HyperFast: Instant Classification for Tabular Data
David Bonet Daniel Mas Montserrat Xavier Giró-i-Nieto Alexander G. Ioannidis
Training deep learning models and performing hyperparameter tuning can be
computationally demanding and time-consuming. Meanwhile, traditional machine
learning methods like gradient-boosting algorithms remain the preferred choice
for most tabular data applications, while neural network alternatives require
extensive hyperparameter tuning or work only in toy datasets under limited
settings. In this paper, we introduce HyperFast, a meta-trained hypernetwork
designed for instant classification of tabular data in a single forward pass.
HyperFast generates a task-specific neural network tailored to an unseen
dataset that can be directly used for classification inference, removing the
need for training a model. We report extensive experiments with OpenML and
genomic data, comparing HyperFast to competing tabular data neural networks,
traditional ML methods, AutoML systems, and boosting machines. HyperFast shows
highly competitive results, while being significantly faster. Additionally, our
approach demonstrates robust adaptability across a variety of classification
tasks with little to no fine-tuning, positioning HyperFast as a strong solution
for numerous applications and rapid model deployment. HyperFast introduces a
promising paradigm for fast classification, with the potential to substantially
decrease the computational burden of deep learning. Our code, which offers a
scikit-learn-like interface, along with the trained HyperFast model, can be
found at https://github.com/AI-sandbox/HyperFast.