RSC-ZO achieves high-probability ε-stationary points for stochastic ZO optimization under weak-L_p heavy-tailed noise with Õ(d^{p/2(p-1)} ε^{-(3p-2)/(p-1)}) function queries.
Title resolution pending
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
representative citing papers
BLOOM is a 176B-parameter open-access multilingual language model trained on the ROOTS corpus that achieves competitive performance on benchmarks, with improved results after multitask prompted finetuning.
Adapted MelBERT MIP-only reaches 0.7281 F1 on Chinese token-level metaphor detection, outperforming RoBERTa and Qwen QLoRA, with all artifacts released for reproducibility.
citing papers explorer
-
Stochastic Zeroth-Order Optimization Under Heavy-Tailed Noise
RSC-ZO achieves high-probability ε-stationary points for stochastic ZO optimization under weak-L_p heavy-tailed noise with Õ(d^{p/2(p-1)} ε^{-(3p-2)/(p-1)}) function queries.
-
BLOOM: A 176B-Parameter Open-Access Multilingual Language Model
BLOOM is a 176B-parameter open-access multilingual language model trained on the ROOTS corpus that achieves competitive performance on benchmarks, with improved results after multitask prompted finetuning.
-
A Reproducible Multi-Architecture Baseline for Token-Level Chinese Metaphor Identification under the MIPVU Framework
Adapted MelBERT MIP-only reaches 0.7281 F1 on Chinese token-level metaphor detection, outperforming RoBERTa and Qwen QLoRA, with all artifacts released for reproducibility.