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HyperFast: Instant Classification for Tabular Data (2402.14335v1)

Published 22 Feb 2024 in cs.LG, cs.AI, and stat.ML

Abstract: 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.

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Citations (7)
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Summary

  • The paper introduces HyperFast, a meta-trained hypernetwork that instantly generates task-specific weights for classification without iterative tuning.
  • It divides the process into meta-training and meta-testing stages, efficiently adapting to a wide range of tabular datasets.
  • Evaluations on OpenML and genomic datasets demonstrate competitive accuracy and markedly faster inference compared to traditional methods.

HyperFast: A Novel Meta-Trained Hypernetwork for Instant Tabular Data Classification

Introduction to HyperFast

Traditional ML methods for supervised classification involve a cumbersome process of training and hyperparameter tuning that can be computationally expensive and time-consuming. This is particularly challenging when dealing with tabular data, where gradient-boosting algorithms still dominate due to their performance, despite the allure of neural network alternatives. However, most neural network approaches require extensive hyperparameter tweaking and are often only viable for small, constrained datasets. Addressing these limitations, this paper introduces HyperFast, a meta-trained hypernetwork designed for instant classification of tabular data.

HyperFast aims to sidestep the tedious training phase by generating a task-specific neural network tailored to an unseen dataset, capable of performing classification inference in a single forward pass. This method significantly reduces the computational overhead typically associated with deep learning while exhibiting adaptability across a wide spectrum of classification tasks without the need for fine-tuning.

Methodology

At its core, HyperFast leverages a hypernetwork - a network that predicts the weights of another network (referred to as the "main network") to perform classification tasks. The process is divided into two stages: during the "meta-training" stage, the hypernetwork learns to predict the optimal set of weights for the main network across a diverse set of datasets. In the "meta-testing" or inference stage, given a new, unseen dataset, HyperFast utilizes information from a support set (features and labels) to generate weights for the main model, which then classifies test samples from the dataset.

One of the key features of HyperFast is its adaptability to datasets of various sizes, making it advantageous over existing pre-trained models that are limited to small datasets with restricted features and classes. By designing the hypernetwork to work with both large and small datasets, HyperFast fills a notable gap in the field of pre-trained models for tabular data.

Performance and Comparison

HyperFast was extensively tested against multiple datasets from OpenML and genomic data, comparing its performance with traditional machine learning methods, AutoML systems, boosting machines, and other neural networks for tabular data. The results demonstrated HyperFast's competitive performance, often outperforming other methods in terms of speed while still delivering robust and competitive accuracy.

Implications and Future Directions

The introduction of HyperFast represents a significant step forward in the field of machine learning, particularly in applications requiring rapid deployment of models, such as healthcare diagnostics and real-time data streaming. Its ability to generate functional, trained models in a single forward pass without the need for extensive hyperparameter optimization or fine-tuning unlocks new possibilities for swift model deployment across various domains.

Looking ahead, expanding HyperFast to accommodate regression tasks and other data types beyond tabular, such as audio, 3D, and video data, could further broaden its applicability and impact. The potential to integrate HyperFast into federated learning scenarios, improve privacy aspects, and facilitate quick prototyping in research underscores its versatile value proposition.

In conclusion, HyperFast proposes a promising new paradigm for dealing with tabular data, emphasizing efficiency, speed, and adaptability. By streamlining the model deployment process, it opens up new avenues for leveraging machine learning in time-sensitive applications, marking a significant stride towards more accessible and efficient AI solutions.

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