Relevant bibliographies by topics / Shortcut learning

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  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

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Journal articles on the topic "Shortcut learning"

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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 (March 24, 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 level and proportion of the candidate shortcut features to be dropped. Extensive experiments on five benchmark datasets demonstrate that, when combined with various OCL algorithms, DropTop increases the average accuracy by up to 10.4% and decreases the forgetting by up to 63.2%.
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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 (December 24, 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 practice. This study presents a method to detect and quantify this shortcut learning in trained classifiers for skin cancer diagnosis, since it is known that dermoscopy images can contain artefacts. Specifically, we train a standard VGG16-based skin cancer classifier on the public ISIC dataset, for which colour calibration charts (elliptical, coloured patches) occur only in benign images and not in malignant ones. Our methodology artificially inserts those patches and uses inpainting to automatically remove patches from images to assess the changes in predictions. We find that our standard classifier partly bases its predictions of benign images on the presence of such a coloured patch. More importantly, by artificially inserting coloured patches into malignant images, we show that shortcut learning results in a significant increase in misdiagnoses, making the classifier unreliable when used in clinical practice. With our results, we, therefore, want to increase awareness of the risks of using black box machine learning models trained on potentially biased datasets. Finally, we present a model-agnostic method to neutralise shortcut learning by removing the bias in the training dataset by exchanging coloured patches with benign skin tissue using image inpainting and re-training the classifier on this de-biased dataset.
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Geirhos, Robert, Jörn-Henrik Jacobsen, Claudio Michaelis, Richard Zemel, Wieland Brendel, Matthias Bethge, and Felix A. Wichmann. "Shortcut learning in deep neural networks." Nature Machine Intelligence 2, no. 11 (November 2020): 665–73. http://dx.doi.org/10.1038/s42256-020-00257-z.

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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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POTAPOV, ALEXEI B., and M. K. ALI. "LEARNING, EXPLORATION AND CHAOTIC POLICIES." International Journal of Modern Physics C 11, no. 07 (October 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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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 (October 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 performance of the exploration than in the stochastic random generator. This has been made clear by analyzing the difference of the performances between the two generators in terms of the patterns of exploration occurrence. We also examine the tent map, which is homeomorphic to the logistic map, compared with other generators.
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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 (December 21, 2023): 110–20. http://dx.doi.org/10.1145/3596490.

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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 (October 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 coupled network but impairs synchronization for the chemically coupled one. Both the introduction of shortcuts and the increase of the coupling strength improve synchronization and they are helpful in increasing the excitement for the chemically coupled network, but have little effect on the excitement of the electrically coupled one.
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Hu, Ruilin, Yajun Du, Jingrong Hu, and Hui Li. "Cross-community shortcut detection based on network representation learning and structural features." Intelligent Data Analysis 27, no. 3 (May 18, 2023): 709–32. http://dx.doi.org/10.3233/ida-216513.

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As social networks continue to expand, an increasing number of people prefer to use social networks to post their comments and express their feelings, and as a result, the information contained in social networks has grown explosively. The effective extraction of valuable information from social networks has attracted the attention of many researchers. It can mine hidden information from social networks and promote the development of social network structures. At present, many ranking node approaches, such as structural hole spanners and opinion leaders, are widely adopted to extract valuable information and knowledge. However, approaches for analyzing edge influences are seldom considered. In this study, we proposed an edge PageRank to mine shortcuts (these edges without direct mutual friends) that are located among communities and play an important role in the spread of public opinion. We first used a network-embedding algorithm to order the spanners and determine the direction of every edge. Then, we transferred the graphs of social networks into edge graphs according to the ordering. We considered the nodes and edges of the graphs of the social networks as edges and nodes of the edge graphs, respectively. Finally, we improved the PageRank algorithm on the edge graph to obtained the edge ranking and extracted the shortcuts of social networks. The experimental results for five different sizes of social networks, such as email, YouTube, DBLP-L, DBLP-M, and DBLP-S, verify whether the inferred shortcut is indeed more useful for information dissemination, and the utility of three sets of edges inferred by different methods is compared, namely, the edge inferred by ER, the edge inferred by the Jaccard index. The ER approach improves by approximately 10%, 9.9%, and 8.3% on DBLP, YouTube, and Orkut. Our method is more effective than the edge ranked by the Jaccard index.
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Zhong, Yujie, Xiao Li, Jiangjian Xie, and Junguo Zhang. "A Lightweight Automatic Wildlife Recognition Model Design Method Mitigating Shortcut Learning." Animals 13, no. 5 (February 25, 2023): 838. http://dx.doi.org/10.3390/ani13050838.

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Recognizing wildlife based on camera trap images is challenging due to the complexity of the wild environment. Deep learning is an optional approach to solve this problem. However, the backgrounds of images captured from the same infrared camera trap are rather similar, and shortcut learning of recognition models occurs, resulting in reduced generality and poor recognition model performance. Therefore, this paper proposes a data augmentation strategy that integrates image synthesis (IS) and regional background suppression (RBS) to enrich the background scene and suppress the existing background information. This strategy alleviates the model’s focus on the background, guiding it to focus on the wildlife in order to improve the model’s generality, resulting in better recognition performance. Furthermore, to offer a lightweight recognition model for deep learning-based real-time wildlife monitoring on edge devices, we develop a model compression strategy that combines adaptive pruning and knowledge distillation. Specifically, a student model is built using a genetic algorithm-based pruning technique and adaptive batch normalization (GA-ABN). A mean square error (MSE) loss-based knowledge distillation method is then used to fine-tune the student model so as to generate a lightweight recognition model. The produced lightweight model can reduce the computational effort of wildlife recognition with only a 4.73% loss in accuracy. Extensive experiments have demonstrated the advantages of our method, which is beneficial for real-time wildlife monitoring with edge intelligence.
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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 loss spécifique. Nous proposons ensuite SCN, un modèle pour la tâche de comptage visuel, avec une architecture conçue pour être robuste aux changements de distribution. Nous étudions ensuite les raccourcis multimodaux dans le VQA. Nous montrons qu'ils ne sont pas seulement basés sur des corrélations entre la question et la réponse, mais qu'ils peuvent aussi impliquer des informations sur l'image. Nous concevons un benchmark d'évaluation pour mesurer la robustesse des modèles aux raccourcis multimodaux. L'apprentissage de ces raccourcis est particulièrement problématique lorsque les modèles sont testés dans un contexte de changement de distribution. C'est pourquoi il est important de pouvoir évaluer la fiabilité des modèles VQA. Nous proposons une méthode pour leur permettre de s'abstenir de répondre lorsque leur confiance est trop faible. Cette méthode consiste à entraîner un modèle externe, dit "sélecteur", pour prédire la confiance du modèle VQA. Nous montrons que notre méthode peut améliorer la fiabilité des modèles VQA existants
This thesis is focused on the task of VQA: it consists in answering textual questions about images. We investigate Shortcut Learning in this task: the literature reports the tendency of models to learn superficial correlations leading them to correct answers in most cases, but which can fail when encountering unusual input data. We first propose two methods to reduce shortcut learning on VQA. The first, which we call RUBi, consists of an additional loss to encourage the model to learn from the most difficult and less biased examples -- those which cannot be answered solely from the question. We then propose SCN, a model for the more specific task of visual counting, which incorporates architectural priors designed to make it more robust to distribution shifts. We then study the existence of multimodal shortcuts in the VQA dataset. We show that shortcuts are not only based on correlations between the question and the answer but can also involve image information. We design an evaluation benchmark to measure the robustness of models to multimodal shortcuts. We show that existing models are vulnerable to multimodal shortcut learning. The learning of those shortcuts is particularly harmful when models are evaluated in an out-of-distribution context. Therefore, it is important to evaluate the reliability of VQA models, i.e. We propose a method to improve their ability to abstain from answering when their confidence is too low. It consists of training an external ``selector'' model to predict the confidence of the VQA model. This selector is trained using a cross-validation-like scheme in order to avoid overfitting on the training set
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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 evaluate the effectiveness of the neural network is arrival rate. The quality of generated paths is evaluated by time efficiency. The real-time performance of the model is also compared between pathfinding in dynamic and static graphs, using theabove metrics. A staggered approach is applied in progressing this investigation. The first step is to generate random graphs, which allows us to monitor the size and properties of the training graph without caring too many details in a road network. The next step is to determine, as a proof of concept, if a neural network can learn to traverse simple graphs with multiple strategies, given that road networks are in effect complex graphs. Finally, we scale up by including factors that might affect the pathfinding in real road networks. Overall, the training data is optimal paths in a graph generated by a shortest path algorithm. The model is then applied to new graphs to generate a path given a pair of origin and destination. The arrival rate and time efficiency are calculated and compared with that of the corresponding optimal path. Experimental results show that the effectiveness, i.e., arrival rate ofthe model is 90% and the path quality, i.e., time efficiency has a medianof 0.88 and a large variance. The experiment shows that the model has better performance in dynamic graphs than in static graphs. Overall, the answer to the research question is positive. However, there is still room to improve the effectiveness of the model and the paths generated by the model. This work shows that a neural network trained to make locally optimal choices can hardly give a globally optimal solution. We also show that our method, only making locally optimal choices, can adapt to dynamic graphs with little performance overhead.
Traditionella algoritmer för att hitta den kortaste vägen kan effektivt hitta de optimala vägarna i grafer med enkel heuristik. Att formulera en enkel heuristik är dock utmanande för vägnätverk eftersom det finns flera faktorer att överväga, såsom vägsegmentlängd, kantcentralitet och hastighetsbegränsningar. Denna studie undersöker hur ett neuralt nätverk kan lära sig att ta dessa faktorer som indata och finna en väg utifrån start- och slutpunkt. Forskningsfrågan är formulerad som: Är neuronnätverket tillämpliga på realtidsplaneringsuppgifter i ett vägnät?. Det föreslagna måttet för att utvärdera effektiviteten hos det neuronnätverket är ankomstgrad. Kvaliteten på genererade vägar utvärderas av tidseffektivitet. Prestandan hos modellen jämförs också mellan sökningen i dynamiska och statiska grafer, med hjälp av ovanstående mätvärden. Undersökningen bedrivs i flera steg. Det första steget är att generera slumpmässiga grafer, vilket gör det möjligt för oss att övervaka träningsdiagrammets storlek och egenskaper utan att ta hand om för många detaljer i ett vägnät. Nästa steg är att, som ett bevis på konceptet, undersöka om ett neuronnätverk kan lära sig att korsa enkla grafer med flera strategier, eftersom vägnätverk är i praktiken komplexa grafer. Slutligen skalas studien upp genom att inkludera faktorer som kan påverka sökningen i riktiga vägnät. Träningsdata utgörs av optimala vägar i en graf som genereras av en algoritm för att finna den kortaste vägen. Modellen appliceras sedan i nya grafer för att hitta en väg mellan start och slutpunkt. Ankomstgrad och tidseffektivitet beräknas och jämförs med den motsvarande optimala sökvägen. De experimentella resultaten visar att effektiviteten, dvs ankomstgraden av modellen är 90% och vägkvaliteten dvs tidseffektiviteten har en median på 0,88 och en stor varians. Experimentet visar att modellen har bättre prestanda i dynamiska grafer än i statiska grafer. Sammantaget är svaret på forskningsfrågan positivt. Det finns dock fortfarande utrymme att förbättra modellens effektivitet och de vägar som genereras av modellen. Detta arbete visar att ett neuronnätverk tränat för att göra lokalt optimala val knappast kan ge globalt optimal lösning. Vi visar också att vår metod, som bara gör lokalt optimala val, kan anpassa sig till dynamiska grafer med begränsad prestandaförlust.
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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 it: Extraction: How do we generate "high quality" random bits from "imperfect" sources? Randomization: How do we use randomness to obtain efficient algorithms? Derandomization: How (and when) can we "remove" our dependence on random bits? In this thesis, we consider important problems in these three prominent and diverse areas pertaining to randomness. In randomness extraction, we present extractors for "oblivious bit fixing sources". In (a non-traditional use of) randomization, we have obtained results in machine learning (learning juntas) and proved hardness of lattice problems. While in derandomization, we present a deterministic algorithm for a fundamental problem called "identity testing". In this thesis we also initiate a complexity theoretic study of Hilbert's 17th problem. Here identity testing is used in an interesting manner. A common theme in this work has been the use of tools from areas such as number theory in a variety of ways, and often the techniques themselves are quite interesting.
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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)
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
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 are the problems of Shortest Path (SPP), Max Flow (MFP) and Traveling Salesman (TSP), which are usually studied in undergraduate and graduate courses such as Industrial Engineering, Computer Science, Information Systems and Logistics, with the use of resources such as chalk and blackboard that hinder the teacher's work, in the sense of showing the functioning of algorithms that solve these problems while maintaining students' motivation for learning. In this context, it is proposed in this research, a computational tool, characterized as a Learning Object (OA) and called TASNOP - Teaching Algorithms for Solving Network Optimization Problems, whose purpose is to contribute to students' understanding about concepts from NOP and, mainly, the functioning of algorithms A*, Greedy Search and Dijkstra used for resolution of SPP, Ford-Fulkerson employed in the resolution of MFP and the Nearest Neighbor to solve the TSP. It is important to highlight that the proposed OA can be accessed through web and also employed in distance learning environments (DLE). Experiments conducted in 2014 with 129 students of Computer Science, from which 51 performed an exercise using the TASNOP and 78 without this tool, confirm that students who used the TASNOP performed better in solving the proposed exercise, corroborating the idea that the OA helped to improve their understanding about the algorithms discussed in this research. In addition, the 51 students who employed the TASNOP answered a questionnaire about it use and, the answers indicated that the TASNOP shows a potential to be used as a learning support tool.
Os problemas de otimização em redes (POR) são comuns a diversas áreas como engenharia, transportes e telecomunicações, e têm sido objetos de intensas pesquisas e estudos. Entre os POR clássicos estão os problemas de Caminho Mínimo (PCM), Fluxo Máximo (PFM) e Caixeiro Viajante (PCV), os quais normalmente são estudados em cursos de graduação e pós-graduação tais como Engenharia de Produção, Ciência da Computação, Sistemas de Informação e Logística, com a utilização de recursos como giz e lousa, o que dificulta o trabalho do professor, no sentido de mostrar o funcionamento dos algoritmos que solucionam esses problemas, mantendo a motivação dos alunos para a aprendizagem. Neste contexto, propõe-se nesta pesquisa, uma ferramenta computacional, caracterizada como um Objeto de Aprendizagem (OA) denominado TASNOP - Teaching Algorithms for Solving Network Optimization Problems, cuja finalidade é contribuir para compreensão dos alunos sobre conceitos de POR e, principalmente, sobre o funcionamento dos algoritmos A*, Busca Gulosa, e Dijkstra, usados para resolução do PCM, Ford-Fulkerson empregado na resolução de PFM e o algoritmo Vizinho mais Próximo para resolução do PCV. É importante ressaltar que o OA proposto pode ser acessado via web e, inclusive, ser acoplado em ambientes de ensino a distância (EaD). Experimentos realizados no ano de 2014 envolvendo 129 alunos do curso de Ciência da Computação, dos quais 51 resolveram um exercício com o uso do TASNOP e 78 sem o seu uso, permitiram verificar que os alunos que utilizaram o TASNOP obtiveram melhor desempenho na resolução do exercício proposto, corroborando a ideia de que o OA contribuiu para melhorar suas compreensões acerca dos algoritmos abordados nesta pesquisa. Em adição, os 51 alunos que usaram o TASNOP responderam a um questionário sobre o seu uso e, com base nessas respostas, ficou evidente o potencial do TASNOP como uma ferramenta de apoio ao ensino.
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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 dynamique. Tout d'abord, dans l'environnement statique, nous proposons de nouvelles heuristiques en deux phases pour calculer une approximation de l'ensemble des solutions efficaces pour un problème bi-objectif. Dans la première phase, nous générons l'ensemble des solutions supportées par un algorithme dichotomique standard. Dans la deuxième phase, nous proposons quatre métaheuristiques pour générer une approximation des solutions non supportées. Les approches proposées sont testées sur le problème du plus court chemin bi-objectif et le problème d'affectation bi-objectif. Dans le contexte de l'environnement dynamique, nous proposons une formulation du problème sous forme d'un programme linéaire en nombres entiers mixtes qui est résolue à plusieurs reprises sur un horizon de temps donné. Les fonctions objectives considérées, permettent un équilibre entre la satisfaction des conducteurs et l'intérêt du gestionnaire de parking. Deux approches sont proposées pour résoudre ce problème d'affectation dynamique avec ou sans phase d'apprentissage. Pour renforcer la phase d'apprentissage, un algorithme à estimation de distribution est proposé pour prévoir la demande future. Pour évaluer l'efficacité des algorithmes proposés, des essais de simulation ont été effectués. Aussi une mise en œuvre pilote a été menée dans le parking à l'Université de Valenciennes en utilisant une plateforme existante, appelée Context Aware Transportation Services (CATS), qui permet le déploiement dynamique de services. Cette plate-forme peut dynamiquement passer d'une approche à l'autre en fonction du contexte. Enfin cette thèse s'inscrit dans le projet SYstem For Smart Road Applications ( SYFRA)
The parking problem is nowadays one of the major issues in urban transportation planning and traffic management research. In fact, the consequences of the lack of parking slots along with the inadequate management of these facilities are tremendous. The aim of this thesis is to provide efficient and robust algorithms in order to save time and money for drivers and to increase the income of parking managers. The problem is formulated as a multi-objective assignment problem in static and dynamic environments. First, for the static environment, we propose new two-phase heuristics to calculate an approximation of the set of efficient solutions for a bi-objective problem. In the first phase, we generate the supported efficient set with a standard dichotomic algorithm. In the second phase we use four metaheuristics to generate an approximation of the non-supported efficient solutions. The proposed approaches are tested on the bi-objective shortest path problem and the biobjective assignment problem. For the dynamic environment, we propose a mixed integer linear programming formulation that is solved several times over a given horizon. The objective functions consist of a balance between the satisfaction of drivers and the interest of the parking managers. Two approaches are proposed for this dynamic assignment problem with or without learning phase. To reinforce the learning phase, an estimation of distribution algorithm is proposed to predict the future demand. In order to evaluate the effectiveness of the proposed algorithms, simulation tests have been carried out. A pilot implementation has also been conducted in the parking of the University of Valenciennes, using an existing platform called framework for context aware transportation services, which allows dynamic deployment of services. This platform can dynamically switch from one approach to another depending on the context. This thesis is part of the project SYstem For Smart Road Applications (SYFRA)
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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 reflétant la charge réelle du réseau lors de la phase de prise de décision. Pour remédier aux inconvénients des techniques utilisant le QR, nous avons proposé deux algorithmes de RA. Le premier, appelé Q-Neural Routing, s'appuie sur un modèle neuronal stochastique pour estimer et mettre à jour les paramètres nécessaires au RA. Afin d'accélérer le temps de convergence, une deuxième approche est proposée : K-Shortest path Q-Routing. Elle est basée sur la technique de routage multi chemin combiné avec l'algorithme QR, l'espace d'exploration étant réduit aux k meilleurs chemins. Les deux algorithmes proposés sont validés et comparés aux approches traditionnelles en utilisant la plateforme de simulation OPNET, leur efficacité au niveau du RA est mise particulièrement en évidence. En effet, ceux-ci permettent une meilleure prise en compte de l'état du réseau contrairement aux approches classiques.
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Перепеліцин, Сергій Олександрович, and 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 настройка рівня шумів або сигналу перешкоди на вхідному тракті комунікатора та контроль зв'язності комутації вузлів мобільної радіомережі. Запропоновано новий метод самонастроювання радіомережі на основі градієнтного підходу, що відрізняється від відомих, інтеграцією градієнтного алгоритму налаштування ваги сусідніх вузлів і пошуку найкоротшого маршруту в мережі, що підлягає впливу перешкод, що адекватно вирішення завдань ітераційної оптимізації. Отримано нові результати для моделювання радіомережі, що відрізняються від відомих тим, що виконано моделювання радіомережі на основі градієнтного алгоритму навчання, які підтверджуються теоретичними дослідженнями й практичними результатами. Запропоновано технологію автоматизованої обробки даних з графічним представленням топології радіомережі за допомогою геоінформаційної системи ArcGIS-10 американської компанії ESRI, яка дозволяє оцінити стійкість мережевої структури в динамічній зміні та виявити кордони стійкої зв'язності вузлів комутації радіомережі. Такий підхід є новою варіацією яка розширює межі розв'язання задачі розподілу трафіку і перешкодостійкості радіомережі з урахуванням структури мережевої топології. Практичне значення отриманих результатів моделювання та експериментальне дослідження підтвердило правильність запропонованих рішень та отриманих теоретичних результатів.
The scientific degree thesis is devoted to solve the task to create an efficient modeling technology for network topology of peer-to-peer mobile self-adaptive tactical military radio network and to manage the changing performance indicators of such radio network under radio frequency interference and defense. The scientific thesis first time offered a brand new topology differing from existing ones, that researches network behavior under circumstances of interference and radiofrequency defense. Innovative intellect management of mobile radio network node were introduced: search adjustment of the noise level or interference signal on entry of communicator and connectivity control of the radio network nodes. Main difference of current intellectual system is mechanism of data/knowledge storage and processing (knowledge base block) for efficient activities in uncertain (lack of information) and random circumstance. The knowledgebase contains the control system, it’s goals and management principles, decision making structure and the control object itself. The control system can be contributed with learning sub-system, that generalizes the accumulated experience, which is show on pic [55]. The subsystem for control, gathering, storage and processing of data measures mobile nodes and general radio network parameters. The decision making subsystem was build thinking about unification of control functions into independent groups to separate network management on subsystems and ensure easier math modeling of network management. The new gradient approach of self-adapted radio network was proposed, that differs from known methods by gradient setting of neighboring nodes weight and search of close path in network affected by interference. Dijkstra algorithm is a search procedure of the shortest path at weighted oriented graph. Algorithm works by steps, starting from first radio network node: on each step it refers to one node, and reduces marks and stops execution when all radio network nodes are visited. Dijkstra algorithm is resourceful, but given the knowledge of network topology and path to necessary peak, the router always knows an alternative route to the required node, in case of fall of any node of the path. Self-learning is a key feature for solving complex problems, that cannot be solved in usual way. The difficulty of constructing such network is to choose invariant features for describing of input data so the differences are caused only by random factors, such as noise. In this case, the informative features will be the vector representation of the symbols on which the noise component or interference was applied. Among the major types of neuro networks, including deep learning networks, the BP (back propagation) structure of neuro network is widely used, because it has features of self-adaptation, and recognition is computation-efficient. The algorithm of non-linear optimization (Levenberg–Marquardt algorithm) which is applied for search of minimal strategy – linear approximation and gradient descent. According to the simulation procedure, we determine the neural network BP with three layers. The initial structure has two layers, the number of neurons in the first layer is 33, and in the second - 27, which corresponds to the number of network outputs. The network training function allows to assess the quality of network configuration by constructing a regression line in which the proportionality factor allows to determine the degree of correlation between input and output data. In this case, there is a high degree of correlation between input and output data, R = 0.999. Training in this example results in an error of 1.52 · 10-5, due to the complexity of the output data. The learning took only eight epochs. The BP multilayer neural network self-adapting algorithm is a controlled algorithm. In fact, it's an iterative method of gradient search for the best parameters in these conditions, which is characterized by the simplicity of the classification task in terms of "input-output" and reliability. New results of radio network modeling are obtained. On the one hand, they differ from the known ones in that the radio network modeling is performed on the basis of gradient learning algorithm. On the other hand, the results are confirmed by theoretical researches and practical results. The proposed geo information technology of automated data processing with a graphical representation of the radio network topology using the geographic information system ArcGIS-10 of the American company ESRI, which allows to assess the stability of the network structure in dynamic change and identify the limits of stable connectivity of radio switching nodes. This approach is a new variation that expands the boundaries of solving the problem of traffic distribution and noise immunity of the radio network, taking into account the structure of the network topology. The practical significance of the obtained simulation results and experimental research confirmed the correctness of the proposed solutions and the obtained theoretical results.
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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
國立臺灣師範大學
教育政策與行政研究所
103
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 was a case study based on first-hand observation, document reviews, interviews with ten teachers and administrators in Lotus Chinese Independent School, and the resarcher’s reflection notes. The findings of the study were the following. Lotus Chinese Independent School adopted top-down leadership to promote the goal of providing students equal learning qualities. Led by various subject leaders, PLCs were built through cooperation, shared practice, and shared leadership. Senior teachers’ willing to take part also played a significant role to smooth the process. In addition, three obstacles (on the levels of institution, individual, and society, respectively) of the operation of PLCs were spotted. On the bright side, PLCs improved teachers’ professional capacity and advanced the professional conversations between senior teachers and less-experienced ones. It trimed the time needed for new teachers to fit in. On the down side, however, there were obstacles on both the levels of institution and individual which weakened teachers’ sense of identity as Chinese Independent School’s teachers. Based on these findings, suggestions are generated for the administrators, senior teachers, and new teachers of Lotus Chinese Independent School, as well as for other Chinese Independent Schools interested in starting their own PLCs and for the United Chinese School Committees Association of Malaysia (UCSCAM) on this regard. Suggestions for further studies are provided.
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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. London: Thorsons, 2003.

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Buzan, Tony. Mind maps for kids: Rev up for revision : the shortcut to exam success. London: Thorsons, 2004.

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

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

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Spanish for gringos: Shortcuts, tips, and secrets to successful learning. New York: Barron's, 1990.

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

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WordPerfect shortcuts for lawyers: Learning merge and macros in one hour. Chicago, Ill: American Bar Association, Section of Law Practice Management, 1994.

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Aber, Joanne. Getting a college degree fast: Testing out & other accredited shortcuts. Amherst, N.Y: 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. St. Louis: 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. St. Louis: Mosby, 2000.

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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, 305–30. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-06649-8_10.

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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, 427–45. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-19815-1_25.

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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, 30–39. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68612-7_4.

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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, 558–69. Cham: 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, 224–37. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-75101-4_21.

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Porisky, Adam, Tom Brosch, Emil Ljungberg, Lisa Y. W. Tang, Youngjin Yoo, Benjamin De Leener, Anthony Traboulsee, Julien Cohen-Adad, and Roger Tam. "Grey Matter Segmentation in Spinal Cord MRIs via 3D Convolutional Encoder Networks with Shortcut Connections." In Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, 330–37. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67558-9_38.

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Oyedotun, Oyebade K., Abd El Rahman Shabayek, Djamila Aouada, and Björn Ottersten. "Training Very Deep Networks via Residual Learning with Stochastic Input Shortcut Connections." In Neural Information Processing, 23–33. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70096-0_3.

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Bentley, Peter J., Soo Ling Lim, Adam Gaier, and Linh Tran. "Evolving Through the Looking Glass: Learning Improved Search Spaces with Variational Autoencoders." In Lecture Notes in Computer Science, 371–84. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-14714-2_26.

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AbstractNature has spent billions of years perfecting our genetic representations, making them evolvable and expressive. Generative machine learning offers a shortcut: learn an evolvable latent space with implicit biases towards better solutions. We present SOLVE: Search space Optimization with Latent Variable Evolution, which creates a dataset of solutions that satisfy extra problem criteria or heuristics, generates a new latent search space, and uses a genetic algorithm to search within this new space to find solutions that meet the overall objective. We investigate SOLVE on five sets of criteria designed to detrimentally affect the search space and explain how this approach can be easily extended as the problems become more complex. We show that, compared to an identical GA using a standard representation, SOLVE with its learned latent representation can meet extra criteria and find solutions with distance to optimal up to two orders of magnitude closer. We demonstrate that SOLVE achieves its results by creating better search spaces that focus on desirable regions, reduce discontinuities, and enable improved search by the genetic algorithm.
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Iwata, Hajime. "Method to Generate an Operation Learning Support System by Shortcut Key Differences in Similar Software." In Lecture Notes in Computer Science, 332–40. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-20618-9_33.

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Saranrittichai, Piyapat, Chaithanya Kumar Mummadi, Claudia Blaiotta, Mauricio Munoz, and Volker Fischer. "Overcoming Shortcut Learning in a Target Domain by Generalizing Basic Visual Factors from a Source Domain." In Lecture Notes in Computer Science, 294–309. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-19806-9_17.

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Conference papers on the topic "Shortcut learning"

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Si, Qingyi, Fandong Meng, Mingyu Zheng, Zheng Lin, Yuanxin Liu, Peng Fu, Yanan Cao, Weiping Wang, and Jie Zhou. "Language Prior Is Not the Only Shortcut: A Benchmark for Shortcut Learning in VQA." In Findings of the Association for Computational Linguistics: EMNLP 2022. Stroudsburg, PA, USA: 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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Kawase, Ricardo, Patrick Siehndel, and Bernardo Pereira Nunes. "To the Point: A Shortcut to Essential Learning." In 2014 IEEE 14th International Conference on Advanced Learning Technologies (ICALT). IEEE, 2014. http://dx.doi.org/10.1109/icalt.2014.210.

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Wen, Jiaxin, Yeshuang Zhu, Jinchao Zhang, Jie Zhou, and Minlie Huang. "AutoCAD: Automatically Generate Counterfactuals for Mitigating Shortcut Learning." In Findings of the Association for Computational Linguistics: EMNLP 2022. Stroudsburg, PA, USA: Association for Computational Linguistics, 2022. http://dx.doi.org/10.18653/v1/2022.findings-emnlp.170.

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Du, Yanrui, Jing Yan, Yan Chen, Jing Liu, Sendong Zhao, Qiaoqiao She, Hua Wu, Haifeng Wang, and Bing Qin. "Less Learn Shortcut: Analyzing and Mitigating Learning of Spurious Feature-Label Correlation." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. California: International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/560.

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Recent research has revealed that deep neural networks often take dataset biases as a shortcut to make decisions rather than understand tasks, leading to failures in real-world applications. In this study, we focus on the spurious correlation between word features and labels that models learn from the biased data distribution of training data. In particular, we define the word highly co-occurring with a specific label as biased word, and the example containing biased word as biased example. Our analysis shows that biased examples are easier for models to learn, while at the time of prediction, biased words make a significantly higher contribution to the models' predictions, and models tend to assign predicted labels over-relying on the spurious correlation between words and labels. To mitigate models' over-reliance on the shortcut (i.e. spurious correlation), we propose a training strategy Less-Learn-Shortcut (LLS): our strategy quantifies the biased degree of the biased examples and down-weights them accordingly. Experimental results on Question Matching, Natural Language Inference and Sentiment Analysis tasks show that LLS is a task-agnostic strategy and can improve the model performance on adversarial data while maintaining good performance on in-domain data.
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Deng, Yuhui, and Le Dong. "Removing Adverse Background Shortcut with Text for Few-Shot Classification." In 2023 International Conference on Image Processing, Computer Vision and Machine Learning (ICICML). IEEE, 2023. http://dx.doi.org/10.1109/icicml60161.2023.10424747.

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Du, Mengnan, Varun Manjunatha, Rajiv Jain, Ruchi Deshpande, Franck Dernoncourt, Jiuxiang Gu, Tong Sun, and Xia Hu. "Towards Interpreting and Mitigating Shortcut Learning Behavior of NLU models." In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Stroudsburg, PA, USA: Association for Computational Linguistics, 2021. http://dx.doi.org/10.18653/v1/2021.naacl-main.71.

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Wang, Shunxin, Christoph Brune, Raymond Veldhuis, and Nicola Strisciuglio. "DFM-X: Augmentation by Leveraging Prior Knowledge of Shortcut Learning." In 2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW). IEEE, 2023. http://dx.doi.org/10.1109/iccvw60793.2023.00020.

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Shen, Xin, and Wai Lam. "Towards Domain-Generalizable Paraphrase Identification by Avoiding the Shortcut Learning." In International Conference Recent Advances in Natural Language Processing. INCOMA Ltd. Shoumen, BULGARIA, 2021. http://dx.doi.org/10.26615/978-954-452-072-4_148.

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Hoftijzer, Dennis, Gertjan Burghouts, and Luuk Spreeuwers. "Language-Based Augmentation to Address Shortcut Learning in Object-Goal Navigation." In 2023 Seventh IEEE International Conference on Robotic Computing (IRC). IEEE, 2023. http://dx.doi.org/10.1109/irc59093.2023.00007.

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