Deep Epidemiological Modeling by Black-box Knowledge Distillation: An Accurate Deep Learning Model for COVID-19

• URL: http://arxiv.org/abs/2101.10280v1
• Date: Wed, 20 Jan 2021 19:49:00 GMT
• Title: Deep Epidemiological Modeling by Black-box Knowledge Distillation: An Accurate Deep Learning Model for COVID-19
• Authors: Dongdong Wang, Shunpu Zhang, and Liqiang Wang
• Abstract summary: We propose a novel deep learning approach using black-box knowledge distillation for both accurate and efficient transmission dynamics prediction. We use simulated observation sequences to query the simulation system to retrieve simulated projection sequences as knowledge. Finally, we train a student deep neural network with the retrieved and mixed observation-projection sequences for practical use.
• Score: 16.442483223157975
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: An accurate and efficient forecasting system is imperative to the prevention of emerging infectious diseases such as COVID-19 in public health. This system requires accurate transient modeling, lower computation cost, and fewer observation data. To tackle these three challenges, we propose a novel deep learning approach using black-box knowledge distillation for both accurate and efficient transmission dynamics prediction in a practical manner. First, we leverage mixture models to develop an accurate, comprehensive, yet impractical simulation system. Next, we use simulated observation sequences to query the simulation system to retrieve simulated projection sequences as knowledge. Then, with the obtained query data, sequence mixup is proposed to improve query efficiency, increase knowledge diversity, and boost distillation model accuracy. Finally, we train a student deep neural network with the retrieved and mixed observation-projection sequences for practical use. The case study on COVID-19 justifies that our approach accurately projects infections with much lower computation cost when observation data are limited.

Related papers

• An Investigation on Machine Learning Predictive Accuracy Improvement and Uncertainty Reduction using VAE-based Data Augmentation [2.517043342442487]
  Deep generative learning uses certain ML models to learn the underlying distribution of existing data and generate synthetic samples that resemble the real data. In this study, our objective is to evaluate the effectiveness of data augmentation using variational autoencoder (VAE)-based deep generative models. We investigated whether the data augmentation leads to improved accuracy in the predictions of a deep neural network (DNN) model trained using the augmented data.
  arXiv  Detail & Related papers  (2024-10-24T18:15:48Z)
• Diffusion posterior sampling for simulation-based inference in tall data settings [53.17563688225137]
  Simulation-based inference ( SBI) is capable of approximating the posterior distribution that relates input parameters to a given observation. In this work, we consider a tall data extension in which multiple observations are available to better infer the parameters of the model. We compare our method to recently proposed competing approaches on various numerical experiments and demonstrate its superiority in terms of numerical stability and computational cost.
  arXiv  Detail & Related papers  (2024-04-11T09:23:36Z)
• Inpainting Computational Fluid Dynamics with Deep Learning [8.397730500554047]
  An effective fluid data completion method reduces the required number of sensors in a fluid dynamics experiment. The ill-posed nature of the fluid data completion problem makes it prohibitively difficult to obtain a theoretical solution. We employ the vector quantization technique to map both complete and incomplete fluid data spaces onto discrete-valued lower-dimensional representations.
  arXiv  Detail & Related papers  (2024-02-27T03:44:55Z)
• Explainable Contrastive and Cost-Sensitive Learning for Cervical Cancer Classification [0.0]
  We first fine-tune five pre-trained CNNs and minimize the overall cost of misclassification. supervised contrastive learning is included to make the models more adept at capturing important features and patterns. The experimental results demonstrate the effectiveness of the developed system, achieving an accuracy of 97.29%.
  arXiv  Detail & Related papers  (2024-02-24T21:03:30Z)
• Neural parameter calibration and uncertainty quantification for epidemic forecasting [0.0]
  We apply a novel and powerful computational method to the problem of learning probability densities on contagion parameters. Using a neural network, we calibrate an ODE model to data of the spread of COVID-19 in Berlin in 2020. We show convergence of our method to the true posterior on a simplified SIR model of epidemics, and also demonstrate our method's learning capabilities on a reduced dataset.
  arXiv  Detail & Related papers  (2023-12-05T21:34:59Z)
• An Adversarial Active Sampling-based Data Augmentation Framework for Manufacturable Chip Design [55.62660894625669]
  Lithography modeling is a crucial problem in chip design to ensure a chip design mask is manufacturable. Recent developments in machine learning have provided alternative solutions in replacing the time-consuming lithography simulations with deep neural networks. We propose a litho-aware data augmentation framework to resolve the dilemma of limited data and improve the machine learning model performance.
  arXiv  Detail & Related papers  (2022-10-27T20:53:39Z)
• Neural Posterior Estimation with Differentiable Simulators [58.720142291102135]
  We present a new method to perform Neural Posterior Estimation (NPE) with a differentiable simulator. We demonstrate how gradient information helps constrain the shape of the posterior and improves sample-efficiency.
  arXiv  Detail & Related papers  (2022-07-12T16:08:04Z)
• Convolutional generative adversarial imputation networks for spatio-temporal missing data in storm surge simulations [86.5302150777089]
  Generative Adversarial Imputation Nets (GANs) and GAN-based techniques have attracted attention as unsupervised machine learning methods. We name our proposed method as Con Conval Generative Adversarial Imputation Nets (Conv-GAIN)
  arXiv  Detail & Related papers  (2021-11-03T03:50:48Z)
• Efficient training of lightweight neural networks using Online Self-Acquired Knowledge Distillation [51.66271681532262]
  Online Self-Acquired Knowledge Distillation (OSAKD) is proposed, aiming to improve the performance of any deep neural model in an online manner. We utilize k-nn non-parametric density estimation technique for estimating the unknown probability distributions of the data samples in the output feature space.
  arXiv  Detail & Related papers  (2021-08-26T14:01:04Z)
• Deep Bayesian Active Learning for Accelerating Stochastic Simulation [74.58219903138301]
  Interactive Neural Process (INP) is a deep active learning framework for simulations and with active learning approaches. For active learning, we propose a novel acquisition function, Latent Information Gain (LIG), calculated in the latent space of NP based models. The results demonstrate STNP outperforms the baselines in the learning setting and LIG achieves the state-of-the-art for active learning.
  arXiv  Detail & Related papers  (2021-06-05T01:31:51Z)
• Leveraging Vision and Kinematics Data to Improve Realism of Biomechanic Soft-tissue Simulation for Robotic Surgery [13.657060682152409]
  We investigate how live data acquired during any robotic endoscopic surgical procedure may be used to correct for inaccurate FEM simulation results. We use an open-source da Vinci Surgical System to probe a soft-tissue phantom and replay the interaction in simulation. We train the network to correct for the difference between the predicted mesh position and the measured point cloud.
  arXiv  Detail & Related papers  (2020-03-14T00:16:08Z)

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.