未使用分类
来自Big Physics
以下分类页面实际存在，即使没有其它页面或分类利用它们。
下面显示区间#41至#90的50条结果。
查看（前50个 | 后50个）（20 | 50 | 100 | 250 | 500）
- GPT等当前人工智能的知识层次人才层次
- HEM中心性
- High-Precision Extraction of Emerging Concepts from Scientific Literature
- Highway
- Hijack
- Hotness Tracing
- Hybrid neural tagging model for open relation extraction
- IESS网站部署和更新
- IOFactor微扰计算
- Identifying Meaningful Citations
- Integer
- Integrate
- Inventor-patent-technology network
- Inverse Ising Inference Using All the Data
- Invitation to scientometrics
- Jacker
- Jacklight
- Joint Extraction of Entities and Relations Based on a Novel Decomposition Strategy
- Lynkage概念地图软件
- Lynkage的实践
- Lynkage的测试
- Markov过程的阶和转移矩阵的计算程序
- Mathematical Thought from Ancient to Modern Times
- Measurement of risk attitude
- Measuring Meaningful Learning Experience: Confirmatory Factor Analysis
- Measuring academic influence: Not all citations are equal
- Measuring prerequisite relations among concepts
- Modeling the competing phase transition pathways in nanoscale olivine electrodes
- Multilevel development of cognitive abilities in an artificial neural network
- NER以及RE的负采样方法
- Neural Representations of Physics Concepts
- Newton第三定律
- Newton第二定律
- PaperRobot: Incremental Draft Generation of Scientific Ideas
- Poincaré Embeddings for Learning Hierarchical Representations
- Publication-patent-economics network
- P值和样本大小的关系
- S2ORC: The Semantic Scholar Open Research Corpus
- SPECTER: Document-level Representation Learning using Citation-informed Transformers
- San Francisco DORA
- Sci-Tech Linkage
- Sci2100第一次讨论纪要
- SciBERT: A Pretrained Language Model for Scientific Text
- SciREX: A Challenge Dataset for Document-Level Information Extraction
- ScienceBeam: Using open technology to extract knowledge from research PDFs
- ScienceWISE: Topic Modeling over Scientific Literature Networks
- Scihub-Libgen资源
- ScispaCy: Fast and Robust Models for Biomedical Natural Language Processing
- Simultaneous and selective inference: Current successes and future challenges
- Span-based Joint Entity and Relation Extraction withTransformer Pre-training
