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From Feature To Paradigm: Deep Learning In Machine Translation

Marta R. Costa-jussà
2018 The Journal of Artificial Intelligence Research  
Integration of deep learning in MT varies from re-modeling existing features into standard statistical systems to the development of a new architecture.  ...  Finally, we include an analysis of the corresponding challenges and future work in using deep learning in MT.  ...  This manuscript presents an overview from the early stages of how deep learning has started as a feature function in statistical MT (Schwenk, Costa-Jussà, & Fonollosa, 2006) to become an entire new paradigm  ... 
doi:10.1613/jair.1.11198 fatcat:zk7tszi4dfboxmeg2vfm2xbtje

From Feature to Paradigm: Deep Learning in Machine Translation (Extended Abstract)

Marta R. Costa-jussà
2018 Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence  
Integration of deep learning in MT varies from re-modeling existing features into standard statistical systems to the development of a new architecture.  ...  The specific field of Machine Translation (MT) has not remained invariant.  ...  Acknowledgements The authors would like to specially thank Prof. Kobi Gal for his invitation to participate in the IJCAI journal track.  ... 
doi:10.24963/ijcai.2018/789 dblp:conf/ijcai/Costa-Jussa18 fatcat:uwapnqtfive3zn4wbfnqmuvcbe

Curriculum Learning and Minibatch Bucketing in Neural Machine Translation

Tom Kocmi, Ondřej Bojar
2017 RANLP 2017 - Recent Advances in Natural Language Processing Meet Deep Learning  
We examine the effects of particular orderings of sentence pairs on the on-line training of neural machine translation (NMT).  ...  "curriculum learning").  ...  Multidisciplinarity to Interdisciplinarity.  ... 
doi:10.26615/978-954-452-049-6_050 dblp:conf/ranlp/KocmiB17 fatcat:3432tnq73zezvpzkymhdwcmhqe

Persian–Spanish Low-Resource Statistical Machine Translation Through English as Pivot Language

Benyamin Ahmadnia, Javier Serrano, Gholamreza Haffari
2017 RANLP 2017 - Recent Advances in Natural Language Processing Meet Deep Learning  
Machine Translation (SMT).  ...  Finally we suggest a method called combination model in which the standard direct model and the best triangulation pivoting model are blended in order to reach a high-quality translation.  ...  The authors have benefited from her erudition and thoughtful comments which truly enriched the present work.  ... 
doi:10.26615/978-954-452-049-6_004 dblp:conf/ranlp/AhmadniaSH17 fatcat:ugrtyxbksnhwlhflj3lcjegqw4

Feature Extraction for Hyperspectral Imagery: The Evolution from Shallow to Deep [article]

Behnood Rasti, Danfeng Hong, Renlong Hang, Pedram Ghamisi, Xudong Kang, Jocelyn Chanussot, Jon Atli Benediktsson
2020 arXiv   pre-print
The advances in feature extraction have been inspired by two fields of research, including the popularization of image and signal processing as well as machine (deep) learning, leading to two types of  ...  In more detail, this paper provides a bird's eye view over shallow (both supervised and unsupervised) and deep feature extraction approaches specifically dedicated to the topic of hyperspectral feature  ...  ACKNOWLEDGMENT The authors would like to thank Prof.  ... 
arXiv:2003.02822v2 fatcat:2l37q46y6ndqjooo6pkcqezmzi

Image Matching from Handcrafted to Deep Features: A Survey

Jiayi Ma, Xingyu Jiang, Aoxiang Fan, Junjun Jiang, Junchi Yan
2020 International Journal of Computer Vision  
Following the feature-based image matching pipeline, we first introduce feature detection, description, and matching techniques from handcrafted methods to trainable ones and provide an analysis of the  ...  Over the past decades, growing amount and diversity of methods have been proposed for image matching, particularly with the development of deep learning techniques over the recent years.  ...  Classical Learning-Based Detectors Early from the past decade, classical learning-based methods, such as decision tree, support vector machine (SVM), and other classifiers by opposition to Deep Learning  ... 
doi:10.1007/s11263-020-01359-2 fatcat:a2epfaolwjfm5mcrsmn7g6sd7m

Deep Learning Training and Benchmarks for Earth Observation Images: Data Sets, Features, and Procedures [chapter]

Mihai Datcu, Gottfried Schwarz, Corneliu Octavian Dumitru
2020 Recent Trends in Artificial Neural Networks - from Training to Prediction  
Therefore, we need reliably labeled data sets and tools to train the developed algorithms and to assess the performance of our deep learning paradigms.  ...  Deep learning methods are often used for image classification or local object segmentation.  ...  Acknowledgements We appreciate the cooperation with Politehnica University of Bucharest (UPB) in Romania and our project partners from the European H2020 projects CANDELA (under grant agreement No. 776193  ... 
doi:10.5772/intechopen.90910 fatcat:ajwyldahcvggnfwax7ecl6wx4u

Network Representation Learning: From Preprocessing, Feature Extraction to Node Embedding [article]

Jingya Zhou, Ling Liu, Wenqi Wei, Jianxi Fan
2021 arXiv   pre-print
This survey not only helps researchers and practitioners to gain an in-depth understanding of different network representation learning techniques but also provides practical guidelines for designing and  ...  Over dozens of network representation learning algorithms have been reported in the literature.  ...  Different from traditional feature engineering that relies heavily on handcrafted statistics to extract structural information, NRL introduces a new data-driven deep learning paradigm to capture, encode  ... 
arXiv:2110.07582v1 fatcat:gbjn3evwwzf4xkeobrsfo6hope

Deep Learning Paradigm with Transformed Monolingual Word Embeddings for Multilingual Sentiment Analysis [article]

Yujie Lu, Tatsunori Mori
2017 arXiv   pre-print
However, this paradigm is conditioned by the quality of machine translation. In this paper, we propose a new deep learning paradigm to assimilate the differences between languages for MSA.  ...  So far, traditional methods resorted to machine translation---translating texts in other languages to English, and then adopt the methods once worked in English.  ...  In this paper, we proposed a novel deep learning paradigm for MSA.  ... 
arXiv:1710.03203v2 fatcat:2uwd5egwlfg5tp76ypwggw4z5i

Network Representation Learning: From Traditional Feature Learning to Deep Learning

Ke Sun, Lei Wang, Bo Xu, Wenhong Zhao, Shyh Wei Teng, Feng Xia
2020 IEEE Access  
INTRODUCTION R EPRESENTATION learning is a new paradigm in the machine learning field aiming at representing information efficiently.  ...  In this survey, we try to go through the development of data representation in graphstructured data from TFL to recent NRL based on deep learning.  ... 
doi:10.1109/access.2020.3037118 fatcat:kca6htfarjdjpmtwcvbsppfzui

Python as Multi Paradigm Programming Language

Nimit Thaker, Abhilash Shukla
2020 International Journal of Computer Applications  
Like IoT Applications, Machine Learning, Deep Learning, Artificial Intelligence, Cyber Security, etc.  ...  This Paper will also include various characteristics and features of python proving why it is the widely used programming language in recent times.  ...  Python for Machine Learning & Deep Learning AI, Machine Learning and Deep Learning are the future, and are the topics that are at a boom these days, and as is Python.  ... 
doi:10.5120/ijca2020919775 fatcat:kpyncdxxarbblocbpib6bcap6m

Transferring ConvNet Features from Passive to Active Robot Self-Localization: The Use of Ego-Centric and World-Centric Views [article]

Kanya Kurauchi, Kanji Tanaka, Ryogo Yamamoto, Mitsuki Yoshida
2022 arXiv   pre-print
In our framework, the ILC and OLC are mapped to a state vector and subsequently used to train a multiview NBV planner via deep reinforcement learning.  ...  Specifically, we divide the visual cues that are available from the CNN model into two types: the output layer cue (OLC) and intermediate layer cue (ILC).  ...  Although most existing active VPR methods are non-deep, in recent years, attempts have been made to boost active VPR using deep learning.  ... 
arXiv:2204.10497v1 fatcat:uofb3ikgz5bqnga2623uxiy4gm

Video Coding for Machines: A Paradigm of Collaborative Compression and Intelligent Analytics [article]

Ling-Yu Duan, Jiaying Liu, Wenhan Yang, Tiejun Huang, Wen Gao
2020 arXiv   pre-print
The recent endeavors in imminent trends of video compression, e.g. deep learning based coding tools and end-to-end image/video coding, and MPEG-7 compact feature descriptor standards, i.e.  ...  In this paper, thanks to booming AI technology, e.g. prediction and generation models, we carry out exploration in the new area, Video Coding for Machines (VCM), arising from the emerging MPEG standardization  ...  As illustrated in Fig. 2 , VCM attempts to connect the features of different granularities to the human/machine vision tasks from the perspective of a general deep learning framework.  ... 
arXiv:2001.03569v2 fatcat:22dsiwby6nfrbicoxyk2ufwhjy

Paradigm shift in electron-based crystallography via machine learning [article]

Kevin Kaufmann, Chaoyi Zhu, Alexander S. Rosengarten, Daniel Maryanovsky, Tyler J. Harrington, Eduardo Marin, Kenneth S. Vecchio
2019 arXiv   pre-print
Electron backscatter diffraction patterns are collected from materials with well-known crystal structures, then a deep neural network model is constructed for classification to a specific Bravais lattice  ...  This paper presents a newly developed methodology that represents a paradigm change in electron diffraction-based structure analysis techniques, with the potential to revolutionize multiple crystallography-related  ...  Other discovered features might be obvious to experts, but difficult to translate into specific logic.  ... 
arXiv:1902.03682v1 fatcat:ytqneqfm4nf3rov4b6wossqpoe

Machine Teaching: A New Paradigm for Building Machine Learning Systems [article]

Patrice Y. Simard, Saleema Amershi, David M. Chickering, Alicia Edelman Pelton, Soroush Ghorashi, Christopher Meek, Gonzalo Ramos, Jina Suh, Johan Verwey, Mo Wang, John Wernsing
2017 arXiv   pre-print
The current processes for building machine learning systems require practitioners with deep knowledge of machine learning.  ...  We believe that in order to meet this growing demand for machine learning systems we must significantly increase the number of individuals that can teach machines.  ...  Acknowledgements We thank Jason Williams for his support and contributions to Machine Teaching.  ... 
arXiv:1707.06742v3 fatcat:4btdbfgwkvhdtpantomqlxs4qy
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