Global optimization of expensive, potentially gradient-free functions has long been a critical component of many complex problems in science and engineering.

The "black-box" nature of representations, combined with the known susceptibility of networks to learn spurious correlations [

To address this problem, existing methods partition the overall DPDP into fixed-size sub-problems by caching online generated orders and solve each sub-problem, or on this basis to utilize the predicted future orders to optimize each sub-problem further. However, the solution quality and efficiency of these methods are unsatisfactory, especially when the problem scale is very large.

Big Tech Says Generative AI Will Save the Planet. A new report finds that of 154 specific claims about how AI will benefit the climate, just a quarter cited academic research. A third included no evidence at all. A few years ago, Ketan Joshi read a statistic about artificial intelligence and climate change that caught his eye. In late 2023, Google began claiming that AI could help cut global greenhouse gas emissions by between 5 and 10 percent by 2030.

Considering different geographic regions generally have their own native sign languages, it is valuable to establish corresponding SL T datasets to support related communication and research. Auslan, as a sign language specific to Australia, still lacks a dedicated large-scale dataset for SL T.

However, many use cases of neural network verification require solving the inverse problem, or over-approximating the set of inputs that lead to certain outputs.

GNNs to mitigate the bias as well as boost their utility in an end-to-end manner.