Graph-based Reinforcement Learning for Active Learning in Real Time: An Application in Modeling River Networks

by   Xiaowei Jia, et al.

Effective training of advanced ML models requires large amounts of labeled data, which is often scarce in scientific problems given the substantial human labor and material cost to collect labeled data. This poses a challenge on determining when and where we should deploy measuring instruments (e.g., in-situ sensors) to collect labeled data efficiently. This problem differs from traditional pool-based active learning settings in that the labeling decisions have to be made immediately after we observe the input data that come in a time series. In this paper, we develop a real-time active learning method that uses the spatial and temporal contextual information to select representative query samples in a reinforcement learning framework. To reduce the need for large training data, we further propose to transfer the policy learned from simulation data which is generated by existing physics-based models. We demonstrate the effectiveness of the proposed method by predicting streamflow and water temperature in the Delaware River Basin given a limited budget for collecting labeled data. We further study the spatial and temporal distribution of selected samples to verify the ability of this method in selecting informative samples over space and time.



There are no comments yet.


page 1

page 2

page 3

page 4


The Effectiveness of Variational Autoencoders for Active Learning

The high cost of acquiring labels is one of the main challenges in deplo...

On Discarding, Caching, and Recalling Samples in Active Learning

We address challenges of active learning under scarce informational reso...

Active Learning-based Classification in Automated Connected Vehicles

Machine learning has emerged as a promising paradigm for enabling connec...

Active Learning for Structured Prediction from Partially Labeled Data

We propose a general purpose active learning algorithm for structured pr...

Human-In-The-Loop Document Layout Analysis

Document layout analysis (DLA) aims to divide a document image into diff...

Reducing Annotating Load: Active Learning with Synthetic Images in Surgical Instrument Segmentation

Accurate instrument segmentation in endoscopic vision of robot-assisted ...

Robust Active Learning for Electrocardiographic Signal Classification

The classification of electrocardiographic (ECG) signals is a challengin...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.