Random-Set Neural Networks (RS-NN)

• URL: http://arxiv.org/abs/2307.05772v5
• Date: Fri, 14 Feb 2025 20:42:16 GMT
• Title: Random-Set Neural Networks (RS-NN)
• Authors: Shireen Kudukkil Manchingal, Muhammad Mubashar, Kaizheng Wang, Keivan Shariatmadar, Fabio Cuzzolin,
• Abstract summary: We propose a novel Random-Set Neural Network (RS-NN) approach to classification. RS-NN encodes the 'epistemic' uncertainty induced by training sets that are insufficiently representative. Our approach outperforms state-of-the-art Bayesian and Ensemble methods in terms of accuracy, uncertainty estimation and out-of-distribution (OoD) detection.
• Score: 4.549947259731147
• License:
• Abstract: Machine learning is increasingly deployed in safety-critical domains where erroneous predictions may lead to potentially catastrophic consequences, highlighting the need for learning systems to be aware of how confident they are in their own predictions: in other words, 'to know when they do not know'. In this paper, we propose a novel Random-Set Neural Network (RS-NN) approach to classification which predicts belief functions (rather than classical probability vectors) over the class list using the mathematics of random sets, i.e., distributions over the collection of sets of classes. RS-NN encodes the 'epistemic' uncertainty induced by training sets that are insufficiently representative or limited in size via the size of the convex set of probability vectors associated with a predicted belief function. Our approach outperforms state-of-the-art Bayesian and Ensemble methods in terms of accuracy, uncertainty estimation and out-of-distribution (OoD) detection on multiple benchmarks (CIFAR-10 vs SVHN/Intel-Image, MNIST vs FMNIST/KMNIST, ImageNet vs ImageNet-O). RS-NN also scales up effectively to large-scale architectures (e.g. WideResNet-28-10, VGG16, Inception V3, EfficientNetB2 and ViT-Base-16), exhibits remarkable robustness to adversarial attacks and can provide statistical guarantees in a conformal learning setting.

Related papers

• Enhancing Trustworthiness of Graph Neural Networks with Rank-Based Conformal Training [17.120502204791407]
  Conformal Prediction can produce statistically guaranteed uncertainty estimates. We propose a Rank-based CP during training framework to GNNs (RCP-GNN) for reliable uncertainty estimates.
  arXiv  Detail & Related papers  (2025-01-06T05:19:24Z)
• A Benchmark on Uncertainty Quantification for Deep Learning Prognostics [0.0]
  We assess some of the latest developments in the field of uncertainty quantification for prognostics deep learning. This includes the state-of-the-art variational inference algorithms for Bayesian neural networks (BNN) as well as popular alternatives such as Monte Carlo Dropout (MCD), deep ensembles (DE) and heteroscedastic neural networks (HNN) The performance of the methods is evaluated on a subset of the large NASA NCMAPSS dataset for aircraft engines.
  arXiv  Detail & Related papers  (2023-02-09T16:12:47Z)
• Robust-by-Design Classification via Unitary-Gradient Neural Networks [66.17379946402859]
  The use of neural networks in safety-critical systems requires safe and robust models, due to the existence of adversarial attacks. Knowing the minimal adversarial perturbation of any input x, or, equivalently, the distance of x from the classification boundary, allows evaluating the classification robustness, providing certifiable predictions. A novel network architecture named Unitary-Gradient Neural Network is presented. Experimental results show that the proposed architecture approximates a signed distance, hence allowing an online certifiable classification of x at the cost of a single inference.
  arXiv  Detail & Related papers  (2022-09-09T13:34:51Z)
• $p$-DkNN: Out-of-Distribution Detection Through Statistical Testing of Deep Representations [32.99800144249333]
  We introduce $p$-DkNN, a novel inference procedure that takes a trained deep neural network and analyzes the similarity structures of its intermediate hidden representations. We find that $p$-DkNN forces adaptive attackers crafting adversarial examples, a form of worst-case OOD inputs, to introduce semantically meaningful changes to the inputs.
  arXiv  Detail & Related papers  (2022-07-25T21:42:08Z)
• A Simple Approach to Improve Single-Model Deep Uncertainty via Distance-Awareness [33.09831377640498]
  We study approaches to improve uncertainty property of a single network, based on a single, deterministic representation. We propose Spectral-normalized Neural Gaussian Process (SNGP), a simple method that improves the distance-awareness ability of modern DNNs. On a suite of vision and language understanding benchmarks, SNGP outperforms other single-model approaches in prediction, calibration and out-of-domain detection.
  arXiv  Detail & Related papers  (2022-05-01T05:46:13Z)
• Comparative Analysis of Interval Reachability for Robust Implicit and Feedforward Neural Networks [64.23331120621118]
  We use interval reachability analysis to obtain robustness guarantees for implicit neural networks (INNs) INNs are a class of implicit learning models that use implicit equations as layers. We show that our approach performs at least as well as, and generally better than, applying state-of-the-art interval bound propagation methods to INNs.
  arXiv  Detail & Related papers  (2022-04-01T03:31:27Z)
• NUQ: Nonparametric Uncertainty Quantification for Deterministic Neural Networks [151.03112356092575]
  We show the principled way to measure the uncertainty of predictions for a classifier based on Nadaraya-Watson's nonparametric estimate of the conditional label distribution. We demonstrate the strong performance of the method in uncertainty estimation tasks on a variety of real-world image datasets.
  arXiv  Detail & Related papers  (2022-02-07T12:30:45Z)
• Rethinking Nearest Neighbors for Visual Classification [56.00783095670361]
  k-NN is a lazy learning method that aggregates the distance between the test image and top-k neighbors in a training set. We adopt k-NN with pre-trained visual representations produced by either supervised or self-supervised methods in two steps. Via extensive experiments on a wide range of classification tasks, our study reveals the generality and flexibility of k-NN integration.
  arXiv  Detail & Related papers  (2021-12-15T20:15:01Z)
• Unifying supervised learning and VAEs -- coverage, systematics and goodness-of-fit in normalizing-flow based neural network models for astro-particle reconstructions [0.0]
  Statistical uncertainties, coverage, systematic uncertainties or a goodness-of-fit measure are often not calculated. We show that a KL-divergence objective of the joint distribution of data and labels allows to unify supervised learning and variational autoencoders. We discuss how to calculate coverage probabilities without numerical integration for specific "base-ordered" contours.
  arXiv  Detail & Related papers  (2020-08-13T11:28:57Z)
• Frequentist Uncertainty in Recurrent Neural Networks via Blockwise Influence Functions [121.10450359856242]
  Recurrent neural networks (RNNs) are instrumental in modelling sequential and time-series data. Existing approaches for uncertainty quantification in RNNs are based predominantly on Bayesian methods. We develop a frequentist alternative that: (a) does not interfere with model training or compromise its accuracy, (b) applies to any RNN architecture, and (c) provides theoretical coverage guarantees on the estimated uncertainty intervals.
  arXiv  Detail & Related papers  (2020-06-20T22:45:32Z)
• Approximation and Non-parametric Estimation of ResNet-type Convolutional Neural Networks [52.972605601174955]
  We show a ResNet-type CNN can attain the minimax optimal error rates in important function classes. We derive approximation and estimation error rates of the aformentioned type of CNNs for the Barron and H"older classes.
  arXiv  Detail & Related papers  (2019-03-24T19:42:39Z)

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.