Relevant bibliographies by topics / Long-tail Problem

Academic literature on the topic 'Long-tail Problem'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Long-tail Problem"

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Sunitha, M., and T. Adilakshmi. "Addressing long tail problem in music recommendation systems." International Journal of Computational Systems Engineering 6, no. 5 (2021): 246. http://dx.doi.org/10.1504/ijcsyse.2021.10045577.

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Sunitha, M., and T. Adilakshmi. "Addressing long tail problem in music recommendation systems." International Journal of Computational Systems Engineering 6, no. 5 (2021): 246. http://dx.doi.org/10.1504/ijcsyse.2021.121367.

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Alshammari, Gharbi, Jose L. Jorro-Aragoneses, Nikolaos Polatidis, Stelios Kapetanakis, Elias Pimenidis, and Miltos Petridis. "A switching multi-level method for the long tail recommendation problem." Journal of Intelligent & Fuzzy Systems 37, no. 6 (December 23, 2019): 7189–98. http://dx.doi.org/10.3233/jifs-179331.

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Chavez, G., C. Proescholdt, and G. Lavy-Shahaf. "The Long Tail Problem: Novel Parametric Methods Still Underestimate Conditional Long-Term Survival In Glioblastoma." International Journal of Radiation Oncology*Biology*Physics 108, no. 3 (November 2020): e783-e784. http://dx.doi.org/10.1016/j.ijrobp.2020.07.247.

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Shiba, Hayato, and Nobuyasu Ito. "Long-Time Tail Problem and Anomalous Transport in Three-Dimensional Nonlinear Lattices." Progress of Theoretical Physics Supplement 178 (2009): 79–85. http://dx.doi.org/10.1143/ptps.178.79.

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Yoon-Joo Park. "The Adaptive Clustering Method for the Long Tail Problem of Recommender Systems." IEEE Transactions on Knowledge and Data Engineering 25, no. 8 (August 2013): 1904–15. http://dx.doi.org/10.1109/tkde.2012.119.

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Zhao, Yan, Weicong Chen, Xu Tan, Kai Huang, and Jihong Zhu. "Adaptive Logit Adjustment Loss for Long-Tailed Visual Recognition." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 3 (June 28, 2022): 3472–80. http://dx.doi.org/10.1609/aaai.v36i3.20258.

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Data in the real world tends to exhibit a long-tailed label distribution, which poses great challenges for the training of neural networks in visual recognition. Existing methods tackle this problem mainly from the perspective of data quantity, i.e., the number of samples in each class. To be specific, they pay more attention to tail classes, like applying larger adjustments to the logit. However, in the training process, the quantity and difficulty of data are two intertwined and equally crucial problems. For some tail classes, the features of their instances are distinct and discriminative, which can also bring satisfactory accuracy; for some head classes, although with sufficient samples, the high semantic similarity with other classes and lack of discriminative features will bring bad accuracy. Based on these observations, we propose Adaptive Logit Adjustment Loss (ALA Loss) to apply an adaptive adjusting term to the logit. The adaptive adjusting term is composed of two complementary factors: 1) quantity factor, which pays more attention to tail classes, and 2) difficulty factor, which adaptively pays more attention to hard instances in the training process. The difficulty factor can alleviate the over-optimization on tail yet easy instances and under-optimization on head yet hard instances. The synergy of the two factors can not only advance the performance on tail classes even further, but also promote the accuracy on head classes. Unlike previous logit adjusting methods that only concerned about data quantity, ALA Loss tackles the long-tailed problem from a more comprehensive, fine-grained and adaptive perspective. Extensive experimental results show that our method achieves the state-of-the-art performance on challenging recognition benchmarks, including ImageNet-LT, iNaturalist 2018, and Places-LT.
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Zhang, Ziqi, and Lei Luo. "Hate speech detection: A solved problem? The challenging case of long tail on Twitter." Semantic Web 10, no. 5 (September 26, 2019): 925–45. http://dx.doi.org/10.3233/sw-180338.

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Politi, A., and T. Schneider. "Corrections to the Lifshitz Tail and the Long-Time Behaviour of the Trapping Problem." Europhysics Letters (EPL) 5, no. 8 (April 15, 1988): 715–20. http://dx.doi.org/10.1209/0295-5075/5/8/009.

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Fan, Xinyue, Teng Liu, Hong Bao, Weiguo Pan, Tianjiao Liang, and Han Li. "Long-Tail Instance Segmentation Based on Memory Bank and Confidence Calibration." Applied Sciences 12, no. 18 (September 19, 2022): 9366. http://dx.doi.org/10.3390/app12189366.

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In the field of computer vision, training a well-performing model on a dataset with a long-tail distribution is a challenging task. To address this challenge, image resampling is usually introduced as a simple and effective solution. However, when performing instance segmentation tasks, there may be multiple classes in one image. Hence, image resampling alone is not enough to obtain a sufficiently balanced distribution at the level of target data volume. In this paper, we propose an improved instance segmentation method for long-tail datasets based on Mask R-CNN. Specifically, an object-centric memory bank is used to establish an object-centric storage strategy that can solve the imbalance problem with respect to categories. In the testing phase, a post-processing calibration is used to adjust each class logit to change the confidence score, which improves the prediction score of tail classes. A discrete cosine transform-based mask is used to obtain high-quality masks, which improves segmentation accuracy. The evaluation of the proposed method on the LVIS dataset demonstrates its effectiveness. The proposed method improves the AP performance of EQL by 2.2%.
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Dissertations / Theses on the topic "Long-tail Problem"

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Wang, Qingyang. "A study of transient bottlenecks: understanding and reducing latency long-tail problem in n-tier web applications." Diss., Georgia Institute of Technology, 2014. http://hdl.handle.net/1853/54002.

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An essential requirement of cloud computing or data centers is to simultaneously achieve good performance and high utilization for cost efficiency. High utilization through virtualization and hardware resource sharing is critical for both cloud providers and cloud consumers to reduce management and infrastructure costs (e.g., energy cost, hardware cost) and to increase cost-efficiency. Unfortunately, achieving good performance (e.g., low latency) for web applications at high resource utilization remains an elusive goal. Both practitioners and researchers have experienced the latency long-tail problem in clouds during periods of even moderate utilization (e.g., 50%). In this dissertation, we show that transient bottlenecks are an important contributing factor to the latency long-tail problem. Transient bottlenecks are bottlenecks with a short lifespan on the order of tens of milliseconds. Though short-lived, transient bottleneck can cause a long-tail response time distribution that spans a spectrum of 2 to 3 orders of magnitude, from tens of milliseconds to tens of seconds, due to the queuing effect propagation and amplification caused by complex inter-tier resource dependencies in the system. Transient bottlenecks can arise from a wide range of factors at different system layers. For example, we have identified transient bottlenecks caused by CPU dynamic voltage and frequency scaling (DVFS) control at the CPU architecture layer, Java garbage collection (GC) at the system software layer, and virtual machine (VM) consolidation at the application layer. These factors interact with naturally bursty workloads from clients, often leading to transient bottlenecks that cause overall performance degradation even if all the system resources are far from being saturated (e.g., less than 50%). By combining fine-grained monitoring tools and a sophisticated analytical method to generate and analyze monitoring data, we are able to detect and study transient bottlenecks in a systematic way.
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Book chapters on the topic "Long-tail Problem"

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Fan, Baoyu, Yue Liu, and Laurie Cuthbert. "Improvement of DGA Long Tail Problem Based on Transfer Learning." In Computer and Information Science, 139–52. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-12127-2_10.

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Alshammari, Gharbi, Jose L. Jorro-Aragoneses, Stelios Kapetanakis, Miltos Petridis, Juan A. Recio-García, and Belén Díaz-Agudo. "A Hybrid CBR Approach for the Long Tail Problem in Recommender Systems." In Case-Based Reasoning Research and Development, 35–45. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-61030-6_3.

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Pang, Jiaona, Jun Guo, and Wei Zhang. "Using Multi-objective Optimization to Solve the Long Tail Problem in Recommender System." In Advances in Knowledge Discovery and Data Mining, 302–13. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-16142-2_24.

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Maynard Smith, John, and Eors Szathmary. "The origin of protocells." In The Major Transitions in Evolution. Oxford University Press, 1997. http://dx.doi.org/10.1093/oso/9780198502944.003.0011.

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Cellularization has the following main aspects that we have to explain: • The need for active (self-generated) compartmentation when metabolism is liberated from the surface. • The origin of membranogenic molecules and membranes. • The origin and mechanism of spontaneous protocell fission. • The transportation problem. Simple membranes are not ‘leaky’ enough to permit important nutrients to pass through. • Were the first protocells autotrophs or heterotrophs? The evolution of the first autocatalytic metabolic cycle. • The iron-sulphur world and the RNA world: are they mutually exclusive or complementary? • The problem of the origin of the two membranes of negibacteria, the most ancient existing group of organisms. • The origin of chromosomes and DNA synthesis. We shall discuss these problems in turn. As we discussed before, the prebiotic pizza has the ability to localize metabolites and genes. This is advantageous for two reasons: Reactants are kept in each other’s proximity, which ensures that reaction rates will be high enough and that important compounds do not drift away. Genes will interact, directly (e.g. by influencing each other’s replication) or indirectly (by catalysing steps of metabolism), only with their neighbours: selection will thus be able to ensure cooperation among genes that would otherwise compete against each other. Life liberated itself from surfaces a long time ago. Somehow, passive localization must have been replaced by an active process of membrane generation, maintenance and fission. The basic structure of contemporary biomembranes is as follows. There is a molecular bilayer of lipids, to which proteins are attached in various ways. The bilayer is formed because the membrane constituents are so-called amphipathic molecules: they have a hydrophilic head and a hydrophobic tail. Since the binding interaction of water with itself is much stronger than that between water and hydrophobic compounds, the latter are expelled by water as much as possible; this results in tails coming together. A simple sheet of bilayer would still be not at the energy minimum because its edges would be exposed to water. An energetically favourable solution is the formation of a lipid vesicle.
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Long, Jeffery D. "Like a Dog’s Curly Tail: Finding Perfection in a World of Imperfection." In Comparing Faithfully. Fordham University Press, 2016. http://dx.doi.org/10.5422/fordham/9780823274666.003.0006.

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Jeffery Long, a Hindu theologian, explores the problem of evil as it is raised and addressed by thinkers in the Ramakrishna Vedanta tradition of Hinduism and by two separate schools of thought from contemporary Christianity. The textual sources used from the Ramakrishna tradition consist of the teachings of Sri Ramakrishna as found in the primary sources on his life, as well as the Complete Works of Swami Vivekananda. From Christianity, Long employs works of John Hick and David Ray Griffin on the topic of theodicy. Despite the fact that the latter two authors hail from the same religious tradition, Long shows that Hick and Ramakrishna are in closer agreement on this topic than either is with Griffin’s process theology. The essay offers a revised version of the Ramakrishna-Hick theodicy that takes Griffin’s objections into account.
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Conference papers on the topic "Long-tail Problem"

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Li, Wei, and Zhixin Li. "Domain Adaptative Semantic Segmentation by alleviating Long-tail Problem." In 2021 International Joint Conference on Neural Networks (IJCNN). IEEE, 2021. http://dx.doi.org/10.1109/ijcnn52387.2021.9533948.

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Li, Jingyi, Hui Zhang, and Yidong Li. "Parallel Detection Architecture for long-tail Problem in Intelligent Transportation Systems." In 2021 IEEE 1st International Conference on Digital Twins and Parallel Intelligence (DTPI). IEEE, 2021. http://dx.doi.org/10.1109/dtpi52967.2021.9540174.

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Winata, Genta Indra, Guangsen Wang, Caiming Xiong, and Steven Hoi. "Adapt-and-Adjust: Overcoming the Long-Tail Problem of Multilingual Speech Recognition." In Interspeech 2021. ISCA: ISCA, 2021. http://dx.doi.org/10.21437/interspeech.2021-1390.

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Somha, Worawit, Hiroyuki Yamauchi, and Ma YuYu. "A discussion on SRAM forward/inverse problem analyses for RTN long-tail distributions." In 2013 IEEE Computer Society Annual Symposium on VLSI (ISVLSI). IEEE, 2013. http://dx.doi.org/10.1109/isvlsi.2013.6654623.

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Nie, Weihang, Jiawei Ren, Gaofeng Cheng, Wenchao Wang, and Ji Xu. "Tackling long-tail data distribution problem of deep learning based underwater target recognition system." In OCEANS 2021: San Diego – Porto. IEEE, 2021. http://dx.doi.org/10.23919/oceans44145.2021.9706030.

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He, Tao, Lianli Gao, Jingkuan Song, Jianfei Cai, and Yuan-Fang Li. "Learning from the Scene and Borrowing from the Rich: Tackling the Long Tail in Scene Graph Generation." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/82.

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Despite the huge progress in scene graph generation in recent years, its long-tail distribution in object relationships remains a challenging and pestering issue. Existing methods largely rely on either external knowledge or statistical bias information to alleviate this problem. In this paper, we tackle this issue from another two aspects: (1) scene-object interaction aiming at learning specific knowledge from a scene via an additive attention mechanism; and (2) long-tail knowledge transfer which tries to transfer the rich knowledge learned from the head into the tail. Extensive experiments on the benchmark dataset Visual Genome on three tasks demonstrate that our method outperforms current state-of-the-art competitors. Our source code is available at https://github.com/htlsn/issg.
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Shang, Xinyi, Yang Lu, Gang Huang, and Hanzi Wang. "Federated Learning on Heterogeneous and Long-Tailed Data via Classifier Re-Training with Federated Features." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/308.

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Federated learning (FL) provides a privacy-preserving solution for distributed machine learning tasks. One challenging problem that severely damages the performance of FL models is the co-occurrence of data heterogeneity and long-tail distribution, which frequently appears in real FL applications. In this paper, we reveal an intriguing fact that the biased classifier is the primary factor leading to the poor performance of the global model. Motivated by the above finding, we propose a novel and privacy-preserving FL method for heterogeneous and long-tailed data via Classifier Re-training with Federated Features (CReFF). The classifier re-trained on federated features can produce comparable performance as the one re-trained on real data in a privacy-preserving manner without information leakage of local data or class distribution. Experiments on several benchmark datasets show that the proposed CReFF is an effective solution to obtain a promising FL model under heterogeneous and long-tailed data. Comparative results with the state-of-the-art FL methods also validate the superiority of CReFF. Our code is available at https://github.com/shangxinyi/CReFF-FL.
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Sun, H. G., W. Chen, and K. Y. Sze. "A Novel Finite Element Method for a Class of Time Fractional Diffusion Equations." In ASME 2011 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2011. http://dx.doi.org/10.1115/detc2011-48079.

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Anomalous transport of contaminants in groundwater or porous soil is a research focus in hydrology and soil science for decades. Because fractional diffusion equations can well characterize early breakthrough and heavy tail decay features of contaminant transport process, they have been considered as promising tools to simulate anomalous transport processes in complex media. However, the efficient and accurate computation of fractional diffusion equations is a main task in their applications. In this paper, we introduce a novel numerical method which captures the critical Mittag-Leffler decay feature of subdiffusion in time direction, to solve a class of time fractional diffusion equations. A key advantage of the new method is that it overcomes the critical problem in the application of time fractional differential equations: long-time range computation. To illustrate its efficiency and simplicity, three typical academic examples are presented. Numerical results show a good agreement with the exact solutions.
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Bengio, Samy. "Sharing Representations for Long Tail Computer Vision Problems." In ICMI '15: INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2818346.2818348.

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Hountalas, Dimitrios T., Nikolaos F. Sakellaridis, Efthimios Pariotis, Antonis K. Antonopoulos, Leonidas Zissimatos, and Nikiforos Papadakis. "Effect of Turbocharger Cut Out on Two-Stroke Marine Diesel Engine Performance and NOx Emissions at Part Load Operation." In ASME 2014 12th Biennial Conference on Engineering Systems Design and Analysis. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/esda2014-20514.

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The diesel engine is widely used for marine vessel propulsion due to its relatively high efficiency compared to existing alternative propulsion systems. The majority of these engines are slow speed two stroke ones. Despite the improvement of their efficiency there now exists a demand for drastic reduction of daily fuel oil consumption as a result of the global financial situation and continuously increasing fuel prices. Towards this effort, slow steaming is a promising solution for the drastic reduction of daily and specific fuel consumption when expressed in tn/mile. This requires engine operation in the low load (low speed) range where these engines are not designed to operate for long term. The main problem related to slow-steaming, is the lack of air which has a negative impact on the engine and its subsystems. A promising solution to the problem is turbocharger (T/C) cut-out at low load when more than one T/C exists. In the present work a combined computational and experimental investigation is conducted to evaluate the operation potential of a large two stroke marine diesel engine equipped with two T/Cs using T/C cut-out, for which the specific technology presents various challenges. This is achieved using an in-house engine simulation model and measurements with and without T/C cut-out. From the results it is revealed that using this technique the scavenging air and peak firing pressure increase while the specific fuel consumption decreases. In this way, some major problems related with the long term operation of the engine under low load conditions, i.e. accumulation of carbon deposits on the exhaust gas side and continuous operation of the auxiliary air blowers, are surpassed. Moreover, a theoretical investigation is conducted considering fuel injection retard to minimize the peak firing pressure penalty while taking care to limit the corresponding negative impact on specific fuel consumption. For NOx emissions the effect of T/C cut-out is also considered using tail pipe emission data measured during the official shop tests. From the analysis conducted it has been revealed that the technique of turbocharger cut–out (one of two) is technically feasible and could offer certain advantages when slow-steaming is implemented. Moreover, comparing the calculated with the measured results, it has been revealed that the simulation model successfully estimates engine operation with and without T/C cut-out, being a valuable tool for the engineers to investigate combustion and pollutant formation mechanisms under various engine configurations.
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