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Zeitschriftenartikel zum Thema „Context Encoder“

Autor: Grafiati

Veröffentlicht am 28. Juni 2021

Zuletzt aktualisiert am 18. Februar 2022

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1

Pinho, M. S., and W. A. Finamore. "Context-based LZW encoder." Electronics Letters 38, no. 20 (2002): 1172. http://dx.doi.org/10.1049/el:20020807.

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Han, Jialong, Aixin Sun, Haisong Zhang, Chenliang Li, and Shuming Shi. "CASE: Context-Aware Semantic Expansion." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (2020): 7871–78. http://dx.doi.org/10.1609/aaai.v34i05.6293.

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In this paper, we define and study a new task called Context-Aware Semantic Expansion (CASE). Given a seed term in a sentential context, we aim to suggest other terms that well fit the context as the seed. CASE has many interesting applications such as query suggestion, computer-assisted writing, and word sense disambiguation, to name a few. Previous explorations, if any, only involve some similar tasks, and all require human annotations for evaluation. In this study, we demonstrate that annotations for this task can be harvested at scale from existing corpora, in a fully automatic manner. On

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Marafioti, Andres, Nathanael Perraudin, Nicki Holighaus, and Piotr Majdak. "A Context Encoder For Audio Inpainting." IEEE/ACM Transactions on Audio, Speech, and Language Processing 27, no. 12 (2019): 2362–72. http://dx.doi.org/10.1109/taslp.2019.2947232.

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Yun, Hyeongu, Yongkeun Hwang, and Kyomin Jung. "Improving Context-Aware Neural Machine Translation Using Self-Attentive Sentence Embedding." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (2020): 9498–506. http://dx.doi.org/10.1609/aaai.v34i05.6494.

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Fully Attentional Networks (FAN) like Transformer (Vaswani et al. 2017) has shown superior results in Neural Machine Translation (NMT) tasks and has become a solid baseline for translation tasks. More recent studies also have reported experimental results that additional contextual sentences improve translation qualities of NMT models (Voita et al. 2018; Müller et al. 2018; Zhang et al. 2018). However, those studies have exploited multiple context sentences as a single long concatenated sentence, that may cause the models to suffer from inefficient computational complexities and long-range dep

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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.

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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

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Dligach, Dmitriy, Majid Afshar, and Timothy Miller. "Toward a clinical text encoder: pretraining for clinical natural language processing with applications to substance misuse." Journal of the American Medical Informatics Association 26, no. 11 (2019): 1272–78. http://dx.doi.org/10.1093/jamia/ocz072.

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Abstract Objective Our objective is to develop algorithms for encoding clinical text into representations that can be used for a variety of phenotyping tasks. Materials and Methods Obtaining large datasets to take advantage of highly expressive deep learning methods is difficult in clinical natural language processing (NLP). We address this difficulty by pretraining a clinical text encoder on billing code data, which is typically available in abundance. We explore several neural encoder architectures and deploy the text representations obtained from these encoders in the context of clinical te

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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.

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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

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Cai, Yuanyuan, Min Zuo, Qingchuan Zhang, Haitao Xiong, and Ke Li. "A Bichannel Transformer with Context Encoding for Document-Driven Conversation Generation in Social Media." Complexity 2020 (September 17, 2020): 1–13. http://dx.doi.org/10.1155/2020/3710104.

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Along with the development of social media on the internet, dialogue systems are becoming more and more intelligent to meet users’ needs for communication, emotion, and social intercourse. Previous studies usually use sequence-to-sequence learning with recurrent neural networks for response generation. However, recurrent-based learning models heavily suffer from the problem of long-distance dependencies in sequences. Moreover, some models neglect crucial information in the dialogue contexts, which leads to uninformative and inflexible responses. To address these issues, we present a bichannel

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Zhang, Biao, Deyi Xiong, Jinsong Su, and Hong Duan. "A Context-Aware Recurrent Encoder for Neural Machine Translation." IEEE/ACM Transactions on Audio, Speech, and Language Processing 25, no. 12 (2017): 2424–32. http://dx.doi.org/10.1109/taslp.2017.2751420.

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Pan, Yirong, Xiao Li, Yating Yang, and Rui Dong. "Multi-Source Neural Model for Machine Translation of Agglutinative Language." Future Internet 12, no. 6 (2020): 96. http://dx.doi.org/10.3390/fi12060096.

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Benefitting from the rapid development of artificial intelligence (AI) and deep learning, the machine translation task based on neural networks has achieved impressive performance in many high-resource language pairs. However, the neural machine translation (NMT) models still struggle in the translation task on agglutinative languages with complex morphology and limited resources. Inspired by the finding that utilizing the source-side linguistic knowledge can further improve the NMT performance, we propose a multi-source neural model that employs two separate encoders to encode the source word

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He, Xiang, Sibei Yang, Guanbin Li, Haofeng Li, Huiyou Chang, and Yizhou Yu. "Non-Local Context Encoder: Robust Biomedical Image Segmentation against Adversarial Attacks." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 8417–24. http://dx.doi.org/10.1609/aaai.v33i01.33018417.

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Recent progress in biomedical image segmentation based on deep convolutional neural networks (CNNs) has drawn much attention. However, its vulnerability towards adversarial samples cannot be overlooked. This paper is the first one that discovers that all the CNN-based state-of-the-art biomedical image segmentation models are sensitive to adversarial perturbations. This limits the deployment of these methods in safety-critical biomedical fields. In this paper, we discover that global spatial dependencies and global contextual information in a biomedical image can be exploited to defend against

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Sediqi, Khwaja Monib, and Hyo Jong Lee. "A Novel Upsampling and Context Convolution for Image Semantic Segmentation." Sensors 21, no. 6 (2021): 2170. http://dx.doi.org/10.3390/s21062170.

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Semantic segmentation, which refers to pixel-wise classification of an image, is a fundamental topic in computer vision owing to its growing importance in the robot vision and autonomous driving sectors. It provides rich information about objects in the scene such as object boundary, category, and location. Recent methods for semantic segmentation often employ an encoder-decoder structure using deep convolutional neural networks. The encoder part extracts features of the image using several filters and pooling operations, whereas the decoder part gradually recovers the low-resolution feature m

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Gu, Zaiwang, Jun Cheng, Huazhu Fu, et al. "CE-Net: Context Encoder Network for 2D Medical Image Segmentation." IEEE Transactions on Medical Imaging 38, no. 10 (2019): 2281–92. http://dx.doi.org/10.1109/tmi.2019.2903562.

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Wen, F., Y. Zhang, and B. Zhang. "GLOBAL CONTEXT AIDED SEMANTIC SEGMENTATION FOR CLOUD DETECTION OF REMOTE SENSING IMAGES." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences V-2-2020 (August 3, 2020): 583–89. http://dx.doi.org/10.5194/isprs-annals-v-2-2020-583-2020.

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Abstract. Cloud detection is a vital preprocessing step for remote sensing image applications, which has been widely studied through Convolutional Neural Networks (CNNs) in recent years. However, the available CNN-based works only extract local/non-local features by stacked convolution and pooling layers, ignoring global contextual information of the input scenes. In this paper, a novel segmentation-based network is proposed for cloud detection of remote sensing images. We add a multi-class classification branch to a U-shaped semantic segmentation network. Through the encoder-decoder architect

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Cheng, Jinfeng, Weiqin Tong, and Weian Yan. "Capsule Network Improved Multi-Head Attention for Word Sense Disambiguation." Applied Sciences 11, no. 6 (2021): 2488. http://dx.doi.org/10.3390/app11062488.

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Word sense disambiguation (WSD) is one of the core problems in natural language processing (NLP), which is to map an ambiguous word to its correct meaning in a specific context. There has been a lively interest in incorporating sense definition (gloss) into neural networks in recent studies, which makes great contribution to improving the performance of WSD. However, disambiguating polysemes of rare senses is still hard. In this paper, while taking gloss into consideration, we further improve the performance of the WSD system from the perspective of semantic representation. We encode the conte

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Su, Shaojing, Jing Zhou, Zhiping Huang, Chunwu Liu, and Yimeng Zhang. "Blind Identification of Convolutional Encoder Parameters." Scientific World Journal 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/798612.

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This paper gives a solution to the blind parameter identification of a convolutional encoder. The problem can be addressed in the context of the noncooperative communications or adaptive coding and modulations (ACM) for cognitive radio networks. We consider an intelligent communication receiver which can blindly recognize the coding parameters of the received data stream. The only knowledge is that the stream is encoded using binary convolutional codes, while the coding parameters are unknown. Some previous literatures have significant contributions for the recognition of convolutional encoder

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Sybrandt, Justin, and Ilya Safro. "CBAG: Conditional biomedical abstract generation." PLOS ONE 16, no. 7 (2021): e0253905. http://dx.doi.org/10.1371/journal.pone.0253905.

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Biomedical research papers often combine disjoint concepts in novel ways, such as when describing a newly discovered relationship between an understudied gene with an important disease. These concepts are often explicitly encoded as metadata keywords, such as the author-provided terms included with many documents in the MEDLINE database. While substantial recent work has addressed the problem of text generation in a more general context, applications, such as scientific writing assistants, or hypothesis generation systems, could benefit from the capacity to select the specific set of concepts

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López-Granado, Otoniel Mario, Miguel Onofre Martínez-Rach, Antonio Martí-Campoy, Marco Antonio Cruz-Chávez, and Manuel Pérez Malumbres. "A General Model for the Design of Efficient Sign-Coding Tools for Wavelet-Based Encoders." Electronics 9, no. 11 (2020): 1899. http://dx.doi.org/10.3390/electronics9111899.

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Traditionally, it has been assumed that the compression of the sign of wavelet coefficients is not worth the effort because they form a zero-mean process. However, several image encoders such as JPEG 2000 include sign-coding capabilities. In this paper, we analyze the convenience of including sign-coding techniques into wavelet-based image encoders and propose a methodology that allows the design of sign-prediction tools for whatever kind of wavelet-based encoder. The proposed methodology is based on the use of metaheuristic algorithms to find the best sign prediction with the most appropriate

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Messaoudi, Mohamed, Majdi Benzarti, and Salem Hasnaoui. "4x4 Time-Domain MIMO encoder with OFDM Scheme in WIMAX Context." International Journal of Management Excellence 1, no. 1 (2013): 01. http://dx.doi.org/10.17722/ijme.v1i1.2.

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Li, Zhiqiang, Zhouzhong Zhang, and Hongchen Guo. "An Improved Image Inpainting Method Based on Feature Similarity Context Encoder." Journal of Physics: Conference Series 1069 (August 2018): 012181. http://dx.doi.org/10.1088/1742-6596/1069/1/012181.

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Xiaohua Tian, T. M. Le, Xi Jiang, and Yong Lian. "Full RDO-Support Power-Aware CABAC Encoder With Efficient Context Access." IEEE Transactions on Circuits and Systems for Video Technology 19, no. 9 (2009): 1262–73. http://dx.doi.org/10.1109/tcsvt.2009.2020326.

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Yun, Hyeongu, Yongil Kim, Taegwan Kang, and Kyomin Jung. "Pairwise Context Similarity for Image Retrieval System Using Variational Auto-Encoder." IEEE Access 9 (2021): 34067–77. http://dx.doi.org/10.1109/access.2021.3061765.

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Deepthi, Godavarthi, and A. Mary Sowjanya. "Query-Based Retrieval Using Universal Sentence Encoder." Revue d'Intelligence Artificielle 35, no. 4 (2021): 301–6. http://dx.doi.org/10.18280/ria.350404.

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In Natural language processing, various tasks can be implemented with the features provided by word embeddings. But for obtaining embeddings for larger chunks like sentences, the efforts applied through word embeddings will not be sufficient. To resolve such issues sentence embeddings can be used. In sentence embeddings, complete sentences along with their semantic information are represented as vectors so that the machine finds it easy to understand the context. In this paper, we propose a Question Answering System (QAS) based on sentence embeddings. Our goal is to obtain the text from the pr

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Wen, Ying, Kai Xie, and Lianghua He. "Segmenting Medical MRI via Recurrent Decoding Cell." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (2020): 12452–59. http://dx.doi.org/10.1609/aaai.v34i07.6932.

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The encoder-decoder networks are commonly used in medical image segmentation due to their remarkable performance in hierarchical feature fusion. However, the expanding path for feature decoding and spatial recovery does not consider the long-term dependency when fusing feature maps from different layers, and the universal encoder-decoder network does not make full use of the multi-modality information to improve the network robustness especially for segmenting medical MRI. In this paper, we propose a novel feature fusion unit called Recurrent Decoding Cell (RDC) which leverages convolutional R

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Lei, S. F., C. C. Lo, C. C. Kuo, and M. D. Shieh. "Low-power context-based adaptive binary arithmetic encoder using an embedded cache." IET Image Processing 6, no. 4 (2012): 309. http://dx.doi.org/10.1049/iet-ipr.2010.0473.

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Yang, Libin, Zeqing Zhang, Xiaoyan Cai, and Tao Dai. "Attention-Based Personalized Encoder-Decoder Model for Local Citation Recommendation." Computational Intelligence and Neuroscience 2019 (June 3, 2019): 1–7. http://dx.doi.org/10.1155/2019/1232581.

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With a tremendous growth in the number of scientific papers, researchers have to spend too much time and struggle to find the appropriate papers they are looking for. Local citation recommendation that provides a list of references based on a text segment could alleviate the problem. Most existing local citation recommendation approaches concentrate on how to narrow the semantic difference between the scientific papers’ and citation context’s text content, completely neglecting other information. Inspired by the successful use of the encoder-decoder framework in machine translation, we develop

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Yang, Zhenjian, Jiamei Shang, Zhongwei Zhang, Yan Zhang, and Shudong Liu. "A new end-to-end image dehazing algorithm based on residual attention mechanism." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 39, no. 4 (2021): 901–8. http://dx.doi.org/10.1051/jnwpu/20213940901.

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Traditional image dehazing algorithms based on prior knowledge and deep learning rely on the atmospheric scattering model and are easy to cause color distortion and incomplete dehazing. To solve these problems, an end-to-end image dehazing algorithm based on residual attention mechanism is proposed in this paper. The network includes four modules: encoder, multi-scale feature extraction, feature fusion and decoder. The encoder module encodes the input haze image into feature map, which is convenient for subsequent feature extraction and reduces memory consumption; the multi-scale feature extra

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Girbau, Dolors, and Humbert Boada. "Accurate Referential Communication and its Relation with Private and Social Speech in a Naturalistic Context." Spanish Journal of Psychology 7, no. 2 (2004): 81–92. http://dx.doi.org/10.1017/s1138741600004789.

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Research into human communication has been grouped under two traditions: referential and sociolinguistic. The study of a communication behavior simultaneously from both paradigms appears to be absent. Basically, this paper analyzes the use of private and social speech, through both a referential task (Word Pairs) and a naturalistic dyadic setting (Lego-set) administered to a sample of 64 children from grades 3 and 5. All children, of 8 and 10 years of age, used speech that was not adapted to the decoder, and thus ineffective for interpersonal communication, in both referential and sociolinguis

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Varade, Saurabh, Ejaaz Sayyed, Vaibhavi Nagtode, and Shilpa Shinde. "Text Summarization using Extractive and Abstractive Methods." ITM Web of Conferences 40 (2021): 03023. http://dx.doi.org/10.1051/itmconf/20214003023.

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Text Summarization is a process where a huge text file is converted into summarized version which will preserve the original meaning and context. The main aim of any text summarization is to provide a accurate and precise summary. One approach is to use a sentence ranking algorithm. This comes under extractive summarization. Here, a graph based ranking algorithm is used to rank the sentences in the text and then top k-scored sentences are included in the summary. The most widely used algorithm to decide the importance of any vertex in a graph based on the information retrieved from the graph i

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Liu, Hai, Yuanxia Liu, Leung-Pun Wong, Lap-Kei Lee, and Tianyong Hao. "A Hybrid Neural Network BERT-Cap Based on Pre-Trained Language Model and Capsule Network for User Intent Classification." Complexity 2020 (November 21, 2020): 1–11. http://dx.doi.org/10.1155/2020/8858852.

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User intent classification is a vital component of a question-answering system or a task-based dialogue system. In order to understand the goals of users’ questions or discourses, the system categorizes user text into a set of pre-defined user intent categories. User questions or discourses are usually short in length and lack sufficient context; thus, it is difficult to extract deep semantic information from these types of text and the accuracy of user intent classification may be affected. To better identify user intents, this paper proposes a BERT-Cap hybrid neural network model with focal

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Fan, Zhun, Chong Li, Ying Chen, et al. "Automatic Crack Detection on Road Pavements Using Encoder-Decoder Architecture." Materials 13, no. 13 (2020): 2960. http://dx.doi.org/10.3390/ma13132960.

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Automatic crack detection from images is an important task that is adopted to ensure road safety and durability for Portland cement concrete (PCC) and asphalt concrete (AC) pavement. Pavement failure depends on a number of causes including water intrusion, stress from heavy loads, and all the climate effects. Generally, cracks are the first distress that arises on road surfaces and proper monitoring and maintenance to prevent cracks from spreading or forming is important. Conventional algorithms to identify cracks on road pavements are extremely time-consuming and high cost. Many cracks show c

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Luo, Junyu, Min Yang, Ying Shen, Qiang Qu, and Haixia Chai. "Learning Document Embeddings with Crossword Prediction." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 9993–94. http://dx.doi.org/10.1609/aaai.v33i01.33019993.

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In this paper, we propose a Document Embedding Network (DEN) to learn document embeddings in an unsupervised manner. Our model uses the encoder-decoder architecture as its backbone, which tries to reconstruct the input document from an encoded document embedding. Unlike the standard decoder for text reconstruction, we randomly block some words in the input document, and use the incomplete context information and the encoded document embedding to predict the blocked words in the document, inspired by the crossword game. Thus, our decoder can keep the balance between the known and unknown inform

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Wang, Shuyang, Xiaodong Mu, Dongfang Yang, Hao He, and Peng Zhao. "Attention Guided Encoder-Decoder Network With Multi-Scale Context Aggregation for Land Cover Segmentation." IEEE Access 8 (2020): 215299–309. http://dx.doi.org/10.1109/access.2020.3040862.

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Dong, Yuying, Liejun Wang, Shuli Cheng, and Yongming Li. "FAC-Net: Feedback Attention Network Based on Context Encoder Network for Skin Lesion Segmentation." Sensors 21, no. 15 (2021): 5172. http://dx.doi.org/10.3390/s21155172.

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Considerable research and surveys indicate that skin lesions are an early symptom of skin cancer. Segmentation of skin lesions is still a hot research topic. Dermatological datasets in skin lesion segmentation tasks generated a large number of parameters when data augmented, limiting the application of smart assisted medicine in real life. Hence, this paper proposes an effective feedback attention network (FAC-Net). The network is equipped with the feedback fusion block (FFB) and the attention mechanism block (AMB), through the combination of these two modules, we can obtain richer and more sp

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Chen, Songle, Xuejian Zhao, Bingqing Luo, and Zhixin Sun. "Visual Browse and Exploration in Motion Capture Data with Phylogenetic Tree of Context-Aware Poses." Sensors 20, no. 18 (2020): 5224. http://dx.doi.org/10.3390/s20185224.

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Visual browse and exploration in motion capture data take resource acquisition as a human–computer interaction problem, and it is an essential approach for target motion search. This paper presents a progressive schema which starts from pose browse, then locates the interesting region and then switches to online relevant motion exploration. It mainly addresses three core issues. First, to alleviate the contradiction between the limited visual space and ever-increasing size of real-world database, it applies affinity propagation to numerical similarity measure of pose to perform data abstractio

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Ai, Xinbo, Yunhao Xie, Yinan He, and Yi Zhou. "Improve SegNet with feature pyramid for road scene parsing." E3S Web of Conferences 260 (2021): 03012. http://dx.doi.org/10.1051/e3sconf/202126003012.

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Road scene parsing is a common task in semantic segmentation. Its images have characteristics of containing complex scene context and differing greatly among targets of the same category from different scales. To address these problems, we propose a semantic segmentation model combined with edge detection. We extend the segmentation network with an encoder-decoder structure by adding an edge feature pyramid module, namely Edge Feature Pyramid Network (EFPNet, for short). This module uses edge detection operators to get boundary information and then combines the multiscale features to improve t

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Zhou, Zexun, Zhongshi He, Yuanyuan Jia, Jinglong Du, Lulu Wang, and Ziyu Chen. "Context prior-based with residual learning for face detection: A deep convolutional encoder–decoder network." Signal Processing: Image Communication 88 (October 2020): 115948. http://dx.doi.org/10.1016/j.image.2020.115948.

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Sriraam, N. "A High-Performance Lossless Compression Scheme for EEG Signals Using Wavelet Transform and Neural Network Predictors." International Journal of Telemedicine and Applications 2012 (2012): 1–8. http://dx.doi.org/10.1155/2012/302581.

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Developments of new classes of efficient compression algorithms, software systems, and hardware for data intensive applications in today's digital health care systems provide timely and meaningful solutions in response to exponentially growing patient information data complexity and associated analysis requirements. Of the different 1D medical signals, electroencephalography (EEG) data is of great importance to the neurologist for detecting brain-related disorders. The volume of digitized EEG data generated and preserved for future reference exceeds the capacity of recent developments in digit

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Tackenberg, Michael C., and Douglas G. McMahon. "Photoperiodic Programming of the SCN and Its Role in Photoperiodic Output." Neural Plasticity 2018 (2018): 1–9. http://dx.doi.org/10.1155/2018/8217345.

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Though the seasonal response of organisms to changing day lengths is a phenomenon that has been scientifically reported for nearly a century, significant questions remain about how photoperiod is encoded and effected neurobiologically. In mammals, early work identified the master circadian clock, the suprachiasmatic nuclei (SCN), as a tentative encoder of photoperiodic information. Here, we provide an overview of research on the SCN as a coordinator of photoperiodic responses, the intercellular coupling changes that accompany that coordination, as well as the SCN’s role in a putative brain net

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Guo, Hui, and Yong Qing Fu. "An Improved CAVLC Entropy Encoder of H.264/AVC and FPGA Implementation." Key Engineering Materials 474-476 (April 2011): 241–46. http://dx.doi.org/10.4028/www.scientific.net/kem.474-476.241.

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Context-based Adaptive Variable Length Coding (CAVLC) as a new entropy coding algorithm has been introduced into H.264/AVC standard. Through analysing the CAVLC coding algorithm detailedly, the paper proposes an overlapping coverage storage method and a new stream merger method, and gives the specific implementation. This idea improves the structure and performance of the complex module and reduces the implementation complexity. The experimental results show that the proposed entropy encoder is correct, and the highest coding frequency of 81.70MHz can be achieved. Meanwhile all the hardware re

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Jin, Shih-Chun, Chia-Jui Hsieh, Jyh-Cheng Chen, et al. "Development of Limited-Angle Iterative Reconstruction Algorithms with Context Encoder-Based Sinogram Completion for Micro-CT Applications." Sensors 18, no. 12 (2018): 4458. http://dx.doi.org/10.3390/s18124458.

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Limited-angle iterative reconstruction (LAIR) reduces the radiation dose required for computed tomography (CT) imaging by decreasing the range of the projection angle. We developed an image-quality-based stopping-criteria method with a flexible and innovative instrument design that, when combined with LAIR, provides the image quality of a conventional CT system. This study describes the construction of different scan acquisition protocols for micro-CT system applications. Fully-sampled Feldkamp (FDK)-reconstructed images were used as references for comparison to assess the image quality produc

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Wu, Yu, Furu Wei, Shaohan Huang, Yunli Wang, Zhoujun Li, and Ming Zhou. "Response Generation by Context-Aware Prototype Editing." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 7281–88. http://dx.doi.org/10.1609/aaai.v33i01.33017281.

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Open domain response generation has achieved remarkable progress in recent years, but sometimes yields short and uninformative responses. We propose a new paradigm, prototypethen-edit for response generation, that first retrieves a prototype response from a pre-defined index and then edits the prototype response according to the differences between the prototype context and current context. Our motivation is that the retrieved prototype provides a good start-point for generation because it is grammatical and informative, and the post-editing process further improves the relevance and coherence

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Liang, Wenkai, Yan Wu, Ming Li, and Yice Cao. "High-Resolution SAR Image Classification Using Context-Aware Encoder Network and Hybrid Conditional Random Field Model." IEEE Transactions on Geoscience and Remote Sensing 58, no. 8 (2020): 5317–35. http://dx.doi.org/10.1109/tgrs.2019.2963699.

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Licciardo, G. D., and L. Freda Albanese. "Design of a context-adaptive variable length encoder for real-time video compression on reconfigurable platforms." IET Image Processing 6, no. 4 (2012): 301. http://dx.doi.org/10.1049/iet-ipr.2010.0510.

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Hwang, Yongkeun, Yanghoon Kim, and Kyomin Jung. "Context-Aware Neural Machine Translation for Korean Honorific Expressions." Electronics 10, no. 13 (2021): 1589. http://dx.doi.org/10.3390/electronics10131589.

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Neural machine translation (NMT) is one of the text generation tasks which has achieved significant improvement with the rise of deep neural networks. However, language-specific problems such as handling the translation of honorifics received little attention. In this paper, we propose a context-aware NMT to promote translation improvements of Korean honorifics. By exploiting the information such as the relationship between speakers from the surrounding sentences, our proposed model effectively manages the use of honorific expressions. Specifically, we utilize a novel encoder architecture that

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Chen, Yunfan, and Hyunchul Shin. "Pedestrian Detection at Night in Infrared Images Using an Attention-Guided Encoder-Decoder Convolutional Neural Network." Applied Sciences 10, no. 3 (2020): 809. http://dx.doi.org/10.3390/app10030809.

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Pedestrian-related accidents are much more likely to occur during nighttime when visible (VI) cameras are much less effective. Unlike VI cameras, infrared (IR) cameras can work in total darkness. However, IR images have several drawbacks, such as low-resolution, noise, and thermal energy characteristics that can differ depending on the weather. To overcome these drawbacks, we propose an IR camera system to identify pedestrians at night that uses a novel attention-guided encoder-decoder convolutional neural network (AED-CNN). In AED-CNN, encoder-decoder modules are introduced to generate multi-

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Liu, Hongtao, Wenjun Wang, Qiyao Peng, Nannan Wu, Fangzhao Wu, and Pengfei Jiao. "Toward Comprehensive User and Item Representations via Three-tier Attention Network." ACM Transactions on Information Systems 39, no. 3 (2021): 1–22. http://dx.doi.org/10.1145/3446341.

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Product reviews can provide rich information about the opinions users have of products. However, it is nontrivial to effectively infer user preference and item characteristics from reviews due to the complicated semantic understanding. Existing methods usually learn features for users and items from reviews in single static fashions and cannot fully capture user preference and item features. In this article, we propose a neural review-based recommendation approach that aims to learn comprehensive representations of users/items under a three-tier attention framework. We design a review encoder

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Xing, Yongfeng, Luo Zhong, and Xian Zhong. "An Encoder-Decoder Network Based FCN Architecture for Semantic Segmentation." Wireless Communications and Mobile Computing 2020 (July 7, 2020): 1–9. http://dx.doi.org/10.1155/2020/8861886.

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In recent years, the convolutional neural network (CNN) has made remarkable achievements in semantic segmentation. The method of semantic segmentation has a desirable application prospect. Nowadays, the methods mostly use an encoder-decoder architecture as a way of generating pixel by pixel segmentation prediction. The encoder is for extracting feature maps and decoder for recovering feature map resolution. An improved semantic segmentation method on the basis of the encoder-decoder architecture is proposed. We can get better segmentation accuracy on several hard classes and reduce the computa

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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.

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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

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Li, Liunian Harold, Patrick H. Chen, Cho-Jui Hsieh, and Kai-Wei Chang. "Efficient Contextual Representation Learning With Continuous Outputs." Transactions of the Association for Computational Linguistics 7 (November 2019): 611–24. http://dx.doi.org/10.1162/tacl_a_00289.

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Contextual representation models have achieved great success in improving various downstream natural language processing tasks. However, these language-model-based encoders are difficult to train due to their large parameter size and high computational complexity. By carefully examining the training procedure, we observe that the softmax layer, which predicts a distribution of the target word, often induces significant overhead, especially when the vocabulary size is large. Therefore, we revisit the design of the output layer and consider directly predicting the pre-trained embedding of the ta

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