James Brofos

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Frame-to-frame stochasticity is a major challenge in video prediction. The use of standard feedforward and recurrent networks for video prediction leads to averaging of future states, which can in part be attributed to the networks’ limited ability to model stochasticity. We propose the use of conditional variational autoencoders (CVAE) for video(More)
Bayesian optimization has emerged as a powerful, new technique for interpolating and optimizing a wide range of functions which are expensive to compute. The primary tool of Bayesian optimization is the Gaussian process, which permits one to define a prior belief, which is then transformed into a posterior through sequential sampling of points.(More)
We prove an upper bound on the cumulative opportunity cost of the online knowledge gradient algorithm. We leverage the theory of martingales to yield a bound under the Gaussian assumption. Using results from information theory we are further able to provide asymptotic bounds on the cumulative opportunity cost with high probability. 1. Supporting Material.(More)
Authors often convey meaning by referring to or imitating prior works of literature, a process that creates complex networks of literary relationships ("intertextuality") and contributes to cultural evolution. In this paper, we use techniques from stylometry and machine learning to address subjective literary critical questions about Latin literature, a(More)
Recently, the Gaussian Mixture Variational Autoencoder (GMVAE) has been introduced to handle unsupervised clustering (Dilokthanakul et al., 2016). However, the existing formulation requires the introduction of the free bits term into the objective function in order to overcome the effects of the uniform prior imposed on the latent categorical variable. By(More)