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Tag: cond-mat.dis-nn
arXiv:2603.13350v1 Announce Type: new Abstract: Understanding whether retrieval in dense associative memories survives thermal noise is essential for bridging zero-temperature capacity proofs with the finite-temperature …
arXiv:2603.11161v1 Announce Type: new Abstract: We formally define Algorithmic Capture (i.e., ``grokking'' an algorithm) as the ability of a neural network to generalize to arbitrary …
arXiv:2603.06142v1 Announce Type: new Abstract: Predictive coding graphs (PCGs) are a recently introduced generalization to predictive coding networks, a neuroscience-inspired probabilistic latent variable model. Here, …
arXiv:2602.13194v1 Announce Type: new Abstract: The entropy rate of printed English is famously estimated to be about one bit per character, a benchmark that modern …
arXiv:2603.03469v1 Announce Type: new Abstract: Generalization in generative modeling is defined as the ability to learn an underlying distribution from a finite dataset and produce …
arXiv:2603.03507v1 Announce Type: new Abstract: Adversarial attacks - input perturbations imperceptible to humans that fool neural networks - remain both a persistent failure mode in …
arXiv:2602.15593v1 Announce Type: new Abstract: Recurrent and deep neural networks (RNNs/DNNs) are cornerstone architectures in machine learning. Remarkably, RNNs differ from DNNs only by weight …
