Fuente: WIPO "tomato" Methods and systems for training machine learning models using contrastive loss are disclosed. The use of these novel contrastive loss methods can improve the accuracy of prediction models trained using such methods. During training, training data elements can be grouped into pairs of training data elements, and a computer system can determine whether such pairs comprise "positive" pairs of training data elements or "negative" pairs of training data elements. Embeddings can be generated for each pair of training data elements using an encoder, and a contrastive loss can be calculated between each pair of embeddings, such that the encoder is rewarded for generating similar embeddings for positive pairs of training data elements and penalized for generating similar embeddings for negative pairs of training data elements. Using contrastive loss, the machine learning model can be trained to develop a more holistic understanding of the training dataset, thereby improving accuracy.