Estimating and disentangling epistemic uncertainty (uncertainty that can be reduced with more training data) and aleatoric uncertainty (uncertainty that is inherent to the task at hand) is critically important when applying machine learning (ML) to high-stakes applications such as medical imaging and weather forecasting. Conditional diffusion models' breakthrough ability to accurately and efficiently sample from the posterior distribution of a dataset now makes uncertainty estimation conceptually straightforward: One need only train and sample from a large ensemble of diffusion models. Unfortunately, training such an ensemble becomes computationally intractable as the complexity of the model architecture grows. In this work we introduce a new approach to ensembling, hyper-diffusion, which allows one to accurately estimate epistemic and aleatoric uncertainty with a single model. Unlike existing Monte Carlo dropout based single-model ensembling methods, hyper-diffusion offers the same prediction accuracy as multi-model ensembles. We validate our approach on two distinct tasks: x-ray computed tomography (CT) reconstruction and weather temperature forecasting.

pdf/hyper-diffusion-estimating-epistemic-and-aleatoric-2lr8gqg0s1.pdf

Hyper-Diffusion: Estimating Epistemic and Aleatoric Uncertainty with a Single Model

Abstract Skin cancer, primarily resulting from the abnormal growth of skin cells, is among the most common cancer types. In recent decades, the incidence of skin cancer cases worldwide has risen significantly (one in every three newly diagnosed cancer cases is a skin cancer). Such an increase can be attributed to changes in our social and lifestyle habits coupled with devastating man-made alterations to the global ecosystem. Despite such a notable increase, diagnosis of skin cancer is still challenging, which becomes critical as its early detection is crucial for increasing the overall survival rate. This calls for advancements of innovative computer-aided systems to assist medical experts with their decision making. In this context, there has been a recent surge of interest in machine learning (ML), in particular, deep neural networks (DNNs), to provide complementary assistance to expert physicians. While DNNs have a high processing capacity far beyond that of human experts, their outputs are deterministic, i.e., providing estimates without prediction confidence. Therefore, it is of paramount importance to develop DNNs with uncertainty-awareness to provide confidence in their predictions. Monte Carlo dropout (MCD) is vastly used for uncertainty quantification; however, MCD suffers from overconfidence and being miss calibrated. In this paper, we use MCD algorithm to develop an uncertainty-aware DNN that assigns high predictive entropy to erroneous predictions and enable the model to optimize the hyper-parameters during training, which leads to more accurate uncertainty quantification. We use two synthetic (two moons and blobs) and a real dataset (skin cancer) to validate our algorithm. Our experiments on these datasets prove effectiveness of our approach in quantifying reliable uncertainty. Our method achieved 85.65 ± 0.18 prediction accuracy, 83.03 ± 0.25 uncertainty accuracy, and 1.93 ± 0.3 expected calibration error outperforming vanilla MCD and MCD with loss enhanced based on predicted entropy. 

A novel uncertainty-aware deep learning technique with an application on skin cancer diagnosis

This paper addresses the problem of selective classification for deep neural networks, where a model is allowed to abstain from low-confidence predictions to avoid potential errors. Specifically, we tackle the problem of optimizing the confidence estimator of a fixed classifier, aiming to enhance its misclassification detection performance, i.e., its ability to discriminate between correct and incorrect predictions by assigning higher confidence values to the correct ones. Previous work has found that different classifiers exhibit varying levels of misclassification detection performance, particularly when using the maximum softmax probability (MSP) as a measure of confidence. However, we argue that these findings are mainly due to a sub-optimal confidence estimator being used for each model. To overcome this issue, we propose a simple and efficient post-hoc confidence estimator, named $p$-NormSoftmax, which consists of transforming the logits through $p$-norm normalization and temperature scaling, followed by taking the MSP, where $p$ and the temperature are optimized based on a hold-out set. This estimator can be easily applied on top of an already trained model and, in many cases, can significantly improve its selective classification performance. When applied to 84 pretrained Imagenet classifiers, our method yields an average improvement of 16% in the area under the risk-coverage curve (AURC), exceeding 40% for some models. Furthermore, after applying $p$-NormSoftmax, we observe that these models exhibit approximately the same level of misclassification detection performance, implying that a model's selective classification performance is almost entirely determined by its accuracy at full coverage.

pdf/improving-selective-classification-performance-of-deep-17ztndg6.pdf

Improving selective classification performance of deep neural networks through post-hoc logit normalization and temperature scaling

As large language models (LLMs) are increasingly deployed in user-facing applications, building trust and maintaining safety by accurately quantifying a model's confidence in its prediction becomes even more important. However, finding effective ways to calibrate LLMs - especially when the only interface to the models is their generated text - remains a challenge. We propose APRICOT (auxiliary prediction of confidence targets): A method to set confidence targets and train an additional model that predicts an LLM's confidence based on its textual input and output alone. This approach has several advantages: It is conceptually simple, does not require access to the target model beyond its output, does not interfere with the language generation, and has a multitude of potential usages, for instance by verbalizing the predicted confidence or adjusting the given answer based on the confidence. We show how our approach performs competitively in terms of calibration error for white-box and black-box LLMs on closed-book question-answering to detect incorrect LLM answers.

pdf/calibrating-large-language-models-using-their-generations-29gadaechi.pdf

Calibrating Large Language Models Using Their Generations Only

ChatGPT took the world by storm for its impressive abilities. Due to its release without documentation, scientists immediately attempted to identify its limits, mainly through its performance in natural language processing (NLP) tasks. This paper aims to join the growing literature regarding ChatGPT's abilities by focusing on its performance in high-resource languages and on its capacity to predict its answers' accuracy by giving a confidence level. The analysis of high-resource languages is of interest as studies have shown that low-resource languages perform worse than English in NLP tasks, but no study so far has analysed whether high-resource languages perform as well as English. The analysis of ChatGPT's confidence calibration has not been carried out before either and is critical to learn about ChatGPT's trustworthiness. In order to study these two aspects, five high-resource languages and two NLP tasks were chosen. ChatGPT was asked to perform both tasks in the five languages and to give a numerical confidence value for each answer. The results show that all the selected high-resource languages perform similarly and that ChatGPT does not have a good confidence calibration, often being overconfident and never giving low confidence values.

ChatGPT Prompting Cannot Estimate Predictive Uncertainty in High-Resource Languages

Although neural networks (especially deep neural networks) have achieved \textit{better-than-human} performance in many fields, their real-world deployment is still questionable due to the lack of awareness about the limitation in their knowledge. To incorporate such awareness in the machine learning model, prediction with reject option (also known as selective classification or classification with abstention) has been proposed in literature. In this paper, we present a systematic review of the prediction with the reject option in the context of various neural networks. To the best of our knowledge, this is the first study focusing on this aspect of neural networks. Moreover, we discuss different novel loss functions related to the reject option and post-training processing (if any) of network output for generating suitable measurements for knowledge awareness of the model. Finally, we address the application of the rejection option in reducing the prediction time for the real-time problems and present a comprehensive summary of the techniques related to the reject option in the context of extensive variety of neural networks. Our code is available on GitHub: \url{https://github.com/MehediHasanTutul/Reject_option}

pdf/survey-on-leveraging-uncertainty-estimation-towards-1dxda8b3.pdf

Survey on Leveraging Uncertainty Estimation Towards Trustworthy Deep Neural Networks: The Case of Reject Option and Post-training Processing

Uncertainty quantification in a neural network is one of the most discussed topics for safety-critical applications. Though Neural Networks (NNs) have achieved state-of-the-art performance for many applications, they still provide unreliable point predictions, which lack information about uncertainty estimates. Among various methods to enable neural networks to estimate uncertainty, Monte Carlo (MC) dropout has gained much popularity in a short period due to its simplicity. In this study, we present a new version of the traditional dropout layer where we are able to fix the number of dropout configurations. As such, each layer can take and apply the new dropout layer in the MC method to quantify the uncertainty associated with NN predictions. We conduct experiments on both toy and realistic datasets and compare the results with the MC method using the traditional dropout layer. Performance analysis utilizing uncertainty evaluation metrics corroborates that our dropout layer offers better performance in most cases.

Controlled Dropout for Uncertainty Estimation

While deep learning models achieve excellent results in image segmentation, their ability to express uncertainty in predictions remains a key limitation. This is especially crucial in safety-critical domains like medical imaging and autonomous driving where prediction errors can lead to life-threatening situations. In this research, we propose an Uncertainty-aware Segmentation Loss (US-Loss) function that focuses on optimizing both: higher segmentation accuracy and reduced epistemic uncertainty. Unlike existing methods that require multiple forward passes or complex architectural modifications, US-Loss provides uncertainty estimates through a single forward pass. Moreover, this loss function can easily be integrated into any segmentation architecture. We evaluated our approach against two popular uncertainty measurement approaches, Deep Ensemble and Monte Carlo Dropout, on two challenging datasets: skin lesion images (ISIC-2018) and breast ultrasound images (BUS). Both quantitative and qualitative results show that our proposed approach performs best across both datasets.

US-Loss: Integrating Uncertainty Estimation in the Loss Function of Image Segmentation

Understanding cognitive load is important due to its inherent implications across many different disciplines. This is, in general, a difficult task due to personal nature of data normally used to infer cognitive load. In addition, an individual changes over time and his/her pattern of data changes as well, which implies past data from an individual may not reliably predict the future cognitive load of the same individual. In this article, we show that utilization of data from other people (a.k.a. crowdsourcing) offers a significant improvement in classifier performance when predicting cognitive load. We reveal that the improvement is substantial compared to an individualistic model and is statistically significant.

Predicting Cognitive Load of an Individual With Knowledge Gained From Others: Improvements in Performance Using Crowdsourcing

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Survey on Leveraging Uncertainty Estimation Towards Trustworthy Deep Neural Networks: The Case of Reject Option and Post-training Processing

Controlled Dropout for Uncertainty Estimation

US-Loss: Integrating Uncertainty Estimation in the Loss Function of Image Segmentation

Predicting Cognitive Load of an Individual With Knowledge Gained From Others: Improvements in Performance Using Crowdsourcing