my_reading_list

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my_reading_list

(★ 16)

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

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* W. Lin, M. E. Khan, and N. Hubacher.  
[**Variational Message Passing with Structured Inference Networks**](https://openreview.net/forum?id=HyH9lbZAW). International Conference on Learning Representations, 2018.

* T. Kurutach, I. Clavera, Y. Duan, A. Tamar, and P. Abbeel.  
[**Model-Ensemble Trust-Region Policy Optimization**](https://openreview.net/forum?id=SJJinbWRZ). International Conference on Learning Representations, 2018.

* B. Athiwaratkun and A. G. Wilson.  
[**On Modeling Hierarchical Data via Probabilistic Order Embeddings**](https://openreview.net/forum?id=HJCXZQbAZ). International Conference on Learning Representations, 2018.

* K. Zolna, D. Arpit, D. Suhubdy, and Y. Bengio.  
[**Fraternal Dropout**](https://openreview.net/forum?id=SJyVzQ-C-). International Conference on Learning Representations, 2018.

* K. Lee, H. Kim, and C. Suh.  
[**Simulated+Unsupervised Learning With Adaptive Data Generation and Bidirectional Mappings**](https://openreview.net/forum?id=SkHDoG-Cb). 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**](https://openreview.net/forum?id=rk07ZXZRb). 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**](http://proceedings.mlr.press/v32/silver14.pdf). 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**](https://arxiv.org/abs/1509.02971). 2015.

* D. Bloembergen, D. Hennes, M. Kaisers, and P. Vrancx.  
[**Multiagent Reinforcement Learning**](http://www.ecmlpkdd2013.org/wp-content/uploads/2013/09/Multiagent-Reinforcement-Learning.pdf). ECML, 2013.

* L. Yu.  
[**Paper Collection of Multi-Agent Reinforcement Learning (MARL)**](https://github.com/LantaoYu/MARL-Papers#review-papers).  GitHub. 2017.

* J. Z. Leibo, V. Zambaldi, M. Lanctot, J. Marecki, and T. Graepel.  
[**Multi-agent Reinforcement Learning in Sequential Social Dilemmas**](https://arxiv.org/abs/1702.03037). 2017.

* A. Marinescu, I. Dusparic, A. Taylor, V. Cahill, and S. Clarke.  
[**Decentralised Multi-Agent Reinforcement Learning for Dynamic and Uncertain Environments**](https://arxiv.org/abs/1409.4561). 2014.

* S. Omidshafiei, J. Pazis, C. Amato, J. P. How, and J. Vian.  
[**Deep Decentralized Multi-task Multi-Agent Reinforcement Learning under Partial Observability**](https://arxiv.org/abs/1703.06182). 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**](https://arxiv.org/abs/1702.08887). 2017.

* R. Lowe, Y. Wu, A. Tamar, J. Harb, P. Abbeel, and I. Mordatch.  
[**Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments**](https://arxiv.org/abs/1706.02275). 2017.

* L. Busoniu, R. Babuska, and B. De Schutter.  
[**A comprehensive survey of multiagent reinforcement learning**](http://www.dcsc.tudelft.nl/~bdeschutter/pub/rep/07_019.pdf). 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**](http://www.dcsc.tudelft.nl/~bdeschutter/pub/rep/10_003.pdf). 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.**](https://jair.org/media/4818/live-4818-8818-jair.pdf). J. Artif. Intell. Res.(JAIR), 53:659--697, 2015.

* A. O. Castaneda.  
[**Deep Reinforcement Learning Variants of Multi-Agent Learning Algorithms**](https://project-archive.inf.ed.ac.uk/msc/20162091/msc_proj.pdf). 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**](http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Szegedy_Going_Deeper_With_2015_CVPR_paper.pdf). 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?**](https://arxiv.org/abs/1411.1792). 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**](https://www.fer.unizg.hr/_download/repository/KDI-Djalto.pdf). Rn ($\Theta$1), 1(2).

* F.-F. Li, A. Karpathy, and J. Johnson.  
[**CS231n: Convolutional Neural Networks for Visual Recognition**](http://cs231n.stanford.edu/). CS231n: Convolutional Neural Networks for Visual Recognition, 2016.

* F.-F. Li, A. Karpathy, and J. Johnson.  
[**CS231n: Convolutional Neural Networks for Visual Recognition**](https://www.youtube.com/playlist?list=PL16j5WbGpaM0_Tj8CRmurZ8Kk1gEBc7fg). 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**](https://arxiv.org/abs/1605.06676v2). 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**](https://arxiv.org/abs/1606.04695). 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**](https://arxiv.org/abs/1605.06676). 2016.

* Alexander, Vezhnevets, V. Mnih, J. Agapiou, S. Osindero, A. Graves, O. Vinyals, and K. Kavukcuoglu.  
[**Strategic Attentive Writer for Learning Macro-Actions**](https://arxiv.org/abs/1606.04695). 2016.

* M. Fraccaro, S. K. Sønderby, U. Paquet, and O. Winther.  
[**Sequential Neural Models with Stochastic Layers**](https://arxiv.org/abs/1605.07571). 2016.

* I. Osband, C. Blundell, A. Pritzel, and B. V. Roy.  
[**Deep Exploration via Bootstrapped DQN**](https://arxiv.org/abs/1602.04621). 2016.

* M. Jaderberg, W. M. Czarnecki, S. Osindero, O. Vinyals, A. Graves, and K. Kavukcuoglu.  
[**Decoupled Neural Interfaces using Synthetic Gradients**](https://arxiv.org/abs/1608.05343). 2016.

* A. A. Rusu, N. C. Rabinowitz, G. Desjardins, H. Soyer, J. Kirkpatrick, K. Kavukcuoglu, R. Pascanu, and R. Hadsell.  
[**Progressive Neural Networks**](https://arxiv.org/abs/1606.04671). 2016.

* D. J. Rezende, S. Mohamed, I. Danihelka, K. Gregor, and D. Wierstra.  
[**One-Shot Generalization in Deep Generative Models**](https://arxiv.org/abs/1603.05106). 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**](https://arxiv.org/abs/1609.08144). 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**](https://arxiv.org/abs/1606.04474). 2016.

* C. Szegedy, S. Ioffe, V. Vanhoucke, and A. Alemi.  
[**Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning**](https://arxiv.org/abs/1602.07261). 2016.

* K. Simonyan and A. Zisserman.  
[**Very Deep Convolutional Networks for Large-Scale Image Recognition**](https://arxiv.org/abs/1409.1556). 2014.

* K. He, X. Zhang, S. Ren, and J. Sun.  
[**Deep Residual Learning for Image Recognition**](https://arxiv.org/abs/1512.03385). 2015.

* D. Andor, C. Alberti, D. Weiss, A. Severyn, A. Presta, K. Ganchev, S. Petrov, and M. Collins.  
[**Globally Normalized Transition-Based Neural Networks**](https://arxiv.org/abs/1603.06042). 2016.

* S. J. Landefeld, E. P. Seskin, and B. M. Fraumeni.  
[**Taking the pulse of the economy: Measuring GDP**](https://www.aeaweb.org/articles?id=10.1257/jep.22.2.193). 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**](https://arxiv.org/abs/1410.5401). 2014.

* J. Chung, C. Gulcehre, K. Cho, and Y. Bengio.  
[**Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling**](https://arxiv.org/abs/1412.3555). 2014.

* G. Huang, Y. Sun, Z. Liu, D. Sedra, and K. Weinberger.  
[**Deep Networks with Stochastic Depth**](https://arxiv.org/abs/1603.09382). 2016.

* J. B. Heaton, N. G. Polson, and J. H. Witte.  
[**Deep Learning in Finance**](https://arxiv.org/abs/1602.06561). 2016.

* S. T. A. Niaki and S. Hoseinzade.  
[**Forecasting S\&amp;P 500 index using artificial neural networks and design of experiments**](http://dx.doi.org/10.1186/2251-712X-9-1). 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**](https://arxiv.org/abs/1411.6422). 2014.

* S. Sukhbaatar, A. Szlam, and R. Fergus.  
[**Learning Multiagent Communication with Backpropagation**](https://arxiv.org/abs/1605.07736). 2016.

* J. Heinrich and D. Silver.  
[**Deep Reinforcement Learning from Self-Play in Imperfect-Information Games**](https://arxiv.org/abs/1603.01121). 2016.

* J. S. Hartford, J. R. Wright, and K. Leyton-Brown.  
[**Deep learning for predicting human strategic behavior**](http://www.cs.ubc.ca/~jasonhar/GameNet-NIPS-2016.pdf). 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**](https://arxiv.org/abs/1612.09596). 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**](https://www.stlouisfed.org/publications/regional-economist/april-2016/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**](https://arxiv.org/abs/1612.08082). 2016.

* Z. Cui, W. Chen, and Y. Chen.  
[**Multi-Scale Convolutional Neural Networks for Time Series Classification**](http://arxiv.org/abs/1603.06995). 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**](https://arxiv.org/abs/1506.00327). 2015.

* L. Liu and Z. Wang.  
[**Encoding Temporal Markov Dynamics in Graph for Time Series Visualization**](https://arxiv.org/abs/1610.07273). 2016.

* L. Ye and E. Keogh.  
[**Time series shapelets: a new primitive for data mining**](http://alumni.cs.ucr.edu/~lexiangy/Shapelet/kdd2009shapelet.pdf). 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**](http://danielnouri.org/notes/2014/01/10/using-deep-learning-to-listen-for-whales/). 2014.

* M. W. McCracken and S. Ng.  
[**FRED-MD: A monthly database for macroeconomic research**](https://research.stlouisfed.org/econ/mccracken/fred-databases/). 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**](http://dx.doi.org/10.1038/323533a0). 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**](https://arxiv.org/abs/1603.09260). 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**](https://arxiv.org/abs/1406.1078). 2014.

* I. Sutskever, O. Vinyals, and Q. V. Le.  
[**Sequence to Sequence Learning with Neural Networks**](https://arxiv.org/abs/1409.3215). 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**](https://arxiv.org/abs/1705.07832). 2017.

* B. Zoph and Q. V. Le.  
[**Neural Architecture Search with Reinforcement Learning**](https://arxiv.org/abs/1611.01578). 2016.

* J. Gehring, M. Auli, D. Grangier, D. Yarats, and Y. N. Dauphin.  
[**Convolutional Sequence to Sequence Learning**](https://arxiv.org/abs/1705.03122). 2017.

* G. Huang, Y. Li, G. Pleiss, Z. Liu, J. E. Hopcroft, and K. Q. Weinberger.  
[**Snapshot Ensembles: Train 1, get M for free**](https://arxiv.org/abs/1704.00109). 2017.

* S. S. Hansen.  
[**Deep Episodic Value Iteration for Model-based Meta-Reinforcement Learning**](https://arxiv.org/abs/1705.03562). 2017.

* Y. Li and Y. Gal.  
[**Dropout Inference in Bayesian Neural Networks with Alpha-divergences**](https://arxiv.org/abs/1703.02914). 2017.

* Y. Li.  
[**Deep Reinforcement Learning: An Overview**](https://arxiv.org/abs/1701.07274). 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**](http://ece.duke.edu/~lcarin/Chunyuan1.15.2016.pdf%20,%20https://arxiv.org/abs/1506.02157). Deep Learning Workshop, ICML, 2015.

* Y. Gal and Z. Ghahramani.  
[**Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning**](https://arxiv.org/abs/1506.02142). 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**](https://arxiv.org/abs/1702.08835). 2017.

* H. Wang and B. Raj.  
[**On the Origin of Deep Learning**](https://arxiv.org/abs/1702.07800). 2017.

* E. Parisotto and R. Salakhutdinov.  
[**Neural Map: Structured Memory for Deep Reinforcement Learning**](https://arxiv.org/abs/1702.08360). 2017.

* X. Mao, Q. Li, H. Xie, R. Y. K. Lau, and Z. Wang.  
[**Least Squares Generative Adversarial Networks**](https://arxiv.org/abs/1611.04076). 2016.

* O. Nachum, M. Norouzi, K. Xu, and D. Schuurmans.  
[**Bridging the Gap Between Value and Policy Based Reinforcement Learning**](https://arxiv.org/abs/1702.08892). 2017.

* B. Amos and J. Z. Kolter.  
[**OptNet: Differentiable Optimization as a Layer in Neural Networks**](https://arxiv.org/abs/1703.00443). 2017.

* L. Kaiser, O. Nachum, A. Roy, and S. Bengio.  
[**Learning to Remember Rare Events**](https://openreview.net/pdf?id=SJTQLdqlg). ICLR, 2017.

* J. Hartford, G. Lewis, K. Leyton-Brown, and M. Taddy.  
[**Counterfactual Prediction with Deep Instrumental Variables Networks**](https://arxiv.org/abs/1612.09596). 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**](https://arxiv.org/abs/1606.07792). 2016.

* D. Kriesel.  
[**A brief Introduction on Neural Networks**](http://www.dkriesel.com/_media/science/neuronalenetze-en-zeta2-2col-dkrieselcom.pdf). 2007.

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