Distilling Bayesian Layers into a pretrained 340M Gated DeltaNet improves RULER long-context retrieval over a matched-compute control, and improves robustness beyond the training regime in controlled collision studies, learned associative recall, and the Zoology MQAR benchmark.
Matthew Dowling, H. Jeon, Cristina Savin et al.· arXiv.org· 0 citations
The central observation is that the negative product-of-scores feedback employed in accelerated score-driven recursions can be read as the stochastic gradient of this predictive loss, offering a new variational perspective.
It is shown that the maximum hypergraph density of any multiclass hypothesis class is upper-bounded by its DS dimension, which proves a longstanding conjecture of Daniely and Shalev-Shwartz (2014) and determines the optimal dependence of the sample complexity on the DS dimension for multiclass as well as list learning.
This analysis identifies a common obstruction: cheap nuisance interpolation causes the refit to underweight the truly predictive coordinate, and an exact target-mass identity and a two-sign argument turn this effect into clipped prediction loss.
Huibo Xu, Shi Fu, Qixin Zhang et al.· 0 citations
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A novel characteristic of dataset heterogeneity is introduced by employing the norm of the difference between the maximum and minimum points in the classical terms of functional analysis.
Maksim V.Kukushkin, M. Arbatskiy, D. Balandin et al.· 0 citations
This paper proposes debiased inference with multiple imperfect measurements (DMM), a framework that combines multiple error-prone AI measurements to enable valid downstream inference without gold-standard labels and proves that the DMM estimator is consistent and asymptotically normal.
A machine-learning approach called CheMeleon provides a practical way to develop new drugs or other molecules with much less detailed, expensive experimentation.
