Humans demonstrate remarkable abilities to predict physical events in dynamic scenes, and to infer the physical properties of objects from static images. We propose a generative model for solving these problems of physical scene understanding from real-world videos and images. At the core of our generative model is a 3D physics engine, operating on an object-based representation of physical properties, including mass, position, 3D shape, and friction. We can infer these latent properties using relatively brief runs of MCMC, which drive simulations in the physics engine to fit key features of visual observations. We further explore directly mapping visual inputs to physical properties, inverting a part of the generative process using deep learning. We name our model Galileo, and evaluate it on a video dataset with simple yet physically rich scenarios. Results show that Galileo is able to infer the physical properties of objects and predict the outcome of a variety of physical events, with an accuracy comparable to human subjects.

The following lemma justifies item 3 in Assumption 1. Consider the following two cases: 1. Density function of the policy is smooth, i.e. We then show how Theorem 4 implies Theorem 1. Assumption 3. F or all x X, there exist constants such that the following hold 1. F or all x, we have null A Now we proceed to prove the main theorem. Then, given the above convergence result on the gradient norm, we proceed to prove the convergence of NAC in terms of the function value.

The generated candidates are novel at both the molecular and chemical scaffold levels when compared to the training data.

This paper presents the Deep Convolution Inverse Graphics Network (DC-IGN), a model that aims to learn an interpretable representation of images, disentangled with respect to three-dimensional scene structure and viewing transformations such as depth rotations and lighting variations. The DC-IGN model is composed of multiple layers of convolution and de-convolution operators and is trained using the Stochastic Gradient Variational Bayes (SGVB) algorithm [10]. We propose a training procedure to encourage neurons in the graphics code layer to represent a specific transformation (e.g.

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The dominant approaches to text representation in natural language rely on learning embeddings on massive corpora which have convenient properties such as compositionality and distance preservation.

Continual lifelong learning is essential to many applications.

CXPlain is significantly more accurate and faster than existing model-agnostic methods for estimating feature importance.