Deep Delta Learning (github.com)

Deep Delta Learning (DDL) introduces a transformative approach to residual network design by enhancing the conventional additive residual connection through a learnable, data-dependent transformation known as the Delta Operator. This innovative architecture reinterprets the residual block to perform as a rank-1 Householder update, integrating identity mapping, orthogonal projection, and geometric reflection into a single, continuously differentiable framework. Such a design not only preserves the advantages of stable training found in gated residual architectures but also significantly expands the network's capacity to model complex, non-monotonic dynamics that can be challenging for traditional models. The implications of DDL for the AI/ML community are profound. By enabling explicit control over the spectrum of layer-wise transitions, DDL allows networks to effectively manage the erasure and rewriting of information at each layer, mitigating the accumulation of interference often seen in standard residual networks. This fundamental shift could enhance the expressiveness of deep learning models, paving the way for more sophisticated sequence modeling applications, as illustrated by its theoretical link to DeltaNet. As researchers explore the capabilities of DDL, it promises to deliver more robust, adaptable architectures suited for modern machine learning challenges.