A Walsh Hadamard Derived Linear Vector Symbolic Architecture

VSAs support the commutativity and associativity of this binding operation, along with an inverse operation, allowing one to construct symbolicstyle manipulations over real-valued vectors. Most VSAs were developed before deep learning and automatic differentiation became popular and instead focused on efficacy in hand-designed systems. In this work, we introduce the Hadamardderived linear Binding (HLB), which is designed to have favorable computational efficiency, and efficacy in classic VSA tasks, and perform well in differentiable systems.