In table 3, we demonstrate the prompt for textualized recaptioning.

Neural Information Processing Systems http://nips.cc/

In the semi-discrete2-Wasserstein problem, we wish to compute the cheapest way to transport all the mass from a continuous distribution µ to a discrete distributionν in Rd for d 1, where the cost of transporting unitmassbetween pointsaandbisd(a,b)= a b 2. When both distributions are discrete, a simple combinatorial framework has been used to find the exact solution (see e.g.

However, it is unclear how to best fit low-rank RNNs to data consisting of noisy observations of an underlying stochastic system. Here, we propose to fit stochastic low-rank RNNs with variational sequential Monte Carlo methods.

A TISS with various transformer models that consider ordering.

This paper presents an end-to-end neural architecture based on Diffusion Models for spatial puzzle solving, particularly jigsaw puzzle and room arrangement tasks. In the latter task, for instance, the proposed system takes a set of room layouts as polygonal curves in the top-down view and aligns the room layout pieces by estimating their 2D translations and rotations, akin to solving the jigsaw puzzle of room layouts. A surprising discovery of the paper is that the simple use of a Diffusion Model effectively solves these challenging spatial puzzle tasks as a conditional generation process. To enable learning of an end-to-end neural system, the paper introduces new datasets with ground-truth arrangements: 1) 2D V oronoi jigsaw dataset, a synthetic one where pieces are generated by V oronoi diagram of 2D pointset; and 2) MagicPlan dataset, a real one offered by MagicPlan from its production pipeline, where pieces are room layouts constructed by augmented reality App by real-estate consumers. The qualitative and quantitative evaluations demonstrate that our approach outperforms the competing methods by significant margins in all the tasks. We have provided code and data here .