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Aalto University is a multidisciplinary science and art community in the fields of technical sciences, business and arts.

Improved Precision and Recall Metric for Assessing Generative Models

The ability to evaluate the performance of a computational model is a vi...
04/15/2019 ∙ by Tuomas Kynkäänniemi, et al. ∙ 52 ∙ share

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SketchTransfer: A Challenging New Task for Exploring Detail-Invariance and the Abstractions Learned by Deep Networks

Deep networks have achieved excellent results in perceptual tasks, yet t...
12/25/2019 ∙ by Alex Lamb, et al. ∙ 40 ∙ share

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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...
12/18/2018 ∙ by Joel Jaskari, et al. ∙ 28 ∙ share

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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...
08/24/2019 ∙ by Thomas Spilsbury, et al. ∙ 26 ∙ share

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Projective Inference in High-dimensional Problems: Prediction and Feature Selection

This paper discusses predictive inference and feature selection for gene...
10/04/2018 ∙ by Juho Piironen, et al. ∙ 24 ∙ share

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Entropy-Regularized 2-Wasserstein Distance between Gaussian Measures

Gaussian distributions are plentiful in applications dealing in uncertai...
06/05/2020 ∙ by Anton Mallasto, et al. ∙ 24 ∙ share

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Likelihood-Free Inference with Deep Gaussian Processes

In recent years, surrogate models have been successfully used in likelih...
06/18/2020 ∙ by Alexander Aushev, et al. ∙ 14 ∙ share

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A Survey on Knowledge Graphs: Representation, Acquisition and Applications

Human knowledge provides a formal understanding of the world. Knowledge ...
02/02/2020 ∙ by Shaoxiong Ji, et al. ∙ 13 ∙ share

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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...
01/22/2020 ∙ by Amin Azari, et al. ∙ 11 ∙ share

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Variance reduction for distributed stochastic gradient MCMC

Stochastic gradient MCMC methods, such as stochastic gradient Langevin d...
04/23/2020 ∙ by Khaoula El Mekkaoui, et al. ∙ 11 ∙ share

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Longitudinal Variational Autoencoder

Longitudinal datasets measured repeatedly over time from individual subj...
06/17/2020 ∙ by Siddharth Ramchandran, et al. ∙ 9 ∙ share

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Human Strategic Steering Improves Performance of Interactive Optimization

A central concern in an interactive intelligent system is optimization o...
05/04/2020 ∙ by Fabio Colella, et al. ∙ 8 ∙ share

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Automated Polysomnography Analysis for Detection of Non-Apneic and Non-Hypopneic Arousals using Feature Engineering and a Bidirectional LSTM Network

Objective: The aim of this study is to develop an automated classificati...
09/06/2019 ∙ by Ali Bahrami Rad, et al. ∙ 7 ∙ share

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Gaussian process classification using posterior linearisation

This paper proposes a new algorithm for Gaussian process classification ...
09/13/2018 ∙ by Ángel F. García-Fernández, et al. ∙ 6 ∙ share

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Classifying Process Instances Using Recurrent Neural Networks

Process Mining consists of techniques where logs created by operative sy...
09/16/2018 ∙ by Markku Hinkka, et al. ∙ 6 ∙ share

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Neural Non-Stationary Spectral Kernel

Standard kernels such as Matérn or RBF kernels only encode simple monoto...
11/27/2018 ∙ by Sami Remes, et al. ∙ 6 ∙ share

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Visualizing Movement Control Optimization Landscapes

A large body of animation research focuses on optimization of movement c...
09/17/2019 ∙ by Perttu Hämäläinen, et al. ∙ 6 ∙ share

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DGC-Net: Dense Geometric Correspondence Network

This paper addresses the challenge of dense pixel correspondence estimat...
10/19/2018 ∙ by Iaroslav Melekhov, et al. ∙ 4 ∙ share

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Scalable Bayesian Non-linear Matrix Completion

Matrix completion aims to predict missing elements in a partially observ...
07/31/2019 ∙ by Xiangju Qin, et al. ∙ 4 ∙ share

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GraphMix: Regularized Training of Graph Neural Networks for Semi-Supervised Learning

We present GraphMix, a regularization technique for Graph Neural Network...
09/25/2019 ∙ by Vikas Verma, et al. ∙ 4 ∙ share

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Interpolation-based semi-supervised learning for object detection

Despite the data labeling cost for the object detection tasks being subs...
06/03/2020 ∙ by Jisoo Jeong, et al. ∙ 3 ∙ share

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KNEEL: Knee Anatomical Landmark Localization Using Hourglass Networks

This paper addresses the challenge of localization of anatomical landmar...
07/29/2019 ∙ by Aleksei Tiulpin, et al. ∙ 2 ∙ share

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How to Construct Deep Recurrent Neural Networks

In this paper, we explore different ways to extend a recurrent neural ne...
12/20/2013 ∙ by Razvan Pascanu, et al. ∙ 0 ∙ share

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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...
11/16/2017 ∙ by Joose Rajamäki, et al. ∙ 0 ∙ share

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A Tutorial on Canonical Correlation Methods

Canonical correlation analysis is a family of multivariate statistical m...
11/07/2017 ∙ by Viivi Uurtio, et al. ∙ 0 ∙ share

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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...
05/17/2017 ∙ by Ankur Sinha, et al. ∙ 0 ∙ share

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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 ...
09/12/2017 ∙ by Wilhelmiina Hämäläinen, et al. ∙ 0 ∙ share

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Interactive Visual Data Exploration with Subjective Feedback: An Information-Theoretic Approach

Visual exploration of high-dimensional real-valued datasets is a fundame...
10/23/2017 ∙ by Kai Puolamäki, et al. ∙ 0 ∙ share

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Subjectively Interesting Subgroup Discovery on Real-valued Targets

Deriving insights from high-dimensional data is one of the core problems...
10/12/2017 ∙ by Jefrey Lijffijt, et al. ∙ 0 ∙ share

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Structural Feature Selection for Event Logs

We consider the problem of classifying business process instances based ...
10/08/2017 ∙ by Markku Hinkka, et al. ∙ 0 ∙ share

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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...
09/15/2017 ∙ by Simo Särkkä, et al. ∙ 0 ∙ share

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Optimal Management of Naturally Regenerating Uneven-aged Forests

A shift from even-aged forest management to uneven-aged management pract...
08/17/2016 ∙ by Ankur Sinha, et al. ∙ 0 ∙ share

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An adaptive prefix-assignment technique for symmetry reduction

This paper presents a technique for symmetry reduction that adaptively a...
06/26/2017 ∙ by Tommi Junttila, et al. ∙ 0 ∙ share

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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...
11/03/2015 ∙ by Diego Marrón, et al. ∙ 0 ∙ share

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Inverse Reinforcement Learning from Incomplete Observation Data

Inverse reinforcement learning (IRL) aims to explain observed strategic ...
03/28/2017 ∙ by Antti Kangasrääsiö, et al. ∙ 0 ∙ share

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DopeLearning: A Computational Approach to Rap Lyrics Generation

Writing rap lyrics requires both creativity to construct a meaningful, i...
05/18/2015 ∙ by Eric Malmi, et al. ∙ 0 ∙ share

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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...
12/22/2014 ∙ by Antti Rasmus, et al. ∙ 0 ∙ share

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Techniques for Learning Binary Stochastic Feedforward Neural Networks

Stochastic binary hidden units in a multi-layer perceptron (MLP) network...
06/11/2014 ∙ by Tapani Raiko, et al. ∙ 0 ∙ share

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Correcting boundary over-exploration deficiencies in Bayesian optimization with virtual derivative sign observations

Bayesian optimization () is a global optimization strategy designed to f...
04/04/2017 ∙ by Eero Siivola, et al. ∙ 0 ∙ share

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Exploiting inter-image similarity and ensemble of extreme learners for fixation prediction using deep features

This paper presents a novel fixation prediction and saliency modeling fr...
10/20/2016 ∙ by Hamed R. Tavakoli, et al. ∙ 0 ∙ share

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Efficient Evolutionary Algorithm for Single-Objective Bilevel Optimization

Bilevel optimization problems are a class of challenging optimization pr...
03/15/2013 ∙ by Ankur Sinha, et al. ∙ 0 ∙ share

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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...
12/03/2010 ∙ by Pekka Malo, et al. ∙ 0 ∙ share

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Gaussian Process Kernels for Popular State-Space Time Series Models

In this paper we investigate a link between state- space models and Gaus...
10/25/2016 ∙ by Alexander Grigorievskiy, et al. ∙ 0 ∙ share

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Parallelizable sparse inverse formulation Gaussian processes (SpInGP)

We propose a parallelizable sparse inverse formulation Gaussian process ...
10/25/2016 ∙ by Alexander Grigorievskiy, et al. ∙ 0 ∙ share

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Peacock Bundles: Bundle Coloring for Graphs with Globality-Locality Trade-off

Bundling of graph edges (node-to-node connections) is a common technique...
09/02/2016 ∙ by Jaakko Peltonen, et al. ∙ 0 ∙ share

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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...
12/21/2017 ∙ by Topi Paananen, et al. ∙ 0 ∙ share

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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...
12/21/2017 ∙ by N. Olspert, et al. ∙ 0 ∙ share

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Estimating activity cycles with probabilistic methods I. Bayesian Generalised Lomb-Scargle Periodogram with Trend

Period estimation is one of the central topics in astronomical time seri...
12/21/2017 ∙ by N. Olspert, et al. ∙ 0 ∙ share

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Studying a set of properties of inconsistency indices for pairwise comparisons

Pairwise comparisons between alternatives are a well-established tool to...
07/31/2015 ∙ by Matteo Brunelli, et al. ∙ 0 ∙ share

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Recent advances on inconsistency indices for pairwise comparisons - a commentary

This paper recalls the definition of consistency for pairwise comparison...
03/28/2015 ∙ by Matteo Brunelli, et al. ∙ 0 ∙ share

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