Second, we show the first core-set w.r.t. the sum-of-nearest-neighbor distances. Finally, we run several experiments showing the effectiveness of our core-set approach. In particular, we apply constrained diversity maximization to summarize a set of timed messages that takes into account the messages' recency.

This prevailing paradigm heavily relies on the out-of-distribution (OOD) effect to influence the model's prediction.

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.

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.