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08/18/2023
Physics-Informed Boundary Integral Networks (PIBI-Nets): A Data-Driven Approach for Solving Partial Differential Equations
Partial differential equations (PDEs) can describe many relevant phenome...
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06/12/2023
Conditional Matrix Flows for Gaussian Graphical Models
Studying conditional independence structure among many variables with fe...
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05/26/2023
Lagrangian Flow Networks for Conservation Laws
We introduce Lagrangian Flow Networks (LFlows) for modeling fluid densit...
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06/03/2022
Truly Mesh-free Physics-Informed Neural Networks
Physics-informed Neural Networks (PINNs) have recently emerged as a prin...
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04/14/2022
Learning Invariances with Generalised Input-Convex Neural Networks
Considering smooth mappings from input vectors to continuous targets, ou...
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11/25/2021
Learning Conditional Invariance through Cycle Consistency
Identifying meaningful and independent factors of variation in a dataset...
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06/24/2020
On the Empirical Neural Tangent Kernel of Standard Finite-Width Convolutional Neural Network Architectures
The Neural Tangent Kernel (NTK) is an important milestone in the ongoing...
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02/07/2020
Inverse Learning of Symmetry Transformations
Symmetry transformations induce invariances and are a crucial building b...
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02/03/2020
Learning Extremal Representations with Deep Archetypal Analysis
Archetypes are typical population representatives in an extremal sense, ...
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12/31/2019
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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08/14/2019
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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08/13/2019
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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04/05/2019
Tensor B-Spline Numerical Methods for PDEs: a High-Performance Alternative to FEM
Tensor B-spline methods are a high-performance alternative to solve part...
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01/30/2019
Deep Archetypal Analysis
"Deep Archetypal Analysis" generates latent representations of high-dime...
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11/26/2018
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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11/19/2018
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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07/06/2018
Causal Deep Information Bottleneck
Estimating causal effects in the presence of latent confounding is a fre...
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04/17/2018
Learning Sparse Latent Representations with the Deep Copula Information Bottleneck
Deep latent variable models are powerful tools for representation learni...
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01/03/2018
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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11/16/2017
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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11/01/2016
Causal Compression
We propose a new method of discovering causal relationships in temporal ...
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10/06/2015
Bayesian Markov Blanket Estimation
This paper considers a Bayesian view for estimating a sub-network in a M...
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04/14/2015
Probabilistic Clustering of Time-Evolving Distance Data
We present a novel probabilistic clustering model for objects that are r...
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06/27/2012