# Targeted Deep Learning: Framework, Methods, and Applications

arXiv: https://arxiv.org/abs/2105.14052 · HTML: https://mlsanity.com/paper/2105.14052/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 47 references that are in the map.
- First posted on arXiv 28 May 2021.

## Builds on (most cited first, 15 of 15)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [A Comprehensive Survey on Transfer Learning](https://mlsanity.com/paper/1911.02685/) (2019)
- [Deep Learning for Classical Japanese Literature](https://mlsanity.com/paper/1812.01718/) (2018)
- [Stochastic Gradient Descent, Weighted Sampling, and the Randomized Kaczmarz algorithm](https://mlsanity.com/paper/1310.5715/) (2013)
- [Learning Invariant Feature Spaces to Transfer Skills with Reinforcement Learning](https://mlsanity.com/paper/1703.02949/) (2017)
- [Label-Free Supervision of Neural Networks with Physics and Domain Knowledge](https://mlsanity.com/paper/1609.05566/) (2016)
- [AdaNet: Adaptive Structural Learning of Artificial Neural Networks](https://mlsanity.com/paper/1607.01097/) (2016)
- [Personalized Machine Learning for Robot Perception of Affect and Engagement in Autism Therapy](https://mlsanity.com/paper/1802.01186/) (2018)
- [Personalization of Deep Learning](https://mlsanity.com/paper/1909.02803/) (2019)
- [An ensemble-based system for automatic screening of diabetic retinopathy](https://mlsanity.com/paper/1410.8576/) (2014)
- [Activation Functions in Artificial Neural Networks: A Systematic Overview](https://mlsanity.com/paper/2101.09957/) (2021)
- [Risk Bounds for Robust Deep Learning](https://mlsanity.com/paper/2009.06202/) (2020)
- [Tuning parameter calibration for prediction in personalized medicine](https://mlsanity.com/paper/1909.10635/) (2019)
- [Penalized angular regression for personalized predictions](https://mlsanity.com/paper/2001.09834/) (2020)

## Abstract (arXiv)

Deep learning systems are typically designed to perform for a wide range of test inputs. For example, deep learning systems in autonomous cars are supposed to deal with traffic situations for which they were not specifically trained. In general, the ability to cope with a broad spectrum of unseen test inputs is called generalization. Generalization is definitely important in applications where the possible test inputs are known but plentiful or simply unknown, but there are also cases where the possible inputs are few and unlabeled but known beforehand. For example, medicine is currently interested in targeting treatments to individual patients; the number of patients at any given time is usually small (typically one), their diagnoses/responses/... are still unknown, but their general characteristics (such as genome information, protein levels in the blood, and so forth) are known before the treatment. We propose to call deep learning in such applications targeted deep learning. In this paper, we introduce a framework for targeted deep learning, and we devise and test an approach for adapting standard pipelines to the requirements of targeted deep learning. The approach is very general yet easy to use: it can be implemented as a simple data-preprocessing step. We demonstrate on a variety of real-world data that our approach can indeed render standard deep learning faster and more accurate when the test inputs are known beforehand.