Zipf
No mentions found
This entity hasn't been tracked yet, or Iris is still building its knowledge base.
Related Articles from SNS
Mediating students' empathy development through play
Mediating students' empathy development through play Sadie Harley Scientific Editor Andrew Zinin Lead Editor Playing a card game can support empathy development in college classrooms, according to a new study led by researchers in Penn State University Libraries' Teaching and Learning with Technology (TLT). The study, "Mediating Students' Empathy Development Through Play," was published in the Journal of Play in Adulthood. It assesses the Inclusive and Multicultural Perspectives with Action,...
SPECTRA: Synthetic IR Test Collections with Relevance Oracles and Controlled Distractor Diagnostics
Announce Type: new Abstract: Scalable information retrieval testing needs corpora that are large enough to stress index construction, ranking latency, query routing, and evaluation tooling, yet human-judged test collections remain expensive and may be unavailable when documents are private or still under design. This paper introduces SPECTRA, a reproducible framework for generating synthetic text corpora and retrieval test collections through a separation of latent topical structure, surface...
Why Muon Outperforms Adam: A Curvature Perspective
arXiv:2606.04662v1 Announce Type: new Abstract: Muon improves training efficiency over Adam in large language-model training by about two times, but the local geometric source of this advantage remains unclear. Our work takes a first step toward demystifying Muon's superiority over Adam from a curvature perspective. First, we apply a second-order Taylor approximation to the training landscape and show that Muon achieves a larger one-step loss decrease than Adam at matched validation loss.
Workload-Aware Autotuning of Block Size in Square-Root Decomposition
arXiv:2606.06145v1 Announce Type: new Abstract: The textbook choice B=sqrt(n) for square-root decomposition is asymptotically natural, but it is not always the fastest implementation choice. We study block-size autotuning as a reproducible algorithm-engineering problem and show that a learned workload model can improve over fixed sqrt(n) on the tested implementation. Under repeated grouped cross-validation, the best policy is a full-feature KNN-9 model that reduces mean regret from 1.2882 to...