The Misclassification Likelihood Matrix: Some Classes Are More Likely To Be Misclassified Than Others

• URL: http://arxiv.org/abs/2407.07818v3
• Date: Tue, 13 Aug 2024 07:18:04 GMT
• Title: The Misclassification Likelihood Matrix: Some Classes Are More Likely To Be Misclassified Than Others
• Authors: Daniel Sikar, Artur Garcez, Robin Bloomfield, Tillman Weyde, Kaleem Peeroo, Naman Singh, Maeve Hutchinson, Dany Laksono, Mirela Reljan-Delaney,
• Abstract summary: This study introduces Misclassification Likelihood Matrix (MLM) as a novel tool for quantifying the reliability of neural network predictions under distribution shifts. The implications of this work extend beyond image classification, with ongoing applications in autonomous systems, such as self-driving cars.
• Score: 1.654278807602897
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This study introduces the Misclassification Likelihood Matrix (MLM) as a novel tool for quantifying the reliability of neural network predictions under distribution shifts. The MLM is obtained by leveraging softmax outputs and clustering techniques to measure the distances between the predictions of a trained neural network and class centroids. By analyzing these distances, the MLM provides a comprehensive view of the model's misclassification tendencies, enabling decision-makers to identify the most common and critical sources of errors. The MLM allows for the prioritization of model improvements and the establishment of decision thresholds based on acceptable risk levels. The approach is evaluated on the MNIST dataset using a Convolutional Neural Network (CNN) and a perturbed version of the dataset to simulate distribution shifts. The results demonstrate the effectiveness of the MLM in assessing the reliability of predictions and highlight its potential in enhancing the interpretability and risk mitigation capabilities of neural networks. The implications of this work extend beyond image classification, with ongoing applications in autonomous systems, such as self-driving cars, to improve the safety and reliability of decision-making in complex, real-world environments.

Related papers

• Quantifying calibration error in modern neural networks through evidence based theory [0.0]
  This paper introduces a novel framework for quantifying the trustworthiness of neural networks by incorporating subjective logic into the evaluation of Expected Error (ECE) We demonstrate the effectiveness of this approach through experiments on MNIST and CIFAR-10 datasets where post-calibration results indicate improved trustworthiness. The proposed framework offers a more interpretable and nuanced assessment of AI models, with potential applications in sensitive domains such as healthcare and autonomous systems.
  arXiv  Detail & Related papers  (2024-10-31T23:54:21Z)
• Detecting Training Data of Large Language Models via Expectation Maximization [62.28028046993391]
  Membership inference attacks (MIAs) aim to determine whether a specific instance was part of a target model's training data. Applying MIAs to large language models (LLMs) presents unique challenges due to the massive scale of pre-training data and the ambiguous nature of membership. We introduce EM-MIA, a novel MIA method for LLMs that iteratively refines membership scores and prefix scores via an expectation-maximization algorithm.
  arXiv  Detail & Related papers  (2024-10-10T03:31:16Z)
• Amortized Bayesian Multilevel Models [9.831471158899644]
  Multilevel models (MLMs) are a central building block of the Bayesian workflow. MLMs pose significant computational challenges, often rendering their estimation and evaluation intractable within reasonable time constraints. Recent advances in simulation-based inference offer promising solutions for addressing complex probabilistic models using deep generative networks. We explore a family of neural network architectures that leverage the probabilistic factorization of multilevel models to facilitate efficient neural network training and subsequent near-instant posterior inference on unseen data sets.
  arXiv  Detail & Related papers  (2024-08-23T17:11:04Z)
• Cycles of Thought: Measuring LLM Confidence through Stable Explanations [53.15438489398938]
  Large language models (LLMs) can reach and even surpass human-level accuracy on a variety of benchmarks, but their overconfidence in incorrect responses is still a well-documented failure mode. We propose a framework for measuring an LLM's uncertainty with respect to the distribution of generated explanations for an answer.
  arXiv  Detail & Related papers  (2024-06-05T16:35:30Z)
• Chain-of-Thought Prompting for Demographic Inference with Large Multimodal Models [58.58594658683919]
  Large multimodal models (LMMs) have shown transformative potential across various research tasks. Our findings indicate LMMs possess advantages in zero-shot learning, interpretability, and handling uncurated 'in-the-wild' inputs. We propose a Chain-of-Thought augmented prompting approach, which effectively mitigates the off-target prediction issue.
  arXiv  Detail & Related papers  (2024-05-24T16:26:56Z)
• Practical Probabilistic Model-based Deep Reinforcement Learning by Integrating Dropout Uncertainty and Trajectory Sampling [7.179313063022576]
  This paper addresses the prediction stability, prediction accuracy and control capability of the current probabilistic model-based reinforcement learning (MBRL) built on neural networks. A novel approach dropout-based probabilistic ensembles with trajectory sampling (DPETS) is proposed.
  arXiv  Detail & Related papers  (2023-09-20T06:39:19Z)
• Probabilistic MIMO U-Net: Efficient and Accurate Uncertainty Estimation for Pixel-wise Regression [1.4528189330418977]
  Uncertainty estimation in machine learning is paramount for enhancing the reliability and interpretability of predictive models. We present an adaptation of the Multiple-Input Multiple-Output (MIMO) framework for pixel-wise regression tasks.
  arXiv  Detail & Related papers  (2023-08-14T22:08:28Z)
• Dynamic Model Agnostic Reliability Evaluation of Machine-Learning Methods Integrated in Instrumentation & Control Systems [1.8978726202765634]
  Trustworthiness of datadriven neural network-based machine learning algorithms is not adequately assessed. In recent reports by the National Institute for Standards and Technology, trustworthiness in ML is a critical barrier to adoption. We demonstrate a real-time model-agnostic method to evaluate the relative reliability of ML predictions by incorporating out-of-distribution detection on the training dataset.
  arXiv  Detail & Related papers  (2023-08-08T18:25:42Z)
• Uncertainty Estimation by Fisher Information-based Evidential Deep Learning [61.94125052118442]
  Uncertainty estimation is a key factor that makes deep learning reliable in practical applications. We propose a novel method, Fisher Information-based Evidential Deep Learning ($mathcalI$-EDL) In particular, we introduce Fisher Information Matrix (FIM) to measure the informativeness of evidence carried by each sample, according to which we can dynamically reweight the objective loss terms to make the network more focused on the representation learning of uncertain classes.
  arXiv  Detail & Related papers  (2023-03-03T16:12:59Z)
• Unlabelled Data Improves Bayesian Uncertainty Calibration under Covariate Shift [100.52588638477862]
  We develop an approximate Bayesian inference scheme based on posterior regularisation. We demonstrate the utility of our method in the context of transferring prognostic models of prostate cancer across globally diverse populations.
  arXiv  Detail & Related papers  (2020-06-26T13:50:19Z)
• Diversity inducing Information Bottleneck in Model Ensembles [73.80615604822435]
  In this paper, we target the problem of generating effective ensembles of neural networks by encouraging diversity in prediction. We explicitly optimize a diversity inducing adversarial loss for learning latent variables and thereby obtain diversity in the output predictions necessary for modeling multi-modal data. Compared to the most competitive baselines, we show significant improvements in classification accuracy, under a shift in the data distribution.
  arXiv  Detail & Related papers  (2020-03-10T03:10:41Z)

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.