Learning with a limited number of labeled data is a central problem in real-world applications of machine learning, as it is often expensive to obtain annotations. To deal with the scarcity of labeled data, transfer learning is a conventional approach; it suggests to learn a transferable knowledge by training a neural network from multiple other sources. In this paper, we investigate transfer learning of tabular tasks, which has been less studied and successful in the literature, compared to other domains, e.g., vision and language. This is because tables are inherently heterogeneous, i.e., they contain different columns and feature spaces, making transfer learning difficult. On the other hand, recent advances in natural language processing suggest that the label scarcity issue can be mitigated by utilizing in-context learning capability of large language models (LLMs). Inspired by this and the fact that LLMs can also process tables within a unified language space, we ask whether LLMs can be effective for tabular transfer learning, in particular, under the scenarios where the source and target datasets are of different format. As a positive answer, we propose a novel tabular transfer learning framework, coined Prompt to Transfer (P2T), that utilizes unlabeled (or heterogeneous) source data with LLMs. Specifically, P2T identifies a column feature in a source dataset that is strongly correlated with a target task feature to create examples relevant to the target task, thus creating pseudo-demonstrations for prompts. Experimental results demonstrate that P2T outperforms previous methods on various tabular learning benchmarks, showing good promise for the important, yet underexplored tabular transfer learning problem. Code is available at https://github.com/jaehyun513/P2T.

Learn how to filter numbers, words, and just about anything in order to reduce bias in your dataset. Filtering through data is a very common transformation; it takes in a conditional and checks through all the data to keep only the data that meets the condition. By filtering you can improve your machine learning models by training on a specific subset of data to specialize the model, remove incorrect data and outliers, or prune biased features. To start off with what filtering does, it takes in a pile of data and turns it into something smaller and (hopefully) easier to work with. When you create a filter, you start from what will stay, not what will go.

The manufacturing industry exhibits some contradictions when it comes to automation and technology. On the one hand, manufacturing was among the first industries to integrate any type of technology more than a century ago, as companies incorporated tools to aid in the production process. On the other hand, manufacturing companies are risk-averse when it comes to implementing new technology quickly, mainly due to the large amount of capital and time at stake. However, according to a new report from Tractica, manufacturing companies are now incorporating artificial intelligence (AI) technology within their environments at a modest, yet steady, pace. The market intelligence firm forecasts that annual worldwide manufacturing sector investment in AI software, hardware, and services will increase from $2.9 billion in 2018 to $13.2 billion by 2025.

Customer segmentation is the practice of dividing a customer base into groups of individuals that are similar in specific ways relevant to marketing, such as age, gender, interests and spending habits. Unsupervised machine learning is a paradigm in machine learning where we build models without relying on labeled training data. One of the most common methods is clustering. You must have heard this term being used quite frequently. We mainly use it for data analysis where we want to find clusters in our data.