b'aalto'

research

∙ 04/15/2019

Improved Precision and Recall Metric for Assessing Generative Models

The ability to evaluate the performance of a computational model is a vi...

Tuomas Kynkäänniemi, et al. ∙

research

∙ 09/08/2021

Knowledge mining of unstructured information: application to cyber-domain

Cyber intelligence is widely and abundantly available in numerous open o...

Tuomas Takko, et al. ∙

research

∙ 12/25/2019

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...

Alex Lamb, et al. ∙

research

∙ 12/18/2018

A Novel Variational Autoencoder with Applications to Generative Modelling, Classification, and Ordinal Regression

We develop a novel probabilistic generative model based on the variation...

Joel Jaskari, et al. ∙

research

∙ 08/24/2019

Don't ignore Dropout in Fully Convolutional Networks

Data for Image segmentation models can be costly to obtain due to the pr...

Thomas Spilsbury, et al. ∙

research

∙ 10/04/2018

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

This paper discusses predictive inference and feature selection for gene...

Juho Piironen, et al. ∙

research

∙ 06/05/2020

Entropy-Regularized 2-Wasserstein Distance between Gaussian Measures

Gaussian distributions are plentiful in applications dealing in uncertai...

Anton Mallasto, et al. ∙

research

∙ 08/26/2021

Fine-tuning Pretrained Language Models with Label Attention for Explainable Biomedical Text Classification

The massive growth of digital biomedical data is making biomedical text ...

Bruce Nguyen, et al. ∙

research

∙ 05/20/2022

Self-Paced Multi-Agent Reinforcement Learning

Curriculum reinforcement learning (CRL) aims to speed up learning of a t...

Wenshuai Zhao, et al. ∙

research

∙ 06/18/2020

Likelihood-Free Inference with Deep Gaussian Processes

In recent years, surrogate models have been successfully used in likelih...

Alexander Aushev, et al. ∙

research

∙ 02/02/2020

A Survey on Knowledge Graphs: Representation, Acquisition and Applications

Human knowledge provides a formal understanding of the world. Knowledge ...

Shaoxiong Ji, et al. ∙

research

∙ 05/19/2022

Why GANs are overkill for NLP

This work offers a novel theoretical perspective on why, despite numerou...

David Alvarez-Melis, et al. ∙

research

∙ 01/22/2020

Machine Learning assisted Handover and Resource Management for Cellular Connected Drones

Enabling cellular connectivity for drones introduces a wide set of chall...

Amin Azari, et al. ∙

research

∙ 04/23/2020

Variance reduction for distributed stochastic gradient MCMC

Stochastic gradient MCMC methods, such as stochastic gradient Langevin d...

Khaoula El Mekkaoui, et al. ∙

research

∙ 11/02/2021

Likelihood-Free Inference in State-Space Models with Unknown Dynamics

We introduce a method for inferring and predicting latent states in the ...

Alexander Aushev, et al. ∙

research

∙ 06/17/2020

Longitudinal Variational Autoencoder

Longitudinal datasets measured repeatedly over time from individual subj...

Siddharth Ramchandran, et al. ∙

research

∙ 12/24/2021

Rediscovering Affordance: A Reinforcement Learning Perspective

Affordance refers to the perception of possible actions allowed by an ob...

Yi-Chi Liao, et al. ∙

research

∙ 05/04/2020

Human Strategic Steering Improves Performance of Interactive Optimization

A central concern in an interactive intelligent system is optimization o...

Fabio Colella, et al. ∙

research

∙ 08/09/2021

On the Transferability of Neural Models of Morphological Analogies

Analogical proportions are statements expressed in the form "A is to B a...

Safa Alsaidi, et al. ∙

research

∙ 11/03/2021

Manipulation of granular materials by learning particle interactions

Manipulation of granular materials such as sand or rice remains an unsol...

Neea Tuomainen, et al. ∙

research

∙ 09/06/2019

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...

Ali Bahrami Rad, et al. ∙

research

∙ 01/05/2022

Formant Tracking Using Quasi-Closed Phase Forward-Backward Linear Prediction Analysis and Deep Neural Networks

Formant tracking is investigated in this study by using trackers based o...

Dhananjaya Gowda, et al. ∙

research

∙ 09/13/2018

Gaussian process classification using posterior linearisation

This paper proposes a new algorithm for Gaussian process classification ...

Ángel F. García-Fernández, et al. ∙

research

∙ 09/16/2018

Classifying Process Instances Using Recurrent Neural Networks

Process Mining consists of techniques where logs created by operative sy...

Markku Hinkka, et al. ∙

research

∙ 11/27/2018

Neural Non-Stationary Spectral Kernel

Standard kernels such as Matérn or RBF kernels only encode simple monoto...

Sami Remes, et al. ∙

research

∙ 09/17/2019

Visualizing Movement Control Optimization Landscapes

A large body of animation research focuses on optimization of movement c...

Perttu Hämäläinen, et al. ∙

research

∙ 01/08/2022

A Unified Review of Deep Learning for Automated Medical Coding

Automated medical coding, an essential task for healthcare operation and...

Shaoxiong Ji, et al. ∙

research

∙ 03/11/2022

The Role of ImageNet Classes in Fréchet Inception Distance

Fréchet Inception Distance (FID) is a metric for quantifying the distanc...

Tuomas Kynkäänniemi, et al. ∙

research

∙ 10/19/2018

DGC-Net: Dense Geometric Correspondence Network

This paper addresses the challenge of dense pixel correspondence estimat...

Iaroslav Melekhov, et al. ∙

research

∙ 07/31/2019

Scalable Bayesian Non-linear Matrix Completion

Matrix completion aims to predict missing elements in a partially observ...

Xiangju Qin, et al. ∙

research

∙ 09/25/2019

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

We present GraphMix, a regularization technique for Graph Neural Network...

Vikas Verma, et al. ∙

research

∙ 06/03/2020

Interpolation-based semi-supervised learning for object detection

Despite the data labeling cost for the object detection tasks being subs...

Jisoo Jeong, et al. ∙

research

∙ 07/29/2019

KNEEL: Knee Anatomical Landmark Localization Using Hourglass Networks

This paper addresses the challenge of localization of anatomical landmar...

Aleksei Tiulpin, et al. ∙

research

∙ 12/20/2013

How to Construct Deep Recurrent Neural Networks

In this paper, we explore different ways to extend a recurrent neural ne...

Razvan Pascanu, et al. ∙

research

∙ 11/16/2017

An Iterative Closest Points Approach to Neural Generative Models

We present a simple way to learn a transformation that maps samples of o...

Joose Rajamäki, et al. ∙

research

∙ 11/07/2017

A Tutorial on Canonical Correlation Methods

Canonical correlation analysis is a family of multivariate statistical m...

Viivi Uurtio, et al. ∙

research

∙ 05/17/2017

A Review on Bilevel Optimization: From Classical to Evolutionary Approaches and Applications

Bilevel optimization is defined as a mathematical program, where an opti...

Ankur Sinha, et al. ∙

research

∙ 09/12/2017

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 ...

Wilhelmiina Hämäläinen, et al. ∙

research

∙ 10/23/2017

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

Visual exploration of high-dimensional real-valued datasets is a fundame...

Kai Puolamäki, et al. ∙

research

∙ 10/12/2017

Subjectively Interesting Subgroup Discovery on Real-valued Targets

Deriving insights from high-dimensional data is one of the core problems...

Jefrey Lijffijt, et al. ∙

research

∙ 10/08/2017

Structural Feature Selection for Event Logs

We consider the problem of classifying business process instances based ...

Markku Hinkka, et al. ∙

research

∙ 09/15/2017

Gaussian Process Latent Force Models for Learning and Stochastic Control of Physical Systems

This paper is concerned with estimation and stochastic control in physic...

Simo Särkkä, et al. ∙

research

∙ 08/17/2016

Optimal Management of Naturally Regenerating Uneven-aged Forests

A shift from even-aged forest management to uneven-aged management pract...

Ankur Sinha, et al. ∙

research

∙ 06/26/2017

An adaptive prefix-assignment technique for symmetry reduction

This paper presents a technique for symmetry reduction that adaptively a...

Tommi Junttila, et al. ∙

research

∙ 11/03/2015

Data Stream Classification using Random Feature Functions and Novel Method Combinations

Big Data streams are being generated in a faster, bigger, and more commo...

Diego Marrón, et al. ∙

research

∙ 03/28/2017

Inverse Reinforcement Learning from Incomplete Observation Data

Inverse reinforcement learning (IRL) aims to explain observed strategic ...

Antti Kangasrääsiö, et al. ∙

research

∙ 05/18/2015

DopeLearning: A Computational Approach to Rap Lyrics Generation

Writing rap lyrics requires both creativity to construct a meaningful, i...

Eric Malmi, et al. ∙

research

∙ 12/22/2014

Denoising autoencoder with modulated lateral connections learns invariant representations of natural images

Suitable lateral connections between encoder and decoder are shown to al...

Antti Rasmus, et al. ∙

research

∙ 06/11/2014

Techniques for Learning Binary Stochastic Feedforward Neural Networks

Stochastic binary hidden units in a multi-layer perceptron (MLP) network...

Tapani Raiko, et al. ∙

research

∙ 04/04/2017

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

Bayesian optimization () is a global optimization strategy designed to f...

Eero Siivola, et al. ∙

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