Differentiable and Scalable Generative Adversarial Models for Data Imputation

• URL: http://arxiv.org/abs/2201.03202v1
• Date: Mon, 10 Jan 2022 08:03:14 GMT
• Title: Differentiable and Scalable Generative Adversarial Models for Data Imputation
• Authors: Yangyang Wu and Jun Wang and Xiaoye Miao and Wenjia Wang and Jianwei Yin
• Abstract summary: SCIS consists of two modules, differentiable imputation modeling (DIM) and sample size estimation (SSE) Experiments upon several real-life large-scale datasets demonstrate that, our proposed system can accelerate the generative adversarial model training by 7.1x.
• Score: 24.111493826345082
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Data imputation has been extensively explored to solve the missing data problem. The dramatically increasing volume of incomplete data makes the imputation models computationally infeasible in many real-life applications. In this paper, we propose an effective scalable imputation system named SCIS to significantly speed up the training of the differentiable generative adversarial imputation models under accuracy-guarantees for large-scale incomplete data. SCIS consists of two modules, differentiable imputation modeling (DIM) and sample size estimation (SSE). DIM leverages a new masking Sinkhorn divergence function to make an arbitrary generative adversarial imputation model differentiable, while for such a differentiable imputation model, SSE can estimate an appropriate sample size to ensure the user-specified imputation accuracy of the final model. Extensive experiments upon several real-life large-scale datasets demonstrate that, our proposed system can accelerate the generative adversarial model training by 7.1x. Using around 7.6% samples, SCIS yields competitive accuracy with the state-of-the-art imputation methods in a much shorter computation time.

Related papers

• CoSTI: Consistency Models for (a faster) Spatio-Temporal Imputation [0.0]
  CoSTI employs Consistency Training to achieve comparable imputation quality to DDPMs while drastically reducing inference times. We evaluate CoSTI across multiple datasets and missing data scenarios, demonstrating up to a 98% reduction in imputation time with performance par with diffusion-based models.
  arXiv  Detail & Related papers  (2025-01-31T18:14:28Z)
• Computation-Aware Gaussian Processes: Model Selection And Linear-Time Inference [55.150117654242706]
  We show that model selection for computation-aware GPs trained on 1.8 million data points can be done within a few hours on a single GPU. As a result of this work, Gaussian processes can be trained on large-scale datasets without significantly compromising their ability to quantify uncertainty.
  arXiv  Detail & Related papers  (2024-11-01T21:11:48Z)
• Towards Theoretical Understandings of Self-Consuming Generative Models [56.84592466204185]
  This paper tackles the emerging challenge of training generative models within a self-consuming loop. We construct a theoretical framework to rigorously evaluate how this training procedure impacts the data distributions learned by future models. We present results for kernel density estimation, delivering nuanced insights such as the impact of mixed data training on error propagation.
  arXiv  Detail & Related papers  (2024-02-19T02:08:09Z)
• Stable Training of Probabilistic Models Using the Leave-One-Out Maximum Log-Likelihood Objective [0.7373617024876725]
  Kernel density estimation (KDE) based models are popular choices for this task, but they fail to adapt to data regions with varying densities. An adaptive KDE model is employed to circumvent this, where each kernel in the model has an individual bandwidth. A modified expectation-maximization algorithm is employed to accelerate the optimization speed reliably.
  arXiv  Detail & Related papers  (2023-10-05T14:08:42Z)
• Neural parameter calibration for large-scale multi-agent models [0.7734726150561089]
  We present a method to retrieve accurate probability densities for parameters using neural equations. The two combined create a powerful tool that can quickly estimate densities on model parameters, even for very large systems.
  arXiv  Detail & Related papers  (2022-09-27T17:36:26Z)
• Mixed Effects Neural ODE: A Variational Approximation for Analyzing the Dynamics of Panel Data [50.23363975709122]
  We propose a probabilistic model called ME-NODE to incorporate (fixed + random) mixed effects for analyzing panel data. We show that our model can be derived using smooth approximations of SDEs provided by the Wong-Zakai theorem. We then derive Evidence Based Lower Bounds for ME-NODE, and develop (efficient) training algorithms.
  arXiv  Detail & Related papers  (2022-02-18T22:41:51Z)
• Data-driven Uncertainty Quantification in Computational Human Head Models [0.6745502291821954]
  Modern biofidelic head model simulations are associated with very high computational cost and high-dimensional inputs and outputs. In this study, a two-stage, data-driven manifold learning-based framework is proposed for uncertainty quantification (UQ) of computational head models. It is demonstrated that the surrogate models provide highly accurate approximations of the computational model while significantly reducing the computational cost.
  arXiv  Detail & Related papers  (2021-10-29T05:42:31Z)
• MoEfication: Conditional Computation of Transformer Models for Efficient Inference [66.56994436947441]
  Transformer-based pre-trained language models can achieve superior performance on most NLP tasks due to large parameter capacity, but also lead to huge computation cost. We explore to accelerate large-model inference by conditional computation based on the sparse activation phenomenon. We propose to transform a large model into its mixture-of-experts (MoE) version with equal model size, namely MoEfication.
  arXiv  Detail & Related papers  (2021-10-05T02:14:38Z)
• CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation [107.63407690972139]
  Conditional Score-based Diffusion models for Imputation (CSDI) is a novel time series imputation method that utilizes score-based diffusion models conditioned on observed data. CSDI improves by 40-70% over existing probabilistic imputation methods on popular performance metrics. In addition, C reduces the error by 5-20% compared to the state-of-the-art deterministic imputation methods.
  arXiv  Detail & Related papers  (2021-07-07T22:20:24Z)
• Closed-form Continuous-Depth Models [99.40335716948101]
  Continuous-depth neural models rely on advanced numerical differential equation solvers. We present a new family of models, termed Closed-form Continuous-depth (CfC) networks, that are simple to describe and at least one order of magnitude faster.
  arXiv  Detail & Related papers  (2021-06-25T22:08:51Z)

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.