The field of machine learning is witnessing significant developments in contrastive learning and human-expert collaboration. Researchers are exploring new frameworks and techniques to improve the performance of contrastive learning methods, including the use of probabilistic variational contrastive learning, projection perspectives, and meta-learning representations. Additionally, there is a growing interest in incorporating human expertise into machine learning models, with studies investigating the use of Bayesian inference for correlated human experts and classifiers. These innovations have the potential to enhance the accuracy and efficiency of machine learning models in various applications, including medical classification and image recognition. Noteworthy papers include: Bayesian Inference for Corated Human Experts and Classifiers, which develops a general Bayesian framework for querying human experts and leveraging class probability estimates of pre-trained classifiers. Probabilistic Variational Contrastive Learning, which proposes a decoder-free framework that maximizes the evidence lower bound by interpreting the InfoNCE loss as a surrogate reconstruction term and adding a KL divergence regularizer to a uniform prior on the unit hypersphere.