transformers in tabular tiny survey 2024.4.8

推荐阅读

TabLLM

pmlr2023,

Few-shot Classification of Tabular Data with Large Language Models

方法

使用把tabular数据序列化成文字的方法进行classification。
使用的序列化方法有几个,有人工也有AI生成。

效果

做few shot learning的效果
看上去一般。

TransTab

Learning Transferable Tabular Transformers Across Tables

方法

属于transfer learning的方法。对category、binary和numeric值进行embedding后再进行transformers最后进行classification。

使用场景

原文:

  • S(1) Transfer learning . We collect data tables from multiple cancer trials for testing the efficacy

of the same drug on different patients. These tables were designed independently with overlapping

columns. How do we learn ML models for one trial by leveraging tables from all trials?

  • S(2) Incremental learning . Additional columns might be added over time. For example, additional

features are collected across different trial phases. How do we update the ML models using tables

from all trial phases?

  • S(3) Pretraining+Finetuning . The trial outcome label (e.g., mortality) might not be always available

from all table sources. Can we benefit pretraining on those tables without labels? How do we finetune

the model on the target table with labels?

  • S(4) Zero-shot inference . We model the drug efficacy based on our trial records. The next step is to

conduct inference with the model to find patients that can benefit from the drug. However, patient

tables do not share the same columns as trial tables so direct inference is not possible.

效果

具体看原文吧,与当时的baseline比有提升。

MET

Masked Encoding for Tabular Data

tabtransformer

2020年,arxiv,TabTransformer: Tabular Data Modeling Using Contextual Embeddings

方法

transformer无监督训练,mlp监督训练。

原文

we introduce a pre-training procedure to train the Transformer layers using unlabeled data . This is followed by fine-tuning of the pre-trained Transformer layers along with the top MLP layer using the labeled data

效果

跟mlp

跟其他模型

tabnet

2020, arxiv,Google Cloud AI,Attentive Interpretable Tabular Learning, 封装的非常好,都可以当工具包使用了。

方法

跟transformer没关系的。
feature selection用的是17年的某个选择模型,最后agg一下做predict。

相关推荐
我有医保我先冲15 分钟前
AI大模型与人工智能的深度融合:重构医药行业数字化转型的底层逻辑
人工智能·重构
pen-ai39 分钟前
【NLP】15. NLP推理方法详解 --- 动态规划:序列标注,语法解析,共同指代
人工智能·自然语言处理·动态规划
Chaos_Wang_1 小时前
NLP高频面试题(二十九)——大模型解码常见参数解析
人工智能·自然语言处理
Acrelhuang1 小时前
8.3MW屋顶光伏+光储协同:上海汽车变速器低碳工厂的能源革命-安科瑞黄安南
大数据·数据库·人工智能·物联网·数据库开发
区块链蓝海1 小时前
沉浸式体验测评|AI Ville:我在Web3小镇“生活”了一周
人工智能·web3·生活
whaosoft-1431 小时前
51c自动驾驶~合集15
人工智能
花楸树1 小时前
前端搭建 MCP Client(Web版)+ Server + Agent 实践
前端·人工智能
用户87612829073741 小时前
前端ai对话框架semi-design-vue
前端·人工智能
量子位1 小时前
稚晖君刚挖来的 90 后机器人大牛:逆袭履历堪比爽文男主
人工智能·llm