Relevant bibliographies by topics / Shortcut learning

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Shortcut learning'

Author: Grafiati
Published: 25 May 2024
Last updated: 31 July 2025

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Journal articles on the topic "Shortcut learning"

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Lyu, Zhaoyan, and Miguel R. D. Rodrigues. "Exploring the Impact of Additive Shortcuts in Neural Networks via Information Bottleneck-like Dynamics: From ResNet to Transformer." Entropy 26, no. 11 (2024): 974. http://dx.doi.org/10.3390/e26110974.

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Deep learning has made significant strides, driving advances in areas like computer vision, natural language processing, and autonomous systems. In this paper, we further investigate the implications of the role of additive shortcut connections, focusing on models such as ResNet, Vision Transformers (ViTs), and MLP-Mixers, given that they are essential in enabling efficient information flow and mitigating optimization challenges such as vanishing gradients. In particular, capitalizing on our recent information bottleneck approach, we analyze how additive shortcuts influence the fitting and com
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Kim, Doyoung, Dongmin Park, Yooju Shin, Jihwan Bang, Hwanjun Song, and Jae-Gil Lee. "Adaptive Shortcut Debiasing for Online Continual Learning." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 12 (2024): 13122–31. http://dx.doi.org/10.1609/aaai.v38i12.29211.

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We propose a novel framework DropTop that suppresses the shortcut bias in online continual learning (OCL) while being adaptive to the varying degree of the shortcut bias incurred by continuously changing environment. By the observed high-attention property of the shortcut bias, highly-activated features are considered candidates for debiasing. More importantly, resolving the limitation of the online environment where prior knowledge and auxiliary data are not ready, two novel techniques---feature map fusion and adaptive intensity shifting---enable us to automatically determine the appropriate
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Li, Zihao, Ruixiang Tang, Lu Cheng, Shuaiqiang Wang, Dawei Yin, and Mengnan Du. "DBR: Divergence-Based Regularization for Debiasing Natural Language Understanding Models." ACM SIGKDD Explorations Newsletter 27, no. 1 (2025): 1–9. https://doi.org/10.1145/3748239.3748241.

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Pre-trained language models (PLMs) have achieved impressive results on various natural language processing tasks. However, recent research has revealed that these models often rely on superficial features and shortcuts instead of developing a genuine understanding of language, especially for natural language understanding (NLU) tasks. Consequently, the models struggle to generalize to out-of-domain data. In this work, we propose Divergence Based Regularization (DBR) to mitigate this shortcut learning behavior. Our method measures the divergence between the output distributions for original exa
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Nauta, Meike, Ricky Walsh, Adam Dubowski, and Christin Seifert. "Uncovering and Correcting Shortcut Learning in Machine Learning Models for Skin Cancer Diagnosis." Diagnostics 12, no. 1 (2021): 40. http://dx.doi.org/10.3390/diagnostics12010040.

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Machine learning models have been successfully applied for analysis of skin images. However, due to the black box nature of such deep learning models, it is difficult to understand their underlying reasoning. This prevents a human from validating whether the model is right for the right reasons. Spurious correlations and other biases in data can cause a model to base its predictions on such artefacts rather than on the true relevant information. These learned shortcuts can in turn cause incorrect performance estimates and can result in unexpected outcomes when the model is applied in clinical
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Geirhos, Robert, Jörn-Henrik Jacobsen, Claudio Michaelis, et al. "Shortcut learning in deep neural networks." Nature Machine Intelligence 2, no. 11 (2020): 665–73. http://dx.doi.org/10.1038/s42256-020-00257-z.

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POTAPOV, ALEXEI B., and M. K. ALI. "LEARNING, EXPLORATION AND CHAOTIC POLICIES." International Journal of Modern Physics C 11, no. 07 (2000): 1455–64. http://dx.doi.org/10.1142/s0129183100001309.

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We consider different versions of exploration in reinforcement learning. For the test problem, we use navigation in a shortcut maze. It is shown that chaotic ∊-greedy policy may be as efficient as a random one. The best results were obtained with a model chaotic neuron. Therefore, exploration strategy can be implemented in a deterministic learning system such as a neural network.
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Fay, Louisa, Erick Cobos, Bin Yang, Sergios Gatidis, and Thomas Küstner. "Avoiding Shortcut-Learning by Mutual Information Minimization in Deep Learning-Based Image Processing." IEEE Access 11 (2023): 64070–86. http://dx.doi.org/10.1109/access.2023.3289397.

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HAN, FANG, MARIAN WIERCIGROCH, JIAN-AN FANG, and ZHIJIE WANG. "EXCITEMENT AND SYNCHRONIZATION OF SMALL-WORLD NEURONAL NETWORKS WITH SHORT-TERM SYNAPTIC PLASTICITY." International Journal of Neural Systems 21, no. 05 (2011): 415–25. http://dx.doi.org/10.1142/s0129065711002924.

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Excitement and synchronization of electrically and chemically coupled Newman-Watts (NW) small-world neuronal networks with a short-term synaptic plasticity described by a modified Oja learning rule are investigated. For each type of neuronal network, the variation properties of synaptic weights are examined first. Then the effects of the learning rate, the coupling strength and the shortcut-adding probability on excitement and synchronization of the neuronal network are studied. It is shown that the synaptic learning suppresses the over-excitement, helps synchronization for the electrically co
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MORIHIRO, KOICHIRO, NOBUYUKI MATSUI, and HARUHIKO NISHIMURA. "CHAOTIC EXPLORATION EFFECTS ON REINFORCEMENT LEARNING IN SHORTCUT MAZE TASK." International Journal of Bifurcation and Chaos 16, no. 10 (2006): 3015–22. http://dx.doi.org/10.1142/s0218127406016616.

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Reinforcement learning is usually required in the process of trial and error called exploration, and the uniform pseudorandom number generator is considered effective in that process. As a generator for the exploration, chaotic sources are also useful in creating a random-like sequence such as in the case of stochastic sources. In this research, we investigate the efficiency of the deterministic chaotic generator for the exploration in learning a nonstationary shortcut maze problem. As a result, it is found that the deterministic chaotic generator based on the logistic map is better in the per
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Du, Mengnan, Fengxiang He, Na Zou, Dacheng Tao, and Xia Hu. "Shortcut Learning of Large Language Models in Natural Language Understanding." Communications of the ACM 67, no. 1 (2023): 110–20. http://dx.doi.org/10.1145/3596490.

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Dissertations / Theses on the topic "Shortcut learning"

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Dancette, Corentin. "Shortcut Learning in Visual Question Answering." Electronic Thesis or Diss., Sorbonne université, 2023. http://www.theses.fr/2023SORUS073.

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Cette thèse se concentre sur la tâche de VQA, c'est à dire les systèmes questions-réponses visuelles. Nous étudions l'apprentissage des biais dans cette tâche. Les modèles ont tendance à apprendre des corrélations superficielles les conduisant à des réponses correctes dans la plupart des cas, mais qui peuvent échouer lorsqu'ils rencontrent des données d'entrée inhabituelles. Nous proposons deux méthodes pour réduire l'apprentissage par raccourci sur le VQA. La première, RUBi, consiste à encourager le modèle à apprendre à partir des exemples les plus difficiles et les moins biaisés grâce à une
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Zhou, Tianyu. "Deep Learning Models for Route Planning in Road Networks." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-235216.

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Traditional shortest path algorithms can efficiently find the optimal paths in graphs using simple heuristics. However, formulating a simple heuristic is challenging under the road network setting since there are multiple factors to consider, such as road segment length, edge centrality, and speed limit. This study investigates how a neural network can learn to take these factors as inputs and yield a path given a pair of origin and destination. The research question is formulated as: Are neural networks applicable to real-time route planning tasks in a roadnetwork?. The proposed metric to eva
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Vishnoi, Nisheeth Kumar. "Theoretical Aspects of Randomization in Computation." Diss., Georgia Institute of Technology, 2004. http://hdl.handle.net/1853/6424.

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Randomness has proved to be a powerful tool in all of computation. It is pervasive in areas such as networking, machine learning, computer graphics, optimization, computational number theory and is "necessary" for cryptography. Though randomized algorithms and protocols assume access to "truly" random bits, in practice, they rely on the output of "imperfect" sources of randomness such as pseudo-random number generators or physical sources. Hence, from a theoretical standpoint, it becomes important to view randomness as a resource and to study the following fundamental questions pertaining to
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Lourenço, Wilson Da Silva. "Objeto de aprendizagem para o ensino de algoritmos solucionadores de problemas de otimização em redes." Universidade Nove de Julho, 2015. http://bibliotecadigital.uninove.br/handle/tede/1122.

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Submitted by Nadir Basilio (nadirsb@uninove.br) on 2015-07-17T15:18:49Z No. of bitstreams: 1 Wilson da Silva Lourenco.pdf: 1321079 bytes, checksum: ea090b0df77d0c04ef1dde30e7b41558 (MD5)<br>Made available in DSpace on 2015-07-17T15:18:49Z (GMT). No. of bitstreams: 1 Wilson da Silva Lourenco.pdf: 1321079 bytes, checksum: ea090b0df77d0c04ef1dde30e7b41558 (MD5) Previous issue date: 2015-02-26<br>The network optimization problems (NOP) are common to several areas such as engineering, transport and telecommunications, and have been objects of intense research and studies. Among the classical NOP
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Ratli, Mustapha. "Système de gestion du stationnement dans un environnement dynamique et multi-objectifs." Thesis, Valenciennes, 2014. http://www.theses.fr/2014VALE0035/document.

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Aujourd'hui, le problème de stationnement devient l'un des enjeux majeurs de la recherche dans la planification des transports urbains et la gestion du trafic. En fait, les conséquences de l'absence de places de stationnement ainsi que la gestion inadéquate de ces installations sont énormes. L'objectif de cette thèse est de fournir des algorithmes efficaces et robustes afin que les conducteurs gagnent du temps et de l'argent et aussi augmenter les revenus des gestionnaires de parking. Le problème est formulé comme un problème d'affectation multi-objectifs dans des environnements statique et dy
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HOCEINI, SAID. "Techniques d'Apprentissage par Renforcement pour le Routage Adaptatif dans les Réseaux de Télécommunication à Trafic Irrégulie." Phd thesis, Université Paris XII Val de Marne, 2004. http://tel.archives-ouvertes.fr/tel-00010430.

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L'objectif de ce travail de thèse est de proposer des approches algorithmiques permettant de traiter la problématique du routage adaptatif (RA) dans un réseau de communication à trafic irrégulier. L'analyse des algorithmes existants nous a conduit à retenir comme base de travail l'algorithme Q-Routing (QR); celui-ci s'appuie sur la technique d'apprentissage par renforcement basée sur les modèles de Markov. L'efficacité de ce type de routage dépend fortement des informations sur la charge et la nature du trafic sur le réseau. Ces dernières doivent être à la fois, suffisantes, pertinentes et ref
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Перепеліцин, Сергій Олександрович, та Sergiy Perepelitsyn. "Технологія налаштовування радіомережі в умовах завад інтеграцією маршрутизації та самонавчання". Thesis, Національний авіаційний університет, 2021. https://er.nau.edu.ua/handle/NAU/49767.

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Дисертаційна робота присвячене розв'язання науково-технічної задачі зі створення інформаційної технології моделювання ефективного контролю за топологією однорангової мобільної радіомережі, що само налагоджується, тактичного рівня й управління зміною показників її функціонування в умовах впливу радіоперешкод та радіоелектронної протидії (РЕБ). У дисертаційній роботі вперше запропоновано нова топологія, що відрізняється від відомих тим, що включає елементи навчання поведінки мережі в умовах перешкод. Введені нові процеси інтелектуальної системи керування вузлом мобільної радіомережі: пошукова
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Ng, Amy Kah-Mei, and 吳佳美. "The Shortcut to Professionals:A Case Study on Professional Learning Community of Chinese Independent School in Malaysia." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/f5ufyc.

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碩士<br>國立臺灣師範大學<br>教育政策與行政研究所<br>103<br>The Shortcut to Professionals: A Case Study on Professional Learning Community of Chinese Independent School in Malaysia Abstract The purpose of this study was to understand the operation of teachers’ professional learning communities (PLCs) in Lotus Chinese Independent School in the past four years. The research aimed to explore how PLCs practices affected teachers’ professional growth in Lotus Chinese Independent School, including their professional capacity and sense of identity as teachers working in a Chinese Independent School. The research w
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Books on the topic "Shortcut learning"

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Buzan, Tony. Mind maps for kids: The shortcut to success at school. Thorsons, 2003.

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Consultants, PSD, ed. Learn anything: Shortcuts to learning. PSD Consultants, 1994.

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Brandimarte, Paolo. From Shortest Paths to Reinforcement Learning. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-61867-4.

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Paul, Meisel, ed. Spanish for gringos: Shortcuts, tips, and secrets to successful learning. Barron's, 1999.

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Aber, Joanne. Getting a college degree fast: Testing out & other accredited shortcuts. Prometheus Books, 1996.

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Patton, Kevin T. Student survival guide for anatomy and physiology: Tips, techniques and shortcuts for learning about the structure and function of the human body with style, ease, and good humor. Mosby, 1999.

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Patton, Kevin T. Student survival guide for structure and function of the body: Tips, techniques and shortcuts for learning about human anatomy and physiology with style, ease, and good humor. Mosby, 2000.

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Patton, Kevin T. Survival guide for anatomy & physiology: Tips, techniques, and shortcuts for learning about the structure and function of the human body with style, ease, and good humor. Mosby Elsevier, 2006.

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Oglander, Susan, ed. I'm Ready to Learn About Following Directions. Kids Stuff: Collins Publishers [Canada], 1986.

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A Shortcut to Learning Chinese, Book I. Dorrance Pub Co, 1998.

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Book chapters on the topic "Shortcut learning"

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Vovk, Vladimir, Alexander Gammerman, and Glenn Shafer. "Non-conformal Shortcut." In Algorithmic Learning in a Random World. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-06649-8_10.

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Lin, Manxi, Nina Weng, Kamil Mikolaj, et al. "Shortcut Learning in Medical Image Segmentation." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-72111-3_59.

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Nuriel, Oren, Sharon Fogel, and Ron Litman. "TextAdaIN: Paying Attention to Shortcut Learning in Text Recognizers." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-19815-1_25.

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Bransby, Kit M., Arian Beqiri, Woo-Jin Cho Kim, Jorge Oliveira, Agisilaos Chartsias, and Alberto Gomez. "BackMix: Mitigating Shortcut Learning in Echocardiography with Minimal Supervision." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-72083-3_53.

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Zhang, Ting, Yujian Li, and Zhaoying Liu. "Shortcut Convolutional Neural Networks for Classification of Gender and Texture." In Artificial Neural Networks and Machine Learning – ICANN 2017. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68612-7_4.

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Asazuma, Yuya, Hiroaki Funayama, Yuichiroh Matsubayashi, Tomoya Mizumoto, Paul Reisert, and Kentaro Inui. "Take No Shortcuts! Stick to the Rubric: A Method for Building Trustworthy Short Answer Scoring Models." In Communications in Computer and Information Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-67351-1_23.

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AbstractThis paper introduces a new strategy to enhance the trustworthiness of Short Answer Scoring (SAS) systems used in educational settings. Although the development of scoring models with high accuracy has become feasible due to advancements in machine learning methods, particularly recent Transformers, there is a risk of shortcut learning using superficial cues present in training data, leading to behaviors that contradict rubric standards and thus raising issues of model trustworthiness. To address this issue, we introduce an efficient strategy that aligns the features of responses with
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Müller, Sarah, Louisa Fay, Lisa M. Koch, Sergios Gatidis, Thomas Küstner, and Philipp Berens. "Benchmarking Dependence Measures to Prevent Shortcut Learning in Medical Imaging." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-73290-4_6.

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Correia, Pedro Gonçalo, and Henrique Lopes Cardoso. "Towards Explaining Shortcut Learning Through Attention Visualization and Adversarial Attacks." In Engineering Applications of Neural Networks. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-34204-2_45.

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Sveen, Finn Olav, Jose Manuel Torres, and Jose Maria Sarriegi. "Learning from Your Elders: A Shortcut to Information Security Management Success." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-75101-4_21.

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Wu, Shaoxuan, Xiao Zhang, Bin Wang, Zhuo Jin, Hansheng Li, and Jun Feng. "Gaze-Directed Vision GNN for Mitigating Shortcut Learning in Medical Image." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-72378-0_48.

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Conference papers on the topic "Shortcut learning"

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Yuan, Yu, Lili Zhao, Kai Zhang, Guangting Zheng, and Qi Liu. "Do LLMs Overcome Shortcut Learning? An Evaluation of Shortcut Challenges in Large Language Models." In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.emnlp-main.679.

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Tasawong, Panuthep, Peerat Limkonchotiwat, Potsawee Manakul, Can Udomcharoenchaikit, Ekapol Chuangsuwanich, and Sarana Nutanong. "Efficient Overshadowed Entity Disambiguation by Mitigating Shortcut Learning." In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.emnlp-main.855.

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Zhou, Yuqing, Ruixiang Tang, Ziyu Yao, and Ziwei Zhu. "Navigating the Shortcut Maze: A Comprehensive Analysis of Shortcut Learning in Text Classification by Language Models." In Findings of the Association for Computational Linguistics: EMNLP 2024. Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.findings-emnlp.146.

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Stanley, Emma, Nils D. Forkert, and Matthias Wilms. "Does a diffusion-based generative classifier avoid shortcut learning in medical image analysis? An initial investigation using synthetic neuroimaging data." In Imaging Informatics, edited by Shandong Wu. SPIE, 2025. https://doi.org/10.1117/12.3046811.

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Wu, Zhijing, Jingbo He, Jingtong Zhang, and Wenyi Pan. "Simulating Physical Climate Models Using Temporal Transformers and Spatial Data as Computational Shortcuts." In 2024 15th International Conference on Distance Learning and Education (ICDLE). IEEE, 2024. https://doi.org/10.1109/icdle63439.2024.00011.

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Xie, Xiaopeng, Ming Yan, Xiwen Zhou, et al. "Shortcuts Arising from Contrast: Towards Effective and Lightweight Clean-Label Attacks in Prompt-Based Learning." In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.emnlp-main.834.

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Syafalni, Infall, Mohamad Imam Firdaus, Nana Sutisna, Trio Adiono, Tutun Juhana, and Rahmat Mulyawan. "DMQ: Dual-Mode Q-Learning Hardware Accelerator for Shortest Path and Coverage Area." In 2024 IEEE 37th International System-on-Chip Conference (SOCC). IEEE, 2024. http://dx.doi.org/10.1109/socc62300.2024.10737818.

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S, Bose, Maheswaran N, Logeswari G, Anitha T, Prabhu D, and Gokulraj G. "Adaptive Deep Learning Techniques for Real-Time Shortest Path Optimization in Drone Ambulance Operations during Disaster." In 2024 8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC). IEEE, 2024. http://dx.doi.org/10.1109/i-smac61858.2024.10714626.

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Si, Qingyi, Fandong Meng, Mingyu Zheng, et al. "Language Prior Is Not the Only Shortcut: A Benchmark for Shortcut Learning in VQA." In Findings of the Association for Computational Linguistics: EMNLP 2022. Association for Computational Linguistics, 2022. http://dx.doi.org/10.18653/v1/2022.findings-emnlp.271.

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Song, Jifei, Kaiyue Pang, Yi-Zhe Song, Tao Xiang, and Timothy M. Hospedales. "Learning to Sketch with Shortcut Cycle Consistency." In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2018. http://dx.doi.org/10.1109/cvpr.2018.00090.

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