Papers

• Notes on: Fischer, A. (2015): Training Restricted Boltzmann Machines
• Notes on: Kocsis, L., & Szepesvári, C. (2006): Bandit Based Monte-Carlo Planning
• Notes on: Olah, C. (2014): Neural networks, manifolds and topology
• Notes on: Albanna, B. F., Hillar, C., Sohl-Dickstein, J., & DeWeese, M. R. (2012): Minimum and maximum entropy distributions for binary systems with known means and pairwise correlations
• Notes on: Alberg, J., & Lipton, Z. C. (2017): Improving Factor-Based Quantitative Investing By Forecasting Company Fundamentals
• Notes on: Arjovsky, M., Chintala, S., & Bottou, L\'eon (2017): Wasserstein Gan
• Notes on: Auer, P., Cesa-Bianchi, N., & Fischer, P. (2002): Finite-time analysis of the multiarmed bandit problem
• Notes on: Baez, J. C., & Stay, M. (2009): Physics, topology, logic and computation: a rosetta stone
• Notes on: Baylor, D., Koc, L., Koo, C. Y., Lew, L., Mewald, C., Modi, A. N., Polyzotis, N., … (2017): Tfx: a tensorflow-based production-scale machine learning platform
• Notes on: Biondo, A. P. A. E., & Rapisarda, A. (2018): Talent vs luck: the role of randomness in success and failure
• Notes on: Bojanowski, P., Grave, E., Joulin, A., & Mikolov, T. (2016): Enriching Word Vectors With Subword Information
• Notes on: Bourely, A., Boueri, J. P., & Choromonski, K. (2017): Sparse Neural Networks Topologies
• Notes on: Brochu, E., Cora, V. M., & Freitas, N. d. (2010): A tutorial on bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning
• Notes on: Bronstein, M. M., Bruna, J., LeCun, Y., Szlam, A., & Vandergheynst, P. (2017): Geometric deep learning: going beyond euclidean data
• Notes on: Brown, N., & Sandholm, T. (2017): Safe and nested subgame solving for imperfect-information games
• Notes on: Brown, N., & Sandholm, T. (2017): Libratus: the superhuman ai for no-limit poker
• Notes on: Bruna, J., Zaremba, W., Szlam, A., & LeCun, Y. (2013): Spectral networks and locally connected networks on graphs
• Notes on: Burda, Y., Grosse, R. B., & Salakhutdinov, R. (2014): Accurate and conservative estimates of mrf log-likelihood using reverse annealing
• Notes on: Caticha, A. (2014): The basics of information geometry
• Notes on: Choromanska, A., Henaff, M., Mathieu, M., Arous, G\'erard Ben, & LeCun, Y. (2014): The loss surfaces of multilayer networks
• Notes on: Chwialkowski, K., Strathmann, H., & Gretton, A. (2016): A kernel test of goodness of fit
• Notes on: Crewe, P., Gratwick, R., & Grafen, A. (2017): Defining fitness in an uncertain world
• Notes on: Ekert, A., Hayden, P., & Inamori, H. (2000): Basic concepts in quantum computation
• Notes on: Friston, K. J., Parr, T., & de Vries, B. (2017): The graphical brain: belief propagation and active inference
• Notes on: Gilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O., & Dahl, G. E. (2017): Neural message passing for quantum chemistry
• Notes on: Gorham, J., & Mackey, L. (2015): Measuring Sample Quality With Stein's Method
• Notes on: Gretton, A., Borgwardt, K. M., Rasch, M. J., Sch\"olkopf, Bernhard, & Smola, A. (2012): A kernel two-sample test
• Notes on: Hall, B. C. (2013): Quantum theory for mathematicians
• Notes on: Hansen, N. (2016): The cma evolution strategy: a tutorial
• Notes on: Havrylov, S., & Titov, I. (2017): Emergence of language with multi-agent games: learning to communicate with sequences of symbols
• Notes on: He, X., Pan, J., Jin, O., Xu, T., Liu, B., Xu, T., Shi, Y., … (2014): Practical lessons from predicting clicks on ads at facebook
• Notes on: Henaff, M., Bruna, J., & LeCun, Y. (2015): Deep convolutional networks on graph-structured data
• Notes on: Jitkrittum, W., Xu, W., Szabo, Z., Fukumizu, K., & Gretton, A. (2017): A Linear-Time Kernel Goodness-Of-Fit Test
• Notes on: Joulin, A., Grave, E., Bojanowski, P., & Mikolov, T. (2016): Bag of tricks for efficient text classification
• Notes on: Joulin, A., Grave, E., Bojanowski, P., Nickel, M., & Mikolov, T. (2017): Fast linear model for knowledge graph embeddings
• "Notes on: Jung, A. (2017): A fixed-point of view on gradient methods for big data"
• Notes on: Kipf, T. N., & Welling, M. (2016): Semi-Supervised Classification With Graph Convolutional Networks
• Notes on: Kitchen, A., & Benedetti, M. (2018): Expit-oos: towards learning from planning in imperfect information games
• Notes on: Klambauer, G\"unter, Unterthiner, T., Mayr, A., & Hochreiter, S. (2017): Self-Normalizing Neural Networks
• Notes on: Lanctot, M., Lis\`y, Viliam, & Bowling, M. (2014): Search in imperfect information games using online monte carlo counterfactual regret minimization
• Notes on: Lillicrap, T. P., Hunt, J. J., Pritzel, A., Heess, N., Erez, T., Tassa, Y., Silver, D., … (2015): Continuous Control With Deep Reinforcement Learning
• Notes on: Liu, Q., & Wang, D. (2016): Stein variational gradient descent: a general purpose bayesian inference algorithm
• Notes on: Lundberg, S. M., & Lee, S. (2017): Consistent feature attribution for tree ensembles
• Notes on: McInnes, L., & Healy, J. (2018): Umap: uniform manifold approximation and projection for dimension reduction
• Notes on: Mehta, P., Bukov, M., Wang, C., Day, A. G. R., Richardson, C., Fisher, C. K., & Schwab, D. J. (2018): A high-bias, low-variance introduction to machine learning for physicists
• Notes on: Srinivasa, C., Givoni, I., Ravanbakhsh, S., & Frey, B. J. (2017): Min-Max Propagation
• Notes on: Mohri, M., Rostamizadeh, A., & Talwalkar, A. (2012): Foundations of machine learning
• Notes on: Montavon, Gr\'egoire, Bach, S., Binder, A., Samek, W., & M\"uller, Klaus-Robert (2015): Explaining Nonlinear Classification Decisions With Deep Taylor Decomposition
• Notes on: Moore, C. J., Berry, C. P. L., Chua, A. J. K., & Gair, J. R. (2016): Improving gravitational-wave parameter estimation using gaussian process regression
• Notes on: Muandet, K., Fukumizu, K., Sriperumbudur, B., & Sch\"olkopf, Bernhard (2016): Kernel mean embedding of distributions: a review and beyond
• Notes on: Neal, R. M. (1998): Annealed Importance Sampling
• Notes on: Nguyen, H. C., Zecchina, R., & Berg, J. (2017): Inverse statistical problems: from the inverse ising problem to data science
• Notes on: Nickel, M., & Kiela, D. (2017): Poincaré embeddings for learning hierarchical representations
• Notes on: Ollivier, Y. (2013): Riemannian metrics for neural networks i: feedforward networks
• Notes on: Osogami, T. (2017): Boltzmann machines for time-series
• Notes on: Papamakarios, G., Pavlakou, T., & Murray, I. (2017): Masked autoregressive flow for density estimation
• Notes on: Park, M., Jitkrittum, W., & Sejdinovic, D. (2016): K2-abc: approximate bayesian computation with kernel embeddings
• Notes on: Qin, Q., & Hobert, J. P. (2017): Asymptotically stable drift and minorization for markov chains with application to albert and chib's algorithm
• Notes on: Rahimi, A., & Recht, B. (2008): Uniform approximation of functions with random bases
• Notes on: Ratner, A., Sa, C. D., Wu, S., Selsam, D., & R\'e, Christopher (2016): Data programming: creating large training sets, quickly
• Notes on: Rosenthal, J. S. (1995): Minorization conditions and convergence rates for markov chain monte carlo
• Notes on: Russo, D. J., Van Roy, B., Kazerouni, A., Osband, I., Wen, Z., & others, (2018): A tutorial on thompson sampling
• Notes on: Schlichtkrull, M., Kipf, T. N., Bloem, P., Berg, R. v. d., Titov, I., & Welling, M. (2017): Modeling Relational Data With Graph Convolutional Networks
• Notes on: Shalev-Shwartz, S., & Ben-David, S. (2014): Understanding machine learning: from theory to algorithms
• Notes on: Srivastava, A., Valkov, L., Russell, C., Gutmann, M. U., & Sutton, C. (2017): Veegan: reducing mode collapse in gans using implicit variational learning
• Notes on: Tayefi, M., & Ramanathan, T. (2016): An overview of figarch and related time series models
• Notes on: Tosh, C. (): Mixing rates for the gibbs sampler over restricted boltzmann machines
• Notes on: Toutanova, K., Chen, D., Pantel, P., Poon, H., Choudhury, P., & Gamon, M. (2015): Representing text for joint embedding of text and knowledge bases
• Notes on: Trouillon, Th\'eo, Welbl, J., Riedel, S., Gaussier, \'Eric, & Bouchard, G. (2016): Complex embeddings for simple link prediction
• Notes on: Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., … (2017): Attention Is All You Need
• Notes on: Walker, C. L. (2004): Categories of fuzzy sets