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Статті в журналах з теми "Metric Learning Approaches"
Sandiwarno, Sulis. "Empirical lecturers’ and students’ satisfaction assessment in e-learning systems based on the usage metrics." Research and Evaluation in Education 7, no. 2 (December 30, 2021): 118–31. http://dx.doi.org/10.21831/reid.v7i2.39642.
Повний текст джерелаLi, Zilong. "A Boosting-Based Deep Distance Metric Learning Method." Computational Intelligence and Neuroscience 2022 (March 15, 2022): 1–9. http://dx.doi.org/10.1155/2022/2665843.
Повний текст джерелаDutta, Ujjal Kr, Mehrtash Harandi, and C. Chandra Sekhar. "Unsupervised Metric Learning with Synthetic Examples." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 3834–41. http://dx.doi.org/10.1609/aaai.v34i04.5795.
Повний текст джерелаYang, Lu, Peng Wang, and Yanning Zhang. "Stop-Gradient Softmax Loss for Deep Metric Learning." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 3 (June 26, 2023): 3164–72. http://dx.doi.org/10.1609/aaai.v37i3.25421.
Повний текст джерелаDutta, Ujjal Kr, Mehrtash Harandi, and C. Chandra Shekhar. "Semi-Supervised Metric Learning: A Deep Resurrection." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 8 (May 18, 2021): 7279–87. http://dx.doi.org/10.1609/aaai.v35i8.16894.
Повний текст джерелаKaya and Bilge. "Deep Metric Learning: A Survey." Symmetry 11, no. 9 (August 21, 2019): 1066. http://dx.doi.org/10.3390/sym11091066.
Повний текст джерелаSyed, Muhamamd Adnan, Zhenjun Han, Zhaoju Li, and Jianbin Jiao. "Impostor Resilient Multimodal Metric Learning for Person Reidentification." Advances in Multimedia 2018 (2018): 1–11. http://dx.doi.org/10.1155/2018/3202495.
Повний текст джерелаSaha, Soumadeep, Utpal Garain, Arijit Ukil, Arpan Pal, and Sundeep Khandelwal. "MedTric : A clinically applicable metric for evaluation of multi-label computational diagnostic systems." PLOS ONE 18, no. 8 (August 10, 2023): e0283895. http://dx.doi.org/10.1371/journal.pone.0283895.
Повний текст джерелаBhukar, Karan, Harshit Kumar, Dinesh Raghu, and Ajay Gupta. "End-to-End Deep Reinforcement Learning for Conversation Disentanglement." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 11 (June 26, 2023): 12571–79. http://dx.doi.org/10.1609/aaai.v37i11.26480.
Повний текст джерелаKomamizu, Takahiro. "Combining Multi-ratio Undersampling and Metric Learning for Imbalanced Classification." Journal of Data Intelligence 2, no. 4 (December 2021): 462–75. http://dx.doi.org/10.26421/jdi2.4-5.
Повний текст джерелаДисертації з теми "Metric Learning Approaches"
Abou-Moustafa, Karim. "Metric learning revisited: new approaches for supervised and unsupervised metric learning with analysis and algorithms." Thesis, McGill University, 2012. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=106370.
Повний текст джерелаDans cette thèse, je propose deux algorithmes pour l'apprentissage de la métrique dX; le premier pour l'apprentissage supervisé, et le deuxième pour l'apprentissage non-supervisé, ainsi que pour l'apprentissage supervisé et semi-supervisé. En particulier, je propose des algorithmes qui prennent en considération la structure et la géométrie de X d'une part, et les caractéristiques des ensembles de données du monde réel d'autre part. Cependant, si on cherche également la réduction de dimension, donc sous certaines hypothèses légères sur la topologie de X, et en même temps basé sur des informations disponibles a priori, on peut apprendre une intégration de X dans un espace Euclidien de petite dimension Rp0 p0 << p, où la distance Euclidienne révèle mieux les ressemblances entre les éléments de X et leurs groupements (clusters). Alors, comme un sous-produit, on obtient simultanément une réduction de dimension et un apprentissage métrique. Pour l'apprentissage supervisé, je propose PARDA, ou Pareto discriminant analysis, pour la discriminante réduction linéaire de dimension. PARDA est basé sur le mécanisme d'optimisation à multi-objectifs; optimisant simultanément plusieurs fonctions objectives, éventuellement des fonctions contradictoires. Cela permet à PARDA de s'adapter à la topologie de classe dans un espace dimensionnel plus petit, et naturellement gère le problème de masquage de classe associé au discriminant Fisher dans le cadre d'analyse de problèmes à multi-classes. En conséquence, PARDA permet des meilleurs résultats de classification par rapport aux techniques modernes de réduction discriminante de dimension. Pour l'apprentissage non-supervisés, je propose un cadre algorithmique, noté par ??, qui encapsule les algorithmes spectraux d'apprentissage formant an algorithme d'apprentissage de métrique. Le cadre ?? capture la structure locale et la densité locale d'information de chaque point dans un ensemble de données, et donc il porte toutes les informations sur la densité d'échantillon différente dans l'espace d'entrée. La structure de ?? induit deux métriques de distance pour ses éléments: la métrique Bhattacharyya-Riemann dBR et la métrique Jeffreys-Riemann dJR. Les deux mesures réorganisent la proximité entre les points de X basé sur la structure locale et la densité autour de chaque point. En conséquence, lorsqu'on combine l'espace métrique (??, dBR) ou (??, dJR) avec les algorithmes de "spectral clustering" et "Euclidean embedding", ils donnent des améliorations significatives dans les précisions de regroupement et les taux d'erreur pour une grande variété de tâches de clustering et de classification.
Zheng, Lilei. "Triangular similarity metric learning : A siamese architecture approach." Thesis, Lyon, 2016. http://www.theses.fr/2016LYSEI045/document.
Повний текст джерелаIn many machine learning and pattern recognition tasks, there is always a need for appropriate metric functions to measure pairwise distance or similarity between data, where a metric function is a function that defines a distance or similarity between each pair of elements of a set. In this thesis, we propose Triangular Similarity Metric Learning (TSML) for automatically specifying a metric from data. A TSML system is loaded in a siamese architecture which consists of two identical sub-systems sharing the same set of parameters. Each sub-system processes a single data sample and thus the whole system receives a pair of data as the input. The TSML system includes a cost function parameterizing the pairwise relationship between data and a mapping function allowing the system to learn high-level features from the training data. In terms of the cost function, we first propose the Triangular Similarity, a novel similarity metric which is equivalent to the well-known Cosine Similarity in measuring a data pair. Based on a simplified version of the Triangular Similarity, we further develop the triangular loss function in order to perform metric learning, i.e. to increase the similarity between two vectors in the same class and to decrease the similarity between two vectors of different classes. Compared with other distance or similarity metrics, the triangular loss and its gradient naturally offer us an intuitive and interesting geometrical interpretation of the metric learning objective. In terms of the mapping function, we introduce three different options: a linear mapping realized by a simple transformation matrix, a nonlinear mapping realized by Multi-layer Perceptrons (MLP) and a deep nonlinear mapping realized by Convolutional Neural Networks (CNN). With these mapping functions, we present three different TSML systems for various applications, namely, pairwise verification, object identification, dimensionality reduction and data visualization. For each application, we carry out extensive experiments on popular benchmarks and datasets to demonstrate the effectiveness of the proposed systems
Rossi, Alex. "Self-supervised information retrieval: a novel approach based on Deep Metric Learning and Neural Language Models." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021.
Знайти повний текст джерелаDahab, Sarah. "An approach to measuring software systems using new combined metrics of complex test." Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLL015/document.
Повний текст джерелаMost of the measurable software quality metrics are currently based on low level metrics, such as cyclomatic complexity, number of comment lines or number of duplicated blocks. Likewise, quality of software engineering is more related to technical or management factoid, and should provide useful metrics for quality requirements. Currently the assessment of these quality requirements is not automated, not empirically validated in real contexts, and the assessment is defined without considering principles of measurement theory. Therefore it is difficult to understand where and how to improve the software following the obtained result. In this domain, the main challenges are to define adequate and useful metrics for quality requirements, software design documents and other software artifacts, including testing activities.The main scientific problematic that are tackled in this proposed thesis are the following : defining metrics and its supporting tools for measuring modern software engineering activities with respect to efficiency and quality. The second consists in analyzing measurement results for identifying what and how to improve automatically. The last one consists in the measurement process automation in order to reduce the development time. Such highly automated and easy to deploy solution will be a breakthrough solution, as current tools do not support it except for very limited scope
Rydell, Christopher. "Deep Learning for Whole Slide Image Cytology : A Human-in-the-Loop Approach." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-450356.
Повний текст джерелаGhadie, Mohamed A. "Analysis and Reconstruction of the Hematopoietic Stem Cell Differentiation Tree: A Linear Programming Approach for Gene Selection." Thesis, Université d'Ottawa / University of Ottawa, 2015. http://hdl.handle.net/10393/32048.
Повний текст джерелаNOTARANGELO, NICLA MARIA. "A Deep Learning approach for monitoring severe rainfall in urban catchments using consumer cameras. Models development and deployment on a case study in Matera (Italy) Un approccio basato sul Deep Learning per monitorare le piogge intense nei bacini urbani utilizzando fotocamere generiche. Sviluppo e implementazione di modelli su un caso di studio a Matera (Italia)." Doctoral thesis, Università degli studi della Basilicata, 2021. http://hdl.handle.net/11563/147016.
Повний текст джерелаNegli ultimi 50 anni, le alluvioni si sono confermate come il disastro naturale più frequente e diffuso a livello globale. Tra gli impatti degli eventi meteorologici estremi, conseguenti ai cambiamenti climatici, rientrano le alterazioni del regime idrogeologico con conseguente incremento del rischio alluvionale. Il monitoraggio delle precipitazioni in tempo quasi reale su scala locale è essenziale per la mitigazione del rischio di alluvione in ambito urbano e periurbano, aree connotate da un'elevata vulnerabilità. Attualmente, la maggior parte dei dati sulle precipitazioni è ottenuta da misurazioni a terra o telerilevamento che forniscono informazioni limitate in termini di risoluzione temporale o spaziale. Ulteriori problemi possono derivare dagli elevati costi. Inoltre i pluviometri sono distribuiti in modo non uniforme e spesso posizionati piuttosto lontano dai centri urbani, comportando criticità e discontinuità nel monitoraggio. In questo contesto, un grande potenziale è rappresentato dall'utilizzo di tecniche innovative per sviluppare sistemi inediti di monitoraggio a basso costo. Nonostante la diversità di scopi, metodi e campi epistemologici, la letteratura sugli effetti visivi della pioggia supporta l'idea di sensori di pioggia basati su telecamera, ma tende ad essere specifica per dispositivo scelto. La presente tesi punta a indagare l'uso di dispositivi fotografici facilmente reperibili come rilevatori-misuratori di pioggia, per sviluppare una fitta rete di sensori a basso costo a supporto dei metodi tradizionali con una soluzione rapida incorporabile in dispositivi intelligenti. A differenza dei lavori esistenti, lo studio si concentra sulla massimizzazione del numero di fonti di immagini (smartphone, telecamere di sorveglianza generiche, telecamere da cruscotto, webcam, telecamere digitali, ecc.). Ciò comprende casi in cui non sia possibile regolare i parametri fotografici o ottenere scatti in timeline o video. Utilizzando un approccio di Deep Learning, la caratterizzazione delle precipitazioni può essere ottenuta attraverso l'analisi degli aspetti percettivi che determinano se e come una fotografia rappresenti una condizione di pioggia. Il primo scenario di interesse per l'apprendimento supervisionato è una classificazione binaria; l'output binario (presenza o assenza di pioggia) consente la rilevazione della presenza di precipitazione: gli apparecchi fotografici fungono da rivelatori di pioggia. Analogamente, il secondo scenario di interesse è una classificazione multi-classe; l'output multi-classe descrive un intervallo di intensità delle precipitazioni quasi istantanee: le fotocamere fungono da misuratori di pioggia. Utilizzando tecniche di Transfer Learning con reti neurali convoluzionali, i modelli sviluppati sono stati compilati, addestrati, convalidati e testati. La preparazione dei classificatori ha incluso la preparazione di un set di dati adeguato con impostazioni verosimili e non vincolate: dati aperti, diversi dati di proprietà del National Research Institute for Earth Science and Disaster Prevention - NIED (telecamere dashboard in Giappone accoppiate con dati radar multiparametrici ad alta precisione) e attività sperimentali condotte nel simulatore di pioggia su larga scala del NIED. I risultati sono stati applicati a uno scenario reale, con la sperimentazione attraverso una telecamera di sorveglianza preesistente che utilizza la connettività 5G fornita da Telecom Italia S.p.A. nella città di Matera (Italia). L'analisi si è svolta su più livelli, fornendo una panoramica sulle questioni relative al paradigma del rischio di alluvione in ambito urbano e questioni territoriali specifiche inerenti al caso di studio. Queste ultime includono diversi aspetti del contesto, l'importante ruolo delle piogge dal guidare l'evoluzione millenaria della morfologia urbana alla determinazione delle criticità attuali, oltre ad alcune componenti di un prototipo Web per la comunicazione del rischio alluvionale su scala locale. I risultati ottenuti e l'implementazione del modello corroborano la possibilità che le tecnologie a basso costo e le capacità locali possano aiutare a caratterizzare la forzante pluviometrica a supporto dei sistemi di allerta precoce basati sull'identificazione di uno stato meteorologico significativo. Il modello binario ha raggiunto un'accuratezza e un F1-score di 85,28% e 0,86 per il set di test e di 83,35% e 0,82 per l'implementazione nel caso di studio. Il modello multi-classe ha raggiunto un'accuratezza media e F1-score medio (macro-average) di 77,71% e 0,73 per il classificatore a 6 vie e 78,05% e 0,81 per quello a 5 classi. Le prestazioni migliori sono state ottenute nelle classi relative a forti precipitazioni e assenza di pioggia, mentre le previsioni errate sono legate a precipitazioni meno estreme. Il metodo proposto richiede requisiti operativi limitati, può essere implementato facilmente e rapidamente in casi d'uso reali, sfruttando dispositivi preesistenti con un uso parsimonioso di risorse economiche e computazionali. La classificazione può essere eseguita su singole fotografie scattate in condizioni disparate da dispositivi di acquisizione di uso comune, ovvero da telecamere statiche o in movimento senza regolazione dei parametri. Questo approccio potrebbe essere particolarmente utile nelle aree urbane in cui i metodi di misurazione come i pluviometri incontrano difficoltà di installazione o limitazioni operative o in contesti in cui non sono disponibili dati di telerilevamento o radar. Il sistema non si adatta a scene che sono fuorvianti anche per la percezione visiva umana. I limiti attuali risiedono nelle approssimazioni intrinseche negli output. Per colmare le lacune evidenti e migliorare l'accuratezza della previsione dell'intensità di precipitazione, sarebbe possibile un'ulteriore raccolta di dati. Sviluppi futuri potrebbero riguardare l'integrazione con ulteriori esperimenti in campo e dati da crowdsourcing, per promuovere comunicazione, partecipazione e dialogo aumentando la resilienza attraverso consapevolezza pubblica e impegno civico in una concezione di comunità smart.
Kim, Junae. "Efficient and scalable approaches to Mahalanobis distance metric learning." Phd thesis, 2012. http://hdl.handle.net/1885/151771.
Повний текст джерелаYeh, Yin-Cheng, and 葉胤呈. "A Thresholded Discriminative Metric Learning Approach for Deep Speaker Recognition." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/3yekj7.
Повний текст джерела國立交通大學
電子研究所
106
Speaker recognition has been widely used in many biometric security applications for decades. With the deep learning thriving today, deep models has out-performed the traditional probability-based models in many speaker recognition applications. However, compared with the studio-quality audio samples, the performance of deep models still fluctuate dramatically when background noises involved in the real-world scenario. In this thesis, we aim to build a robust speaker identification, verification, and clustering system and solve the degradation brought by background noise. To be more specific, the deep model will be refined from two perspectives, the data pre-processing and the model training stage. In the data preparation stage, noise datasets and environment filters are used to augment the data to help the model adapting the noise environment and prevent the model from over-fitting. In the model training stage, classification would be used as the initial model for further embedding training. Next, we applied our proposed embedding optimization approach, threshold center loss, to further discriminate speakers to achieve noise-resisted model on the speaker verification and clustering tasks. To sum up, this model is capable to achieve 6.48\% equal error rate and the accuracy of the speaker clustering more then 90\% if the number of speakers less than 20 in VoxCeleb Dataset.
Sanyal, Soubhik. "Discriminative Descriptors for Unconstrained Face and Object Recognition." Thesis, 2017. http://etd.iisc.ac.in/handle/2005/4177.
Повний текст джерелаКниги з теми "Metric Learning Approaches"
Conference, Ontario Educational Research Council. [Papers presented at the 31st Annual Conference of the Ontario Educational Research Council, Toronto, Ontario, December 8-9, 1989]. [Toronto, ON: s.n.], 1989.
Знайти повний текст джерелаConference, Ontario Educational Research Council. [Papers presented at the 30th Annual Conference of the Ontario Educational Research Council, Toronto, Ontario, December 2-3, 1988]. [Toronto, ON: s.n.], 1988.
Знайти повний текст джерелаOntario Educational Research Council. Conference. [Papers presented at the 32nd Annual Conference of the Ontario Educational Research Council, Toronto, Ontario, December 7-8, 1990]. [Ontario: s.n.], 1990.
Знайти повний текст джерелаOntario Educational Research Council. Conference. [Papers presented at the 34th Annual Conference of the Ontario Educational Research Council, Toronto, Ontario, December 4 - 5, 1992]. [Ontario: s.n.], 1992.
Знайти повний текст джерелаOntario Educational Research Council. Conference. [Papers presented at the 35th Annual Conference of the Ontario Educational Research Council, Toronto, Ontario, December 3-4, 1993]. [Toronto, Ont: s.n, 1993.
Знайти повний текст джерелаOntario Educational Research Council. Conference. [Papers presented at the 36th Annual Conference of the Ontario Educational Research Council, Toronto, Ontario, December 2-3, 1994]. [Toronto, ON: s.n.], 1994.
Знайти повний текст джерелаOntario Educational Research Council. Conference. [Papers presented at the 28th Annual Conference of the Ontario Educational Research Council, Toronto, Ontario, Dec. 1986]. [Toronto, ON: s.n.]., 1986.
Знайти повний текст джерелаOntario Educational Research Council. Conference. [Papers presented at the 33rd Annual Conference of the Ontario Educational Research Council, Toronto, Ontario, December 6-7, 1991]. [Ontario: s.n.], 1991.
Знайти повний текст джерелаWinters, Bradford D., and Peter J. Pronovost. Patient safety in the ICU. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199600830.003.0016.
Повний текст джерелаЧастини книг з теми "Metric Learning Approaches"
Fay, Damien, Hamed Haddadi, Andrew W. Moore, Richard Mortier, Andrew G. Thomason, and Steve Uhlig. "Weighted Spectral Distribution: A Metric for Structural Analysis of Networks." In Statistical and Machine Learning Approaches for Network Analysis, 153–89. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781118346990.ch6.
Повний текст джерелаVaish, Ashutosh, Sagar Gupta, and Neeru Rathee. "Enhancing Emotion Detection Using Metric Learning Approach." In Innovations in Computer Science and Engineering, 317–23. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8201-6_36.
Повний текст джерелаKunapuli, Gautam, and Jude Shavlik. "Mirror Descent for Metric Learning: A Unified Approach." In Machine Learning and Knowledge Discovery in Databases, 859–74. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33460-3_60.
Повний текст джерелаPerez-Suay, Adrian, Francesc J. Ferri, and Jesús V. Albert. "An Online Metric Learning Approach through Margin Maximization." In Pattern Recognition and Image Analysis, 500–507. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21257-4_62.
Повний текст джерелаLiu, Meizhu, and Baba C. Vemuri. "A Robust and Efficient Doubly Regularized Metric Learning Approach." In Computer Vision – ECCV 2012, 646–59. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33765-9_46.
Повний текст джерелаBabagholami-Mohamadabadi, Behnam, Seyed Mahdi Roostaiyan, Ali Zarghami, and Mahdieh Soleymani Baghshah. "Multi-Modal Distance Metric Learning: ABayesian Non-parametric Approach." In Computer Vision - ECCV 2014 Workshops, 63–77. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-16199-0_5.
Повний текст джерелаGonzález-Vanegas, W., A. Álvarez-Meza, and A. Orozco-Gutiérrez. "An Automatic Approximate Bayesian Computation Approach Using Metric Learning." In Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, 12–19. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-13469-3_2.
Повний текст джерелаLuo, Changchun, Mu Li, Hongzhi Zhang, Faqiang Wang, David Zhang, and Wangmeng Zuo. "Metric Learning with Relative Distance Constraints: A Modified SVM Approach." In Communications in Computer and Information Science, 242–49. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-46248-5_30.
Повний текст джерелаCaione, Adriana, Anna Lisa Guido, Roberto Paiano, Andrea Pandurino, and Stefania Pasanisi. "A Social Metric Approach to E-Learning Evaluation in Education." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 3–11. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-49625-2_1.
Повний текст джерелаSuárez, Juan Luis, Germán González-Almagro, Salvador García, and Francisco Herrera. "A Preliminary Approach for using Metric Learning in Monotonic Classification." In Advances and Trends in Artificial Intelligence. Theory and Practices in Artificial Intelligence, 773–84. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-08530-7_65.
Повний текст джерелаТези доповідей конференцій з теми "Metric Learning Approaches"
Lehinevych, Taras, and Hlybovets Andii. "Analysis of Deep Metric Learning Approaches." In 2019 IEEE International Conference on Advanced Trends in Information Theory (ATIT). IEEE, 2019. http://dx.doi.org/10.1109/atit49449.2019.9030440.
Повний текст джерелаWohlwend, Jeremy, Ethan R. Elenberg, Sam Altschul, Shawn Henry, and Tao Lei. "Metric Learning for Dynamic Text Classification." In Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019). Stroudsburg, PA, USA: Association for Computational Linguistics, 2019. http://dx.doi.org/10.18653/v1/d19-6116.
Повний текст джерелаMao, Jun-Xiang, Wei Wang, and Min-Ling Zhang. "Label Specific Multi-Semantics Metric Learning for Multi-Label Classification: Global Consideration Helps." 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.2023/451.
Повний текст джерелаGuillaumin, Matthieu, Jakob Verbeek, and Cordelia Schmid. "Is that you? Metric learning approaches for face identification." In 2009 IEEE 12th International Conference on Computer Vision (ICCV). IEEE, 2009. http://dx.doi.org/10.1109/iccv.2009.5459197.
Повний текст джерелаBuris, Luiz H., Daniel C. G. Pedronette, Joao P. Papa, Jurandy Almeida, Gustavo Carneiro, and Fabio A. Faria. "Mixup-Based Deep Metric Learning Approaches for Incomplete Supervision." In 2022 IEEE International Conference on Image Processing (ICIP). IEEE, 2022. http://dx.doi.org/10.1109/icip46576.2022.9897167.
Повний текст джерелаKumari, Priyadarshini, Ritesh Goru, Siddhartha Chaudhuri, and Subhasis Chaudhuri. "Batch Decorrelation for Active Metric Learning." 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.2020/312.
Повний текст джерелаLuo, Yong, Tongliang Liu, Yonggang Wen, and Dacheng Tao. "Online Heterogeneous Transfer Metric Learning." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/350.
Повний текст джерелаChen, Pu, Xinyi Xu, and Cheng Deng. "Deep View-Aware Metric Learning for Person Re-Identification." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/86.
Повний текст джерелаPecorelli, Fabiano, Fabio Palomba, Dario Di Nucci, and Andrea De Lucia. "Comparing Heuristic and Machine Learning Approaches for Metric-Based Code Smell Detection." In 2019 IEEE/ACM 27th International Conference on Program Comprehension (ICPC). IEEE, 2019. http://dx.doi.org/10.1109/icpc.2019.00023.
Повний текст джерелаOregi, Izaskun, Javier Del Ser, Aritz Perez, and Jose A. Lozano. "Nature-inspired approaches for distance metric learning in multivariate time series classification." In 2017 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2017. http://dx.doi.org/10.1109/cec.2017.7969545.
Повний текст джерелаЗвіти організацій з теми "Metric Learning Approaches"
Zheng, Zhonghua, Nicole Riemer, Matthew West, and Valentine G. Anantharaj. Evaluation of Machine Learning Approaches to Estimate Aerosol Mixing State Metrics in Atmospheric Models. Office of Scientific and Technical Information (OSTI), May 2019. http://dx.doi.org/10.2172/1513380.
Повний текст джерелаIdakwo, Gabriel, Sundar Thangapandian, Joseph Luttrell, Zhaoxian Zhou, Chaoyang Zhang, and Ping Gong. Deep learning-based structure-activity relationship modeling for multi-category toxicity classification : a case study of 10K Tox21 chemicals with high-throughput cell-based androgen receptor bioassay data. Engineer Research and Development Center (U.S.), July 2021. http://dx.doi.org/10.21079/11681/41302.
Повний текст джерелаValencia, Oscar, Juan José Díaz, and Diego A. Parra. Assessing Macro-Fiscal Risk for Latin American and Caribbean Countries. Inter-American Development Bank, November 2022. http://dx.doi.org/10.18235/0004530.
Повний текст джерелаHlushak, Oksana M., Svetlana O. Semenyaka, Volodymyr V. Proshkin, Stanislav V. Sapozhnykov, and Oksana S. Lytvyn. The usage of digital technologies in the university training of future bachelors (having been based on the data of mathematical subjects). [б. в.], July 2020. http://dx.doi.org/10.31812/123456789/3860.
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