# Robust and Active Learning for Deep Neural Network Regression

arXiv: https://arxiv.org/abs/2107.13124 · HTML: https://mlsanity.com/paper/2107.13124/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 8 references that are in the map.
- First posted on arXiv 28 Jul 2021.

## Builds on (most cited first, 5 of 5)

- [An overview of gradient descent optimization algorithms](https://mlsanity.com/paper/1609.04747/) (2016)
- [A Survey of Deep Active Learning](https://mlsanity.com/paper/2009.00236/) (2020)
- [A Comprehensive Analysis of Deep Regression](https://mlsanity.com/paper/1803.08450/) (2018)
- [Active Learning for Regression Using Greedy Sampling](https://mlsanity.com/paper/1808.04245/) (2018)
- [Dropout-based Active Learning for Regression](https://mlsanity.com/paper/1806.09856/) (2018)

## Built on by (most cited first, 1 of 1)

- [Backdoor Attack and Defense for Deep Regression](https://mlsanity.com/paper/2109.02381/) (2021)

## Abstract (arXiv)

We describe a gradient-based method to discover local error maximizers of a deep neural network (DNN) used for regression, assuming the availability of an "oracle" capable of providing real-valued supervision (a regression target) for samples. For example, the oracle could be a numerical solver which, operationally, is much slower than the DNN. Given a discovered set of local error maximizers, the DNN is either fine-tuned or retrained in the manner of active learning.