Point Cloud Generation

Pointer Networks tackle problems where input and output data are sequential data, but can't be solved by seq2seq type models because discrete categories of output elements depend on the variable input size (and are not decided in advance). A Pointer Network learns the conditional probability of an output sequence with elements that are discrete tokens corresponding to positions in an input sequence. They solve the problem of variable size output dictionaries using additive attention. But instead of using attention to blend hidden units of an encoder to a context vector at each decoder step, Pointer Networks use attention as a pointer to select a member of the input sequence as the output. Pointer-Nets can be used to learn approximate solutions to challenging geometric problems such as finding planar convex hulls, computing Delaunay triangulations, and the planar Travelling Salesman Problem.

Energy Based Processes extend energy based models to exchangeable data while allowing neural network parameterizations of the energy function. They extend the previously separate stochastic process and latent variable model perspectives in a common framework. The result is a generalization of Gaussian processes and Student-t processes that exploits EBMs for greater flexibility.