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.,

Orbital MCMC

Markov Chain Monte Carlo (MCMC) is a computational approach to fundament...
10/15/2020 ∙ by Kirill Neklyudov, et al. ∙ 0 ∙ share

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Natural Graph Networks

Conventional neural message passing algorithms are invariant under permu...
07/16/2020 ∙ by Pim de Haan, et al. ∙ 179 ∙ share

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Federated Learning of User Authentication Models

Machine learning-based User Authentication (UA) models have been widely ...
07/09/2020 ∙ by Hossein Hosseini, et al. ∙ 47 ∙ share

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SurVAE Flows: Surjections to Bridge the Gap between VAEs and Flows

Normalizing flows and variational autoencoders are powerful generative m...
07/06/2020 ∙ by Didrik Nielsen, et al. ∙ 12 ∙ share

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RE-MIMO: Recurrent and Permutation Equivariant Neural MIMO Detection

In this paper, we present a novel neural network for MIMO symbol detecti...
06/30/2020 ∙ by Kumar Pratik, et al. ∙ 2 ∙ share

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MDP Homomorphic Networks: Group Symmetries in Reinforcement Learning

This paper introduces MDP homomorphic networks for deep reinforcement le...
06/30/2020 ∙ by Elise van der Pol, et al. ∙ 1 ∙ share

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Involutive MCMC: a Unifying Framework

Markov Chain Monte Carlo (MCMC) is a computational approach to fundament...
06/30/2020 ∙ by Kirill Neklyudov, et al. ∙ 8 ∙ share

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Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series Data

Standard causal discovery methods must fit a new model whenever they enc...
06/18/2020 ∙ by Sindy Löwe, et al. ∙ 41 ∙ share

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SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks

We introduce the SE(3)-Transformer, a variant of the self-attention modu...
06/18/2020 ∙ by Fabian B. Fuchs, et al. ∙ 0 ∙ share

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The Convolution Exponential and Generalized Sylvester Flows

This paper introduces a new method to build linear flows, by taking the ...
06/02/2020 ∙ by Emiel Hoogeboom, et al. ∙ 10 ∙ 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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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An Introduction to Variational Autoencoders

Variational autoencoders provide a principled framework for learning dee...
06/06/2019 ∙ by Diederik P. Kingma, et al. ∙ 0 ∙ 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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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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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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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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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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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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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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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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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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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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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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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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Predictive Uncertainty through Quantization

High-risk domains require reliable confidence estimates from predictive ...
10/12/2018 ∙ by Bastiaan S. Veeling, et al. ∙ 0 ∙ 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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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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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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3D Steerable CNNs: Learning Rotationally Equivariant Features in Volumetric Data

We present a convolutional network that is equivariant to rigid body mot...
07/06/2018 ∙ by Maurice Weiler, et al. ∙ 0 ∙ share

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Sample Efficient Semantic Segmentation using Rotation Equivariant Convolutional Networks

We propose a semantic segmentation model that exploits rotation and refl...
07/02/2018 ∙ by Jasper Linmans, et al. ∙ 0 ∙ share

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Rotation Equivariant CNNs for Digital Pathology

We propose a new model for digital pathology segmentation, based on the ...
06/08/2018 ∙ by Bastiaan S. Veeling, et al. ∙ 0 ∙ 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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Max Welling

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