Log in

Relevant bibliographies by topics / Encoder-Decoder Models / Journal articles

To see the other types of publications on this topic, follow the link: Encoder-Decoder Models.

Journal articles on the topic 'Encoder-Decoder Models'

Author: Grafiati

Published: 4 June 2021

Last updated: 5 February 2022

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the top 50 journal articles for your research on the topic 'Encoder-Decoder Models.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.

1

Zhang, Wenbo, Xiao Li, Yating Yang, Rui Dong, and Gongxu Luo. "Keeping Models Consistent between Pretraining and Translation for Low-Resource Neural Machine Translation." Future Internet 12, no. 12 (2020): 215. http://dx.doi.org/10.3390/fi12120215.

Full text

Abstract:

Recently, the pretraining of models has been successfully applied to unsupervised and semi-supervised neural machine translation. A cross-lingual language model uses a pretrained masked language model to initialize the encoder and decoder of the translation model, which greatly improves the translation quality. However, because of a mismatch in the number of layers, the pretrained model can only initialize part of the decoder’s parameters. In this paper, we use a layer-wise coordination transformer and a consistent pretraining translation transformer instead of a vanilla transformer as the tra

APA, Harvard, Vancouver, ISO, and other styles

2

Lamar, Annie K. "Generating Metrically Accurate Homeric Poetry with Recurrent Neural Networks." International Journal of Transdisciplinary Artificial Intelligence 2, no. 1 (2020): 1–25. http://dx.doi.org/10.35708/tai1869-126247.

Full text

Abstract:

We investigate the generation of metrically accurate Homeric poetry using recurrent neural networks (RNN). We assess two models: a basic encoder-decoder RNN and the hierarchical recurrent encoderdecoder model (HRED). We assess the quality of the generated lines of poetry using quantitative metrical analysis and expert evaluation. This evaluation reveals that while the basic encoder-decoder is able to capture complex poetic meter, it under performs in terms of semantic coherence. The HRED model, however, produces more semantically coherent lines of poetry but is unable to capture the meter. Our

APA, Harvard, Vancouver, ISO, and other styles

3

Markovnikov, Nikita, and Irina Kipyatkova. "Encoder-decoder models for recognition of Russian speech." Information and Control Systems, no. 4 (October 4, 2019): 45–53. http://dx.doi.org/10.31799/1684-8853-2019-4-45-53.

Full text

Abstract:

Problem: Classical systems of automatic speech recognition are traditionally built using an acoustic model based on hidden Markovmodels and a statistical language model. Such systems demonstrate high recognition accuracy, but consist of several independentcomplex parts, which can cause problems when building models. Recently, an end-to-end recognition method has been spread, usingdeep artificial neural networks. This approach makes it easy to implement models using just one neural network. End-to-end modelsoften demonstrate better performance in terms of speed and accuracy of speech recognitio

APA, Harvard, Vancouver, ISO, and other styles

4

Meng, Zhaorui, and Xianze Xu. "A Hybrid Short-Term Load Forecasting Framework with an Attention-Based Encoder–Decoder Network Based on Seasonal and Trend Adjustment." Energies 12, no. 24 (2019): 4612. http://dx.doi.org/10.3390/en12244612.

Full text

Abstract:

Accurate electrical load forecasting plays an important role in power system operation. An effective load forecasting approach can improve the operation efficiency of a power system. This paper proposes the seasonal and trend adjustment attention encoder–decoder (STA–AED), a hybrid short-term load forecasting approach based on a multi-head attention encoder–decoder module with seasonal and trend adjustment. A seasonal and trend decomposing technique is used to preprocess the original electrical load data. Each decomposed datum is regressed to predict the future electric load value by utilizing

APA, Harvard, Vancouver, ISO, and other styles

5

Dabre, Raj, and Atsushi Fujita. "Recurrent Stacking of Layers for Compact Neural Machine Translation Models." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 6292–99. http://dx.doi.org/10.1609/aaai.v33i01.33016292.

Full text

Abstract:

In encoder-decoder based sequence-to-sequence modeling, the most common practice is to stack a number of recurrent, convolutional, or feed-forward layers in the encoder and decoder. While the addition of each new layer improves the sequence generation quality, this also leads to a significant increase in the number of parameters. In this paper, we propose to share parameters across all layers thereby leading to a recurrently stacked sequence-to-sequence model. We report on an extensive case study on neural machine translation (NMT) using our proposed method, experimenting with a variety of dat

APA, Harvard, Vancouver, ISO, and other styles

6

Oh, Jiun, and Yong-Suk Choi. "Reusing Monolingual Pre-Trained Models by Cross-Connecting Seq2seq Models for Machine Translation." Applied Sciences 11, no. 18 (2021): 8737. http://dx.doi.org/10.3390/app11188737.

Full text

Abstract:

This work uses sequence-to-sequence (seq2seq) models pre-trained on monolingual corpora for machine translation. We pre-train two seq2seq models with monolingual corpora for the source and target languages, then combine the encoder of the source language model and the decoder of the target language model, i.e., the cross-connection. We add an intermediate layer between the pre-trained encoder and the decoder to help the mapping of each other since the modules are pre-trained completely independently. These monolingual pre-trained models can work as a multilingual pre-trained model because one

APA, Harvard, Vancouver, ISO, and other styles

7

Khanh, Trinh Le Ba, Duy-Phuong Dao, Ngoc-Huynh Ho, et al. "Enhancing U-Net with Spatial-Channel Attention Gate for Abnormal Tissue Segmentation in Medical Imaging." Applied Sciences 10, no. 17 (2020): 5729. http://dx.doi.org/10.3390/app10175729.

Full text

Abstract:

In recent years, deep learning has dominated medical image segmentation. Encoder-decoder architectures, such as U-Net, can be used in state-of-the-art models with powerful designs that are achieved by implementing skip connections that propagate local information from an encoder path to a decoder path to retrieve detailed spatial information lost by pooling operations. Despite their effectiveness for segmentation, these naïve skip connections still have some disadvantages. First, multi-scale skip connections tend to use unnecessary information and computational sources, where likable low-level

APA, Harvard, Vancouver, ISO, and other styles

8

Zheng, Chuanpan, Xiaoliang Fan, Cheng Wang, and Jianzhong Qi. "GMAN: A Graph Multi-Attention Network for Traffic Prediction." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 01 (2020): 1234–41. http://dx.doi.org/10.1609/aaai.v34i01.5477.

Full text

Abstract:

Long-term traffic prediction is highly challenging due to the complexity of traffic systems and the constantly changing nature of many impacting factors. In this paper, we focus on the spatio-temporal factors, and propose a graph multi-attention network (GMAN) to predict traffic conditions for time steps ahead at different locations on a road network graph. GMAN adapts an encoder-decoder architecture, where both the encoder and the decoder consist of multiple spatio-temporal attention blocks to model the impact of the spatio-temporal factors on traffic conditions. The encoder encodes the input

APA, Harvard, Vancouver, ISO, and other styles

9

Monteiro, João, Bruno Martins, Miguel Costa, and João M. Pires. "Geospatial Data Disaggregation through Self-Trained Encoder–Decoder Convolutional Models." ISPRS International Journal of Geo-Information 10, no. 9 (2021): 619. http://dx.doi.org/10.3390/ijgi10090619.

Full text

Abstract:

Datasets collecting demographic and socio-economic statistics are widely available. Still, the data are often only released for highly aggregated geospatial areas, which can mask important local hotspots. When conducting spatial analysis, one often needs to disaggregate the source data, transforming the statistics reported for a set of source zones into values for a set of target zones, with a different geometry and a higher spatial resolution. This article reports on a novel dasymetric disaggregation method that uses encoder–decoder convolutional neural networks, similar to those adopted in i

APA, Harvard, Vancouver, ISO, and other styles

10

Özkaya Eren, Ayşegül, and Mustafa Sert. "Audio Captioning with Composition of Acoustic and Semantic Information." International Journal of Semantic Computing 15, no. 02 (2021): 143–60. http://dx.doi.org/10.1142/s1793351x21400018.

Full text

Abstract:

Generating audio captions is a new research area that combines audio and natural language processing to create meaningful textual descriptions for audio clips. To address this problem, previous studies mostly use the encoder–decoder-based models without considering semantic information. To fill this gap, we present a novel encoder–decoder architecture using bi-directional Gated Recurrent Units (BiGRU) with audio and semantic embeddings. We extract semantic embedding by obtaining subjects and verbs from the audio clip captions and combine these embedding with audio embedding to feed the BiGRU-b

APA, Harvard, Vancouver, ISO, and other styles

11

Liu, Tianyu, Fuli Luo, Qiaolin Xia, Shuming Ma, Baobao Chang, and Zhifang Sui. "Hierarchical Encoder with Auxiliary Supervision for Neural Table-to-Text Generation: Learning Better Representation for Tables." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 6786–93. http://dx.doi.org/10.1609/aaai.v33i01.33016786.

Full text

Abstract:

Generating natural language descriptions for the structured tables which consist of multiple attribute-value tuples is a convenient way to help people to understand the tables. Most neural table-to-text models are based on the encoder-decoder framework. However, it is hard for a vanilla encoder to learn the accurate semantic representation of a complex table. The challenges are two-fold: firstly, the table-to-text datasets often contain large number of attributes across different domains, thus it is hard for the encoder to incorporate these heterogeneous resources. Secondly, the single encoder

APA, Harvard, Vancouver, ISO, and other styles

12

Schmitz, M., W. Brandenburger, and H. Mayer. "SEMANTIC SEGMENTATION OF AIRBORNE IMAGES AND CORRESPONDING DIGITAL SURFACE MODELS – ADDITIONAL INPUT DATA OR ADDITIONAL TASK?" ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W16 (September 17, 2019): 195–200. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w16-195-2019.

Full text

Abstract:

<p><strong>Abstract.</strong> We analyze the effects of additional height data for semantic segmentation of aerial images with a convolutional encoder-decoder network. Besides a merely image-based semantic segmentation, we trained the same network with height as additional input and furthermore, we defined a multi-task model, where we trained the network to estimate the relative height of objects in parallel to semantic segmentation on the image data only. Our findings are, that excellent results are possible for image data only and additional height information has no signif

APA, Harvard, Vancouver, ISO, and other styles

13

Patel, Ankit, Yi-Ting Cheng, Radhika Ravi, Yi-Chun Lin, Darcy Bullock, and Ayman Habib. "Transfer Learning for LiDAR-Based Lane Marking Detection and Intensity Profile Generation." Geomatics 1, no. 2 (2021): 287–309. http://dx.doi.org/10.3390/geomatics1020016.

Full text

Abstract:

Recently, light detection and ranging (LiDAR)-based mobile mapping systems (MMS) have been utilized for extracting lane markings using deep learning frameworks. However, huge datasets are required for training neural networks. Furthermore, with accurate lane markings being detected utilizing LiDAR data, an algorithm for automatically reporting their intensity information is beneficial for identifying worn-out or missing lane markings. In this paper, a transfer learning approach based on fine-tuning of a pretrained U-net model for lane marking extraction and a strategy for generating intensity

APA, Harvard, Vancouver, ISO, and other styles

14

Pipiras, Laurynas, Rytis Maskeliūnas, and Robertas Damaševičius. "Lithuanian Speech Recognition Using Purely Phonetic Deep Learning." Computers 8, no. 4 (2019): 76. http://dx.doi.org/10.3390/computers8040076.

Full text

Abstract:

Automatic speech recognition (ASR) has been one of the biggest and hardest challenges in the field. A large majority of research in this area focuses on widely spoken languages such as English. The problems of automatic Lithuanian speech recognition have attracted little attention so far. Due to complicated language structure and scarcity of data, models proposed for other languages such as English cannot be directly adopted for Lithuanian. In this paper we propose an ASR system for the Lithuanian language, which is based on deep learning methods and can identify spoken words purely from their

APA, Harvard, Vancouver, ISO, and other styles

15

Asakawa, Shin, and Takashi Ogata. "Comparison Between Variational Autoencoder and Encoder-Decoder Models for Short Conversation." Proceedings of International Conference on Artificial Life and Robotics 22 (January 19, 2017): 639–42. http://dx.doi.org/10.5954/icarob.2017.os1-4.

Full text

APA, Harvard, Vancouver, ISO, and other styles

16

Jiang, Wenhao, Lin Ma, and Wei Lu. "Recurrent Nested Model for Sequence Generation." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (2020): 11117–24. http://dx.doi.org/10.1609/aaai.v34i07.6768.

Full text

Abstract:

Depth has been shown beneficial to neural network models. In this paper, we make an attempt to make the encoder-decoder model deeper for sequence generation. We propose a module that can be plugged into the middle between the encoder and decoder to increase the depth of the whole model. The proposed module follows a nested structure, which is divided into blocks with each block containing several recurrent transition steps. To reduce the training difficulty and preserve the necessary information for the decoder during transitions, inter-block connections and intra-block connections are constru

APA, Harvard, Vancouver, ISO, and other styles

17

Trisedya, Bayu, Jianzhong Qi, and Rui Zhang. "Sentence Generation for Entity Description with Content-Plan Attention." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (2020): 9057–64. http://dx.doi.org/10.1609/aaai.v34i05.6439.

Full text

Abstract:

We study neural data-to-text generation. Specifically, we consider a target entity that is associated with a set of attributes. We aim to generate a sentence to describe the target entity. Previous studies use encoder-decoder frameworks where the encoder treats the input as a linear sequence and uses LSTM to encode the sequence. However, linearizing a set of attributes may not yield the proper order of the attributes, and hence leads the encoder to produce an improper context to generate a description. To handle disordered input, recent studies propose two-stage neural models that use pointer

APA, Harvard, Vancouver, ISO, and other styles

18

Tang, Shijie, Yuan Yao, Suwei Zhang, et al. "An Integral Tag Recommendation Model for Textual Content." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 5109–16. http://dx.doi.org/10.1609/aaai.v33i01.33015109.

Full text

Abstract:

Recommending suitable tags for online textual content is a key building block for better content organization and consumption. In this paper, we identify three pillars that impact the accuracy of tag recommendation: (1) sequential text modeling meaning that the intrinsic sequential ordering as well as different areas of text might have an important implication on the corresponding tag(s) , (2) tag correlation meaning that the tags for a certain piece of textual content are often semantically correlated with each other, and (3) content-tag overlapping meaning that the vocabularies of content an

APA, Harvard, Vancouver, ISO, and other styles

19

Dakwale, Praveen, and Christof Monz. "Convolutional over Recurrent Encoder for Neural Machine Translation." Prague Bulletin of Mathematical Linguistics 108, no. 1 (2017): 37–48. http://dx.doi.org/10.1515/pralin-2017-0007.

Full text

Abstract:

AbstractNeural machine translation is a recently proposed approach which has shown competitive results to traditional MT approaches. Standard neural MT is an end-to-end neural network where the source sentence is encoded by a recurrent neural network (RNN) called encoder and the target words are predicted using another RNN known as decoder. Recently, various models have been proposed which replace the RNN encoder with a convolutional neural network (CNN). In this paper, we propose to augment the standard RNN encoder in NMT with additional convolutional layers in order to capture wider context

APA, Harvard, Vancouver, ISO, and other styles

20

van der Putten, Joost, Fons van der Sommen, Jeroen de Groof, et al. "Modeling clinical assessor intervariability using deep hypersphere encoder–decoder networks." Neural Computing and Applications 32, no. 14 (2019): 10705–17. http://dx.doi.org/10.1007/s00521-019-04607-w.

Full text

Abstract:

AbstractIn medical imaging, a proper gold-standard ground truth as, e.g., annotated segmentations by assessors or experts is lacking or only scarcely available and suffers from large intervariability in those segmentations. Most state-of-the-art segmentation models do not take inter-observer variability into account and are fully deterministic in nature. In this work, we propose hypersphere encoder–decoder networks in combination with dynamic leaky ReLUs, as a new method to explicitly incorporate inter-observer variability into a segmentation model. With this model, we can then generate multip

APA, Harvard, Vancouver, ISO, and other styles

21

Belaid, Mohamed Karim, Maximilian Rabus, and Ralf Krestel. "CrashNet: an encoder–decoder architecture to predict crash test outcomes." Data Mining and Knowledge Discovery 35, no. 4 (2021): 1688–709. http://dx.doi.org/10.1007/s10618-021-00761-9.

Full text

Abstract:

AbstractDestructive car crash tests are an elaborate, time-consuming, and expensive necessity of the automotive development process. Today, finite element method (FEM) simulations are used to reduce costs by simulating car crashes computationally. We propose CrashNet, an encoder–decoder deep neural network architecture that reduces costs further and models specific outcomes of car crashes very accurately. We achieve this by formulating car crash events as time series prediction enriched with a set of scalar features. Traditional sequence-to-sequence models are usually composed of convolutional

APA, Harvard, Vancouver, ISO, and other styles

22

Wijanarko, Bambang Dwi. "Encoder-Decoder with Attention Mechanisms for Developing Question Generation Models in Education." International Journal of Advanced Trends in Computer Science and Engineering 9, no. 4 (2020): 5994–6000. http://dx.doi.org/10.30534/ijatcse/2020/266942020.

Full text

APA, Harvard, Vancouver, ISO, and other styles

23

Huang, Kefan, Kevin P. Hallinan, Robert Lou, Abdulrahman Alanezi, Salahaldin Alshatshati, and Qiancheng Sun. "Self-Learning Algorithm to Predict Indoor Temperature and Cooling Demand from Smart WiFi Thermostat in a Residential Building." Sustainability 12, no. 17 (2020): 7110. http://dx.doi.org/10.3390/su12177110.

Full text

Abstract:

Smart WiFi thermostats have moved well beyond the function they were originally designed for; namely, controlling heating and cooling comfort in buildings. They are now also learning from occupant behaviors and permit occupants to control their comfort remotely. This research seeks to go beyond this state of the art by utilizing smart WiFi thermostat data in residences to develop dynamic predictive models for room temperature and cooling/heating demand. These models can then be used to estimate the energy savings from new thermostat temperature schedules and estimate peak load reduction achiev

APA, Harvard, Vancouver, ISO, and other styles

24

Subramaniam, Sudha, K. B. Jayanthi, C. Rajasekaran, and C. Sunder. "Measurement of Intima-Media Thickness Depending on Intima Media Complex Segmentation by Deep Neural Networks." Journal of Medical Imaging and Health Informatics 11, no. 10 (2021): 2546–57. http://dx.doi.org/10.1166/jmihi.2021.3841.

Full text

Abstract:

Intima Media Thickness (IMT) of the carotid artery is an important marker indicating the sign of cardiovascular disease. Automated measurement of IMT requires segmentation of intima media complex (IMC).Traditional methods which use shape, color and texture for classification have poor generalization capability. This paper proposes two models- the pipeline model and the end-to-end model using Convolutional Neural Networks (CNN) and auto encoder–decoder network respectively. CNN architecture is implemented and tested by varying the number of convolutional layer, size of the kernel as well as the

APA, Harvard, Vancouver, ISO, and other styles

25

Kaneko, Masahiro. "Encoder-Decoder Models Can Benefit from Pre-trained Masked Language Models in Grammatical Error Correction." Journal of Natural Language Processing 27, no. 3 (2020): 683–87. http://dx.doi.org/10.5715/jnlp.27.683.

Full text

APA, Harvard, Vancouver, ISO, and other styles

26

Adoui, Mahmoudi, Larhmam, and Benjelloun. "MRI Breast Tumor Segmentation Using Different Encoder and Decoder CNN Architectures." Computers 8, no. 3 (2019): 52. http://dx.doi.org/10.3390/computers8030052.

Full text

Abstract:

Breast tumor segmentation in medical images is a decisive step for diagnosis and treatment follow-up. Automating this challenging task helps radiologists to reduce the high manual workload of breast cancer analysis. In this paper, we propose two deep learning approaches to automate the breast tumor segmentation in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) by building two fully convolutional neural networks (CNN) based on SegNet and U-Net. The obtained models can handle both detection and segmentation on each single DCE-MRI slice. In this study, we used a dataset of 86 DCE-

APA, Harvard, Vancouver, ISO, and other styles

27

Teng, Lin, Hang Li, and Shahid Karim. "DMCNN: A Deep Multiscale Convolutional Neural Network Model for Medical Image Segmentation." Journal of Healthcare Engineering 2019 (December 27, 2019): 1–10. http://dx.doi.org/10.1155/2019/8597606.

Full text

Abstract:

Medical image segmentation is one of the hot issues in the related area of image processing. Precise segmentation for medical images is a vital guarantee for follow-up treatment. At present, however, low gray contrast and blurred tissue boundaries are common in medical images, and the segmentation accuracy of medical images cannot be effectively improved. Especially, deep learning methods need more training samples, which lead to time-consuming process. Therefore, we propose a novelty model for medical image segmentation based on deep multiscale convolutional neural network (CNN) in this artic

APA, Harvard, Vancouver, ISO, and other styles

28

Jishan, Md Asifuzzaman, Khan Raqib Mahmud, Abul Kalam Al Azad, Mohammad Rifat Ahmmad Rashid, Bijan Paul, and Md Shahabub Alam. "Bangla language textual image description by hybrid neural network model." Indonesian Journal of Electrical Engineering and Computer Science 21, no. 2 (2021): 757. http://dx.doi.org/10.11591/ijeecs.v21.i2.pp757-767.

Full text

Abstract:

Automatic image captioning task in different language is a challenging task which has not been well investigated yet due to the lack of dataset and effective models. It also requires good understanding of scene and contextual embedding for robust semantic interpretation of images for natural language image descriptor. To generate image descriptor in Bangla, we created a new Bangla dataset of images paired with target language label, named as Bangla Natural Language Image to Text (BNLIT) dataset. To deal with the image understanding, we propose a hybrid encoder-decoder model based on encoder-de

APA, Harvard, Vancouver, ISO, and other styles

29

Akyürek, Ekin, Erenay Dayanık, and Deniz Yuret. "Morphological Analysis Using a Sequence Decoder." Transactions of the Association for Computational Linguistics 7 (November 2019): 567–79. http://dx.doi.org/10.1162/tacl_a_00286.

Full text

Abstract:

We introduce Morse, a recurrent encoder-decoder model that produces morphological analyses of each word in a sentence. The encoder turns the relevant information about the word and its context into a fixed size vector representation and the decoder generates the sequence of characters for the lemma followed by a sequence of individual morphological features. We show that generating morphological features individually rather than as a combined tag allows the model to handle rare or unseen tags and to outperform whole-tag models. In addition, generating morphological features as a sequence rathe

APA, Harvard, Vancouver, ISO, and other styles

30

Qiao, Yuchen, Kazuma Hashimoto, Akiko Eriguchi, et al. "Parallelizing and optimizing neural Encoder–Decoder models without padding on multi-core architecture." Future Generation Computer Systems 108 (July 2020): 1206–13. http://dx.doi.org/10.1016/j.future.2018.04.070.

Full text

APA, Harvard, Vancouver, ISO, and other styles

31

Li, Haotian, Zhuang Yue, Jingyu Liu, et al. "SCCDNet: A Pixel-Level Crack Segmentation Network." Applied Sciences 11, no. 11 (2021): 5074. http://dx.doi.org/10.3390/app11115074.

Full text

Abstract:

Cracks are one of the most serious defects that threaten the safety of bridges. In order to detect different forms of cracks in different collection environments quickly and accurately, we proposed a pixel-level crack segmentation network based on convolutional neural networks, which is called the Skip Connected Crack Detection Network (SCCDNet). The network is composed of three parts: the Encoder module with 13 convolutional layers pretrained in the VGG-16 network, the Decoder module with a densely connected structure, and the Skip-Squeeze-and-Excitation (SSE) module which connects the featur

APA, Harvard, Vancouver, ISO, and other styles

32

Kao, Johnny W. H., Stevan M. Berber, and Abbas Bigdeli. "A General Rate K/N Convolutional Decoder Based on Neural Networks with Stopping Criterion." Advances in Artificial Intelligence 2009 (June 18, 2009): 1–11. http://dx.doi.org/10.1155/2009/356120.

Full text

Abstract:

A novel algorithm for decoding a general rate K/N convolutional code based on recurrent neural network (RNN) is described and analysed. The algorithm is introduced by outlining the mathematical models of the encoder and decoder. A number of strategies for optimising the iterative decoding process are proposed, and a simulator was also designed in order to compare the Bit Error Rate (BER) performance of the RNN decoder with the conventional decoder that is based on Viterbi Algorithm (VA). The simulation results show that this novel algorithm can achieve the same bit error rate and has a lower d

APA, Harvard, Vancouver, ISO, and other styles

33

Wei, Xiangpeng, Yue Hu, Luxi Xing, Yipeng Wang, and Li Gao. "Translating with Bilingual Topic Knowledge for Neural Machine Translation." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 7257–64. http://dx.doi.org/10.1609/aaai.v33i01.33017257.

Full text

Abstract:

The dominant neural machine translation (NMT) models that based on the encoder-decoder architecture have recently achieved the state-of-the-art performance. Traditionally, the NMT models only depend on the representations learned during training for mapping a source sentence into the target domain. However, the learned representations often suffer from implicit and inadequately informed properties. In this paper, we propose a novel bilingual topic enhanced NMT (BLTNMT) model to improve translation performance by incorporating bilingual topic knowledge into NMT. Specifically, the bilingual topi

APA, Harvard, Vancouver, ISO, and other styles

34

Geng, Haibo, Ying Hu, and Hao Huang. "Monaural Singing Voice and Accompaniment Separation Based on Gated Nested U-Net Architecture." Symmetry 12, no. 6 (2020): 1051. http://dx.doi.org/10.3390/sym12061051.

Full text

Abstract:

This paper proposes a separation model adopting gated nested U-Net (GNU-Net) architecture, which is essentially a deeply supervised symmetric encoder–decoder network that can generate full-resolution feature maps. Through a series of nested skip pathways, it can reduce the semantic gap between the feature maps of encoder and decoder subnetworks. In the GNU-Net architecture, only the backbone not including nested part is applied with gated linear units (GLUs) instead of conventional convolutional networks. The outputs of GNU-Net are further fed into a time-frequency (T-F) mask layer to generate

APA, Harvard, Vancouver, ISO, and other styles

35

Yoo, Jaechang, Heesong Eom, and Yong Suk Choi. "Image-To-Image Translation Using a Cross-Domain Auto-Encoder and Decoder." Applied Sciences 9, no. 22 (2019): 4780. http://dx.doi.org/10.3390/app9224780.

Full text

Abstract:

Recently, several studies have focused on image-to-image translation. However, the quality of the translation results is lacking in certain respects. We propose a new image-to-image translation method to minimize such shortcomings using an auto-encoder and an auto-decoder. This method includes pre-training two auto-encoders and decoder pairs for each source and target image domain, cross-connecting two pairs and adding a feature mapping layer. Our method is quite simple and straightforward to adopt but very effective in practice, and we experimentally demonstrated that our method can significa

APA, Harvard, Vancouver, ISO, and other styles

36

Choi, Dongho, Janghyuk Yim, Minjin Baek, and Sangsun Lee. "Machine Learning-Based Vehicle Trajectory Prediction Using V2V Communications and On-Board Sensors." Electronics 10, no. 4 (2021): 420. http://dx.doi.org/10.3390/electronics10040420.

Full text

Abstract:

Predicting the trajectories of surrounding vehicles is important to avoid or mitigate collision with traffic participants. However, due to limited past information and the uncertainty in future driving maneuvers, trajectory prediction is a challenging task. Recently, trajectory prediction models using machine learning algorithms have been addressed solve to this problem. In this paper, we present a trajectory prediction method based on the random forest (RF) algorithm and the long short term memory (LSTM) encoder-decoder architecture. An occupancy grid map is first defined for the region surro

APA, Harvard, Vancouver, ISO, and other styles

37

Cho, Choongsang, Young Han Lee, Jongyoul Park, and Sangkeun Lee. "A Self-Spatial Adaptive Weighting Based U-Net for Image Segmentation." Electronics 10, no. 3 (2021): 348. http://dx.doi.org/10.3390/electronics10030348.

Full text

Abstract:

Semantic image segmentation has a wide range of applications. When it comes to medical image segmentation, its accuracy is even more important than those of other areas because the performance gives useful information directly applicable to disease diagnosis, surgical planning, and history monitoring. The state-of-the-art models in medical image segmentation are variants of encoder-decoder architecture, which is called U-Net. To effectively reflect the spatial features in feature maps in encoder-decoder architecture, we propose a spatially adaptive weighting scheme for medical image segmentati

APA, Harvard, Vancouver, ISO, and other styles

38

Nissimagoudar, P. C., A. V. Nandi, Aakanksha Patil, and Gireesha H. M. "AlertNet: Deep convolutional-recurrent neural network model for driving alertness detection." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 4 (2021): 3529. http://dx.doi.org/10.11591/ijece.v11i4.pp3529-3538.

Full text

Abstract:

Drowsy driving is one of the major problems which has led to many road accidents. Electroencephalography (EEG) is one of the most reliable sources to detect sleep on-set while driving as there is the direct involvement of biological signals. The present work focuses on detecting driver’s alertness using the deep neural network architecture, which is built using ResNets and encoder-decoder based sequence to sequence models with attention decoder. The ResNets with the skip connections allow training the network deeper with a reduced loss function and training error. The model is built to reduce

APA, Harvard, Vancouver, ISO, and other styles

39

Li, Naihan, Yanqing Liu, Yu Wu, Shujie Liu, Sheng Zhao, and Ming Liu. "RobuTrans: A Robust Transformer-Based Text-to-Speech Model." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (2020): 8228–35. http://dx.doi.org/10.1609/aaai.v34i05.6337.

Full text

Abstract:

Recently, neural network based speech synthesis has achieved outstanding results, by which the synthesized audios are of excellent quality and naturalness. However, current neural TTS models suffer from the robustness issue, which results in abnormal audios (bad cases) especially for unusual text (unseen context). To build a neural model which can synthesize both natural and stable audios, in this paper, we make a deep analysis of why the previous neural TTS models are not robust, based on which we propose RobuTrans (Robust Transformer), a robust neural TTS model based on Transformer. Comparin

APA, Harvard, Vancouver, ISO, and other styles

40

Zang, Tianzi, Yanmin Zhu, Yanan Xu, and Jiadi Yu. "Jointly Modeling Spatio–Temporal Dependencies and Daily Flow Correlations for Crowd Flow Prediction." ACM Transactions on Knowledge Discovery from Data 15, no. 4 (2021): 1–20. http://dx.doi.org/10.1145/3439346.

Full text

Abstract:

Crowd flow prediction is a vital problem for an intelligent transportation system construction in a smart city. It plays a crucial role in traffic management and behavioral analysis, thus it has raised great attention from many researchers. However, predicting crowd flows timely and accurately is a challenging task that is affected by many complex factors such as the dependencies of adjacent regions or recent crowd flows. Existing models mainly focus on capturing such dependencies in spatial or temporal domains and fail to model relations between crowd flows of distant regions. We notice that

APA, Harvard, Vancouver, ISO, and other styles

41

Yousefi, Ali, Ishita Basu, Angelique C. Paulk, et al. "Decoding Hidden Cognitive States From Behavior and Physiology Using a Bayesian Approach." Neural Computation 31, no. 9 (2019): 1751–88. http://dx.doi.org/10.1162/neco_a_01196.

Full text

Abstract:

Cognitive processes, such as learning and cognitive flexibility, are both difficult to measure and to sample continuously using objective tools because cognitive processes arise from distributed, high-dimensional neural activity. For both research and clinical applications, that dimensionality must be reduced. To reduce dimensionality and measure underlying cognitive processes, we propose a modeling framework in which a cognitive process is defined as a low-dimensional dynamical latent variable—called a cognitive state, which links high-dimensional neural recordings and multidimensional behavi

APA, Harvard, Vancouver, ISO, and other styles

42

Kiperwasser, Eliyahu, and Miguel Ballesteros. "Scheduled Multi-Task Learning: From Syntax to Translation." Transactions of the Association for Computational Linguistics 6 (December 2018): 225–40. http://dx.doi.org/10.1162/tacl_a_00017.

Full text

Abstract:

Neural encoder-decoder models of machine translation have achieved impressive results, while learning linguistic knowledge of both the source and target languages in an implicit end-to-end manner. We propose a framework in which our model begins learning syntax and translation interleaved, gradually putting more focus on translation. Using this approach, we achieve considerable improvements in terms of BLEU score on relatively large parallel corpus (WMT14 English to German) and a low-resource (WIT German to English) setup.

APA, Harvard, Vancouver, ISO, and other styles

43

Wang, Yao, Zujun Yu, and Liqiang Zhu. "Foreground Detection with Deeply Learned Multi-Scale Spatial-Temporal Features." Sensors 18, no. 12 (2018): 4269. http://dx.doi.org/10.3390/s18124269.

Full text

Abstract:

Foreground detection, which extracts moving objects from videos, is an important and fundamental problem of video analysis. Classic methods often build background models based on some hand-craft features. Recent deep neural network (DNN) based methods can learn more effective image features by training, but most of them do not use temporal feature or use simple hand-craft temporal features. In this paper, we propose a new dual multi-scale 3D fully-convolutional neural network for foreground detection problems. It uses an encoder–decoder structure to establish a mapping from image sequences to

APA, Harvard, Vancouver, ISO, and other styles

44

Kwak, Jeong gi, and Hanseok Ko. "Unsupervised Generation and Synthesis of Facial Images via an Auto-Encoder-Based Deep Generative Adversarial Network." Applied Sciences 10, no. 6 (2020): 1995. http://dx.doi.org/10.3390/app10061995.

Full text

Abstract:

The processing of facial images is an important task, because it is required for a large number of real-world applications. As deep-learning models evolve, they require a huge number of images for training. In reality, however, the number of images available is limited. Generative adversarial networks (GANs) have thus been utilized for database augmentation, but they suffer from unstable training, low visual quality, and a lack of diversity. In this paper, we propose an auto-encoder-based GAN with an enhanced network structure and training scheme for Database (DB) augmentation and image synthe

APA, Harvard, Vancouver, ISO, and other styles

45

Jin, Xue-Bo, Wei-Zhen Zheng, Jian-Lei Kong, et al. "Deep-Learning Forecasting Method for Electric Power Load via Attention-Based Encoder-Decoder with Bayesian Optimization." Energies 14, no. 6 (2021): 1596. http://dx.doi.org/10.3390/en14061596.

Full text

Abstract:

Short-term electrical load forecasting plays an important role in the safety, stability, and sustainability of the power production and scheduling process. An accurate prediction of power load can provide a reliable decision for power system management. To solve the limitation of the existing load forecasting methods in dealing with time-series data, causing the poor stability and non-ideal forecasting accuracy, this paper proposed an attention-based encoder-decoder network with Bayesian optimization to do the accurate short-term power load forecasting. Proposed model is based on an encoder-de

APA, Harvard, Vancouver, ISO, and other styles

46

Hamed Mozaffari, M., and Won-Sook Lee. "Encoder-decoder CNN models for automatic tracking of tongue contours in real-time ultrasound data." Methods 179 (July 2020): 26–36. http://dx.doi.org/10.1016/j.ymeth.2020.05.011.

Full text

APA, Harvard, Vancouver, ISO, and other styles

47

Nayak, Tapas, and Hwee Tou Ng. "Effective Modeling of Encoder-Decoder Architecture for Joint Entity and Relation Extraction." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (2020): 8528–35. http://dx.doi.org/10.1609/aaai.v34i05.6374.

Full text

Abstract:

A relation tuple consists of two entities and the relation between them, and often such tuples are found in unstructured text. There may be multiple relation tuples present in a text and they may share one or both entities among them. Extracting such relation tuples from a sentence is a difficult task and sharing of entities or overlapping entities among the tuples makes it more challenging. Most prior work adopted a pipeline approach where entities were identified first followed by finding the relations among them, thus missing the interaction among the relation tuples in a sentence. In this

APA, Harvard, Vancouver, ISO, and other styles

48

Lin, Kangling, Sheng Sheng, Yanlai Zhou, et al. "The exploration of a Temporal Convolutional Network combined with Encoder-Decoder framework for runoff forecasting." Hydrology Research 51, no. 5 (2020): 1136–49. http://dx.doi.org/10.2166/nh.2020.100.

Full text

Abstract:

Abstract The Temporal Convolutional Network (TCN) and TCN combined with the Encoder-Decoder architecture (TCN-ED) are proposed to forecast runoff in this study. Both models are trained and tested using the hourly data in the Jianxi basin, China. The results indicate that the forecast horizon has a great impact on the forecast ability, and the concentration time of the basin is a critical threshold to the effective forecast horizon for both models. Both models perform poorly in the low flow and well in the medium and high flow at most forecast horizons, while it is subject to the forecast horiz

APA, Harvard, Vancouver, ISO, and other styles

49

Mohammad, Faisal, Mohamed A. Ahmed, and Young-Chon Kim. "Efficient Energy Management Based on Convolutional Long Short-Term Memory Network for Smart Power Distribution System." Energies 14, no. 19 (2021): 6161. http://dx.doi.org/10.3390/en14196161.

Full text

Abstract:

An efficient energy management system is integrated with the power grid to collect information about the energy consumption and provide the appropriate control to optimize the supply–demand pattern. Therefore, there is a need for intelligent decisions for the generation and distribution of energy, which is only possible by making the correct future predictions. In the energy market, future knowledge of the energy consumption pattern helps the end-user to decide when to buy or sell the energy to reduce the energy cost and decrease the peak consumption. The Internet of things (IoT) and energy da

APA, Harvard, Vancouver, ISO, and other styles

50

Zemouri, Ryad. "Semi-Supervised Adversarial Variational Autoencoder." Machine Learning and Knowledge Extraction 2, no. 3 (2020): 361–78. http://dx.doi.org/10.3390/make2030020.

Full text

Abstract:

We present a method to improve the reconstruction and generation performance of a variational autoencoder (VAE) by injecting an adversarial learning. Instead of comparing the reconstructed with the original data to calculate the reconstruction loss, we use a consistency principle for deep features. The main contributions are threefold. Firstly, our approach perfectly combines the two models, i.e., GAN and VAE, and thus improves the generation and reconstruction performance of the VAE. Secondly, the VAE training is done in two steps, which allows to dissociate the constraints used for the const

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!