trcook / my_reading_list

A list of various articles that I find helpful for reading about deep learning, forecasting, or macroeconomics

• W. Lin, M. E. Khan, and N. Hubacher.
  Variational Message Passing with Structured Inference Networks. International Conference on Learning Representations, 2018.

• T. Kurutach, I. Clavera, Y. Duan, A. Tamar, and P. Abbeel.
  Model-Ensemble Trust-Region Policy Optimization. International Conference on Learning Representations, 2018.

• B. Athiwaratkun and A. G. Wilson.
  On Modeling Hierarchical Data via Probabilistic Order Embeddings. International Conference on Learning Representations, 2018.

• K. Zolna, D. Arpit, D. Suhubdy, and Y. Bengio.
  Fraternal Dropout. International Conference on Learning Representations, 2018.

• K. Lee, H. Kim, and C. Suh.
  Simulated+Unsupervised Learning With Adaptive Data Generation and Bidirectional Mappings. International Conference on Learning Representations, 2018.

• K. Hausman, J. T. Springenberg, Z. Wang, N. Heess, and M. Riedmiller.
  Learning an Embedding Space for Transferable Robot Skills. International Conference on Learning Representations, 2018.

• K. Lee, H. Kim, and C. Suh.
  Simulated+Unsupervised Learning With Adaptive Data Generation and Bidirectional Mappings. International Conference on Learning Representations, 2018.

• A. R. Barron.
  Approximation and estimation bounds for artificial neural networks. Machine Learning, 14(1):115--133, 1994.

• D. Silver, G. Lever, N. Heess, T. Degris, D. Wierstra, and M. Riedmiller.
  Deterministic policy gradient algorithms. ICML, 2014.

• T. P. Lillicrap, J. J. Hunt, A. Pritzel, N. Heess, T. Erez, Y. Tassa, D. Silver, and D. Wierstra.
  Continuous control with deep reinforcement learning. 2015.

• D. Bloembergen, D. Hennes, M. Kaisers, and P. Vrancx.
  Multiagent Reinforcement Learning. ECML, 2013.

• L. Yu.
  Paper Collection of Multi-Agent Reinforcement Learning (MARL). GitHub. 2017.

• J. Z. Leibo, V. Zambaldi, M. Lanctot, J. Marecki, and T. Graepel.
  Multi-agent Reinforcement Learning in Sequential Social Dilemmas. 2017.

• A. Marinescu, I. Dusparic, A. Taylor, V. Cahill, and S. Clarke.
  Decentralised Multi-Agent Reinforcement Learning for Dynamic and Uncertain Environments. 2014.

• S. Omidshafiei, J. Pazis, C. Amato, J. P. How, and J. Vian.
  Deep Decentralized Multi-task Multi-Agent Reinforcement Learning under Partial Observability. 2017.

• J. Foerster, N. Nardelli, G. Farquhar, T. Afouras, P. H. S. Torr, P. Kohli, and S. Whiteson.
  Stabilising Experience Replay for Deep Multi-Agent Reinforcement Learning. 2017.

• R. Lowe, Y. Wu, A. Tamar, J. Harb, P. Abbeel, and I. Mordatch.
  Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments. 2017.

• L. Busoniu, R. Babuska, and B. De Schutter.
  A comprehensive survey of multiagent reinforcement learning. IEEE Transactions on Systems, Man, And Cybernetics-Part C: Applications and Reviews, 38 (2), 2008, 2008.

• L. Busoniu, R. Babu\vska, and B. De Schutter.
  Multi-agent reinforcement learning: An overview. Innovations in multi-agent systems and applications-1, 310:183--221, 2010.

• D. Bloembergen, K. Tuyls, D. Hennes, and M. Kaisers.
  Evolutionary Dynamics of Multi-Agent Learning: A Survey.. J. Artif. Intell. Res.(JAIR), 53:659--697, 2015.

• A. O. Castaneda.
  Deep Reinforcement Learning Variants of Multi-Agent Learning Algorithms. Master's thesis, School of Informatics, University of Edinburgh, 2016.

• C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich.
  Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1--9, 2015.

• H. Bourlard and Y. Kamp.
  Auto-association by multilayer perceptrons and singular value decomposition. Biological cybernetics, 59(4-5):291--294, 1988.

• M. D. Zeiler, D. Krishnan, G. W. Taylor, and R. Fergus.
  Deconvolutional networks. Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on, 2528--2535, 2010.

• J. Yosinski, J. Clune, Y. Bengio, and H. Lipson.
  How transferable are features in deep neural networks?. Advances in neural information processing systems, 3320--3328, 2014.

• M. Dalto.
  Deep neural networks for time series prediction with applications in ultra-short-term wind forecasting. Rn ($\Theta$1), 1(2).

• F.-F. Li, A. Karpathy, and J. Johnson.
  CS231n: Convolutional Neural Networks for Visual Recognition. CS231n: Convolutional Neural Networks for Visual Recognition, 2016.

• F.-F. Li, A. Karpathy, and J. Johnson.
  CS231n: Convolutional Neural Networks for Visual Recognition. CS231n: Convolutional Neural Networks for Visual Recognition, 2016.

• J. N. Foerster, Y. M. Assael, N. de Freitas, and S. Whiteson.
  Learning to Communicate with Deep Multi-Agent Reinforcement Learning. arXiv preprint arXiv:1605.06676, 2016.

• V. Mnih, J. Agapiou, S. Osindero, A. Graves, O. Vinyals, K. Kavukcuoglu, and others.
  Strategic Attentive Writer for Learning Macro-Actions. arXiv preprint arXiv:1606.04695, 2016.

• J. N. Foerster, Y. M. Assael, N. de Freitas, and S. Whiteson.
  Learning to Communicate with Deep Multi-Agent Reinforcement Learning. 2016.

• Alexander, Vezhnevets, V. Mnih, J. Agapiou, S. Osindero, A. Graves, O. Vinyals, and K. Kavukcuoglu.
  Strategic Attentive Writer for Learning Macro-Actions. 2016.

• M. Fraccaro, S. K. Sønderby, U. Paquet, and O. Winther.
  Sequential Neural Models with Stochastic Layers. 2016.

• I. Osband, C. Blundell, A. Pritzel, and B. V. Roy.
  Deep Exploration via Bootstrapped DQN. 2016.

• M. Jaderberg, W. M. Czarnecki, S. Osindero, O. Vinyals, A. Graves, and K. Kavukcuoglu.
  Decoupled Neural Interfaces using Synthetic Gradients. 2016.

• A. A. Rusu, N. C. Rabinowitz, G. Desjardins, H. Soyer, J. Kirkpatrick, K. Kavukcuoglu, R. Pascanu, and R. Hadsell.
  Progressive Neural Networks. 2016.

• D. J. Rezende, S. Mohamed, I. Danihelka, K. Gregor, and D. Wierstra.
  One-Shot Generalization in Deep Generative Models. 2016.

• Y. Wu, M. Schuster, Z. Chen, Q. V. Le, M. Norouzi, W. Macherey, M. Krikun, Y. Cao, Q. Gao, K. Macherey, J. Klingner, A. Shah, M. Johnson, X. Liu, Ł. Kaiser, S. Gouws, Y. Kato, T. Kudo, H. Kazawa, K. Stevens, G. Kurian, N. Patil, W. Wang, C. Young, J. Smith, J. Riesa, A. Rudnick, O. Vinyals, G. Corrado, M. Hughes, and J. Dean.
  Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation. 2016.

• M. Andrychowicz, M. Denil, S. Gomez, M. W. Hoffman, D. Pfau, T. Schaul, and N. de Freitas.
  Learning to learn by gradient descent by gradient descent. 2016.

• C. Szegedy, S. Ioffe, V. Vanhoucke, and A. Alemi.
  Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning. 2016.

• K. Simonyan and A. Zisserman.
  Very Deep Convolutional Networks for Large-Scale Image Recognition. 2014.

• K. He, X. Zhang, S. Ren, and J. Sun.
  Deep Residual Learning for Image Recognition. 2015.

• D. Andor, C. Alberti, D. Weiss, A. Severyn, A. Presta, K. Ganchev, S. Petrov, and M. Collins.
  Globally Normalized Transition-Based Neural Networks. 2016.

• S. J. Landefeld, E. P. Seskin, and B. M. Fraumeni.
  Taking the pulse of the economy: Measuring GDP. The Journal of Economic Perspectives, 22(2):193--193, 2008.

• A. Graves, G. Wayne, M. Reynolds, T. Harley, I. Danihelka, A. Grabska-Barwińska, S. G. Colmenarejo, E. Grefenstette, T. Ramalho, J. Agapiou, and others.
  Hybrid computing using a neural network with dynamic external memory. Nature, 2016.

• A. Graves, G. Wayne, and I. Danihelka.
  Neural Turing Machines. 2014.

• J. Chung, C. Gulcehre, K. Cho, and Y. Bengio.
  Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling. 2014.

• G. Huang, Y. Sun, Z. Liu, D. Sedra, and K. Weinberger.
  Deep Networks with Stochastic Depth. 2016.

• J. B. Heaton, N. G. Polson, and J. H. Witte.
  Deep Learning in Finance. 2016.

• S. T. A. Niaki and S. Hoseinzade.
  Forecasting S&P 500 index using artificial neural networks and design of experiments. Journal of Industrial Engineering International, 9(1):1, 2013.

• L. Deng and J. C. Platt.
  Ensemble deep learning for speech recognition. INTERSPEECH, 2014.

• U. G"u\ccl"u and M. A. J. van Gerven.
  Deep Neural Networks Reveal a Gradient in the Complexity of Neural Representations across the Brain's Ventral Visual Pathway. 2014.

• S. Sukhbaatar, A. Szlam, and R. Fergus.
  Learning Multiagent Communication with Backpropagation. 2016.

• J. Heinrich and D. Silver.
  Deep Reinforcement Learning from Self-Play in Imperfect-Information Games. 2016.

• J. S. Hartford, J. R. Wright, and K. Leyton-Brown.
  Deep learning for predicting human strategic behavior. Advances in Neural Information Processing Systems, 2424--2432, 2016.

• J. Hartford, G. Lewis, K. Leyton-Brown, and M. Taddy.
  Counterfactual Prediction with Deep Instrumental Variables Networks. 2016.

• L. R. Klein.
  Economic fluctuations in the United States, 1921-1941. 1950.

• K. L. Kliesen and M. W. McCracken.
  Tracking the US Economy with Nowcasts. The Regional Economist, 2016.

• O. Atan, W. R. Zame, Q. Feng, and M. van der Schaar.
  Constructing Effective Personalized Policies Using Counterfactual Inference from Biased Data Sets with Many Features. 2016.

• Z. Cui, W. Chen, and Y. Chen.
  Multi-Scale Convolutional Neural Networks for Time Series Classification. CoRR, abs/1603.069952016.

• O. Abdel-Hamid, A.-R. Mohamed, H. Jiang, L. Deng, G. Penn, and D. Yu.
  Convolutional neural networks for speech recognition. IEEE/ACM Transactions on audio, speech, and language processing, 22(10):1533--1545, 2014.

• Z. Wang and T. Oates.
  Imaging Time-Series to Improve Classification and Imputation. 2015.

• L. Liu and Z. Wang.
  Encoding Temporal Markov Dynamics in Graph for Time Series Visualization. 2016.

• L. Ye and E. Keogh.
  Time series shapelets: a new primitive for data mining. Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, 947--956, 2009.

• D. Nouri.
  Using deep learning to listen for whales. 2014.

• M. W. McCracken and S. Ng.
  FRED-MD: A monthly database for macroeconomic research. Journal of Business & Economic Statistics, 2015.

• C. E. Walsh.
  Optimal contracts for central bankers. The American Economic Review, :150--167, 1995.

• R. Barnichon and C. J. Nekarda.
  The ins and outs of forecasting unemployment: Using labor force flows to forecast the labor market. Brookings Papers on Economic Activity, 2012(2):83--131, 2012.

• M. Tasci, C. Treanor, and others.
  Forecasting Unemployment in Real Time during the Great Recession: An Elusive Task. Economic Commentary, (Nov)2015.

• F. Rosenblatt.
  The perceptron: A probabilistic model for information storage and organization in the brain.. Psychological review, 65(6):386, 1958.

• R. E. Lucas.
  Econometric policy evaluation: A critique. Carnegie-Rochester conference series on public policy, 1, 19--46, 1976.

• A. Pescatori, S. Zaman, and others.
  Macroeconomic models, forecasting, and policymaking. Economic Commentary, 19(20):1, 2011.

• C. A. Sims.
  Macroeconomics and reality. Econometrica: Journal of the Econometric Society, :1--48, 1980.

• T. Stark.
  SPF panelists' forecasting methods: A note on the aggregate results of a November 2009 special survey. Federal Reserve Bank of Philadelphia, 2013.

• D. E. Rumelhart, G. E. Hinton, and R. J. Williams.
  Learning representations by back-propagating errors. Nature, 323(6088):533--536, 1986.

• M. Creel.
  Neural Nets for Indirect Inference. Econometrics and Statistics, 2016.

• T. Gao and V. Jojic.
  Degrees of Freedom in Deep Neural Networks. 2016.

• J. Ye.
  On measuring and correcting the effects of data mining and model selection. Journal of the American Statistical Association, 93(441):120--131, 1998.

• K. Cho, B. van Merrienboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, and Y. Bengio.
  Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation. 2014.

• I. Sutskever, O. Vinyals, and Q. V. Le.
  Sequence to Sequence Learning with Neural Networks. 2014.

• M. P. Clements.
  Forecast uncertainty---ex ante and ex post: US inflation and output growth. Journal of Business & Economic Statistics, 32(2):206--216, 2014.

• Y. Gal, J. Hron, and A. Kendall.
  Concrete Dropout. 2017.

• B. Zoph and Q. V. Le.
  Neural Architecture Search with Reinforcement Learning. 2016.

• J. Gehring, M. Auli, D. Grangier, D. Yarats, and Y. N. Dauphin.
  Convolutional Sequence to Sequence Learning. 2017.

• G. Huang, Y. Li, G. Pleiss, Z. Liu, J. E. Hopcroft, and K. Q. Weinberger.
  Snapshot Ensembles: Train 1, get M for free. 2017.

• S. S. Hansen.
  Deep Episodic Value Iteration for Model-based Meta-Reinforcement Learning. 2017.

• Y. Li and Y. Gal.
  Dropout Inference in Bayesian Neural Networks with Alpha-divergences. 2017.

• Y. Li.
  Deep Reinforcement Learning: An Overview. 2017.

• M. Qi and G. P. Zhang.
  Trend time--series modeling and forecasting with neural networks. IEEE Transactions on neural networks, 19(5):808--816, 2008.

• M. A. Nielsen.
  Neural networks and deep learning. URL: http://neuralnetworksanddeeplearning. com/.(visited: 01.11. 2014), 2015.

• Y. Gal and Z. Ghahramani.
  Dropout as a Bayesian approximation: Insights and applications. Deep Learning Workshop, ICML, 2015.

• Y. Gal and Z. Ghahramani.
  Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning. 2015.

• Y. Gal.
  Uncertainty in Deep Learning. University of Cambridge, 2016.

• Z.-H. Zhou and J. Feng.
  Deep Forest: Towards An Alternative to Deep Neural Networks. 2017.

• H. Wang and B. Raj.
  On the Origin of Deep Learning. 2017.

• E. Parisotto and R. Salakhutdinov.
  Neural Map: Structured Memory for Deep Reinforcement Learning. 2017.

• X. Mao, Q. Li, H. Xie, R. Y. K. Lau, and Z. Wang.
  Least Squares Generative Adversarial Networks. 2016.

• O. Nachum, M. Norouzi, K. Xu, and D. Schuurmans.
  Bridging the Gap Between Value and Policy Based Reinforcement Learning. 2017.

• B. Amos and J. Z. Kolter.
  OptNet: Differentiable Optimization as a Layer in Neural Networks. 2017.

• L. Kaiser, O. Nachum, A. Roy, and S. Bengio.
  Learning to Remember Rare Events. ICLR, 2017.

• J. Hartford, G. Lewis, K. Leyton-Brown, and M. Taddy.
  Counterfactual Prediction with Deep Instrumental Variables Networks. 2016.

• R. H. Webb and others.
  Two approaches to macroeconomic forecasting. Economic Quarterly-Federal Reserve Bank of Richmond, 85(3):23--40, 1999.

• H.-T. Cheng, L. Koc, J. Harmsen, T. Shaked, T. Chandra, H. Aradhye, G. Anderson, G. Corrado, W. Chai, M. Ispir, R. Anil, Z. Haque, L. Hong, V. Jain, X. Liu, and H. Shah.
  Wide & Deep Learning for Recommender Systems. 2016.

• D. Kriesel.
  A brief Introduction on Neural Networks. 2007.