1 Aronson JK, Heneghan C, Ferner RE (2020) Medical devices: definition, classification, and regulatory implications. Drug Saf 43(2):83–93. https://doi.org/10.1007/s40264-019-00878-3 2 Bianco C (2010) Integrating a ris...
1. Mathematics for Machine Learning Before mastering machine learning, it is important to understand the fundamental mathematical concepts that power these algorithms. Linear Algebra: This is crucial for understanding many algorithms, especially those used in deep learning. Key concepts include vectors, ...
[4] Ning Ding et al. Parameter-efficient Fine-tuning for Large-scale Pre-trained Language Models. Nature Machine Intelligence. [5] Neil Houlsby et al. Parameter-Efficient Transfer Learning for NLP. ICML 2020. [6] Edward Hu et al. LoRA: Low-Rank Adaptation of Large Language Models. ICLR 2...
Testing the Reliability of ChatGPT for Text Annotation and Classification: A Cautionary Remark ~ arxiv.org/pdf/2304.1108,指出直接用大模型做标注有问题,太敏感/不确定高。 "Genlangs" and Zipf's Law: Do languages generated by ChatGPT statistically look human? ~ arxiv.org/pdf/2304.1219,对比人和大...
Classification finetuning, the topic of this chapter, is a procedure you may already be familiar with if you have a background in machine learning -- it's similar to training aconvolutional networkto classify handwritten digits, for example ...
$schema: https://azuremlschemas.azureedge.net/promptflow/latest/Run.schema.json flow: <path_to_flow> data: <path_to_flow>/data.jsonl run: <id of web-classification flow run> column_mapping: groundtruth: ${data.answer} prediction: ${run.outputs.category} # define cloud resource #...
[8]El-Yaniv R. On the Foundations of Noise-free Selective Classification[J]. Journal of Machine Learning Research, 2010, 11(5). [9]Dhole K D, Gangal V, Gehrmann S, et al. Nl-augmenter: A framework for task-sensitive natural language augmentation[J]. arXiv preprint arXiv:2112.02721,...
这个可以理解为machine learning的范畴,也是实验室最近想要搞的方向。 其次,是否LLMs在reasoning task上的刷榜,就意味着它真正具有了causal discovery的能力了呢?我对因果推断的理解是,发现数据内在的因果关系。这就意味着,它不拘泥于task的形式:不仅是reasoning task,一般的classification,generation task中,也应包含着...
F1 Score: The F1 score balances precision and recall, often used in classification tasks. It provides a single metric that considers both false positives and false negatives. The F1 score is important for tasks like sentiment analysis and question answering, where both precision and recall are cri...
Code4Struct: Code Generation for Few-Shot Event Structure Prediction ACL 2023-07 GitHub Event Extraction as Question Generation and Answering ACL short 2023-07 GitHub Global Constraints with Prompting for Zero-Shot Event Argument Classification EACL Findings 2023-05 Prompt for extraction? PAIE: promp...