# Deeper Connections between Neural Networks and Gaussian Processes Speed-up Active Learning

arXiv: https://arxiv.org/abs/1902.10350 · HTML: https://mlsanity.com/paper/1902.10350/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Feb 2019.

## Built on by (most cited first, 4 of 4)

- [The Neural Process Family: Survey, Applications and Perspectives](https://mlsanity.com/paper/2209.00517/) (2022)
- [Querying Easily Flip-flopped Samples for Deep Active Learning](https://mlsanity.com/paper/2401.09787/) (2024)
- [Scalable Batch Acquisition for Deep Bayesian Active Learning](https://mlsanity.com/paper/2301.05490/) (2023)
- [Surrogate uncertainty estimation for your time series forecasting black-box: learn when to trust](https://mlsanity.com/paper/2302.02834/) (2023)

## Abstract (arXiv)

Active learning methods for neural networks are usually based on greedy criteria which ultimately give a single new design point for the evaluation. Such an approach requires either some heuristics to sample a batch of design points at one active learning iteration, or retraining the neural network after adding each data point, which is computationally inefficient. Moreover, uncertainty estimates for neural networks sometimes are overconfident for the points lying far from the training sample. In this work we propose to approximate Bayesian neural networks (BNN) by Gaussian processes, which allows us to update the uncertainty estimates of predictions efficiently without retraining the neural network, while avoiding overconfident uncertainty prediction for out-of-sample points. In a series of experiments on real-world data including large-scale problems of chemical and physical modeling, we show superiority of the proposed approach over the state-of-the-art methods.