Pattern Exploiting Training

Pattern Exploiting Training (PET) focuses on enhancing machine learning models by leveraging patterns within data to improve their reasoning and predictive capabilities. Current research explores PET's application across diverse domains, including visual reasoning, traffic forecasting, and tabular data analysis, often employing techniques like adversarial learning and attention mechanisms within neural network architectures such as Graph Convolutional Recurrent Networks and pre-trained language models. This approach offers significant potential for improving model performance and robustness in various applications by explicitly incorporating learned patterns into the training process, leading to more accurate and reliable predictions.