Rényi Neural Processes

• URL: http://arxiv.org/abs/2405.15991v3
• Date: Tue, 03 Jun 2025 13:31:34 GMT
• Title: Rényi Neural Processes
• Authors: Xuesong Wang, He Zhao, Edwin V. Bonilla,
• Abstract summary: We show that Neural Processes enforce parameterization coupling between the conditional prior model and the posterior model.<n>We propose R'enyi Neural Processes (RNP), a method that replaces the standard KL divergence with the R'enyi divergence.<n>We show significant performance improvements of RNPs in real-world problems.
• Score: 14.11793373584558
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Neural Processes (NPs) are deep probabilistic models that represent stochastic processes by conditioning their prior distributions on a set of context points. Despite their advantages in uncertainty estimation for complex distributions, NPs enforce parameterization coupling between the conditional prior model and the posterior model. We show that this coupling amounts to prior misspecification and revisit the NP objective to address this issue. More specifically, we propose R\'enyi Neural Processes (RNP), a method that replaces the standard KL divergence with the R\'enyi divergence, dampening the effects of the misspecified prior during posterior updates. We validate our approach across multiple benchmarks including regression and image inpainting tasks, and show significant performance improvements of RNPs in real-world problems. Our extensive experiments show consistently better log-likelihoods over state-of-the-art NP models.

Related papers

• Diffusion Models for Solving Inverse Problems via Posterior Sampling with Piecewise Guidance [52.705112811734566]
  A novel diffusion-based framework is introduced for solving inverse problems using a piecewise guidance scheme.<n>The proposed method is problem-agnostic and readily adaptable to a variety of inverse problems.<n>The framework achieves a reduction in inference time of (25%) for inpainting with both random and center masks, and (23%) and (24%) for (4times) and (8times) super-resolution tasks.
  arXiv  Detail & Related papers  (2025-07-22T19:35:14Z)
• Exploring Pseudo-Token Approaches in Transformer Neural Processes [0.0]
  We introduce the Induced Set Attentive Neural Processes (ISANPs)<n>ISANPs perform competitively with Transformer Neural Processes (TNPs) and often surpass state-of-the-art models in 1D regression, image completion, contextual bandits, and Bayesian optimization.<n>ISANPs offer a tunable balance between performance and computational complexity, which scale well to larger datasets.
  arXiv  Detail & Related papers  (2025-04-19T22:47:59Z)
• Fixed-Mean Gaussian Processes for Post-hoc Bayesian Deep Learning [11.22428369342346]
  We introduce a novel family of sparse variational Gaussian processes (GPs), where the posterior mean is fixed to any continuous function when using a universal kernel. Specifically, we fix the mean of this GP to the output of the pre-trained DNN, allowing our approach to effectively fit the GP's predictive variances to estimate the prediction uncertainty. Experimental results demonstrate that FMGP improves both uncertainty estimation and computational efficiency when compared to state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-12-05T14:17:16Z)
• Inference at the data's edge: Gaussian processes for modeling and inference under model-dependency, poor overlap, and extrapolation [0.0]
  The Gaussian Process (GP) is a flexible non-linear regression approach. It provides a principled approach to handling our uncertainty over predicted (counterfactual) values. This is especially valuable under conditions of extrapolation or weak overlap.
  arXiv  Detail & Related papers  (2024-07-15T05:09:50Z)
• Spectral Convolutional Conditional Neural Processes [4.52069311861025]
  Conditional Neural Processes (CNPs) constitute a family of probabilistic models that harness the flexibility of neural networks to parameterize processes. We propose Spectral Convolutional Conditional Neural Processes (SConvCNPs), a new addition to the NPs family that allows for more efficient representation of functions in the frequency domain.
  arXiv  Detail & Related papers  (2024-04-19T21:13:18Z)
• Improving Diffusion Models for Inverse Problems Using Optimal Posterior Covariance [52.093434664236014]
  Recent diffusion models provide a promising zero-shot solution to noisy linear inverse problems without retraining for specific inverse problems. Inspired by this finding, we propose to improve recent methods by using more principled covariance determined by maximum likelihood estimation.
  arXiv  Detail & Related papers  (2024-02-03T13:35:39Z)
• SPDE priors for uncertainty quantification of end-to-end neural data assimilation schemes [4.213142548113385]
  Recent advances in the deep learning community enables to adress this problem as neural architecture embedding data assimilation variational framework. In this work, we draw from SPDE-based Processes to estimate prior models able to handle non-stationary covariances in both space and time. Our neural variational scheme is modified to embed an augmented state formulation with both state SPDE parametrization to estimate.
  arXiv  Detail & Related papers  (2024-02-02T19:18:12Z)
• Model-Based Epistemic Variance of Values for Risk-Aware Policy Optimization [59.758009422067]
  We consider the problem of quantifying uncertainty over expected cumulative rewards in model-based reinforcement learning. We propose a new uncertainty Bellman equation (UBE) whose solution converges to the true posterior variance over values. We introduce a general-purpose policy optimization algorithm, Q-Uncertainty Soft Actor-Critic (QU-SAC) that can be applied for either risk-seeking or risk-averse policy optimization.
  arXiv  Detail & Related papers  (2023-12-07T15:55:58Z)
• Variational Laplace Autoencoders [53.08170674326728]
  Variational autoencoders employ an amortized inference model to approximate the posterior of latent variables. We present a novel approach that addresses the limited posterior expressiveness of fully-factorized Gaussian assumption. We also present a general framework named Variational Laplace Autoencoders (VLAEs) for training deep generative models.
  arXiv  Detail & Related papers  (2022-11-30T18:59:27Z)
• Transformer Neural Processes: Uncertainty-Aware Meta Learning Via Sequence Modeling [26.377099481072992]
  We propose Transformer Neural Processes (TNPs) for uncertainty-aware meta learning. We learn TNPs via an autoregressive likelihood-based objective and instantiate it with a novel transformer-based architecture. We show that TNPs achieve state-of-the-art performance on various benchmark problems.
  arXiv  Detail & Related papers  (2022-07-09T02:28:58Z)
• Improving Robustness and Uncertainty Modelling in Neural Ordinary Differential Equations [0.2538209532048866]
  We propose a novel approach to model uncertainty in NODE by considering a distribution over the end-time $T$ of the ODE solver. We also propose, adaptive latent time NODE (ALT-NODE), which allow each data point to have a distinct posterior distribution over end-times. We demonstrate the effectiveness of the proposed approaches in modelling uncertainty and robustness through experiments on synthetic and several real-world image classification data.
  arXiv  Detail & Related papers  (2021-12-23T16:56:10Z)
• Loss function based second-order Jensen inequality and its application to particle variational inference [112.58907653042317]
  Particle variational inference (PVI) uses an ensemble of models as an empirical approximation for the posterior distribution. PVI iteratively updates each model with a repulsion force to ensure the diversity of the optimized models. We derive a novel generalization error bound and show that it can be reduced by enhancing the diversity of models.
  arXiv  Detail & Related papers  (2021-06-09T12:13:51Z)
• GP-ConvCNP: Better Generalization for Convolutional Conditional Neural Processes on Time Series Data [4.141867179461668]
  Convolutional Conditional Neural Processes (ConvCNP) have shown remarkable improvement in performance over prior art. We find that they sometimes struggle to generalize when applied to time series data. In particular, they are not robust to distribution shifts and fail to extrapolate observed patterns into the future.
  arXiv  Detail & Related papers  (2021-06-09T10:26:39Z)
• When in Doubt: Neural Non-Parametric Uncertainty Quantification for Epidemic Forecasting [70.54920804222031]
  Most existing forecasting models disregard uncertainty quantification, resulting in mis-calibrated predictions. Recent works in deep neural models for uncertainty-aware time-series forecasting also have several limitations. We model the forecasting task as a probabilistic generative process and propose a functional neural process model called EPIFNP.
  arXiv  Detail & Related papers  (2021-06-07T18:31:47Z)
• Sampling-free Variational Inference for Neural Networks with Multiplicative Activation Noise [51.080620762639434]
  We propose a more efficient parameterization of the posterior approximation for sampling-free variational inference. Our approach yields competitive results for standard regression problems and scales well to large-scale image classification tasks.
  arXiv  Detail & Related papers  (2021-03-15T16:16:18Z)
• Reducing the Amortization Gap in Variational Autoencoders: A Bayesian Random Function Approach [38.45568741734893]
  Inference in our GP model is done by a single feed forward pass through the network, significantly faster than semi-amortized methods. We show that our approach attains higher test data likelihood than the state-of-the-arts on several benchmark datasets.
  arXiv  Detail & Related papers  (2021-02-05T13:01:12Z)
• On Signal-to-Noise Ratio Issues in Variational Inference for Deep Gaussian Processes [55.62520135103578]
  We show that the gradient estimates used in training Deep Gaussian Processes (DGPs) with importance-weighted variational inference are susceptible to signal-to-noise ratio (SNR) issues. We show that our fix can lead to consistent improvements in the predictive performance of DGP models.
  arXiv  Detail & Related papers  (2020-11-01T14:38:02Z)
• Bootstrapping Neural Processes [114.97111530885093]
  Neural Processes (NPs) implicitly define a broad class of processes with neural networks. NPs still rely on an assumption that uncertainty in processes is modeled by a single latent variable. We propose the Boostrapping Neural Process (BNP), a novel extension of the NP family using the bootstrap.
  arXiv  Detail & Related papers  (2020-08-07T02:23:34Z)
• Meta-Learning Stationary Stochastic Process Prediction with Convolutional Neural Processes [32.02612871707347]
  We propose ConvNP, which endows Neural Processes (NPs) with translation equivariance and extends convolutional conditional NPs to allow for dependencies in the predictive distribution. We demonstrate the strong performance and generalization capabilities of ConvNPs on 1D, regression image completion, and various tasks with real-world-temporal data.
  arXiv  Detail & Related papers  (2020-07-02T18:25:27Z)
• NP-PROV: Neural Processes with Position-Relevant-Only Variances [113.20013269514327]
  We present a new member named Neural Processes with Position-Relevant-Only Variances (NP-PROV) NP-PROV hypothesizes that a target point close to a context point has small uncertainty, regardless of the function value at that position. Our evaluation on synthetic and real-world datasets reveals that NP-PROV can achieve state-of-the-art likelihood while retaining a bounded variance.
  arXiv  Detail & Related papers  (2020-06-15T06:11:21Z)

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.