DNF literals explanation.

NIAID Data Ecosystem2026-03-08 收录

规则学习

模型可解释性

数据链接：

https://figshare.com/articles/dataset/_DNF_literals_explanation_/986259 数据链接链接失效反馈

官方服务：

资源简介：

Note. *: when missing values present in the data, they are treated as a literal, but they are never selected in the DNF learning.

创建时间：

2014-04-03

hmao/rule_learning_data_v0_w_old_instruction

规则学习

--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: old_instruction dtype: string - name: prompt dtype: string - n

Hugging Face2023-10-01 更新90

Motor Demands Constrain Cognitive Rule Structures

运动-认知交互

规则学习

Study of human executive function focuses on our ability to represent cognitive rules independently of stimulus or response modality. However, recent findings suggest that executive functions cannot b

NIAID Data Ecosystem60

ANB-Ablation-Surrogates

消融实验

模型可解释性

Ablation surrogate models trained on different portions of the total dataset. Meant to reproduce results in Section. 4 of the paper.

Figshare2023-04-17 更新30

SolshineMisfit/nla-qwen2.5-7b-L20-results-famous-ads

模型可解释性

广告分析

该数据集名为著名广告标语——NLA激活分析（Qwen2.5-7B，第20层），旨在探究语言模型在读取著名广告标语时的内部表示。它使用自然语言自编码器（NLA）将Qwen2.5-7B-Instruct模型在第20层最后一个token的残差流激活解码为自然语言描述。数据集包含约146条著名广告标语（源自维基百科，附有品牌、年份和上下文信息），每条标语都对应一个由AV生成的英文解释（描述模型对该标语的表

Hugging Face2026-05-27 更新70

Importance coefficient and ranking of features in LDA model.

线性判别分析特征选择

模型可解释性

Importance coefficient and ranking of features in LDA model.

Figshare2022-03-24 更新50