Non-Markovian Memory-Based Model for Sequence Prediction

Recognizing the non-Markovian nature of natural language and visual information, we introduce a Non-Markovian Memory-Based Model (NMM) that adjusts dynamically to the training data sequence. This novel approach uses memory-augmented networks and non-Markovian learning to capture complex dependencies in sequential data such as text and images. Our model features a memory module for storing and retrieving past input information and a learning mechanism for long-term dependencies, dynamically updating with new embedding and positional memory vectors as new tokens appear. Our extensive experiments show that using memory vectors based on token position significantly improves learning effectiveness over traditional DNN models. The NMM's performance, demonstrated across various datasets, highlights its superior accuracy and adaptability in tasks such as token prediction and image recognition, marking a significant advancement in machine learning research.