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

• URL: http://arxiv.org/abs/2010.14000v2
• Date: Tue, 8 Dec 2020 18:04:58 GMT
• Title: Graph-based Reinforcement Learning for Active Learning in Real Time: An Application in Modeling River Networks
• Authors: Xiaowei Jia, Beiyu Lin, Jacob Zwart, Jeffrey Sadler, Alison Appling, Samantha Oliver, Jordan Read
• Abstract summary: 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. 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.
• Score: 2.8631830115500394
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Downstream-Pretext Domain Knowledge Traceback for Active Learning [138.02530777915362]
  We propose a downstream-pretext domain knowledge traceback (DOKT) method that traces the data interactions of downstream knowledge and pre-training guidance. DOKT consists of a traceback diversity indicator and a domain-based uncertainty estimator. Experiments conducted on ten datasets show that our model outperforms other state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-07-20T01:34:13Z)
• A Survey on Data Selection for Language Models [151.6210632830082]
  Data selection methods aim to determine which data points to include in a training dataset. Deep learning is mostly driven by empirical evidence and experimentation on large-scale data is expensive. Few organizations have the resources for extensive data selection research.
  arXiv  Detail & Related papers  (2024-02-26T18:54:35Z)
• The Why, When, and How to Use Active Learning in Large-Data-Driven 3D Object Detection for Safe Autonomous Driving: An Empirical Exploration [1.2815904071470705]
  entropy querying is a promising strategy for selecting data that enhances model learning in resource-constrained environments. Our findings suggest that entropy querying is a promising strategy for selecting data that enhances model learning in resource-constrained environments.
  arXiv  Detail & Related papers  (2024-01-30T00:14:13Z)
• Learn to Unlearn for Deep Neural Networks: Minimizing Unlearning Interference with Gradient Projection [56.292071534857946]
  Recent data-privacy laws have sparked interest in machine unlearning. Challenge is to discard information about the forget'' data without altering knowledge about remaining dataset. We adopt a projected-gradient based learning method, named as Projected-Gradient Unlearning (PGU) We provide empirically evidence to demonstrate that our unlearning method can produce models that behave similar to models retrained from scratch across various metrics even when the training dataset is no longer accessible.
  arXiv  Detail & Related papers  (2023-12-07T07:17:24Z)
• Towards Free Data Selection with General-Purpose Models [71.92151210413374]
  A desirable data selection algorithm can efficiently choose the most informative samples to maximize the utility of limited annotation budgets. Current approaches, represented by active learning methods, typically follow a cumbersome pipeline that iterates the time-consuming model training and batch data selection repeatedly. FreeSel bypasses the heavy batch selection process, achieving a significant improvement in efficiency and being 530x faster than existing active learning methods.
  arXiv  Detail & Related papers  (2023-09-29T15:50:14Z)
• Exploring Data Redundancy in Real-world Image Classification through Data Selection [20.389636181891515]
  Deep learning models often require large amounts of data for training, leading to increased costs. We present two data valuation metrics based on Synaptic Intelligence and gradient norms, respectively, to study redundancy in real-world image data. Online and offline data selection algorithms are then proposed via clustering and grouping based on the examined data values.
  arXiv  Detail & Related papers  (2023-06-25T03:31:05Z)
• Temporal Output Discrepancy for Loss Estimation-based Active Learning [65.93767110342502]
  We present a novel deep active learning approach that queries the oracle for data annotation when the unlabeled sample is believed to incorporate high loss. Our approach achieves superior performances than the state-of-the-art active learning methods on image classification and semantic segmentation tasks.
  arXiv  Detail & Related papers  (2022-12-20T19:29:37Z)
• Stream-based active learning with linear models [0.7734726150561089]
  In production, instead of performing random inspections to obtain product information, labels are collected by evaluating the information content of the unlabeled data. We propose a new strategy for the stream-based scenario, where instances are sequentially offered to the learner. The iterative aspect of the decision-making process is tackled by setting a threshold on the informativeness of the unlabeled data points.
  arXiv  Detail & Related papers  (2022-07-20T13:15:23Z)
• Transfer learning to improve streamflow forecasts in data sparse regions [0.0]
  We study the methodology behind Transfer Learning (TL) through fine-tuning and parameter transferring for better generalization performance of streamflow prediction in data-sparse regions. We propose a standard recurrent neural network in the form of Long Short-Term Memory (LSTM) to fit on a sufficiently large source domain dataset. We present a methodology to implement transfer learning approaches for hydrologic applications by separating the spatial and temporal components of the model and training the model to generalize.
  arXiv  Detail & Related papers  (2021-12-06T14:52:53Z)
• Robust Event Classification Using Imperfect Real-world PMU Data [58.26737360525643]
  We study robust event classification using imperfect real-world phasor measurement unit (PMU) data. We develop a novel machine learning framework for training robust event classifiers.
  arXiv  Detail & Related papers  (2021-10-19T17:41:43Z)
• Message Passing Adaptive Resonance Theory for Online Active Semi-supervised Learning [30.19936050747407]
  We propose Message Passing Adaptive Resonance Theory (MPART) for online active semi-supervised learning. MPART infers the class of unlabeled data and selects informative and representative samples through message passing between nodes on the topological graph. We evaluate our model with comparable query selection strategies and frequencies, showing that MPART significantly outperforms the competitive models in online active learning environments.
  arXiv  Detail & Related papers  (2020-12-02T14:14:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.