Researchers from Stanford University have introduced a new deep-learning framework for tabular data called PLATO, leveraging a knowledge graph (KG) for auxiliary domain information. It regulates a multilayer perceptron (MLP) by inferring weight vectors based on KG node similarity, addressing the challenge of high-dimensional features and limited samples. PLATO outperforms 13 baselines by up to 10.19% on six datasets. [48 words]
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Introducing PLATO: A Practical AI Solution for Tabular Data
A knowledge graph (KG) is a graph-based database that stores information as nodes and edges. On the other hand, a multilayer perceptron (MLP) is a type of neural network used in machine learning. MLPs are composed of interconnected nodes arranged in multiple layers. Each node obtains input from the previous layer and sends output to the next layer.
What is PLATO?
PLATO is a new machine learning model introduced by researchers from Stanford University. It leverages a KG to provide auxiliary domain information and regularizes an MLP by introducing an inductive bias that ensures similar nodes in the KG have equivalent weight vectors in the MLP’s first layer.
Practical Solutions and Value
PLATO addresses the challenge of machine learning models needing help with tabular datasets featuring many dimensions compared to samples. It enables deep learning for tabular data with features > samples and achieves superior performance on datasets with high-dimensional features and limited models.
PLATO surpasses 13 cutting-edge baselines by up to 10.19% across six datasets, demonstrating consistent performance and robustness in low-data regimes. It infers weight vectors for an MLP model, utilizing the graph as a prior for predictions on a distinct tabular dataset.
Key Features of PLATO
- Each input feature resembles a node in an auxiliary KG.
- PLATO regulates an MLP and achieves robust performance on tabular data with high-dimensional features and limited samples.
- The framework infers weight vectors based on KG node similarity, capturing the inductive bias that similar input features should share similar weight vectors.
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