Max Welling

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University of California, Irvine ∙ Qualcomm ∙ University of Amsterdam ∙ Google Inc

Vice President Technologies at Qualcomm Technologies Netherlands, Senior Fellow Canadian Institute for Advanced Research, Co-founder and Chief Scientific Advisor Scyfer B.V.,

Gauge Equivariant Mesh CNNs: Anisotropic convolutions on geometric graphs

A common approach to define convolutions on meshes is to interpret them ...
03/11/2020 ∙ by Pim de Haan, et al. ∙ 115 ∙ share

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Taxonomy and Evaluation of Structured Compression of Convolutional Neural Networks

The success of deep neural networks in many real-world applications is l...
12/20/2019 ∙ by Andrey Kuzmin, et al. ∙ 47 ∙ share

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Simple and Accurate Uncertainty Quantification from Bias-Variance Decomposition

Accurate uncertainty quantification is crucial for many applications whe...
02/13/2020 ∙ by Shi Hu, et al. ∙ 43 ∙ share

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i-RIM applied to the fastMRI challenge

We, team AImsterdam, summarize our submission to the fastMRI challenge (...
10/20/2019 ∙ by Patrick Putzky, et al. ∙ 30 ∙ share

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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 ∙ share

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Estimating Gradients for Discrete Random Variables by Sampling without Replacement

We derive an unbiased estimator for expectations over discrete random va...
02/14/2020 ∙ by Wouter Kool, et al. ∙ 21 ∙ share

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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 ∙ share

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Learning Likelihoods with Conditional Normalizing Flows

Normalizing Flows (NFs) are able to model complicated distributions p(y)...
11/29/2019 ∙ by Christina Winkler, et al. ∙ 17 ∙ share

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Neural Enhanced Belief Propagation on Factor Graphs

A graphical model is a structured representation of locally dependent ra...
03/04/2020 ∙ by Victor Garcia Satorras, et al. ∙ 16 ∙ share

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Gradient ℓ_1 Regularization for Quantization Robustness

We analyze the effect of quantizing weights and activations of neural ne...
02/18/2020 ∙ by Milad Alizadeh, et al. ∙ 14 ∙ share

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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 ∙ share

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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 ∙ share

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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 ∙ share

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DP-MAC: 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 ∙ share

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Invert to Learn to Invert

Iterative learning to infer approaches have become popular solvers for i...
11/25/2019 ∙ by Patrick Putzky, et al. ∙ 11 ∙ share

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Probabilistic Binary Neural Networks

Low bit-width weights and activations are an effective way of combating ...
09/10/2018 ∙ by Jorn W. T. Peters, et al. ∙ 10 ∙ share

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Graph Refinement based Tree Extraction using Mean-Field 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 ∙ share

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Stochastic Beams and Where to Find Them: The Gumbel-Top-k Trick for Sampling Sequences Without Replacement

The well-known Gumbel-Max trick for sampling from a categorical distribu...
03/14/2019 ∙ by Wouter Kool, et al. ∙ 10 ∙ share

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Guided Variational Autoencoder for Disentanglement Learning

We propose an algorithm, guided variational autoencoder (Guided-VAE), th...
04/02/2020 ∙ by Zheng Ding, et al. ∙ 9 ∙ share

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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 ∙ share

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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 ∙ share

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Integer Discrete Flows and Lossless Compression

Lossless compression methods shorten the expected representation size of...
05/17/2019 ∙ by Emiel Hoogeboom, et al. ∙ 7 ∙ share

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Sinkhorn AutoEncoders

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

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Deep Scale-spaces: Equivariance Over Scale

We introduce deep scale-spaces (DSS), a generalization of convolutional ...
05/28/2019 ∙ by Daniel E. Worrall, et al. ∙ 5 ∙ share

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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 ∙ share

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The Functional Neural Process

We present a new family of exchangeable stochastic processes, the Functi...
06/19/2019 ∙ by Christos Louizos, et al. ∙ 4 ∙ share

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A Data and Compute Efficient Design for Limited-Resources Deep Learning

Thanks to their improved data efficiency, equivariant neural networks ha...
04/21/2020 ∙ by Mirgahney Mohamed, et al. ∙ 4 ∙ share

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Bayesian Bits: Unifying Quantization and Pruning

We introduce Bayesian Bits, a practical method for joint mixed precision...
05/14/2020 ∙ by Mart van Baalen, et al. ∙ 4 ∙ share

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Relational Generalized Few-Shot Learning

Transferring learned models to novel tasks is a challenging problem, par...
07/22/2019 ∙ by Xiahan Shi, et al. ∙ 3 ∙ share

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Primal-Dual Wasserstein GAN

We introduce Primal-Dual Wasserstein GAN, a new learning algorithm for b...
05/24/2018 ∙ by Mevlana Gemici, et al. ∙ 2 ∙ share

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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 ∙ share

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Combinatorial Bayesian Optimization using Graph Representations

This paper focuses on Bayesian Optimization - typically considered with ...
02/01/2019 ∙ by ChangYong Oh, et al. ∙ 2 ∙ share

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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 ∙ share

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Data-Free Quantization through Weight Equalization and Bias Correction

We introduce a data-free quantization method for deep neural networks th...
06/11/2019 ∙ by Markus Nagel, et al. ∙ 1 ∙ share

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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 ∙ share

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Batch-Shaped Channel Gated Networks

We present a method for gating deep-learning architectures on a fine-gra...
07/15/2019 ∙ by Babak Ehteshami Bejnordi, et al. ∙ 1 ∙ share

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Improved Bayesian Compression

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

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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 ∙ share

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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 ∙ share

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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 ∙ share

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Improving Variational Auto-Encoders using convex combination linear Inverse Autoregressive Flow

In this paper, we propose a new volume-preserving flow and show that it ...
06/07/2017 ∙ by Jakub M. Tomczak, et al. ∙ 0 ∙ share

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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 ∙ share

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Causal Effect Inference with Deep Latent-Variable Models

Learning individual-level causal effects from observational data, such a...
05/24/2017 ∙ by Christos Louizos, et al. ∙ 0 ∙ share

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Bayesian Compression for Deep Learning

Compression and computational efficiency in deep learning have become a ...
05/24/2017 ∙ by Christos Louizos, et al. ∙ 0 ∙ share

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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 ∙ share

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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 ∙ share

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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 ∙ share

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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 ∙ share

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Soft Weight-Sharing for Neural Network Compression

The success of deep learning in numerous application domains created the...
02/13/2017 ∙ by Karen Ullrich, et al. ∙ 0 ∙ share

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Steerable CNNs

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

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Max Welling

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