Generative AI is exasperating due to prompt sensitivities, i.e., that the AI misinterprets your entered prompt. Here are ...

In this paper, we leverage casual inference to precisely model inter-airport relationships and propose a self-corrective spatio-temporal graph neural network (named CausalNet) for flight delay ...

Enter machine learning (ML), the process through which software and hardware use algorithms, data analysis and other procedures to expand their understanding of concepts. To join this emerging ...

a machine limited to rule-based operations could not be expert. 2 Not even an enormous database of common-sense facts could make these systems as smart as experts. Many expert systems are useful ...

Unlike traditional autoencoder approaches, DiGIT separates the training of encoders and decoders, starting with the encoder-only training through a discriminative self-supervised model ... After ...

DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical ...

To this end, we present a novel self-supervised learning (SSL) framework, called multiview subgraph neural networks ( Muse), for handling the long-range dependencies. In particular, we propose an ...