# Learning What Data to Learn

arXiv: https://arxiv.org/abs/1702.08635 · HTML: https://mlsanity.com/paper/1702.08635/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Feb 2017.

## Built on by (most cited first, 21 of 21)

- [MEST: Accurate and Fast Memory-Economic Sparse Training Framework on the Edge](https://mlsanity.com/paper/2110.14032/) (2021)
- [Simfluence: Modeling the Influence of Individual Training Examples by Simulating Training Runs](https://mlsanity.com/paper/2303.08114/) (2023)
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- [Seeking the Shape of Sound: An Adaptive Framework for Learning Voice-Face Association](https://mlsanity.com/paper/2103.07293/) (2021)
- [CLASTER: Clustering with Reinforcement Learning for Zero-Shot Action Recognition](https://mlsanity.com/paper/2101.07042/) (2021)
- [Layer Freezing & Data Sieving: Missing Pieces of a Generic Framework for Sparse Training](https://mlsanity.com/paper/2209.11204/) (2022)
- [Towards High-Quality and Efficient Video Super-Resolution via Spatial-Temporal Data Overfitting](https://mlsanity.com/paper/2303.08331/) (2023)
- [A Survey of Dataset Refinement for Problems in Computer Vision Datasets](https://mlsanity.com/paper/2210.11717/) (2022)
- [AutoSampling: Search for Effective Data Sampling Schedules](https://mlsanity.com/paper/2105.13695/) (2021)
- [MERCURY: Accelerating DNN Training By Exploiting Input Similarity](https://mlsanity.com/paper/2110.14904/) (2021)
- [Efficient Backpropagation with Variance-Controlled Adaptive Sampling](https://mlsanity.com/paper/2402.17227/) (2024)
- [A Bayesian Approach to Data Point Selection](https://mlsanity.com/paper/2411.03768/) (2024)
- [Stop Filtering: Multi-View Attribute-Enhanced Dialogue Learning](https://mlsanity.com/paper/2205.11206/) (2022)
- [Importance Sampling for Stochastic Gradient Descent in Deep Neural Networks](https://mlsanity.com/paper/2303.16529/) (2023)
- [Relation Extraction in underexplored biomedical domains: A diversity-optimised sampling and synthetic data generation approach](https://mlsanity.com/paper/2311.06364/) (2023)
- [Fair Bayesian Data Selection via Generalized Discrepancy Measures](https://mlsanity.com/paper/2511.07032/) (2025)
- [A microservice-based framework for exploring data selection in cross-building knowledge transfer](https://mlsanity.com/paper/2102.12970/) (2021)
- [Learning Continuous Cost-to-Go Functions for Non-holonomic Systems](https://mlsanity.com/paper/2103.11168/) (2021)
- [Grad Queue : A probabilistic framework to reinforce sparse gradients](https://mlsanity.com/paper/2404.16917/) (2024)
- [Data Overfitting for On-Device Super-Resolution with Dynamic Algorithm and Compiler Co-Design](https://mlsanity.com/paper/2407.02813/) (2024)
- [Learning What to Fail On: Failure-Mode Contextual Bandits for Adversarial Data Curation](https://mlsanity.com/paper/2608.18681/) (2026)

## Abstract (arXiv)

Machine learning is essentially the sciences of playing with data. An adaptive data selection strategy, enabling to dynamically choose different data at various training stages, can reach a more effective model in a more efficient way. In this paper, we propose a deep reinforcement learning framework, which we call emphNeural Data Filter (NDF), to explore automatic and adaptive data selection in the training process. In particular, NDF takes advantage of a deep neural network to adaptively select and filter important data instances from a sequential stream of training data, such that the future accumulative reward (e.g., the convergence speed) is maximized. In contrast to previous studies in data selection that is mainly based on heuristic strategies, NDF is quite generic and thus can be widely suitable for many machine learning tasks. Taking neural network training with stochastic gradient descent (SGD) as an example, comprehensive experiments with respect to various neural network modeling (e.g., multi-layer perceptron networks, convolutional neural networks and recurrent neural networks) and several applications (e.g., image classification and text understanding) demonstrate that NDF powered SGD can achieve comparable accuracy with standard SGD process by using less data and fewer iterations.