
Interpretations are useful: penalizing explanations to align neural networks with prior knowledge
For an explanation of a deep learning model to be effective, it must pro...
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BatVision: Learning to See 3D Spatial Layout with Two Ears
Virtual camera images showing the correct layout of a space ahead can be...
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Estimating Latent Demand of Shared Mobility through Censored Gaussian Processes
Transport demand is highly dependent on supply, especially for shared tr...
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A ContrastAdaptive Method for Simultaneous WholeBrain and Lesion Segmentation in Multiple Sclerosis
Here we present a method for the simultaneous segmentation of white matt...
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SurVAE Flows: Surjections to Bridge the Gap between VAEs and Flows
Normalizing flows and variational autoencoders are powerful generative m...
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Synthesize, Execute and Debug: Learning to Repair for Neural Program Synthesis
The use of deep learning techniques has achieved significant progress fo...
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A Learning Strategy for Contrastagnostic MRI Segmentation
We present a deep learning strategy that enables, for the first time, co...
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LAVAE: Disentangling Location and Appearance
We propose a probabilistic generative model for unsupervised learning of...
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Geodesic Clustering in Deep Generative Models
Deep generative models are tremendously successful in learning lowdimen...
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Heteroscedastic Gaussian processes for uncertainty modeling in largescale crowdsourced traffic data
Accurately modeling traffic speeds is a fundamental part of efficient in...
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Towards a Flexible Deep Learning Method for Automatic Detection of Clinically Relevant MultiModal Events in the Polysomnogram
Much attention has been given to automatic sleep staging algorithms in p...
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Optimal Variance Control of the Score Function Gradient Estimator for Importance Weighted Bounds
This paper introduces novel results for the score function gradient esti...
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Only Bayes should learn a manifold (on the estimation of differential geometric structure from data)
We investigate learning of the differential geometric structure of a dat...
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Probabilistic PARAFAC2
The PARAFAC2 is a multimodal factor analysis model suitable for analyzin...
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Scalable Population Synthesis with Deep Generative Modeling
Population synthesis is concerned with the generation of synthetic yet r...
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Computing Canonical Bases of Modules of Univariate Relations
We study the computation of canonical bases of sets of univariate relati...
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Fast Computation of the Roots of Polynomials Over the Ring of Power Series
We give an algorithm for computing all roots of polynomials over a univa...
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Recurrent Relational Networks for Complex Relational Reasoning
Humans possess an ability to abstractly reason about objects and their i...
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Latent Space Oddity: on the Curvature of Deep Generative Models
Deep generative models provide a systematic way to learn nonlinear data ...
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Adaptive Smoothing in fMRI Data Processing Neural Networks
Functional Magnetic Resonance Imaging (fMRI) relies on multistep data p...
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Deep learning from crowds
Over the last few years, deep learning has revolutionized the field of m...
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Convolutional LSTM Networks for Subcellular Localization of Proteins
Machine learning is widely used to analyze biological sequence data. Non...
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Protein Secondary Structure Prediction with Long Short Term Memory Networks
Prediction of protein secondary structure from the amino acid sequence i...
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Incorporating Prior Information in Compressive Online Robust Principal Component Analysis
We consider an online version of the robust Principle Component Analysis...
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Finite First Hitting Time versus Stochastic Convergence in Particle Swarm Optimisation
We reconsider stochastic convergence analyses of particle swarm optimisa...
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On the Impact of MutationSelection Balance on the Runtime of Evolutionary Algorithms
The interplay between mutation and selection plays a fundamental role in...
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Towards endtoend optimisation of functional image analysis pipelines
The study of neurocognitive tasks requiring accurate localisation of act...
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Intrinsic Grassmann Averages for Online Linear and Robust Subspace Learning
Principal Component Analysis (PCA) is a fundamental method for estimatin...
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A Locally Adaptive Normal Distribution
The multivariate normal density is a monotonic function of the distance ...
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An Adaptive ResampleMove Algorithm for Estimating Normalizing Constants
The estimation of normalizing constants is a fundamental step in probabi...
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Nonparametric Modeling of Dynamic Functional Connectivity in fMRI Data
Dynamic functional connectivity (FC) has in recent years become a topic ...
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Bayesian inference for spatiotemporal spikeandslab priors
In this work, we address the problem of solving a series of underdetermi...
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Spatiotemporal Spike and Slab Priors for Multiple Measurement Vector Problems
We are interested in solving the multiple measurement vector (MMV) probl...
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Bayesian leaveoneout crossvalidation approximations for Gaussian latent variable models
The future predictive performance of a Bayesian model can be estimated u...
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Efficient inference of overlapping communities in complex networks
We discuss two views on extending existing methods for complex network m...
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Online open neuroimaging mass metaanalysis
We describe a system for metaanalysis where a wiki stores numerical dat...
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Adaptive Reconfiguration Moves for Dirichlet Mixtures
Bayesian mixture models are widely applied for unsupervised learning and...
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A Topic Model Approach to MultiModal Similarity
Calculating similarities between objects defined by many heterogeneous d...
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Dreaming More Data: Classdependent Distributions over Diffeomorphisms for Learned Data Augmentation
Data augmentation is a key element in training highdimensional models. ...
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Dimensionality reduction for clickthrough rate prediction: Dense versus sparse representation
In online advertising, display ads are increasingly being placed based o...
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The Infinite Degree Corrected Stochastic Block Model
In Stochastic blockmodels, which are among the most prominent statistica...
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Semisupervised Eigenvectors for Largescale Locallybiased Learning
In many applications, one has side information, e.g., labels that are pr...
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A TensorBased Dictionary Learning Approach to Tomographic Image Reconstruction
We consider tomographic reconstruction using priors in the form of a dic...
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Perturbative Corrections for Approximate Inference in Gaussian Latent Variable Models
Expectation Propagation (EP) provides a framework for approximate infere...
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Geodesic Exponential Kernels: When Curvature and Linearity Conflict
We consider kernel methods on general geodesic metric spaces and provide...
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Bézier curves that are close to elastica
We study the problem of identifying those cubic Bézier curves that are c...
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Infill Optimization for Additive Manufacturing  Approaching Bonelike Porous Structures
Porous structures such as trabecular bone are widely seen in nature. The...
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Geoplotlib: a Python Toolbox for Visualizing Geographical Data
We introduce geoplotlib, an opensource python toolbox for visualizing g...
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Hash Embeddings for Efficient Word Representations
We present hash embeddings, an efficient method for representing words i...
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CloudScan  A configurationfree invoice analysis system using recurrent neural networks
We present CloudScan; an invoice analysis system that requires zero conf...
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