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Nikhil Muralidhar | Naren Ramakrishnan | B. Aditya Prakash | Alexander Rodriguez | Bijaya Adhikari | Anika Tabassum
[1] Hitoshi Imaoka,et al. An Efficient Method of Training Small Models for Regression Problems with Knowledge Distillation , 2020, 2020 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR).
[2] E. Nsoesie,et al. Monitoring Influenza Epidemics in China with Search Query from Baidu , 2013, PloS one.
[3] Li Li,et al. Using LSTM and GRU neural network methods for traffic flow prediction , 2016, 2016 31st Youth Academic Annual Conference of Chinese Association of Automation (YAC).
[4] Mark Dredze,et al. HealthTweets.org: A Platform for Public Health Surveillance Using Twitter , 2014, AAAI 2014.
[5] Yasin Almalioglu,et al. Distilling Knowledge From a Deep Pose Regressor Network , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[6] Jaime G. Carbonell,et al. Completely Heterogeneous Transfer Learning with Attention - What And What Not To Transfer , 2017, IJCAI.
[7] Dave Osthus,et al. Accuracy of real-time multi-model ensemble forecasts for seasonal influenza in the U.S. , 2019, PLoS Comput. Biol..
[8] Alok N. Choudhary,et al. Real-time disease surveillance using Twitter data: demonstration on flu and cancer , 2013, KDD.
[9] Ronald Rosenfeld,et al. Flexible Modeling of Epidemics with an Empirical Bayes Framework , 2014, PLoS Comput. Biol..
[10] Yoshua Bengio,et al. Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation , 2014, EMNLP.
[11] Daniela Perrotta,et al. Forecasting Seasonal Influenza Fusing Digital Indicators and a Mechanistic Disease Model , 2017, WWW.
[12] Aaron C. Miller,et al. A Smartphone-Driven Thermometer Application for Real-time Population- and Individual-Level Influenza Surveillance , 2018, Clinical infectious diseases : an official publication of the Infectious Diseases Society of America.
[13] Alex C. Kot,et al. Heterogeneous Transfer Learning via Deep Matrix Completion with Adversarial Kernel Embedding , 2019, AAAI.
[14] Reid Priedhorsky,et al. Dynamic Bayesian Influenza Forecasting in the United States with Hierarchical Discrepancy (with Discussion) , 2017, Bayesian Analysis.
[15] Naren Ramakrishnan,et al. EpiDeep: Exploiting Embeddings for Epidemic Forecasting , 2019, KDD.
[16] Alessandro Vespignani,et al. Real-time numerical forecast of global epidemic spreading: case study of 2009 A/H1N1pdm , 2012, BMC Medicine.
[17] Dave Osthus,et al. A Collaborative Multi-Model Ensemble for Real-Time Influenza Season Forecasting in the U.S , 2019, bioRxiv.
[18] P. Young,et al. Time series analysis, forecasting and control , 1972, IEEE Transactions on Automatic Control.
[19] Teck-Hua Ho,et al. Unmasking the Actual COVID-19 Case Count , 2020, Clinical infectious diseases : an official publication of the Infectious Diseases Society of America.
[20] Madhav V. Marathe,et al. Forecasting a Moving Target: Ensemble Models for ILI Case Count Predictions , 2014, SDM.
[21] Vijay V. Raghavan,et al. A novel data-driven model for real-time influenza forecasting , 2017, bioRxiv.
[22] Les E. Atlas,et al. Recurrent neural networks and robust time series prediction , 1994, IEEE Trans. Neural Networks.
[23] Gwilym M. Jenkins,et al. Time series analysis, forecasting and control , 1971 .
[24] Naren Ramakrishnan,et al. Syndromic surveillance of Flu on Twitter using weakly supervised temporal topic models , 2016, Data Mining and Knowledge Discovery.
[25] Jiangzhuo Chen,et al. DEFSI: Deep Learning Based Epidemic Forecasting with Synthetic Information , 2019, AAAI.
[26] Arindam Banerjee,et al. Climate Multi-model Regression Using Spatial Smoothing , 2013, SDM.
[27] Geoffrey E. Hinton,et al. Distilling the Knowledge in a Neural Network , 2015, ArXiv.
[28] Rich Caruana,et al. Do Deep Nets Really Need to be Deep? , 2013, NIPS.
[29] Jeremy Ginsberg,et al. Detecting influenza epidemics using search engine query data , 2009, Nature.
[30] Marc Lipsitch,et al. Absolute humidity and pandemic versus epidemic influenza. , 2011, American journal of epidemiology.
[31] Paola Velardi,et al. Results from the centers for disease control and prevention’s predict the 2013–2014 Influenza Season Challenge , 2016, BMC Infectious Diseases.
[32] J. Shaman,et al. Forecasting seasonal outbreaks of influenza , 2012, Proceedings of the National Academy of Sciences.
[33] Ellyn Ayton,et al. Forecasting influenza-like illness dynamics for military populations using neural networks and social media , 2017, PloS one.
[34] Ravi Sankar,et al. Time Series Prediction Using Support Vector Machines: A Survey , 2009, IEEE Computational Intelligence Magazine.
[35] Wen Li,et al. Semi-Supervised Optimal Transport for Heterogeneous Domain Adaptation , 2018, IJCAI.
[36] Mikhail Belkin,et al. Regularization and Semi-supervised Learning on Large Graphs , 2004, COLT.
[37] Ronald Rosenfeld,et al. Nonmechanistic forecasts of seasonal influenza with iterative one-week-ahead distributions , 2018, PLoS Comput. Biol..
[38] Evan L. Ray,et al. Infectious disease prediction with kernel conditional density estimation , 2017, Statistics in medicine.
[39] Jürgen Schmidhuber,et al. Long Short-Term Memory , 1997, Neural Computation.
[40] Nikhil Muralidhar,et al. DyAt Nets: Dynamic Attention Networks for State Forecasting in Cyber-Physical Systems , 2019, IJCAI.