On the robustness of self-attentive models

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August undefined, 2024

Web14 de abr. de 2024 · Guo et al. proposed a multi-scale self-attentive mechanism model where the selfattentive mechanism is introduced into the multi-scale structure to extract … Webmodel with five semi-supervised approaches on the public 2024 ACDC dataset and 2024 Prostate dataset. Our proposed method achieves better segmentation performance on both datasets under the same settings, demonstrating its effectiveness, robustness, and potential transferability to other medical image segmentation tasks.

Why Robustness is not Enough for Safety and Security in Machine ...

Web13 de dez. de 2024 · A Robust Self-Attentive Capsule Network for Fault Diagnosis of Series-Compensated Transmission Line. ... and which are used to investigate the robustness or representation of every model or ... WebThis work examines the robustness of self-attentive neural networks against adversarial input perturbations. Specifically, we investigate the attention and feature extraction mechanisms of state-of-the-art recurrent neural networks and self-attentive architectures for sentiment analysis, entailment and machine translation under adversarial attacks. hilary genet

On the Robustness of Self-Attentive Models - ACL Anthology

WebAdditionally, a multi-head self-attention module is developed to explicitly model the attribute interactions. Extensive experiments on benchmark datasets have veriﬁed the effectiveness of the proposed NETTENTION model on a variety of tasks, including vertex classiﬁcation and link prediction. Index Terms—network embedding, attributed ... Web31 de mar. de 2024 · DOI: 10.1109/TNSRE.2024.3263570 Corpus ID: 257891756; Self-Supervised EEG Emotion Recognition Models Based on CNN @article{Wang2024SelfSupervisedEE, title={Self-Supervised EEG Emotion Recognition Models Based on CNN}, author={Xingyi Wang and Yuliang Ma and Jared Cammon and … Web12 de out. de 2024 · Robust Models are less Over-Confident. Despite the success of convolutional neural networks (CNNs) in many academic benchmarks for computer … hilary gentile

[2203.03539] Understanding The Robustness of Self-supervised …

WebOn the Robustness of Self Attentive Models In addition, the concept of adversarial attacks has also been explored in more complex NLP tasks. For example, Jia and Liang (2024) … WebDistribution shifts—where a model is deployed on a data distribution different from what it was trained on—pose significant robustness challenges in real-world ML applications. Such shifts are often unavoidable in the wild and have been shown to substantially degrade model performance in applications such as biomedicine, wildlife conservation, … hilary geddes quartetWeb14 de abr. de 2024 · The performance comparisons to several state-of-the-art approaches and variations validate the effectiveness and robustness of our proposed model, and show the positive impact of temporal point process on sequential recommendation. ... McAuley, J.: Self-attentive sequential recommendation. In: ICDM, pp. 197–206 (2024) Google Scholar small world vacations disney cruise

"Web9 de jul. de 2016 · This allows analysts to present their core, preferred estimate in the context of a distribution of plausible estimates. Second, we develop a model influence … " - On the robustness of self-attentive models

On the robustness of self-attentive models

Web11 de nov. de 2024 · To address the above issues, in this paper, we propose Nettention, a self-attentive network embedding approach that can efficiently learn vertex embeddings on attributed network. Instead of sample-wise optimization, Nettention aggregates the two types of information through minimizing the difference between the representation distributions … Webrent neural models, self-attentive models are more robust against adversarial perturbation. In addition, we provide theoretical explana-tions for their superior robustness to support …

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Web7 de abr. de 2024 · Experimental results show that, compared to recurrent neural models, self-attentive models are more robust against adversarial perturbation. In addition, we provide theoretical explanations for their superior robustness to support our claims. …

Web5 de abr. de 2024 · Automatic speech recognition (ASR) that relies on audio input suffers from significant degradation in noisy conditions and is particularly vulnerable to speech interference. However, video recordings of speech capture both visual and audio signals, providing a potent source of information for training speech models. Audiovisual speech … WebThis work examines the robustness of self-attentive neural networks against adversarial input ... Cheng, M., Juan, D. C., Wei, W., Hsu, W. L., & Hsieh, C. J. (2024). On the …

Web10 de ago. de 2024 · Sleep staging is of great importance in the diagnosis and treatment of sleep disorders. Recently, numerous data-driven deep learning models have been proposed for automatic sleep staging. They mainly train the model on a large public labeled sleep dataset and test it on a smaller one with subjects of interest. However, they usually … Web1 de jul. de 2024 · DOI: 10.18653/v1/P19-1147 Corpus ID: 192546007; On the Robustness of Self-Attentive Models @inproceedings{Hsieh2024OnTR, title={On the Robustness …

Web1 de ago. de 2024 · On the robustness of self-attentive models. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, Association for Computational Linguistics, Florence, Italy (2024), pp. 1520-1529. CrossRef Google Scholar [3] Garg Siddhant, Ramakrishnan Goutham.

Webdatasets, its robustness still lags behind [10,15]. Many re-searchers [11,21,22,53] have shown that the performance of deep models trained in high-quality data decreases dra-matically with low-quality data encountered during deploy-ment, which usually contain common corruptions, includ-ing blur, noise, and weather influence. For example, the hilary geller ecccWeb12 de abr. de 2024 · Self-attention is a mechanism that allows a model to attend to different parts of a sequence based on their relevance and similarity. For example, in the sentence "The cat chased the mouse", the ... small world vacations disney cruise creditWebThese will impair the accuracy and robustness of combinational models that use relations and other types of information, especially when iteration is performed. To better explore structural information between entities, we novelly propose a Self-Attentive heterogeneous sequence learning model for Entity Alignment (SAEA) that allows us to capture long … hilary geogheganWebTable 3: Comparison of LSTM and BERT models under human evaluations against GS-EC attack. Readability is a relative quality score between models, and Human Accuracy is … small world vacations employmentWeb11 de jul. de 2024 · Robustness in Statistics. In statistics, the term robust or robustness refers to the strength of a statistical model, tests, and procedures according to the specific conditions of the statistical analysis a study hopes to achieve. Given that these conditions of a study are met, the models can be verified to be true through the use of ... hilary geddes parksideWeb19 de out. de 2024 · We further develop Quaternion-based Adversarial learning along with the Bayesian Personalized Ranking (QABPR) to improve our model's robustness. Extensive experiments on six real-world datasets show that our fused QUALSE model outperformed 11 state-of-the-art baselines, improving 8.43% at [email protected] and … small world vacations disneylandWeb8 de jan. de 2024 · Simultaneously, the self-attention layer highlights the more dominant features that make the network work upon the limited data effectively. A Western-System-Coordinating-Council WSCC 9-bus and 3-machine test model, which was modified with the series capacitor was studied to quantify the robustness of the self-attention WSCN. small world vacations inc