Sequence in a sequence: Learning of auditory but not visual patterns within a multimodal sequence.
Sign Up to like & getrecommendations! Published in 2019 at "Acta psychologica"
DOI: 10.1016/j.actpsy.2019.102905
Abstract: The current study investigates whether a unimodal visual and a unimodal auditory sequence is learned separately in a multimodal learning situation. In two experiments participants faced a modified version of the Serial Reaction-Time task, in… read more here.
Keywords: learning auditory; sequence sequence; auditory visual; sequence learning ... See more keywords
Regional prediction of ground-level ozone using a hybrid sequence-to-sequence deep learning approach
Sign Up to like & getrecommendations! Published in 2020 at "Journal of Cleaner Production"
DOI: 10.1016/j.jclepro.2019.119841
Abstract: Abstract Ozone is one of the most important greenhouse gases and air pollutants in urban areas, and has significantly negative impacts both on the climate change and human health. In addition to alert the public… read more here.
Keywords: sequence sequence; hybrid sequence; ground level; prediction ... See more keywords
Universal Lemmatizer: A sequence-to-sequence model for lemmatizing Universal Dependencies treebanks
Sign Up to like & getrecommendations! Published in 2020 at "Natural Language Engineering"
DOI: 10.1017/s1351324920000224
Abstract: Abstract In this paper, we present a novel lemmatization method based on a sequence-to-sequence neural network architecture and morphosyntactic context representation. In the proposed method, our context-sensitive lemmatizer generates the lemma one character at a… read more here.
Keywords: context representation; sequence sequence; lemmatizer sequence; universal lemmatizer ... See more keywords
EvoLSTM: context-dependent models of sequence evolution using a sequence-to-sequence LSTM
Sign Up to like & getrecommendations! Published in 2020 at "Bioinformatics"
DOI: 10.1093/bioinformatics/btaa447
Abstract: Abstract Motivation Accurate probabilistic models of sequence evolution are essential for a wide variety of bioinformatics tasks, including sequence alignment and phylogenetic inference. The ability to realistically simulate sequence evolution is also at the core… read more here.
Keywords: sequence sequence; evolution; context dependencies; sequence evolution ... See more keywords
Whisper to Normal Speech Conversion Using Sequence-to-Sequence Mapping Model With Auditory Attention
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Access"
DOI: 10.1109/access.2019.2940700
Abstract: Whispering is a special pronunciation style in which the vocal cords do not vibrate. Compared with voiced speech, whispering is noise-like because of the lack of a fundamental frequency. The energy of whispered speech is… read more here.
Keywords: normal speech; conversion; speech; sequence ... See more keywords
Deep Learning for Load Forecasting: Sequence to Sequence Recurrent Neural Networks With Attention
Sign Up to like & getrecommendations! Published in 2020 at "IEEE Access"
DOI: 10.1109/access.2020.2975738
Abstract: The biggest contributor to global warming is energy production and use. Moreover, a push for electrical vehicle and other economic developments are expected to further increase energy use. To combat these challenges, electrical load forecasting… read more here.
Keywords: attention; neural networks; load forecasting; load ... See more keywords
Sequence-to-Sequence Emotional Voice Conversion With Strength Control
Sign Up to like & getrecommendations! Published in 2021 at "IEEE Access"
DOI: 10.1109/access.2021.3065460
Abstract: This paper proposes an improved emotional voice conversion (EVC) method with emotional strength and duration controllability. EVC methods without duration mapping generate emotional speech with identical duration to that of the neutral input speech. In… read more here.
Keywords: voice conversion; strength; sequence; emotional voice ... See more keywords
Spatiotemporal Sequence-to-Sequence Clustering for Electric Load Forecasting
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Access"
DOI: 10.1109/access.2023.3235724
Abstract: Massive electrical load exhibits many patterns making it difficult for forecast algorithms to generalise well. Most learning algorithms produce a better forecast for dominant patterns in the case of weekday consumption and otherwise for less… read more here.
Keywords: time series; electric load; load forecasting; sequence ... See more keywords
A Sequence-to-Sequence Deep Learning Architecture Based on Bidirectional GRU for Type Recognition and Time Location of Combined Power Quality Disturbance
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Transactions on Industrial Informatics"
DOI: 10.1109/tii.2019.2895054
Abstract: In this paper, a sequence-to-sequence deep learning architecture based on the bidirectional gated recurrent unit (Bi-GRU) for type recognition and time location of combined power quality disturbance is proposed. Especially, the proposed methodology can determine… read more here.
Keywords: type recognition; sequence; sequence sequence; input sequence ... See more keywords
Physically Consistent Soft-Sensor Development Using Sequence-to-Sequence Neural Networks
Sign Up to like & getrecommendations! Published in 2020 at "IEEE Transactions on Industrial Informatics"
DOI: 10.1109/tii.2019.2952429
Abstract: Soft sensors attempt to predict the key quality variables that are infrequently available using the sensor and manipulated variables that are readily available. Since only limited amount of labeled data are available, there is always… read more here.
Keywords: physics; physically consistent; sequence; consistent soft ... See more keywords
A systematic review on sequence-to-sequence learning with neural network and its models
Sign Up to like & getrecommendations! Published in 2021 at "International Journal of Electrical and Computer Engineering"
DOI: 10.11591/ijece.v11i3.pp2315-2326
Abstract: We develop a precise writing survey on sequence-to-sequence learning with neural network and its models. The primary aim of this report is to enhance the knowledge of the sequence-to-sequence neural network and to locate the… read more here.
Keywords: neural network; network models; methodology; sequence ... See more keywords