Local Feature Based Online Mode Detection with Recurrent Neural Networks

Otte, Sebastian; Krechel, Dirk; Liwicki, Marcus; Dengel, Andreas

doi:10.1109/icfhr.2012.229

Cited by 25 publications

(5 citation statements)

References 10 publications

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“…On the IAM-OnDoDB benchmark, LSTM RNNs (Sec. 5.13) outperformed all other methods (HMMs, SVMs) on online mode detection (Otte et al, 2012;Indermuhle et al, 2012) and keyword spotting (Indermuhle et al, 2011). On the long time lag problem of language modelling, LSTM RNNs outperformed all statistical approaches on the IAM-DB benchmark (Frinken et al, 2012); improved results were later obtained through a combination of NNs and HMMs (Zamora-Martnez et al, 2014).…”

Section: : First Contests Won On Imagenet Object Detection Segmentationmentioning

confidence: 97%

Deep learning in neural networks: An overview

2015

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In recent years, deep artificial neural networks (including recurrent ones) have won numerous contests in pattern recognition and machine learning. This historical survey compactly summarises relevant work, much of it from the previous millennium. Shallow and deep learners are distinguished by the depth of their credit assignment paths, which are chains of possibly learnable, causal links between actions and effects. I review deep supervised learning (also recapitulating the history of backpropagation), unsupervised learning, reinforcement learning & evolutionary computation, and indirect search for short programs encoding deep and large networks.LATEX source: http://www.idsia.ch/˜juergen/DeepLearning8Oct2014.tex Complete BIBTEX file (888 kB): http://www.idsia.ch/˜juergen/deep.bib Preface This is the preprint of an invited Deep Learning (DL) overview. One of its goals is to assign credit to those who contributed to the present state of the art. I acknowledge the limitations of attempting to achieve this goal. The DL research community itself may be viewed as a continually evolving, deep network of scientists who have influenced each other in complex ways. Starting from recent DL results, I tried to trace back the origins of relevant ideas through the past half century and beyond, sometimes using "local search" to follow citations of citations backwards in time. Since not all DL publications properly acknowledge earlier relevant work, additional global search strategies were employed, aided by consulting numerous neural network experts. As a result, the present preprint mostly consists of references. Nevertheless, through an expert selection bias I may have missed important work. A related bias was surely introduced by my special familiarity with the work of my own DL research group in the past quarter-century. For these reasons, this work should be viewed as merely a snapshot of an ongoing credit assignment process. To help improve it, please do not hesitate to send corrections and suggestions to juergen@idsia.ch.

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Section: : First Contests Won On Imagenet Object Detection Segmentationmentioning

confidence: 97%

Deep learning in neural networks: An overview

2015

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“…In 2012 BSLTM was applied to keyword spotting and mode detection distinguishing different types of content in handwritten documents, such as text, formulas, diagrams and figures, outperforming HMMs and SVMs [44,45,59]. At approximately the same period of time [51] investigated the classification of high-resolution images from the ImageNet database with considerable better results then previous approaches.…”

Section: Image Processingmentioning

confidence: 99%

Understanding LSTM -- a tutorial into Long Short-Term Memory Recurrent Neural Networks

Staudemeyer¹,

Morris²

2019

Preprint

172

100

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Long Short-Term Memory Recurrent Neural Networks (LSTM-RNN) are one of the most powerful dynamic classifiers publicly known. The network itself and the related learning algorithms are reasonably well documented to get an idea how it works. This paper will shed more light into understanding how LSTM-RNNs evolved and why they work impressively well, focusing on the early, ground-breaking publications. We significantly improved documentation and fixed a number of errors and inconsistencies that accumulated in previous publications. To support understanding we as well revised and unified the notation used.

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“…Long-Short Term Memory (LSTM), Gated Recurrent Units (GRU) and control gate-based Recurrent Neural Networks (CGRNN) are the most well-known Recurrent Neural Networks methods to analyse the sequential data and have been applied on GPS trajectories to detect the mode of transport [27,20] . Vu et al [27] analyzed different RNN approaches and found the superiority of GRU and CGRNN models over simple RNN and LSTM models to infer mode of transport from accelerometer data.…”

Section: Mode and Purpose Detectionmentioning

confidence: 99%

Multi-task Recurrent Neural Networks to Simultaneously Infer Mode and Purpose in GPS Trajectories

Yazdizadeh¹,

Kalatian²,

Patterson³

et al. 2021

Preprint

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Multi-task learning is assumed as a powerful inference method, specifically, where there is a considerable correlation between multiple tasks, predicting them in an unique framework may enhance prediction results. This research challenges this assumption by developing several single-task models to compare their results against multi-task learners to infer mode and purpose of trip from smartphone travel survey data collected as part of a smartphone-based travel survey. GPS trajectory data along with socio-demographics and destination-related characteristics are fed into a multi-input neural network framework to predict two outputs; mode and purpose. We deployed Recurrent Neural Networks (RNN) that are fed by sequential GPS trajectories. To process the socio-demographics and destination-related characteristics, another neural network, with different embedding and dense layers is used in parallel with RNN layers in a multi-input multi-output framework. The results are compared against the single-task learners that classify mode and purpose independently. We also investigate different RNN approaches such as Long-Short Term Memory (LSTM), Gated Recurrent Units (GRU) and Bi-directional Gated Recurrent Units (Bi-GRU). The best multitask learner was a Bi-GRU model able to classify mode and purpose with an F1measures of 84.33% and 78.28%, while the best single-task learner to infer mode of transport was a GRU model that achieved an F1-measure of 86.50%, and the best single-task Bi-GRU purpose detection model that reached an F1-measure of 77.38%. While there's an assumption of higher performance of multi-task over sing-task learners, the results of this study does not hold such an assumption and shows, in the context of mode and trip purpose inference from GPS trajectory

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Local Feature Based Online Mode Detection with Recurrent Neural Networks

Cited by 25 publications

References 10 publications

Deep learning in neural networks: An overview

Deep learning in neural networks: An overview

Understanding LSTM -- a tutorial into Long Short-Term Memory Recurrent Neural Networks

Multi-task Recurrent Neural Networks to Simultaneously Infer Mode and Purpose in GPS Trajectories

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