Just-In-Time Learning for Fast and Flexible Inference

By S. M. Ali Eslami, Daniel Tarlow, Pushmeet Kohli, John M. Winn. 2014

25 citations recorded by the source corpus.

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In Efficiency & Compression

Citations in the corpus

Kernel Mean Embedding of Distributions: A Review and Beyonds (2016, methodology): “Inspired by the work of Eslami et al. (2014), Jitkrittum et al.”
Kernel-Based Just-In-Time Learning for Passing Expectation Propagation Messages (2015, methodology): “Experiment 1: Batch Learning As in (Heess et al., 2013; Eslami et al., 2014), we study the logistic factor f(p|z) = δ ( p− 1 1+exp(−z) ) , where δ is the Dirac delta function, in the context of a binary logistic regression model (Fig.”
Mondrian Forests for Large-Scale Regression when Uncertainty Matters (2015, background): “(Hutter et al. [14] proposed a further modiﬁcation, see Appendix C.) Eslami et al. [9] used a non-standard decision forest implementation where a quadratic regressor is ﬁt at each leaf node, rather than a constant regressor as in popular decision forest implementations.”
Applications of Probabilistic Programming (Master's thesis, 2015) (2016, background): “Methods to learn EP operators for inference in Infer.NET have included neural networks (Heess et al., 2013), just-in-time random forests (Eslami et al., 2014) and just-in-time kernel-based regression (Jitkrittum et al., 2015).”
Distributed Bayesian Learning with Stochastic Natural Gradient Expectation Propagation and the Posterior Server (2015, methodology): “SNEP can therefore be used in place of EP in situations where Monte Carlo moment estimates are used, including graphical models (Heess et al., 2013, Eslami et al., 2014, Jitkrittum et al., 2015, Lienart et al., 2015), hierarchical Bayesian models (Gelman et al., 2014), and approximate Bayesian…”