Dual Learning
暫譯: 雙重學習
Qin, Tao
商品描述
Many AI (and machine learning) tasks present in dual forms, e.g., English-to-Chinese translation vs. Chinese-to-English translation, speech recognition vs. speech synthesis, question answering vs. question generation, and image classification vs. image generation. Dual learning is a new learning framework that leverages the primal-dual structure of AI tasks to obtain effective feedback or regularization signals in order to enhance the learning/inference process. Since it was first introduced four years ago, the concept has attracted considerable attention in multiple fields, and been proven effective in numerous applications, such as machine translation, image-to-image translation, speech synthesis and recognition, (visual) question answering and generation, image captioning and generation, and code summarization and generation.
Offering a systematic and comprehensive overview of dual learning, this book enables interested researchers (both established and newcomers) and practitioners to gain a better understanding of the state of the art in the field. It also provides suggestions for further reading and tools to help readers advance the area. The book is divided into five parts. The first part gives a brief introduction to machine learning and deep learning. The second part introduces the algorithms based on the dual reconstruction principle using machine translation, image translation, speech processing and other NLP/CV tasks as the demo applications. It covers algorithms, such as dual semi-supervised learning, dual unsupervised learning and multi-agent dual learning. In the context of image translation, it introduces algorithms including CycleGAN, DualGAN, DiscoGAN cdGAN and more recent techniques/applications. The third part presents various work based on the probability principle, including dual supervised learning and dual inference based on the joint-probability principle and dual semi-supervised learning based on the marginal-probability principle. The fourth part reviews various theoretical studies on dual learning and discusses its connections to other learning paradigms. The fifth part provides a summary and suggests future research directions.
商品描述(中文翻譯)
許多人工智慧（AI）和機器學習任務以雙重形式呈現，例如英語到中文翻譯與中文到英語翻譯、語音識別與語音合成、問題回答與問題生成，以及圖像分類與圖像生成。雙重學習是一種新的學習框架，利用AI任務的原始-對偶結構來獲取有效的反饋或正則化信號，以增強學習/推理過程。自從四年前首次提出以來，這一概念在多個領域引起了相當大的關注，並在許多應用中證明了其有效性，例如機器翻譯、圖像到圖像翻譯、語音合成與識別、（視覺）問題回答與生成、圖像標題生成以及代碼摘要與生成。
本書提供了雙重學習的系統性和全面性概述，使有興趣的研究者（無論是資深還是新手）和從業者能夠更好地理解該領域的最新進展。它還提供了進一步閱讀的建議和工具，以幫助讀者推進該領域。本書分為五個部分。第一部分簡要介紹了機器學習和深度學習。第二部分介紹了基於雙重重建原則的算法，使用機器翻譯、圖像翻譯、語音處理和其他自然語言處理（NLP）/計算機視覺（CV）任務作為示範應用。它涵蓋了雙重半監督學習、雙重無監督學習和多代理雙重學習等算法。在圖像翻譯的背景下，介紹了包括CycleGAN、DualGAN、DiscoGAN、cdGAN以及更近期的技術/應用的算法。第三部分展示了基於概率原則的各種工作，包括基於聯合概率原則的雙重監督學習和雙重推理，以及基於邊際概率原則的雙重半監督學習。第四部分回顧了關於雙重學習的各種理論研究，並討論了其與其他學習範式的聯繫。第五部分提供了總結並建議未來的研究方向。
作者簡介
Dr. Tao Qin is a Senior Principal Researcher and Manager at Microsoft Research Asia, and an Adjunct Professor (PhD advisor) at the University of Science and Technology of China. He coined the term dual learning together with his colleagues, and has published numerous papers on the topic in Neur IPS/ICLR/ICML/AAAI/IJCAI/CVPR/ACL/EMNLP/NAACL. His research interests include machine learning (with the focus on deep learning and reinforcement learning), artificial intelligence (with applications to language understanding, speech processing and computer vision), game theory and multi-agent systems (with applications to cloud computing, online and mobile advertising, ecommerce), information retrieval and computational advertising.
Dr. Qin has published over 100 refereed papers at prestigious conferences such asNeur IPS, ICML, ICLR, AAAI, IJCAI, AAMAS, ACL, EMNLP, NAACL, KDD, WWW, SIGIR, WSDM, JAIR, EC, WINE, and ACM Transactions. He served/is serving as Area Chair for AAAI, IJCAI, EMNLP, AAMAS, SIGIR and ACML, Workshop Chair for WWW 2020, and Industry Chair for DAI 2019. He is a Senior Member of the IEEE and ACM.
作者簡介(中文翻譯)
秦濤博士是微軟亞洲研究院的高級首席研究員及經理，並且是中國科學技術大學的兼任教授（博士生導師）。他與同事共同創造了「雙重學習」（dual learning）這一術語，並在Neur IPS、ICLR、ICML、AAAI、IJCAI、CVPR、ACL、EMNLP、NAACL等會議上發表了多篇相關論文。他的研究興趣包括機器學習（專注於深度學習和強化學習）、人工智慧（應用於語言理解、語音處理和計算機視覺）、博弈論和多智能體系統（應用於雲計算、在線和移動廣告、電子商務）、信息檢索和計算廣告。
秦博士在Neur IPS、ICML、ICLR、AAAI、IJCAI、AAMAS、ACL、EMNLP、NAACL、KDD、WWW、SIGIR、WSDM、JAIR、EC、WINE和ACM Transactions等知名會議上發表了超過100篇經過審核的論文。他曾擔任或正在擔任AAAI、IJCAI、EMNLP、AAMAS、SIGIR和ACML的區域主席，2020年WWW的研討會主席，以及2019年DAI的產業主席。他是IEEE和ACM的資深會員。
