Context Neural Network

Context neural networks enhance traditional models by incorporating contextual information from related data points or sources, improving prediction accuracy and efficiency in various applications. Current research focuses on integrating context into diverse architectures, including Transformers, recurrent neural networks (RNNs, particularly LSTMs), and spiking neural networks, often employing techniques like masked reconstruction pre-training or contextual adapters for personalization. This approach is proving valuable across domains, from time series forecasting and sound event detection to image retrieval and speech recognition, offering improved performance and scalability compared to models lacking contextual awareness.