perpetual pass
Pursuing the development of continuous networks.
A continuous feed-forward network that consumes data particles, processes them through hidden layers, and emits transformed generations in an endless learning cycle.
Visualizes the attention mechanism in transformer models, showing how tokens attend to each other through multiple heads and flow through the architecture.
A network designed to never converge - continuous drift, random walks, and momentum reversals keep the weights perpetually exploring the loss landscape.
Associative memory through energy minimization. Neurons form attractors that recall stored patterns from noisy inputs via recurrent dynamics.
Long Short-Term Memory with gated architecture. Forget, input, and output gates control information flow through the cell state memory highway.
Linear-time sequence modeling through selective state spaces. Input-dependent parameters enable context-aware memory, achieving O(N) complexity vs O(N^2) for attention.
Message passing on graph-structured data. Nodes aggregate neighbor information through learned transformations, with optional attention weighting (GAT).