We design a system that learns how to edit visual programs. Our edit network consumes a complete input program and a visual target. From this input, we task our network with predicting a local edit operation that could be applied to the input program to improve its similarity to the target.

The sample-averaged gradient of the CSO objective is biased due to its nested structure, and therefore requires a high sample complexity for convergence. We introduce a general stochastic extrapolation technique that effectively reduces the bias.

Inatypical activeconfocal NLOS imaging system, as depicted in Figure 1, alaser source and adetector are both focused on the same point onarelay surface.

Recent curriculum Reinforcement Learning (RL) has shown notable progress in solving complex tasks by proposing sequences of surrogate tasks. However, the previous approaches often face challenges when they generate curriculum goals in a high-dimensional space.

Large Language Models (LLMs) have revolutionized the field of natural language processing with their impressive capabilities.

In this work, we introduce the Geometric Diffusion Bridge (GDB), a novel generative modeling framework that accurately bridges initial and target geometric states.

UCB, wherein agents cooperatively minimize the group regret under the coordination of a central server.