powerful tools for representation learni...
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New Directions for Trust in the Certificate Authority Ecosystem
Many of the benefits we derive from the Internet require trust in the au...
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Beyond Sparsity: Tree Regularization of Deep Models for Interpretability
The lack of interpretability remains a key barrier to the adoption of de...
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Causal Compression
We propose a new method of discovering causal relationships in temporal ...
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Bayesian Markov Blanket Estimation
This paper considers a Bayesian view for estimating a subnetwork in a M...
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Probabilistic Clustering of TimeEvolving Distance Data
We present a novel probabilistic clustering model for objects that are r...
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Copula Mixture Model for Dependencyseeking Clustering
We introduce a copula mixture model to perform dependencyseeking cluste...
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Volker Roth
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