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Related Articles from SNS
Residual-Weighted Randomized Jacobi: Sharpened Bounds via Residual Concentration and Asynchronous Extension
arXiv:2606.01232v1 Announce Type: new Abstract: We study randomized stationary methods for symmetric positive definite linear systems in which component $j$ is selected with probability proportional to $|r_j|^\ell$. This power-weighted family interpolates continuously between uniform randomized Jacobi as $\ell \to 0$ and Gauss--Southwell greedy relaxation as $\ell \to \infty$. For the central case $\ell = 2$, we sharpen the standard one-step convergence analysis using the inverse...
WAV: Multi-Resolution Block Residual Routing for Deep Decoder-Only Transformers
Announce Type: new Abstract: Residual connections are central to training deep Transformers, but standard PreNorm residual streams aggregate sublayer updates with fixed unit weights. Recent Attention Residuals replace this fixed accumulation with content-dependent depth-wise routing, and Block Attention Residuals make the mechanism efficient by routing over block-level residual summaries. However, a single block summary stores only the low-frequency total residual displacement inside a...
State Observers for Linear Systems with Prescribed Residual Bounds
Announce Type: new Abstract: This paper presents a state observer design for continuous linear time-invariant (LTI) systems subject to unknown bounded disturbances, that enforces a prescribed bound on the observer residual. The proposed observer augments a continuous-time Luenberger observer with state resets, triggered when the norm of the residual equals a pre-specified bound. The reset map guarantees contraction of the residual at jump instants while preserving the uniform boundedness...
XOResNet: Exclusive-OR Meta-Residuals Facilitate Deep Spiking Neural Networks Learning
arXiv:2605.30362v1 Announce Type: new Abstract: Spiking neural networks (SNNs) hold promise for demonstrating superior learning and representation capabilities in deep models. Given the tremendous success of ResNet in deep learning, it would naturally follow to train deep SNNs with residual learning. However, existing residual structures for constructing deep SNNs still present challenges of spike redundancy or information loss, as well as redundant learning.
KromHC: Manifold-Constrained Hyper-Connections with Kronecker-Product Residual Matrices
Announce Type: replace Abstract: The success of Hyper-Connections (HC) in neural networks (NN) has also highlighted issues related to training instability and restricted scalability. The Manifold-Constrained Hyper-Connections (mHC) mitigate these challenges by projecting the residual connection space onto a Birkhoff polytope, however, it faces two issues: 1) its iterative Sinkhorn-Knopp (SK) algorithm does not always yield exactly doubly stochastic residual matrices; 2) mHC incurs a...
'Married... with Children' star Ted McGinley reveals he gets penny residual checks from his hit TV shows
Ted McGinley is the latest celebrity to reveal the shocking amount he still receives from residual checks. In a new interview with Page Six, the "Married... With Children" star — who also found fame on "Happy Days" and "The Love Boat" throughout the 1970s, ’80s, and '90s — said he's holding on to a "whole stack" of checks each worth a total of one penny."I’ve been getting residuals all the time," said McGinley, who admitted the checks become smaller and smaller as the years go by. While...
DeRes: Decoupling Residual Stability and Adaptivity for Scalable CTR Prediction
arXiv:2606.07980v1 Announce Type: new Abstract: Transformer-based CTR models face a growing bottleneck at the residual connection: under Pre-Norm, early user-interest signals are diluted layer by layer; the identity skip cannot forget stale interests; and each layer sees only its immediate predecessor, losing long-range cross-layer dependencies. Recent attention-based residual variants (AttnRes) address parts of this in language models, but drop the protective identity skip and have not been...
FiberTune: Preserving Action-Fiber Visual Residuals in Vision-Language-Action Fine-Tuning
arXiv:2606.08653v1 Announce Type: new Abstract: Action-supervised fine-tuning of vision-language-action (VLA) policies fits demonstrations effectively but constrains only the directions that change predicted actions, leaving visual structure consistent across action-equivalent states free to collapse. We formalize this as residual visual collapse along local action fibers and propose FiberTune, a training-time objective that preserves teacher-structured visual residuals without adding...
Bellman Residual Minimization for Control: Geometry, Stationarity, and Convergence
arXiv:2601.18840v4 Announce Type: replace Abstract: Markov decision problems are most commonly solved via dynamic programming. Another approach is Bellman residual minimization, which directly minimizes the squared Bellman residual objective function. However, compared to dynamic programming, this approach has received relatively less attention, mainly because it is often less efficient in practice and can be more difficult to extend to model-free settings such as reinforcement learning.
Residual Reservoir Memory Networks
Announce Type: replace Abstract: We introduce a novel class of untrained Recurrent Neural Networks (RNNs) within the Reservoir Computing (RC) paradigm, called Residual Reservoir Memory Networks (ResRMNs). ResRMN combines a linear memory reservoir with a non-linear reservoir, where the latter is based on residual orthogonal connections along the temporal dimension for enhanced long-term propagation of the input. The resulting reservoir state dynamics are studied through the lens of linear...