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The graphical model of DGP is summarized in Figure 1 . First let's define the potential function Now let's define the Gaussian bump. We will write everything in vector form hereafter. We want to "let the data speak" and avoid oversmoothing, so the penalty weights Given the approximate posterior (eq. To understand the various terms in the ELBO above it is helpful to start with a simpler special case.

Noninvasive behavioral tracking of animals is crucial for many scientific investigations. Recent transfer learning approaches for behavioral tracking have considerably advanced the state of the art. Typically these methods treat each video frame and each object to be tracked independently.

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. The paper introduces the case of estimating a sparse chain graphical model in a high-dimensional data setting. It estimates a sparse autoregressive and sparse covariance structure. The method considers one-and multi-levels chain graphical models. The examples and applications are interesting.

We propose meta-curvature (MC), a framework to learn curvature information for better generalization and fast model adaptation.

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. Spectral methods are based on decomposing moment tensors. If data are generated from latent variable models, their empirical moment tensors have a kind of (approximate) low-rank decomposition (approximate due to both noisy observations and finite sample estimation error in the empirical moments). These decompositions can be computed from the moment tensors, e.g. using a kind of power iteration method on related (symmetrized) tensors. The basic ideas of these spectral methods are fairly well established and many examples have been explored, especially in [6]. This paper applies spectral fitting methods to data modeled as being generated from an IBP, including two common emission models (a linear gaussian model and a sparse factor analysis model, described in Section 2). The main ingredients are a calculation of the appropriate moment tensors and corresponding symmetrized versions (Section 3), an application of the standard tensor power decomposition method (Section 4), and concentration proofs to offer recovery guarantees when data are generated from the model (Section 5).