
Emerging Convolutions for Generative Normalizing Flows
Generative flows are attractive because they admit exact likelihood opti...
01/30/2019 ∙ by Emiel Hoogeboom, et al. ∙ 23 ∙ shareread it

Adversarial Variational Inference and Learning in Markov Random Fields
Markov random fields (MRFs) find applications in a variety of machine le...
01/24/2019 ∙ by Chongxuan Li, et al. ∙ 18 ∙ shareread it

The Deep Weight Prior. Modeling a prior distribution for CNNs using generative models
Bayesian inference is known to provide a general framework for incorpora...
10/16/2018 ∙ by Andrei Atanov, et al. ∙ 12 ∙ shareread it

Gauge Equivariant Convolutional Networks and the Icosahedral CNN
The idea of equivariance to symmetry transformations provides one of the...
02/11/2019 ∙ by Taco S Cohen, et al. ∙ 11 ∙ shareread it

DIVA: Domain Invariant Variational Autoencoders
We consider the problem of domain generalization, namely, how to learn r...
05/24/2019 ∙ by Maximilian Ilse, et al. ∙ 11 ∙ shareread it

DPMAC: The Differentially Private Method of Auxiliary Coordinates for Deep Learning
Developing a differentially private deep learning algorithm is challengi...
10/15/2019 ∙ by Frederik Harder, et al. ∙ 11 ∙ shareread it

Probabilistic Binary Neural Networks
Low bitwidth weights and activations are an effective way of combating ...
09/10/2018 ∙ by Jorn W. T. Peters, et al. ∙ 10 ∙ shareread it

Graph Refinement based Tree Extraction using MeanField Networks and Graph Neural Networks
Graph refinement, or the task of obtaining subgraphs of interest from ov...
11/21/2018 ∙ by Raghavendra Selvan, et al. ∙ 10 ∙ shareread it

Stochastic Beams and Where to Find Them: The GumbelTopk Trick for Sampling Sequences Without Replacement
The wellknown GumbelMax trick for sampling from a categorical distribu...
03/14/2019 ∙ by Wouter Kool, et al. ∙ 10 ∙ shareread it

Combining Generative and Discriminative Models for Hybrid Inference
A graphical model is a structured representation of the data generating ...
06/06/2019 ∙ by Victor Garcia Satorras, et al. ∙ 8 ∙ shareread it

Differentiable probabilistic models of scientific imaging with the Fourier slice theorem
Scientific imaging techniques such as optical and electron microscopy an...
06/18/2019 ∙ by Karen Ullrich, et al. ∙ 8 ∙ shareread it

Integer Discrete Flows and Lossless Compression
Lossless compression methods shorten the expected representation size of...
05/17/2019 ∙ by Emiel Hoogeboom, et al. ∙ 7 ∙ shareread it

Sinkhorn AutoEncoders
Optimal Transport offers an alternative to maximum likelihood for learni...
10/02/2018 ∙ by Giorgio Patrini, et al. ∙ 6 ∙ shareread it

Deep Scalespaces: Equivariance Over Scale
We introduce deep scalespaces (DSS), a generalization of convolutional ...
05/28/2019 ∙ by Daniel E. Worrall, et al. ∙ 5 ∙ shareread it

BOCK : Bayesian Optimization with Cylindrical Kernels
A major challenge in Bayesian Optimization is the boundary issue (Swersk...
06/05/2018 ∙ by ChangYong Oh, et al. ∙ 4 ∙ shareread it

The Functional Neural Process
We present a new family of exchangeable stochastic processes, the Functi...
06/19/2019 ∙ by Christos Louizos, et al. ∙ 4 ∙ shareread it

Relational Generalized FewShot Learning
Transferring learned models to novel tasks is a challenging problem, par...
07/22/2019 ∙ by Xiahan Shi, et al. ∙ 3 ∙ shareread it

PrimalDual Wasserstein GAN
We introduce PrimalDual Wasserstein GAN, a new learning algorithm for b...
05/24/2018 ∙ by Mevlana Gemici, et al. ∙ 2 ∙ shareread it

Relaxed Quantization for Discretized Neural Networks
Neural network quantization has become an important research area due to...
10/03/2018 ∙ by Christos Louizos, et al. ∙ 2 ∙ shareread it

Combinatorial Bayesian Optimization using Graph Representations
This paper focuses on Bayesian Optimization  typically considered with ...
02/01/2019 ∙ by ChangYong Oh, et al. ∙ 2 ∙ shareread it

Covariance in Physics and Convolutional Neural Networks
In this proceeding we give an overview of the idea of covariance (or equ...
06/06/2019 ∙ by Miranda C. N. Cheng, et al. ∙ 1 ∙ shareread it

DataFree Quantization through Weight Equalization and Bias Correction
We introduce a datafree quantization method for deep neural networks th...
06/11/2019 ∙ by Markus Nagel, et al. ∙ 1 ∙ shareread it

Supervised Uncertainty Quantification for Segmentation with Multiple Annotations
The accurate estimation of predictive uncertainty carries importance in ...
07/03/2019 ∙ by Shi Hu, et al. ∙ 1 ∙ shareread it

BatchShaped Channel Gated Networks
We present a method for gating deeplearning architectures on a finegra...
07/15/2019 ∙ by Babak Ehteshami Bejnordi, et al. ∙ 1 ∙ shareread it

Improved Bayesian Compression
Compression of Neural Networks (NN) has become a highly studied topic in...
11/17/2017 ∙ by Marco Federici, et al. ∙ 0 ∙ shareread it

Temporally Efficient Deep Learning with Spikes
The vast majority of natural sensory data is temporally redundant. Video...
06/13/2017 ∙ by Peter O'Connor, et al. ∙ 0 ∙ shareread it

Sigma Delta Quantized Networks
Deep neural networks can be obscenely wasteful. When processing video, a...
11/07/2016 ∙ by Peter O'Connor, et al. ∙ 0 ∙ shareread it

Deep Spiking Networks
We introduce an algorithm to do backpropagation on a spiking network. Ou...
02/26/2016 ∙ by Peter O'Connor, et al. ∙ 0 ∙ shareread it

Improving Variational AutoEncoders using convex combination linear Inverse Autoregressive Flow
In this paper, we propose a new volumepreserving flow and show that it ...
06/07/2017 ∙ by Jakub M. Tomczak, et al. ∙ 0 ∙ shareread it

Visualizing Deep Neural Network Decisions: Prediction Difference Analysis
This article presents the prediction difference analysis method for visu...
02/15/2017 ∙ by Luisa M Zintgraf, et al. ∙ 0 ∙ shareread it

Causal Effect Inference with Deep LatentVariable Models
Learning individuallevel causal effects from observational data, such a...
05/24/2017 ∙ by Christos Louizos, et al. ∙ 0 ∙ shareread it

Bayesian Compression for Deep Learning
Compression and computational efficiency in deep learning have become a ...
05/24/2017 ∙ by Christos Louizos, et al. ∙ 0 ∙ shareread it

VAE with a VampPrior
Many different methods to train deep generative models have been introdu...
05/19/2017 ∙ by Jakub M. Tomczak, et al. ∙ 0 ∙ shareread it

Modeling Relational Data with Graph Convolutional Networks
Knowledge graphs enable a wide variety of applications, including questi...
03/17/2017 ∙ by Michael Schlichtkrull, et al. ∙ 0 ∙ shareread it

Recurrent Inference Machines for Solving Inverse Problems
Much of the recent research on solving iterative inference problems focu...
06/13/2017 ∙ by Patrick Putzky, et al. ∙ 0 ∙ shareread it

Multiplicative Normalizing Flows for Variational Bayesian Neural Networks
We reinterpret multiplicative noise in neural networks as auxiliary rand...
03/06/2017 ∙ by Christos Louizos, et al. ∙ 0 ∙ shareread it

Soft WeightSharing for Neural Network Compression
The success of deep learning in numerous application domains created the...
02/13/2017 ∙ by Karen Ullrich, et al. ∙ 0 ∙ shareread it

Steerable CNNs
It has long been recognized that the invariance and equivariance propert...
12/27/2016 ∙ by Taco S Cohen, et al. ∙ 0 ∙ shareread it

Variational Graph AutoEncoders
We introduce the variational graph autoencoder (VGAE), a framework for ...
11/21/2016 ∙ by Thomas N. Kipf, et al. ∙ 0 ∙ shareread it

Accelerating the BSM interpretation of LHC data with machine learning
The interpretation of Large Hadron Collider (LHC) data in the framework ...
11/08/2016 ∙ by Gianfranco Bertone, et al. ∙ 0 ∙ shareread it

Variational Bayes In Private Settings (VIPS)
We provide a general framework for privacypreserving variational Bayes ...
11/01/2016 ∙ by Mijung Park, et al. ∙ 0 ∙ shareread it

Private Topic Modeling
We develop a privatised stochastic variational inference method for Late...
09/14/2016 ∙ by Mijung Park, et al. ∙ 0 ∙ shareread it

SemiSupervised Classification with Graph Convolutional Networks
We present a scalable approach for semisupervised learning on graphstr...
09/09/2016 ∙ by Thomas N. Kipf, et al. ∙ 0 ∙ shareread it

Deep Learning with Permutationinvariant Operator for Multiinstance Histopathology Classification
The computeraided analysis of medical scans is a longstanding goal in t...
12/01/2017 ∙ by Jakub M. Tomczak, et al. ∙ 0 ∙ shareread it

Automatic Variational ABC
Approximate Bayesian Computation (ABC) is a framework for performing lik...
06/28/2016 ∙ by Alexander Moreno, et al. ∙ 0 ∙ shareread it

Improving Variational Inference with Inverse Autoregressive Flow
The framework of normalizing flows provides a general strategy for flexi...
06/15/2016 ∙ by Diederik P. Kingma, et al. ∙ 0 ∙ shareread it

A note on privacy preserving iteratively reweighted least squares
Iteratively reweighted least squares (IRLS) is a widelyused method in m...
05/24/2016 ∙ by Mijung Park, et al. ∙ 0 ∙ shareread it

DPEM: Differentially Private Expectation Maximization
The iterative nature of the expectation maximization (EM) algorithm pres...
05/23/2016 ∙ by Mijung Park, et al. ∙ 0 ∙ shareread it

On the Theory and Practice of PrivacyPreserving Bayesian Data Analysis
Bayesian inference has great promise for the privacypreserving analysis...
03/23/2016 ∙ by James Foulds, et al. ∙ 0 ∙ shareread it

Structured and Efficient Variational Deep Learning with Matrix Gaussian Posteriors
We introduce a variational Bayesian neural network where the parameters ...
03/15/2016 ∙ by Christos Louizos, et al. ∙ 0 ∙ shareread it
Max Welling
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Vice President Technologies at Qualcomm Technologies Netherlands, Senior Fellow Canadian Institute for Advanced Research, Cofounder and Chief Scientific Advisor Scyfer B.V.,