
Improved Precision and Recall Metric for Assessing Generative Models
The ability to evaluate the performance of a computational model is a vi...
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SketchTransfer: A Challenging New Task for Exploring DetailInvariance and the Abstractions Learned by Deep Networks
Deep networks have achieved excellent results in perceptual tasks, yet t...
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A Novel Variational Autoencoder with Applications to Generative Modelling, Classification, and Ordinal Regression
We develop a novel probabilistic generative model based on the variation...
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Don't ignore Dropout in Fully Convolutional Networks
Data for Image segmentation models can be costly to obtain due to the pr...
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Projective Inference in Highdimensional Problems: Prediction and Feature Selection
This paper discusses predictive inference and feature selection for gene...
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EntropyRegularized 2Wasserstein Distance between Gaussian Measures
Gaussian distributions are plentiful in applications dealing in uncertai...
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LikelihoodFree Inference with Deep Gaussian Processes
In recent years, surrogate models have been successfully used in likelih...
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A Survey on Knowledge Graphs: Representation, Acquisition and Applications
Human knowledge provides a formal understanding of the world. Knowledge ...
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Machine Learning assisted Handover and Resource Management for Cellular Connected Drones
Enabling cellular connectivity for drones introduces a wide set of chall...
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Variance reduction for distributed stochastic gradient MCMC
Stochastic gradient MCMC methods, such as stochastic gradient Langevin d...
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Longitudinal Variational Autoencoder
Longitudinal datasets measured repeatedly over time from individual subj...
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Human Strategic Steering Improves Performance of Interactive Optimization
A central concern in an interactive intelligent system is optimization o...
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Automated Polysomnography Analysis for Detection of NonApneic and NonHypopneic Arousals using Feature Engineering and a Bidirectional LSTM Network
Objective: The aim of this study is to develop an automated classificati...
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Gaussian process classification using posterior linearisation
This paper proposes a new algorithm for Gaussian process classification ...
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Classifying Process Instances Using Recurrent Neural Networks
Process Mining consists of techniques where logs created by operative sy...
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Neural NonStationary Spectral Kernel
Standard kernels such as Matérn or RBF kernels only encode simple monoto...
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Visualizing Movement Control Optimization Landscapes
A large body of animation research focuses on optimization of movement c...
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DGCNet: Dense Geometric Correspondence Network
This paper addresses the challenge of dense pixel correspondence estimat...
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Scalable Bayesian Nonlinear Matrix Completion
Matrix completion aims to predict missing elements in a partially observ...
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GraphMix: Regularized Training of Graph Neural Networks for SemiSupervised Learning
We present GraphMix, a regularization technique for Graph Neural Network...
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Interpolationbased semisupervised learning for object detection
Despite the data labeling cost for the object detection tasks being subs...
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KNEEL: Knee Anatomical Landmark Localization Using Hourglass Networks
This paper addresses the challenge of localization of anatomical landmar...
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How to Construct Deep Recurrent Neural Networks
In this paper, we explore different ways to extend a recurrent neural ne...
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An Iterative Closest Points Approach to Neural Generative Models
We present a simple way to learn a transformation that maps samples of o...
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A Tutorial on Canonical Correlation Methods
Canonical correlation analysis is a family of multivariate statistical m...
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A Review on Bilevel Optimization: From Classical to Evolutionary Approaches and Applications
Bilevel optimization is defined as a mathematical program, where an opti...
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Specious rules: an efficient and effective unifying method for removing misleading and uninformative patterns in association rule mining
We present theoretical analysis and a suite of tests and procedures for ...
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Interactive Visual Data Exploration with Subjective Feedback: An InformationTheoretic Approach
Visual exploration of highdimensional realvalued datasets is a fundame...
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Subjectively Interesting Subgroup Discovery on Realvalued Targets
Deriving insights from highdimensional data is one of the core problems...
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Structural Feature Selection for Event Logs
We consider the problem of classifying business process instances based ...
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Gaussian Process Latent Force Models for Learning and Stochastic Control of Physical Systems
This paper is concerned with estimation and stochastic control in physic...
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Optimal Management of Naturally Regenerating Unevenaged Forests
A shift from evenaged forest management to unevenaged management pract...
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An adaptive prefixassignment technique for symmetry reduction
This paper presents a technique for symmetry reduction that adaptively a...
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Data Stream Classification using Random Feature Functions and Novel Method Combinations
Big Data streams are being generated in a faster, bigger, and more commo...
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Inverse Reinforcement Learning from Incomplete Observation Data
Inverse reinforcement learning (IRL) aims to explain observed strategic ...
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DopeLearning: A Computational Approach to Rap Lyrics Generation
Writing rap lyrics requires both creativity to construct a meaningful, i...
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Denoising autoencoder with modulated lateral connections learns invariant representations of natural images
Suitable lateral connections between encoder and decoder are shown to al...
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Techniques for Learning Binary Stochastic Feedforward Neural Networks
Stochastic binary hidden units in a multilayer perceptron (MLP) network...
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Correcting boundary overexploration deficiencies in Bayesian optimization with virtual derivative sign observations
Bayesian optimization () is a global optimization strategy designed to f...
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Exploiting interimage similarity and ensemble of extreme learners for fixation prediction using deep features
This paper presents a novel fixation prediction and saliency modeling fr...
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Efficient Evolutionary Algorithm for SingleObjective Bilevel Optimization
Bilevel optimization problems are a class of challenging optimization pr...
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Automated Query Learning with Wikipedia and Genetic Programming
Most of the existing information retrieval systems are based on bag of w...
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Gaussian Process Kernels for Popular StateSpace Time Series Models
In this paper we investigate a link between state space models and Gaus...
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Parallelizable sparse inverse formulation Gaussian processes (SpInGP)
We propose a parallelizable sparse inverse formulation Gaussian process ...
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Peacock Bundles: Bundle Coloring for Graphs with GlobalityLocality Tradeoff
Bundling of graph edges (nodetonode connections) is a common technique...
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Model selection for Gaussian processes utilizing sensitivity of posterior predictive distribution
We propose two novel methods for simplifying Gaussian process (GP) model...
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Estimating activity cycles with probabilistic methods II. The Mount Wilson Ca H&K data
Debate over the existence versus nonexistence of trends in the stellar a...
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Estimating activity cycles with probabilistic methods I. Bayesian Generalised LombScargle Periodogram with Trend
Period estimation is one of the central topics in astronomical time seri...
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Studying a set of properties of inconsistency indices for pairwise comparisons
Pairwise comparisons between alternatives are a wellestablished tool to...
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Recent advances on inconsistency indices for pairwise comparisons  a commentary
This paper recalls the definition of consistency for pairwise comparison...
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