Evidential deep learning (EDL) has shown remarkable success in uncertainty estimation. However, there is still room for improvement, particularly in out-of-distribution (OOD) detection and classification tasks. The limited OOD detection performance of EDL arises from its inability to reflect the distance between the testing example and training data when quantifying uncertainty, while its limited classification performance stems from its parameterization of the concentration parameters. To address these limitations, we propose a novel method called Density Aware Evidential Deep Learning (DAEDL). DAEDL integrates the feature space density of the testing example with the output of EDL during the prediction stage, while using a novel parameterization that resolves the issues in the conventional parameterization. We prove that DAEDL enjoys a number of favorable theoretical properties. DAEDL demonstrates state-of-the-art performance across diverse downstream tasks related to uncertainty estimation and classification

Secondly, we use the learned This paper presents a novel self-supervised path-planning method world model as an internal generative model enriched with active for UAV-aided networks. First, we employed an optimizer to solve states to simulate the environment and plan actions that minimize training examples offline and then used the resulting solutions as the agent's surprise during online decision-making. This approach demonstrations from which the UAV can learn the world model to enables the UAV to navigate its surroundings with a reference model understand the environment and implicitly discover the optimizer's representing the goal, choosing actions that minimize unexpected or policy. UAV equipped with the world model can make real-time unusual observations (surprise) measured by how much they deviate autonomous decisions and engage in online planning using active from the expected goal. The main contributions of this paper are as inference. During planning, UAV can score different policies based follows: It expands on previous research [11] by exploring online on the expected surprise, allowing it to choose among alternative planning, a prospective form of cognition.

Originally published on Towards AI the World's Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses. Classification, In Machine Learning, is a supervised learning concept where data points are classified into different classes.

Recent work has shown that large pretrained Language Models (LMs) can not only perform remarkably well on a range of Natural Language Processing (NLP) tasks but also start improving on reasoning tasks such as arithmetic induction, symbolic manipulation, and commonsense reasoning with increasing size of models. However, it is still unclear what the underlying capabilities of these LMs are. Surprisingly, we find that these models have limitations on certain basic symbolic manipulation tasks such as copy, reverse, and addition. When the total number of symbols or repeating symbols increases, the model performance drops quickly. We investigate the potential causes behind this phenomenon and examine a set of possible methods, including explicit positional markers, fine-grained computation steps, and LMs with callable programs. Experimental results show that none of these techniques can solve the simplest addition induction problem completely. In the end, we introduce LMs with tutor, which demonstrates every single step of teaching. LMs with tutor is able to deliver 100% accuracy in situations of OOD and repeating symbols, shedding new insights on the boundary of large LMs in induction.

Despite impressive capabilities and outstanding performance, deep neural network(DNN) has captured increasing public concern for its security problem, due to frequent occurrence of erroneous behaviors. Therefore, it is necessary to conduct systematically testing before its deployment to real-world applications. Existing testing methods have provided fine-grained criteria based on neuron coverage and reached high exploratory degree of testing. But there is still a gap between the neuron coverage and model's robustness evaluation. To bridge the gap, we observed that neurons which change the activation value dramatically due to minor perturbation are prone to trigger incorrect corner cases. Motivated by it, we propose neuron sensitivity and develop a novel white-box testing framework for DNN, donated as DeepSensor. The number of sensitive neurons is maximized by particle swarm optimization, thus diverse corner cases could be triggered and neuron coverage be further improved when compared with baselines. Besides, considerable robustness enhancement can be reached when adopting testing examples based on neuron sensitivity for retraining. Extensive experiments implemented on scalable datasets and models can well demonstrate the testing effectiveness and robustness improvement of DeepSensor.

Abstract--The success of deep neural networks (DNNs) in real-world applications has benefited from abundant pre-trained models. However, the backdoored pre-trained models can pose a significant trojan threat to the deployment of downstream DNNs. Existing DNN testing methods are mainly designed to find incorrect corner case behaviors in adversarial settings but fail to discover the backdoors crafted by strong trojan attacks. Observing the trojan network behaviors shows that they are not just reflected by a single compromised neuron as proposed by previous work but attributed to the critical neural paths in the activation intensity and frequency of multiple neurons. This work formulates the DNN backdoor testing and proposes the CatchBackdoor framework. Via differential fuzzing of critical neurons from a small number of benign examples, we identify the trojan paths and particularly the critical ones, and generate backdoor testing examples by simulating the critical neurons in the identified paths. Extensive experiments demonstrate the superiority of CatchBackdoor, with higher detection performance than existing methods. CatchBackdoor works better on detecting backdoors( 1.5) by stealthy blending and adaptive attacks, which existing methods fail to detect. Moreover, our experiments show that CatchBackdoor may reveal the potential backdoors of models in Model Zoo.

Deep neural networks (DNNs) have transformed several artificial intelligence research areas including computer vision, speech recognition, and natural language processing. However, recent studies demonstrated that DNNs are vulnerable to adversarial manipulations at testing time. Specifically, suppose we have a testing example, whose label can be correctly predicted by a DNN classifier. An attacker can add a small carefully crafted noise to the testing example such that the DNN classifier predicts an incorrect label, where the crafted testing example is called adversarial example. Such attacks are called evasion attacks. Evasion attacks are one of the biggest challenges for deploying DNNs in safety and security critical applications such as self-driving cars. In this work, we develop new methods to defend against evasion attacks. Our key observation is that adversarial examples are close to the classification boundary. Therefore, we propose region-based classification to be robust to adversarial examples. For a benign/adversarial testing example, we ensemble information in a hypercube centered at the example to predict its label. In contrast, traditional classifiers are point-based classification, i.e., given a testing example, the classifier predicts its label based on the testing example alone. Our evaluation results on MNIST and CIFAR-10 datasets demonstrate that our region-based classification can significantly mitigate evasion attacks without sacrificing classification accuracy on benign examples. Specifically, our region-based classification achieves the same classification accuracy on testing benign examples as point-based classification, but our region-based classification is significantly more robust than point-based classification to various evasion attacks.

A statistical theory for overtraining is proposed. The analysis treats realizable stochastic neural networks, trained with Kullback Leibler loss in the asymptotic case. It is shown that the asymptotic gain in the generalization error is small if we perform early stopping, even if we have access to the optimal stopping time. Considering cross-validation stopping we answer the question: In what ratio the examples should be divided into training and testing sets in order to obtain the optimum performance. In the non-asymptotic region cross-validated early stopping always decreases the generalization error. Our large scale simulations done on a CM5 are in nice agreement with our analytical findings.

A statistical theory for overtraining is proposed. The analysis treats realizable stochastic neural networks, trained with Kullback Leibler loss in the asymptotic case. It is shown that the asymptotic gain in the generalization error is small if we perform early stopping, even if we have access to the optimal stopping time. Considering cross-validation stopping we answer the question: In what ratio the examples should be divided into training and testing sets in order to obtain the optimum performance. In the non-asymptotic region cross-validated early stopping always decreases the generalization error. Our large scale simulations done on a CM5 are in nice agreement with our analytical findings.

A statistical theory for overtraining is proposed. The analysis treats realizable stochastic neural networks, trained with Kullback Leibler loss in the asymptotic case. It is shown that the asymptotic gain in the generalization error is small if we perform early stopping, evenif we have access to the optimal stopping time. Considering cross-validation stopping we answer the question: In what ratio the examples should be divided into training and testing sets in order toobtain the optimum performance. In the non-asymptotic region cross-validated early stopping always decreases the generalization error.Our large scale simulations done on a CM5 are in nice agreement with our analytical findings.