Vector Symbolic Architecture

Vector Symbolic Architectures (VSAs), also known as hyperdimensional computing, represent information using high-dimensional vectors, enabling efficient and robust computation through vector operations. Current research focuses on improving VSA model architectures, such as exploring sparse binary representations and incorporating techniques like self-attention and residual networks to enhance performance and scalability for tasks ranging from visual question answering to brain-computer interfaces. This approach offers a promising alternative to traditional methods by combining the strengths of symbolic and connectionist paradigms, leading to more interpretable and efficient machine learning models with applications across diverse fields.