Martin Johan Längkvist

Martin Johan Längkvist
Örebro University | oru · AASS Machine Perception and Interaction Lab, School of Science and Technology

PhD Information Technology

About

27
Publications
37,125
Reads
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1,733
Citations
Citations since 2017
12 Research Items
1575 Citations
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Introduction
My research interest is in machine learning and specifically applying unsupervised feature learning and deep learning techniques to multidimensional time-series data.
Additional affiliations
October 2009 - present
Örebro universitet
Education
September 2004 - September 2009
Linköping University
Field of study
  • Mechatronics

Publications

Publications (27)
Conference Paper
Full-text available
Software testing is still heavily dependent on human judgment since a large portion of testing artifacts such as requirements and test cases are written in a natural text by people. Identifying and classifying relevant test cases in large test suites is a challenging and also time-consuming task. Moreover, to optimize the testing process test cases...
Chapter
In this chapter we focus the use of knowledge representation and reasoning (KRR) methods as a guide to machine learning algorithms whereby relevant contextual knowledge can be leveraged upon. In this way, the learning methods improve performance by taking into account causal relationships behind errors. Performance improvement can be obtained by fo...
Conference Paper
Full-text available
Finding a balance between testing goals and testing resources can be considered as a most challenging issue, therefore test optimization plays a vital role in the area of software testing. Moreover, several parameters such as the objectives of the tests, test cases similarities and dependencies between test cases need to be considered, before attem...
Preprint
Short-chain fatty acids (SCFAs), including acetate, propionate and butyrate, are organic fatty-acids that are produced when indigested carbohydrates are fermented in the colon by gut-bacteria. Butyrate is especially considered a beneficial compound in relation to gut health and the maintenance of colonic homeostasis. Little is known about butyrate...
Preprint
Full-text available
Understanding why machine learning algorithms may fail is usually the task of the human expert that uses domain knowledge and contextual information to discover systematic shortcomings in either the data or the algorithm. In this paper, we propose a semantic referee, which is able to extract qualitative features of the errors emerging from deep mac...
Preprint
Full-text available
High resolution computed tomography (HRCT) is the most important imaging modality for interstitial lung diseases, where the radiologists are interested in identifying certain patterns, and their volumetric and regional distribution. The use of machine learning can assist the radiologists with both these tasks by performing semantic segmentation. In...
Article
Full-text available
Computed tomography (CT) is the method of choice for diagnosing ureteral stones - kidney stones that obstruct the ureter. The purpose of this study is to develop a computer aided detection (CAD) algorithm for identifying a ureteral stone in thin slice CT volumes. The challenge in CAD for urinary stones lies in the similarity in shape and intensity...
Article
Full-text available
This paper presents a framework in which satellite images are classified and augmented with additional semantic information to enable queries about what can be found on the map at a particular location, but also about paths that can be taken. This is achieved by a reasoning framework based on qualitative spatial reasoning that is able to find answe...
Article
Full-text available
In this paper, we examine how to ground symbols referring to objects in perceptual data from a robot system by examining object entities and their changes over time. In particular, we approach the challenge by (1) tracking and maintaining object entities over time; and (2) utilizing an artificial neural network to learn the coupling between words r...
Conference Paper
Full-text available
In this paper, we summarize the practical issues met during our interaction with OpenStreetMap for the purpose of automatically generating labelled data used by data classification methods.
Conference Paper
Full-text available
Recently, deep learning models, such as Convolutional Neural Networks, have shown to give good performance for various computer vision tasks. A prerequisite for such models is to have access to lots of labeled data since the most successful ones are trained with supervised learning. The process of labeling data is expensive, time-consuming, tedious...
Article
Full-text available
The availability of high-resolution remote sensing (HRRS) data has opened up thepossibility for new interesting applications, such as per-pixel classification of individual objects ingreater detail. This paper shows how a convolutional neural network (CNN) can be applied tomultispectral orthoimagery and a digital surface model (DSM) of a small city...
Article
Full-text available
There is an increasing interest in the machine learning community to automatically learn feature representations directly from the (unlabeled) data instead of using hand-designed features. The autoencoder is one method that can be used for this purpose. However, for data sets with a high degree of noise, a large amount of the representational capac...
Article
Full-text available
This paper gives a review of the recent developments in deep learning and un-supervised feature learning for time-series problems. While these techniques have shown promise for modeling static data, such as computer vision, ap-plying them to time-series data is gaining increasing attention. This paper overviews the particular challenges present in...
Data
This dataset is consists of readings from an NST electronic nose when sampling viles of blood containing different species of bacteria. The dataset is part of the Novamedtech funded project, Mednose, whose main objective is to identify bacteria in blood when sepsis is suspected. The dataset was collected at Örebro University Hospital together with...
Data
This is the matlab code used in the paper "Sleep Stage Classification Using Unsupervised Feature Learning" by M. Langkvist, L. Karlsson, and A. Loutfi. The dataset can be downloaded from http://www.physionet.org/pn3/ucddb/
Data
This is the poster for "Unsupervised feature learning for electronic nose data applied to Bacteria Identification in Blood" by M. Längkvist and A. Loutfi. Presented at NIPS deep learning workshop 2011.
Data
Full-text available
This is the poster for "Not all signals are created equal: Dynamic Objective Auto-Encoder for Multivariate Data" by M. Langkvist and A. Loutfi. Presented at NIPS deep learning workshop 2012.
Article
Full-text available
This paper investigates a rapid and accurate detection system for spoilage in meat. We use unsupervised feature learning techniques (stacked restricted Boltzmann machines and auto-encoders) that consider only the transient response from undoped zinc oxide, manganese-doped zinc oxide, and fluorine-doped zinc oxide in order to classify three categori...
Conference Paper
Full-text available
There is a representational capacity limit in a neural network defined by the num-ber of hidden units. For multimodal time-series data, there could exist signals with various complexity and redundancy. One way of getting a higher representa-tional capacity for such input data is to increase the number of units in the hidden layer. We propose a step...
Article
Full-text available
Most attempts at training computers for the difficult and time-consuming task of sleep stage classification involve a feature extraction step. Due to the complexity of multimodal sleep data, the size of the feature space can grow to the extent that it is also necessary to include a feature selection step. In this paper, we propose the use of an uns...
Conference Paper
Full-text available
Electronic nose (e-nose) data represents multivariate time-series from an array of chemical gas sensors exposed to a gas. This data is a new data set for use with deep learning methods, and is highly suitable since e-nose data is complex and difficult to interpret for human experts. Furthermore, this data set presents a number of interesting challe...

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