计算机科学 ›› 2022, Vol. 49 ›› Issue (11A): 210900016-17.doi: 10.11896/jsjkx.210900016
周帆, 陈晓蝶, 钟婷, 吴劲
ZHOU Fan, CHEN Xiao-die, ZHONG Ting, WU Jin
摘要: 近年来,深度学习技术被广泛应用于金融领域,并受到了国内外学术界和商业界的广泛关注。研究人员利用深度学习技术对各种金融数据进行发掘和分析,取得了大量的研究成果。深度学习在多个金融关键应用上的表现超过了传统的统计机器学习模型,包括金融市场预测、交易策略改进和金融文本信息挖掘等。为了更全面地把握深度学习技术在金融领域中的研究和应用趋势,促进它们之间的深层次融合和发展,着重梳理了近年来深度学习技术在金融科技研究中的发展脉络及前沿动态,分析和总结了深度学习模型在金融领域的主要应用和最新算法。根据金融领域中的具体应用场景将现有的深度学习金融研究进行详细分类,分析并总结各个领域的最新研究,并展望了金融科技领域未来的研究热点、技术难点和发展趋势等。
中图分类号:
[1]WU J J.Financial distress prediction:The comparison and application of data mining models[J].Journal of Tsinghua University(Philosophy and Social Sciences),2006(S1):45-53. [2]SUN Z J,XUE L,XU Y M,et al.Overview of deep learning[J].Application Research of Computers,2012,29(8):2806-2810. [3]SU Z,LU M,LI D X.Deep learning in Financial Empirical Applications:Dynamics,Contributions and Prospects[J].Journal of Financial Research,2017(5):111-126. [4]HINTON G E,OSINDERO S,TEH Y W.A fast learning algo-rithm for deep belief nets[J].Neural Computation,2006,18(7):1527-1554. [5]LECUN Y,BOTTOU L,BENGIO Y,et al.Gradient-basedlearning applied to document recognition[J].Proceedings of the IEEE,1998,86(11):2278-2324. [6]HOCHREITER S,SCHMIDHUBER J.Long short-term memory[J].Neural Computation,1997,9(8):1735-1780. [7]WU Z,PAN S,CHEN F,et al.A comprehensive survey ongraph neural networks[J].IEEE Transactions on Neural Networks and Learning Systems,2020,32(1):4-24. [8]VASWANI A,SHAZEER N,PARMAR N,et al.Attention is all you need[J].arXiv:1706.03762,2017. [9]XU H R,XU B,XU K W.Analysis on application of machine learning in stock forecasting[J].Computer Engineering and Applications,2020,56(12):19-24. [10]SHAH D,ISAH H,ZULKERNINE F.Stock market analysis:A review and taxonomy of prediction techniques[J].International Journal of Financial Studies,2019,7(2):26. [11]SEZER O B,GUDELEK M U,OZBAYOGLU A M.Financial time series forecasting with deep learning:A systematic literature review:2005-2019[J].Applied Soft Computing,2020,90:106181. [12]JEONG G,KIM H Y.Improving financial trading decisionsusing deep Q-learning:Predicting the number of shares,action strategies,and transfer learning[J].Expert Systems with Applications,2019,117:125-138. [13]CHONG E,HAN C,PARK F C.Deep learning networks for stock market analysis and prediction:Methodology,data representations,and case studies[J].Expert Systems with Applications,2017,83:187-205. [14]CHEN L,QIAO Z L,WANG M G,et al.Which artificial intelligence algorithm better predicts the Chinese stock market?[J].IEEE Access,2018,6:48625-48633. [15]PROSKY J,SONG X Y,TAN A,et al.Sentiment predictability for stocks[J].arXiv:1712.05785,2017. [16]ZHANG J,MARINGER D.Using a genetic algorithm to im-prove recurrent reinforcement learning for equity trading[J].Computational Economics,2016,47(4):551-567. [17]DINGLI A,FOURNIER K S.Financial time series forecasting-a deep learning approach[J].International Journal of Machine Learning and Computing,2017,7(5):118-122. [18]TÜFEKCI P.Predicting the direction of movement for stockprice index using machine learning methods[C]//Second International Afro-European Conference for Industrial Advancement AECIA.Springer International Publishing,2016:477-492. [19]LAGO J,DE RIDDER F,DE SCHUTTER B.Forecasting spot electricity prices:Deep learning approaches and empirical comparison of traditional algorithms[J].Applied Energy,2018,221:386-405. [20]ROY A,SUN J Y,MAHONEY R,et al.Deep learning detecting fraud in credit card transactions[C]//Systems and Information Engineering Design Symposium(SIEDS).IEEE,2018:129-134. [21]JIANG W.Applications of deep learning in stock market prediction:recent progress[J].Expert Systems with Applications,2021,184:115537. [22]SMOLENSKY P.Information processing in dynamical systems:Foundations of harmony theory[R].Massachusetts:Colorado Univ at Boulder Dept of Computer Science,1986. [23]LECUN Y,BENGIO Y,HINTON G.Deep learning[J].Nature,2015,521(7553):436-444. [24]KINGMA D P,WELLING M.Auto-encoding variational bayes[J].arXiv:1312.6114,2013. [25]GOODFELLOW I J,POUGET-ABADIE J,MIRZA M,et al.Generative adversarial networks[J].arXiv:1406.2661,2014. [26]MNIH V,KAVUKCUOGLU K,SILVER D,et al.Playing atari with deep reinforcement learning[J].arXiv:1312.5602,2013. [27]HE K M,ZHANG X Y,REN S Q,et al.Deep residual learning for image recognition[C]//IEEE Conference on Computer Vision and Pattern Recognition.IEEE,2016:770-778. [28]CHO K,VAN MERRIËNBOER B,GULCEHRE C,et al.Learning phrase representations using RNN encoder-decoder for statistical machine translation[J].arXiv:1406.1078,2014. [29]XU Y M,COHEN S B.Stock movement prediction from tweets and historical prices[C]//56th Annual Meeting of the Association for Computational Linguistics.Association for Computational Linguistics,2018:1970-1979. [30]HU Z N,LIU W Q,BIAN J,et al.Listening to chaotic whispers:A deep learning framework for news-oriented stock trend prediction[C]//11th ACM International Conference on Web Search and Data Mining.ACM Press,2018:261-269. [31]ZHOU F,ZHANG S,YANG Y.Interpretable operational risk classification with semi-supervised variational autoencoder[C]//Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics.2020:846-852. [32]ZHOU F,QI X,XIAO C,et al.MetaRisk:Semi-supervised few-shot operational risk classification in banking industry[J].Information Sciences,2021,552:1-16. [33]TAKAHASHI S,CHEN Y,TANAKA-ISHII K.Modeling fi-nancial time-series with generative adversarial networks[J].Physica A:Statistical Mechanics and its Applications,2019,527:121261. [34]BA H.Improving detection of credit card fraudulent transactions using generative adversarial networks[J].arXiv:1907.03355,2019. [35]XU K,HU W,LESKOVEC J,et al.How powerful are graphneural networks?[J].arXiv:1810.00826,2018. [36]DEVLIN J,CHANG M W,LEE K,et al.Bert:Pre-training of deep bidirectional transformers for language understanding[J].arXiv:1810.04805,2018. [37]YANG L Y,NG T L J,SMYTH B,et al.Html:Hierarchical transformer-based multi-task learning for volatility prediction[C]//The Web Conference 2020.Assoc Comp Machinery,2020:441-451. [38]KESKAR N S,MCCANN B,VARSHNEY L R,et al.Ctrl:Aconditional transformer language model for controllable generation[J].arXiv:1909.05858,2019. [39]MISHEV K,GJORGJEVIKJ A,VODENSKA I,et al.Evalu-ation of sentiment analysis in finance:from lexicons to transfor-mers[J].IEEE Access,2020,8:131662-131682. [40]DING Q,WU S,SUN H,et al.Hierarchical Multi-Scale Gaussian Transformer for Stock Movement Prediction[C]//IJCAI.2020:4640-4646. [41]LIANG T X,YANG X P,WANG L,et al.Review on financial trading system based on reinforcement learning[J].Ruan Jian Xue Bao/Journal of Software,2019,30(3):845-864 [42]HAJIABOTORABI Z,KAZEMI A,SAMAVATI F F,et al.Improving DWT-RNN model via B-spline wavelet multiresolution to forecast a high-frequency time series[J].Expert Systems With Applications,2019,138:112842. [43]SI W Y,LI J K,DING P,et al.A multi-objective deep reinforcement learning approach for stock index future’s intraday trading[C]//10th International Symposium on Computational Intelligence and Design(ISCID).IEEE,2017:431-436. [44]ZHOU X Y,PAN Z S,HU G Y,et al.Stock market prediction on high-frequency data using generative adversarial nets[J].Mathematical Problems in Engineering,2018,2018. [45]HUANG T T,YU L.Application of SDAE-LSTM Model on Financial Time Series Forecasting[J].Computer Engineering and Applications,2019,55(1):142-148. [46]ZHANG L H,AGGARWAL C,QI G J.Stock price prediction via discovering multi-frequency trading patterns[C]//23rd ACM SIGKDD International Conference on Knowledge Disco-very and Data Mining.Association for Computing Machinery,2017:2141-2149. [47]PANG X W,ZHOU Y Q,WANG P,et al.An innovative neural network approach for stock market prediction[J].The Journal of Supercomputing,2020,76(3):2098-2118. [48]REZAEI H,FAALJOU H,MANSOURFAR G.Stock price prediction using deep learning and frequency decomposition[J].Expert Systems with Applications,2021,169:114332. [49]MOURELATOS M,ALEXAKOS C,AMORGIANIOTIS T,et al.Financial indices modelling and trading utilizing deep lear-ning techniques:The ATHENS SE FTSE/ASE Large Cap Use Case[C]//Innovations in Intelligent Systems and Applications(INISTA).IEEE,2018:1-7. [50]DING X,ZHANG Y,LIU T,et al.Deep learning for event-driven stock prediction[C]//24th International Joint Conference on Artificial Intelligence.AAAI Press,2015. [51]PUTRI K S,HALIM S.Currency movement forecasting usingtime series analysis and long short-term memory[J].International Journal of Industrial Optimization,2020,1(2):71-80. [52]ZHENG J,FU X,ZHANG G J.Research on exchange rate forecasting based on deep belief network[J].Neural Computing and Applications,2019,31(1):573-582. [53]SHEN F R,CHAO J,ZHAO J X.Forecasting exchange rateusing deep belief networks and conjugate gradient method[J].Neurocomputing,2015,167:243-253. [54]SHEN H,LIANG X.A time series forecasting model based on deep learning integrated algorithm with stacked autoencoders and svr for fx prediction[C]//International Conference on Artificial Neural Networks.Springer,2016:326-335. [55]KORCZAK J,HEMES M.Deep learning for financial time series forecasting in A-Trader system[C]//Federated Conf on Computer Science and Information Systems(FedCSIS).IEEE,2017:905-912. [56]ZHANG R,SHEN F R,ZHAO J X.A model with fuzzy granulation and deep belief networks for exchange rate forecasting[C]//International Joint Conference on Neural Networks(IJCNN).IEEE,2014:366-373. [57]CAO W,ZHU W D,WANG W J,et al.A deep coupled LSTM approach for USD/CNY exchange rate forecasting[J].IEEE Intelligent Systems,2020,35(2):43-53. [58]WIDEGREN P.Deep learning-based forecasting of financial assets[D].KTH Royal Institution Technology,2017. [59]ZHANG P Y,CI B C.Deep belief network for gold price forecasting[J].Resources Policy,2020,69:101806. [60]LASHERAS F S,DE COS JUEZ F J,SÁNCHEZ A S,et al.Forecasting the COMEX copper spot price by means of neural networks and ARIMA models[J].Resources Policy,2015,45:37-43. [61]LIVIERIS I E,PINTELAS E,PINTELAS P.A CNN-LSTMmodel for gold price time-series forecasting[J].Neural Computing and Applications,2020,32(23):17351-17360. [62]CHEN Y H,HE K J,TSO G K.Forecasting crude oil prices:a deep learning based model[J].Procedia Computer Science,2017,122:300-307. [63]WU Y X,WU Q B,ZHU J Q.Improved EEMD-based crude oil price forecasting using LSTM networks[J].Physica A:Statistical Mechanics and its Applications,2019,516:114-124. [64]QIAO W B,YANG Z.Forecast the electricity price of US using a wavelet transform-based hybrid model[J].Energy,2020,193:116704. [65]JIANG Z Y,LIANG J J.Cryptocurrency portfolio management with deep reinforcement learning[C]//Intelligent Systems Conference(IntelliSys).IEEE,2017:905-913. [66]JIANG Z Y,XU D X,LIANG J Y.A deep reinforcement lear-ning framework for the financial portfolio management problem[J].arXiv:1706.10059,2017. [67]LIANG Z P,CHEN H,ZHU J H,et al.Adversarial deep reinforcement learning in portfolio management[J].arXiv:1808.09940,2018. [68]LEE S I,YOO S J.Threshold-based portfolio:the role of the threshold and its applications[J].The Journal of Supercompu-ting,2018:1-18. [69]DENG Y,BAO F,KONG Y Y,et al.Deep Direct Reinforcement Learning for Financial Signal Representation and Trading[J].IEEE Trans. Neural Netw Learn Syst,2017,28(3):653-664. [70]WANG W Y,LI W Z,ZHANG N,et al.Portfolio formationwith preselection using deep learning from long-term financial data[J].Expert Systems with Applications,2020,143:113042. [71]STOEAN C,PAJA W,STOEAN R,et al.Deep architectures for long-term stock price prediction with a heuristic-based strategy for trading simulations[J].PloS one,2019,14(10):e0223593. [72]NGUNYI A,MUNDIA S,OMARI C.Modelling volatility dynamics of cryptocurrencies using GARCH models[J].Journal of Mathemmatical Finance,2019,9:591-615. [73]ZHOU Y L,HAN R J,XU Q,et al.Long shortterm memory networks for CSI300 volatility prediction with Baidu search vo-lume[J].Concurrency and Computation:Practice and Expe-rience,2019,31(10):e4721. [74]JIA F,YANG B L.Forecasting Volatility of Stock Index:Deep Learning Model with Likelihood-Based Loss Function[J].Complexity,2021,2021. [75]KIM H Y,WON C H.Forecasting the volatility of stock price index:A hybrid model integrating LSTM with multiple GARCH-type models[J].Expert Systems with Applications,2018,103:25-37. [76]HU Y,NI J,WEN L.A hybrid deep learning approach by integrating LSTM-ANN networks with GARCH model for copper price volatility prediction[J].Physica A:Statistical Mechanics and its Applications,2020,557:124907. [77]CHEN W L,YEO C K,LAU C T,et al.Leveraging social media news to predict stock index movement using RNN-boost[J].Data & Knowledge Engineering,2018,118:14-24. [78]VIDAL A,KRISTJANPOLLER W.Gold Volatility Predictionusing a CNN-LSTM approach[J].Expert Systems with Applications,2020:113481. [79]MCNALLY S,ROCHE J,CATON S.Predicting the price of bitcoin using machine learning[C]//26th Euromicro International Conference on Parallel,Distributed and Network-based Proces-sing(PDP).IEEE,2018:339-343. [80]LAHMIRI S,BEKIROS S.Cryptocurrency forecasting withdeep learning chaotic neural networks[J].Chaos,Solitons & Fractals,2019,118:35-40. [81]KUMAR D,RATH S.Predicting the Trends of Price forEthereum Using Deep Learning Techniques[M].Singapore:Springer,2020:103-114. [82]ALTAN A,KARASU S,BEKIROS S.Digital currency forecasting with chaotic meta-heuristic bio-inspired signal processing techniques[J].Chaos,Solitons & Fractals,2019,126:325-336. [83]LOPES G D L F.Deep Learning for Market Forecasts[D].University of Porto,2018. [84]DI PERSIO L,HONCHAR O.Artificial neural networks architectures for stock price prediction:Comparisons and applications[J].International Journal of Circuits,Systems and Signal Processing,2016,10(2016):403-413. [85]SEZER O B,OZBAYOGLU A M.Algorithmic financial trading with deep convolutional neural networks:Time series to image conversion approach[J].Applied Soft Computing,2018,70:525-538. [86]NABIPOUR M,NAYYERI P,JABANI H,et al.PredictingStock Market Trends Using Machine Learning and Deep Lear-ning Algorithms Via Continuous and Binary Data;a Comparative Analysis[J].IEEE Access,2020,8:150199-150212. [87]SHARANG A,RAO C.Using machine learning for medium frequency derivative portfolio trading[J].arXiv:1512.06228,2015. [88]KRAUSS C,DO X A,HUCK N.Deep neural networks,gra-dient-boosted trees,random forests:Statistical arbitrage on the S&P 500[J].European Journal of Operational Research,2017,259(2):689-702. [89]HOSEINZADE E,HARATIZADEH S.CNNpred:CNN-basedstock market prediction using a diverse set of variables[J].Expert Systems with Applications,2019,129:273-285. [90]YANG C,ZHAI J J,TAO G H.Deep Learning for Price Movement Prediction Using Convolutional Neural Network and Long Short-Term Memory[J].Mathematical Problems in Engineering,2020,2020. [91]LONG J W,CHEN Z P,HE W B,et al.An integrated framework of deep learning and knowledge graph for prediction of stock price trend:An application in Chinese stock exchange market[J].Applied Soft Computing,2020,91:106205. [92]BUCZKOWSKI P.Predicting stock trends based on expert recommendations using gru/lstm neural networks[C]//International Symposium on Methodologies for Intelligent Systems.Springer,2017:708-717. [93]LUO C C,WU D S,WU D X.A deep learning approach forcredit scoring using credit default swaps[J].Engineering Applications of Artificial Intelligence,2017,65:465-470. [94]HOSAKA T.Bankruptcy prediction using imaged financial ratios and convolutional neural networks[J].Expert Systems with Applications,2019,117:287-299. [95]NEAGOE V E,CIOTEC A D,CUCU G S.Deep convolutional neural networks versus multilayer perceptron for financial prediction[C]//International Conference on Communications(COMM).IEEE,2018:201-206. [96]JURGOVSKY J,GRANITZER M,ZIEGLER K,et al.Sequence classification for credit-card fraud detection[J].Expert Systems with Applications,2018,100:234-245. [97]HERYADI Y,WARNARS H L H S.Learning temporal representation of transaction amount for fraudulent transaction re-cognition using cnn,stacked lstm,and cnn-lstm[C]//IEEE International Conference on Cybernetics and Computational Intelligence(CyberneticsCom).IEEE,2017:84-89. [98]AL-SHABI M.Credit card fraud detection using autoencodermodel in unbalanced datasets[J].Journal of Advances in Mathematics and Computer Science,2019,33(5):1-16. [99]ZHU B,YANG W C,WANG H X,et al.A hybrid deep learning model for consumer credit scoring[C]//2018 International Conference on Artificial Intelligence and Big Data(ICAIBD).IEEE,2018:205-208. [100]KVAMME H,SELLEREITE N,AAS K,et al.Predicting mort-gage default using convolutional neural networks[J].Expert Systems with Applications,2018,102:207-217. [101]YU L,ZHOU R,TANG L,et al.A DBN-based resampling SVM ensemble learning paradigm for credit classification with imba-lanced data[J].Applied Soft Computing,2018,69:192-202. [102]CHATZIS S P,SIAKOULIS V,PETROPOULOS A,et al.Forecasting stock market crisis events using deep and statistical machine learning techniques[J].Expert Systems with Applications,2018,112:353-371. [103]SOHONY I,PRATAP R,NAMBIAR U.Ensemble learning for credit card fraud detection[C]//ACM India Joint International Conference on Data Science and Management of Data(CoDS-COMAD’18).New York,2018:289-294. [104]ZHAO C,YE Y W,YAO M H.Stock volatility forecast based on financial text emotion[J].Computer Science,2020,47(5):79-83. [105]SHI L,TENG Z Y,WANG L,et al.DeepClue:Visual Interpretation of Text-Based Deep Stock Prediction[J].IEEE Trans on Knowledge and Data Engineering,2019,31(6):1094-1108. [106]DOS SANTOS P L,DRAS M.Stock market prediction withdeep learning:A character-based neural language model for event-based trading[C]//Australasian Language Technology Association Workshop 2017.2017:6-15. [107]LEE C Y,SOO V W.Predict stock price with financial newsbased on recurrent convolutional neural networks[C]//Confe-rence on Technologies and Applications of Artificial Intelligence(TAAI).IEEE,2017:160-165. [108]RAWTE V,GUPTA A,ZAKI M J.Analysis of year-over-year changes in Risk Factors Disclosure in 10-K filings[C]//Procee-dings of the Fourth International Workshop on Data Science for Macro-Modeling with Financial and Economic Datasets(DSMM’18).2018:1-4. [109]RöNNQVIST S,SARLIN P.Bank distress in the news:Describing events through deep learning[J].Neurocomputing,2017,264:57-70. [110]CERCHIELLO P,NICOLA G,RONNQVIST S,et al.Deeplearning bank distress from news and numerical financial data[J].arXiv:1706.09627,2017. [111]AKITA R,YOSHIHARA A,MATSUBARA T,et al.Deeplearning for stock prediction using numerical and textual information[C]//IEEE/ACIS 15th International Conference on Computer and Information Science(ICIS).IEEE,2016:1-6. [112]MATSUBARA T,AKITA R,UEHARA K.Stock Price Prediction by Deep Neural Generative Model of News Articles[J].IEICE Transactions on Information and Systems,2018,E101.D(4):901-908. [113]WANG Y B,XU W.Leveraging deep learning with LDA-based text analytics to detect automobile insurance fraud[J].Decision Support Systems,2018,105:87-95. [114]FRANÇOIS-LAVET V,HENDERSON P,ISLAM R,et al.Anintroduction to deep reinforcement learning[J].arXiv:1811.12560,2018. [115]LIU Y,ZENG Q G,YANG H R,et al.Stock price movement prediction from financial news with deep learning and knowledge graph embedding[C]//Pacific Rim Knowledge Acquisition Workshop.Springer,2018:102-113. [116]WANG Q L,XU W,ZHENG H.Combining the wisdom ofcrowds and technical analysis for financial market prediction using deep random subspace ensembles[J].Neurocomputing,2018,299:51-61. [117]LIEN MINH D,SADEGHI-NIARAKI A,HUY H D,et al.Deep Learning Approach for Short-Term Stock Trends Prediction Based on Two-Stream Gated Recurrent Unit Network[J].IEEE Access,2018,6:55392-55404. [118]DAY M Y,LEE C C.Deep learning for financial sentiment ana-lysis on finance news providers[C]//IEEE/ACM International Conferenceon Advances in Social Networks Analysis and Mining(ASONAM).IEEE,2016:1127-1134. [119]DAS S,BEHERA R K,RATH S K.Real-time sentiment analysis of twitter streaming data for stock prediction[J].Procedia Computer Science,2018,132:956-964. [120]ZHUGE Q,XU L Y,ZHANG G W.LSTM Neural Network with Emotional Analysis for prediction of stock price[J].Engineering Letters,2017,25(2). [121]WANG Y W,LI Q,HUANG Z X,et al.EAN:Event attention network for stock price trend prediction based on sentimental embedding[C]//10th ACM Conference on Web Science.Asso-ciation for Computing Machinery,2019:311-320. [122]SOHANGIR S,WANG D D,POMERANETS A,et al.Big Data:Deep Learning for financial sentiment analysis[J].Journal of Big Data,2018,5(1):1-25. [123]SOHANGIR S,WANG D D.Finding Expert Authors in Financial Forum Using Deep Learning Methods[C]//Second IEEE International Conference on Robotic Computing(IRC).IEEE,2018:399-402. [124]IWASAKI H,CHEN Y.Topic sentiment asset pricing with dnn supervised learning[J/OL].http://dx.doi.org/10.2139.ssrn.3228485. [125]WANG G S,YU G J,SHEN X H.The Effect of Online Investor Sentiment on Stock Movements:An LSTM Approach[J/OL].http://dx.doi/ogr/10.2139/ssm.3228485. [126]YING J J C,HUANG P Y,CHANG C K,et al.A preliminary study on deep learning for predicting social insurance payment behavior[C]//IEEE International Conference on Big Data(Big Data).IEEE,2017:1866-1875. [127]KIM K H,LEE C S,JO S M,et al.Predicting the success ofbank telemarketing using deep convolutional neural network[C]//7th International Conference of Soft Computing and Pattern Recognition(SoCPaR).IEEE,2015:314-317. [128]ŁADYZYŃSKI P,ZBIKOWSKI K,GAWRYSIAK P.Direct marketing campaigns in retail banking with the use of deep learning and random forests[J].Expert Systems with Applications,2019,134:28-35. [129]LI Y,LIN X H,WANG X W,et al.Credit risk assessment algorithm using deep neural networks with clustering and merging[C]//13th International Conference on Computational Intelligence and Security(CIS).IEEE,2017:173-176. [130]LIU Z,HUANG D,HUANG K,et al.FinBERT:A Pre-trained Financial Language Representation Model for Financial Text Mining[C]//IJCAI.2020:4513-4519. [131]WU Z,PAN S,LONG G,et al.Connecting the dots:Multivariate time series forecasting with graph neural networks[C]//26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining.Association for Computing Machinery,2020:753-763. [132]WANG X,JI H Y,SHI C,et al.Heterogeneous graph attention network[C]//the World Wide Web Conference.ACM Press,2019:2022-2032. [133]KRITTANAWONG C,JOHNSON K W,ROSENSON R S,et al.Deep learning for cardiovascular medicine:a practical pri-mer[J].European Heart Journal,2019,40(25):2058-2073. |
[1] | 饶志双, 贾真, 张凡, 李天瑞. 基于Key-Value关联记忆网络的知识图谱问答方法 Key-Value Relational Memory Networks for Question Answering over Knowledge Graph 计算机科学, 2022, 49(9): 202-207. https://doi.org/10.11896/jsjkx.220300277 |
[2] | 汤凌韬, 王迪, 张鲁飞, 刘盛云. 基于安全多方计算和差分隐私的联邦学习方案 Federated Learning Scheme Based on Secure Multi-party Computation and Differential Privacy 计算机科学, 2022, 49(9): 297-305. https://doi.org/10.11896/jsjkx.210800108 |
[3] | 柳杰灵, 凌晓波, 张蕾, 王博, 王之梁, 李子木, 张辉, 杨家海, 吴程楠. 基于战术关联的网络安全风险评估框架 Network Security Risk Assessment Framework Based on Tactical Correlation 计算机科学, 2022, 49(9): 306-311. https://doi.org/10.11896/jsjkx.210600171 |
[4] | 徐涌鑫, 赵俊峰, 王亚沙, 谢冰, 杨恺. 时序知识图谱表示学习 Temporal Knowledge Graph Representation Learning 计算机科学, 2022, 49(9): 162-171. https://doi.org/10.11896/jsjkx.220500204 |
[5] | 王剑, 彭雨琦, 赵宇斐, 杨健. 基于深度学习的社交网络舆情信息抽取方法综述 Survey of Social Network Public Opinion Information Extraction Based on Deep Learning 计算机科学, 2022, 49(8): 279-293. https://doi.org/10.11896/jsjkx.220300099 |
[6] | 郝志荣, 陈龙, 黄嘉成. 面向文本分类的类别区分式通用对抗攻击方法 Class Discriminative Universal Adversarial Attack for Text Classification 计算机科学, 2022, 49(8): 323-329. https://doi.org/10.11896/jsjkx.220200077 |
[7] | 姜梦函, 李邵梅, 郑洪浩, 张建朋. 基于改进位置编码的谣言检测模型 Rumor Detection Model Based on Improved Position Embedding 计算机科学, 2022, 49(8): 330-335. https://doi.org/10.11896/jsjkx.210600046 |
[8] | 孙奇, 吉根林, 张杰. 基于非局部注意力生成对抗网络的视频异常事件检测方法 Non-local Attention Based Generative Adversarial Network for Video Abnormal Event Detection 计算机科学, 2022, 49(8): 172-177. https://doi.org/10.11896/jsjkx.210600061 |
[9] | 胡艳羽, 赵龙, 董祥军. 一种用于癌症分类的两阶段深度特征选择提取算法 Two-stage Deep Feature Selection Extraction Algorithm for Cancer Classification 计算机科学, 2022, 49(7): 73-78. https://doi.org/10.11896/jsjkx.210500092 |
[10] | 程成, 降爱莲. 基于多路径特征提取的实时语义分割方法 Real-time Semantic Segmentation Method Based on Multi-path Feature Extraction 计算机科学, 2022, 49(7): 120-126. https://doi.org/10.11896/jsjkx.210500157 |
[11] | 侯钰涛, 阿布都克力木·阿布力孜, 哈里旦木·阿布都克里木. 中文预训练模型研究进展 Advances in Chinese Pre-training Models 计算机科学, 2022, 49(7): 148-163. https://doi.org/10.11896/jsjkx.211200018 |
[12] | 周慧, 施皓晨, 屠要峰, 黄圣君. 基于主动采样的深度鲁棒神经网络学习 Robust Deep Neural Network Learning Based on Active Sampling 计算机科学, 2022, 49(7): 164-169. https://doi.org/10.11896/jsjkx.210600044 |
[13] | 苏丹宁, 曹桂涛, 王燕楠, 王宏, 任赫. 小样本雷达辐射源识别的深度学习方法综述 Survey of Deep Learning for Radar Emitter Identification Based on Small Sample 计算机科学, 2022, 49(7): 226-235. https://doi.org/10.11896/jsjkx.210600138 |
[14] | 赵璐, 袁立明, 郝琨. 多示例学习算法综述 Review of Multi-instance Learning Algorithms 计算机科学, 2022, 49(6A): 93-99. https://doi.org/10.11896/jsjkx.210500047 |
[15] | 吴子斌, 闫巧. 基于动量的映射式梯度下降算法 Projected Gradient Descent Algorithm with Momentum 计算机科学, 2022, 49(6A): 178-183. https://doi.org/10.11896/jsjkx.210500039 |
|