My Next Role

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Beginning the search for an impossibly awesome next role.


Neurally Embedded Emojis

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Convolutional variational autoencoders for emoji generation and Siamese text-question-emoji-answer models. Keras, bidirectional LSTMs and snarky tweets @united within.


Random Effects Neural Networks in Edward and Keras

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Coupling nimble probabilistic models with neural architectures in Edward and Keras: "what worked and what didn't," a conceptual overview of random effects, and directions for further exploration.


Further Exploring Common Probabilistic Models

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Exploring generative vs. discriminative models, and sampling and variational methods for approximate inference through the lens of Bayes' theorem.


Minimizing the Negative Log-Likelihood, in English

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Statistical underpinnings of the machine learning models we know and love. A walk through random variables, entropy, exponential family distributions, generalized linear models, maximum likelihood estimation, cross entropy, KL-divergence, maximum a posteriori estimation and going "fully Bayesian."


Transfer Learning for Flight Delay Prediction via Variational Autoencoders

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Autoencoding airports via variational autoencoders to improve flight delay prediction. Additionally, a principled look at variational inference itself and its connections to machine learning.


Deriving the Softmax from First Principles

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Deriving the softmax from first conditional probabilistic principles, and how this framework extends naturally to define the softmax regression, conditional random fields, naive Bayes and hidden Markov models.


Approximating Implicit Matrix Factorization with Shallow Neural Networks

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In this post, we look to beat the performance of Implicit Matrix Factorization on a recommendation task using 5 different neural network architectures.


Ordered Categorical GLMs for Product Feedback Scores

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A follow-up to Erik Bernhardsson's post "More MCMC – Analyzing a small dataset with 1-5 ratings" using ordered categorical generalized linear models.


Intercausal Reasoning in Bayesian Networks

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Simple intercausal reasoning on a 3-node Bayesian network.


© Will Wolf 2017

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