Sequential Representation

Sequential representation focuses on encoding data with inherent temporal or structural order into effective numerical sequences for machine learning. Current research emphasizes disentangling relevant factors within these sequences, particularly in complex multimodal data like videos and electronic health records, often employing transformer-based architectures and graph neural networks to capture both temporal and structural relationships. These improved representations are crucial for enhancing the performance and interpretability of models in diverse applications, ranging from 3D object generation and code summarization to medical prediction and interactive decision-making. The development of more efficient and informative sequential representations is driving progress across numerous fields.