Relevant bibliographies by topics / Multitask learning

Academic literature on the topic 'Multitask learning'

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Published: 4 June 2021
Last updated: 22 June 2024

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Journal articles on the topic "Multitask learning"

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Qiuhua Liu, Xuejun Liao, Hui Li, J. R. Stack, and L. Carin. "Semisupervised Multitask Learning." IEEE Transactions on Pattern Analysis and Machine Intelligence 31, no. 6 (June 2009): 1074–86. http://dx.doi.org/10.1109/tpami.2008.296.

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Yang, Peng, Peilin Zhao, Jiayu Zhou, and Xin Gao. "Confidence Weighted Multitask Learning." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 5636–43. http://dx.doi.org/10.1609/aaai.v33i01.33015636.

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Traditional online multitask learning only utilizes the firstorder information of the datastream. To remedy this issue, we propose a confidence weighted multitask learning algorithm, which maintains a Gaussian distribution over each task model to guide online learning process. The mean (covariance) of the Gaussian Distribution is a sum of a local component and a global component that is shared among all the tasks. In addition, this paper also addresses the challenge of active learning on the online multitask setting. Instead of requiring labels of all the instances, the proposed algorithm determines whether the learner should acquire a label by considering the confidence from its related tasks over label prediction. Theoretical results show the regret bounds can be significantly reduced. Empirical results demonstrate that the proposed algorithm is able to achieve promising learning efficacy, while simultaneously minimizing the labeling cost.
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Li, Guangxia, Steven C. H. Hoi, Kuiyu Chang, Wenting Liu, and Ramesh Jain. "Collaborative Online Multitask Learning." IEEE Transactions on Knowledge and Data Engineering 26, no. 8 (August 2014): 1866–76. http://dx.doi.org/10.1109/tkde.2013.139.

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Li, Zhen Xing, and Wei Hua Li. "Multitask Similarity Cluster." Advanced Materials Research 765-767 (September 2013): 1662–66. http://dx.doi.org/10.4028/www.scientific.net/amr.765-767.1662.

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Single task learning is widely used training in artificial neural network. Before, people usually see other tasks as noise in same learning machine. However, multitask learning, proposed by Rich Caruana, sees simultaneously training several correlated tasks is helpful to improve single tasks performance. In this paper, we propose a new neural network multitask similarity cluster. Combined with hellinger distance, multitask similarity cluster can estimate distances among clusters more accurate. Experimental results show multitask learning is helpful to improve performance of single task and multitask similarity cluster can get satisfactory result.
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Li, Zhen Xing, and Wei Hua Li. "Multitask Fuzzy Learning with Rule Weight." Advanced Materials Research 774-776 (September 2013): 1883–86. http://dx.doi.org/10.4028/www.scientific.net/amr.774-776.1883.

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In fuzzy learning system based on rule weight, certainty grade, denoted by membership function of fuzzy set, defines how close a rule to a classification. In this system, several rules can correspond to same classification. But it cannot reflect the changing while training several tasks simultaneously. In this paper, we propose multitask fuzzy learning based on error-correction, and define belonging grade to show how much a sample belongs to a rule. Experimental results demonstrate efficiency of multitask fuzzy learning, and multitask learning could help to improve learning machines prediction.
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Menghi, Nicholas, Kemal Kacar, and Will Penny. "Multitask learning over shared subspaces." PLOS Computational Biology 17, no. 7 (July 6, 2021): e1009092. http://dx.doi.org/10.1371/journal.pcbi.1009092.

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This paper uses constructs from machine learning to define pairs of learning tasks that either shared or did not share a common subspace. Human subjects then learnt these tasks using a feedback-based approach and we hypothesised that learning would be boosted for shared subspaces. Our findings broadly supported this hypothesis with either better performance on the second task if it shared the same subspace as the first, or positive correlations over task performance for shared subspaces. These empirical findings were compared to the behaviour of a Neural Network model trained using sequential Bayesian learning and human performance was found to be consistent with a minimal capacity variant of this model. Networks with an increased representational capacity, and networks without Bayesian learning, did not show these transfer effects. We propose that the concept of shared subspaces provides a useful framework for the experimental study of human multitask and transfer learning.
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Kato, Tsuyoshi, Hisashi Kashima, Masashi Sugiyama, and Kiyoshi Asai. "Conic Programming for Multitask Learning." IEEE Transactions on Knowledge and Data Engineering 22, no. 7 (July 2010): 957–68. http://dx.doi.org/10.1109/tkde.2009.142.

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Kong, Yu, Ming Shao, Kang Li, and Yun Fu. "Probabilistic Low-Rank Multitask Learning." IEEE Transactions on Neural Networks and Learning Systems 29, no. 3 (March 2018): 670–80. http://dx.doi.org/10.1109/tnnls.2016.2641160.

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Yin, Jichong, Fang Wu, Yue Qiu, Anping Li, Chengyi Liu, and Xianyong Gong. "A Multiscale and Multitask Deep Learning Framework for Automatic Building Extraction." Remote Sensing 14, no. 19 (September 22, 2022): 4744. http://dx.doi.org/10.3390/rs14194744.

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Detecting buildings, segmenting building footprints, and extracting building edges from high-resolution remote sensing images are vital in applications such as urban planning, change detection, smart cities, and map-making and updating. The tasks of building detection, footprint segmentation, and edge extraction affect each other to a certain extent. However, most previous works have focused on one of these three tasks and have lacked a multitask learning framework that can simultaneously solve the tasks of building detection, footprint segmentation and edge extraction, making it difficult to obtain smooth and complete buildings. This study proposes a novel multiscale and multitask deep learning framework to consider the dependencies among building detection, footprint segmentation, and edge extraction while completing all three tasks. In addition, a multitask feature fusion module is introduced into the deep learning framework to increase the robustness of feature extraction. A multitask loss function is also introduced to balance the training losses among the various tasks to obtain the best training results. Finally, the proposed method is applied to open-source building datasets and large-scale high-resolution remote sensing images and compared with other advanced building extraction methods. To verify the effectiveness of multitask learning, the performance of multitask learning and single-task training is compared in ablation experiments. The experimental results show that the proposed method has certain advantages over other methods and that multitask learning can effectively improve single-task performance.
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Szyszkowska, Joanna, Anna Kinga Zduńczyk-Kłos, Antonina Doroszewska, Barbara Banaszczak, Milena Michalska, and Katarzyna Potocka. "Zdolność do skupienia uwagi i wielozadaniowości u studentów uczelni wyższych w okresie pandemicznej nauki na odległość." Kwartalnik Pedagogiczny 68, no. 3 (2023): 71–90. http://dx.doi.org/10.31338/2657-6007.kp.2023-3.4.

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The study aimed to investigate the impact of the changes in higher education during the COVID-19 pandemic on Polish university students’ ability to focus and multitask, and the presumed disproportions in these skills between medical students and other students. We also analysed the differences in the evaluation of the organisation of classes during the pandemic in medicine and in other programmes. The study consisted of a survey on distance learning during the COVID-19 pandemic, an assessment of cognitive and motivational functions based on the PDQ-20 questionnaire and the authors’ original questions, and a test examining the ability to multitask on the Psytoolkit platform. 201 students participated in the study – 111 medical students and 90 other students. The respondents’ answers indicate their greater exposure to distracting stimuli and their increased tendency to multitask during distance learning. The results of the experimental test show that multitasking affects longer task processing and higher error rates. Medical students were less satisfied with the quality of distance classes. The level of subjective cognitive deficits and multitasking intensity was similar in both respondent groups. According to the above results, the use of methods engaging students in distance learning may be helpful for learning, enhancing the focusing processes. It is the first study investigating university students’ ability to focus and multitask during the pandemic distance learning.
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Dissertations / Theses on the topic "Multitask learning"

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Patel, Vatsa Sanjay. "Masked Face Analysis via Multitask Deep Learning." University of Dayton / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1619637677725646.

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Romera, Paredes B. "Multitask and transfer learning for multi-aspect data." Thesis, University College London (University of London), 2014. http://discovery.ucl.ac.uk/1457869/.

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Supervised learning aims to learn functional relationships between inputs and outputs. Multitask learning tackles supervised learning tasks by performing them simultaneously to exploit commonalities between them. In this thesis, we focus on the problem of eliminating negative transfer in order to achieve better performance in multitask learning. We start by considering a general scenario in which the relationship between tasks is unknown. We then narrow our analysis to the case where data are characterised by a combination of underlying aspects, e.g., a dataset of images of faces, where each face is determined by a person's facial structure, the emotion being expressed, and the lighting conditions. In machine learning there have been numerous efforts based on multilinear models to decouple these aspects but these have primarily used techniques from the field of unsupervised learning. In this thesis we take inspiration from these approaches and hypothesize that supervised learning methods can also benefit from exploiting these aspects. The contributions of this thesis are as follows: 1. A multitask learning and transfer learning method that avoids negative transfer when there is no prescribed information about the relationships between tasks. 2. A multitask learning approach that takes advantage of a lack of overlapping features between known groups of tasks associated with different aspects. 3. A framework which extends multitask learning using multilinear algebra, with the aim of learning tasks associated with a combination of elements from different aspects. 4. A novel convex relaxation approach that can be applied both to the suggested framework and more generally to any tensor recovery problem. Through theoretical validation and experiments on both synthetic and real-world datasets, we show that the proposed approaches allow fast and reliable inferences. Furthermore, when performing learning tasks on an aspect of interest, accounting for secondary aspects leads to significantly more accurate results than using traditional approaches.
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Settipalli, Venkata Sai Sukesh, and Naga Manendra Kumar Dasireddy. "Reducing Unintended bias in Text Classification using Multitask learning." Thesis, Blekinge Tekniska Högskola, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-21174.

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Yu, Qingtian. "Deep Learning-Enabled Multitask System for Exercise Recognition and Counting." Thesis, Université d'Ottawa / University of Ottawa, 2021. http://hdl.handle.net/10393/42686.

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Exercise is a prevailing topic in modern society as more people are pursuing a healthy lifestyle. Physical activities provide unimaginable benefits to human well-being from the inside out. 2D human pose estimation, action recognition and repetitive counting fields developed rapidly in the past several years. However, few works combined them together as a whole system to assist people in evaluating body poses, recognizing exercises and counting repetitive actions. The existing methods estimate pose positions first, and utilize human joints locations in the other two tasks. In this thesis, we propose a multitask system covering the three domains. Different from the methodology used in the literature, heatmaps which are the byproducts of 2D human pose estimation models are adopted for exercise recognition and counting. Recent heatmap processing methods are proven effective in extracting dynamic body pose information. Inspired by this, we propose a new deep-learning multitask model of exercise recognition & repetition counting, and apply these approaches to the multitask for the first time. To meet the needs of the multitask model, we create a new dataset Rep-Penn with action, counting and speed labels. A two-stage training strategy is applied in the training process. Our multitask system can estimate human pose, identify physical activities and count repeated motions. We achieved 95.69% accuracy in exercise recognition on Rep-Penn dataset. The multitask model also performed well in repetitive counting with 0.004 Mean Average Error (MAE) and 0.997 Off-By-One (OBO) accuracy on Rep-Penn dataset. Compared with existing frameworks, our method obtained state-of-the-art results.
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Nina, Oliver A. Nina. "A Multitask Learning Encoder-N-Decoder Framework for Movie and Video Description." The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1531996548147165.

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Lin, Yu-Kai, Hsinchun Chen, Randall A. Brown, Shu-Hsing Li, and Hung-Jen Yang. "HEALTHCARE PREDICTIVE ANALYTICS FOR RISK PROFILING IN CHRONIC CARE: A BAYESIAN MULTITASK LEARNING APPROACH." SOC INFORM MANAGE-MIS RES CENT, 2017. http://hdl.handle.net/10150/625248.

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Clinical intelligence about a patient's risk of future adverse health events can support clinical decision making in personalized and preventive care. Healthcare predictive analytics using electronic health records offers a promising direction to address the challenging tasks of risk profiling. Patients with chronic diseases often face risks of not just one, but an array of adverse health events. However, existing risk models typically focus on one specific event and do not predict multiple outcomes. To attain enhanced risk profiling, we adopt the design science paradigm and propose a principled approach called Bayesian multitask learning (BMTL). Considering the model development for an event as a single task, our BMTL approach is to coordinate a set of baseline models-one for each event-and communicate training information across the models. The BMTL approach allows healthcare providers to achieve multifaceted risk profiling and model an arbitrary number of events simultaneously. Our experimental evaluations demonstrate that the BMTL approach attains an improved predictive performance when compared with the alternatives that model multiple events separately. We also find that, in most cases, the BMTL approach significantly outperforms existing multitask learning techniques. More importantly, our analysis shows that the BMTL approach can create significant potential impacts on clinical practice in reducing the failures and delays in preventive interventions. We discuss several implications of this study for health IT, big data and predictive analytics, and design science research.
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VALSECCHI, CECILE. "Advancing the prediction of Nuclear Receptor modulators through machine learning methods." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2022. http://hdl.handle.net/10281/356289.

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I recettori nucleari sono fattori di trascrizione coinvolti in processi critici per la salute umana e sono un obiettivo rilevante per la valutazione del rischio tossicologico e il processo di scoperta dei farmaci. I modelli computazionali possono essere uno strumento utile (i) per dare priorità alla sperimentazione di sostanze chimiche che possono imitare gli ormoni naturali e quindi essere interferenti endocrini e (ii) per identificare nuovi possibili candidati farmaci. Pertanto, l'obiettivo principale di questo progetto è quello di studiare le potenziali interazioni tra sostanze chimiche e recettori nucleari, con il duplice scopo di sviluppare strumenti in silico per la ricerca di nuovi modulatori e di identificare possibili sostanze chimiche che alterano il sistema endocrino. Dopo aver creato una collezione esaustiva di modulatori di recettori nucleari, abbiamo applicato metodi di apprendimento automatico per colmare il vuoto di dati e predire nuovi possibili modulatori tramite modelli predittivi. In particolare, le strategie di modellazione hanno incluso algoritmi di apprendimento automatico multi-tasking per indagare le complesse relazioni tra sostanze chimiche e diversi recettori nucleari.
Nuclear receptors are transcription factors involved in processes critical to human health and are a relevant target for toxicological risk assessment and the drug discovery process. Computational models can be a useful tool (i) to prioritize chemicals that can mimic natural hormones and thus be endocrine disruptors and (ii) to identify new possible lead for drug discovery. Therefore, the main goal of this project is to study potential interactions between chemicals and nuclear receptors, with the dual purpose of developing in silico tools to search for new modulators and to identify possible endocrine disrupting chemicals. After creating an exhaustive collection of nuclear receptor modulators, we applied machine learning methods to fill the data gap and prioritize modulators by building predictive models. In particular, modeling strategies included multi-tasking machine learning algorithms to investigate the complex relationships between chemicals and multiple nuclear receptors.
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Zylich, Brian Matthew. "Training Noise-Robust Spoken Phrase Detectors with Scarce and Private Data: An Application to Classroom Observation Videos." Digital WPI, 2019. https://digitalcommons.wpi.edu/etd-theses/1289.

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We explore how to automatically detect specific phrases in audio from noisy, multi-speaker videos using deep neural networks. Specifically, we focus on classroom observation videos that contain a few adult teachers and several small children (< 5 years old). At any point in these videos, multiple people may be talking, shouting, crying, or singing simultaneously. Our goal is to recognize polite speech phrases such as "Good job", "Thank you", "Please", and "You're welcome", as the occurrence of such speech is one of the behavioral markers used in classroom observation coding via the Classroom Assessment Scoring System (CLASS) protocol. Commercial speech recognition services such as Google Cloud Speech are impractical because of data privacy concerns. Therefore, we train and test our own custom models using a combination of publicly available classroom videos from YouTube, as well as a private dataset of real classroom observation videos collected by our colleagues at the University of Virginia. We also crowdsource an additional 1152 recordings of polite speech phrases to augment our training dataset. Our contributions are the following: (1) we design a crowdsourcing task for efficiently labeling speech events in classroom videos, (2) we develop a neural network-based architecture for speech recognition, robust to noise and overlapping speech, and (3) we explore methods to synthesize new and authentic audio data, both to increase the training set size and reduce the class imbalance. Finally, using our trained polite speech detector, (4) we investigate the relationship between polite speech and CLASS scores and enable teachers to visualize their use of polite language.
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Bao, Guoqing. "End-to-End Machine Learning Models for Multimodal Medical Data Analysis." Thesis, The University of Sydney, 2022. https://hdl.handle.net/2123/28153.

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The pathogenesis of infectious and severe diseases including COVID-19, metabolic disorders, and cancer can be highly complicated because it involves abnormalities in genetic, metabolic, anatomical as well as functional levels. The deteriorative changes could be quantitatively monitored on biochemical markers, genome-wide assays as well as different imaging modalities including radiographic and pathological data. Multimodal medical data, involving three common and essential diagnostic disciplines, i.e., pathology, radiography, and genomics, are increasingly utilized to unravel the complexity of the diseases. High-throughput and deep features can be extracted from different types of medical data to characterize diseases in various quantitative aspects, e.g., compactness and flatness of tumors, and heterogeneity of tissues. State-of-the-art deep learning methods including convolutional neural networks (CNNs) and Transformer have achieved impressive results in analyses of natural image, text, and voice data through an intrinsic and latent manner. However, there are many obstacles and challenges when applying existing machine learning models that initially tuned on natural image and language data to clinical practice, such as shortage of labeled data, distribution and domain discrepancy, data heterogeneity and imbalance, etc. Moreover, those methods are not designed to harness multimodal data under a unified and end-to-end learning paradigm, making them heavily relying on expert involvement and more prone to be affected by intra- and inter-observer variability. To address those limitations, in this thesis, we present novel end-to-end machine learning methods to learn fused feature representations from multimodal medical data, and perform quantitative analyses to identify significant higher-level features from raw medical data in explanation of the characteristics and outcomes of the infectious and severe diseases. • Starting from gold standard pathology images, we propose a bifocal weakly-supervised method which is able to complementarily and simultaneously capture two types of discriminative regions from both shorter and longer image tiles under a small amount of sparsely labeled data to improve recognition and cross-modality analyses of complex morphological and immunohistochemical structures in entire and adjacent multimodal histological slides. • Then, we expand our research on data collected from non-invasive approaches, we present an end-to-end multitask learning model for automated and simultaneous diagnosis and severity assessment of infectious disease which obviates the need for expert involvement, and Shift3D and Random-weighted multitask loss function are two novel algorithm components proposed to learn shift-invariant and shareable representations from fused radiographic imaging and high-throughput numerical data to accelerate model convergence, improve joint learning performance, and resist the influence of intra- and inter-observer variability. • Next, we further involve time-dimension data and invent the machine learning-based method to locate representative imaging features to tackle the problem of non-invasive diagnostic side effects, i.e., radiation, and the low-radiation and non-invasive solution can be used on progression analysis of metabolic disorders over time and evaluation of surgery-induced weight loss effects. • Lastly, we investigate genomic data given genetic disorders can lead to diverse diseases, we build a machine learning pipeline for processing genomic data and analyzing disease prognosis by incorporating statistical power, biological rationale, and machine learning algorithms as a unified prognostic feature extractor. We carried out rigorous and extensive experiments on two large public datasets and two private cohorts covering various forms of medical data, e.g., biochemical markers, genomic profiles, radiomic features, radiological and pathological imaging data. The experiments demonstrated that our proposed machine learning approaches are able to achieve better performances compared to corresponding state-of-the-art methods, and subsequently improve the diagnostic and/or prognostic workflows of infectious and severe diseases including COVID-19, metabolic disorders, and cancer.
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Widmer, Christian Verfasser], Klaus-Robert [Akademischer Betreuer] [Müller, Gunnar [Akademischer Betreuer] Rätsch, and Klaus [Akademischer Betreuer] Obermayer. "Regularization-based multitask learning with applications in computational biology / Christian Widmer. Gutachter: Klaus-Robert Müller ; Gunnar Rätsch ; Klaus Obermayer. Betreuer: Klaus-Robert Müller ; Gunnar Rätsch." Berlin : Technische Universität Berlin, 2014. http://d-nb.info/1068856017/34.

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Books on the topic "Multitask learning"

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Kovac, Krunoslav. Multitask learning for Bayesian neural networks. 2005.

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Bell, Adam Patrick. Mixing the Multitrack. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190296605.003.0007.

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Employing the metaphor of mixing a multitrack recording, chapter 7 presents a cross-case analysis that irradiates the salient facets of each case study, bringing to the forefront both the consonant and dissonant relationships across cases. From these analyses, a number of important findings are presented. First, the DIY studio as a music-making entity can be conceptualized as functioning in at least two different models: the do-it-alone (DIA) studio and the do-it-with-others (DIWO) studio. Second, existing computer-based compositional and learning models are referenced to demonstrate how these frameworks need to evolve to reflect current music production practices. Lastly, Lucy Green’s criteria of informal learning are used to examine the learning explained and exhibited by the participants profiled in part II, most notably self-teaching.
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Bell, Adam Patrick. Mastering the Multitrack. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190296605.003.0008.

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Chapter 8 discusses the significance of the studio as musical instrument and its implications for music education. The stories of Michael, Tara, Tyler, and Jimmy depict a music education with DIY studios that is largely devoid of teachers and schools. Their collective quest to make new music and realize new sonic textures by their own volition has spawned an approach to making music that is typified by trial-and-error learning. Their end goal is to make music, implying that learning occurs tacitly as a by-process. On the surface, trial-and-error learning appears cumbersome and inefficient, but it is a time-honored practice in music production, and the likes of Michael, Tara, Tyler, and Jimmy are continuing its evolution. Music education would do well to follow in their footsteps.
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Bell, Adam Patrick. Track 1. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190296605.003.0003.

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Fifty-three-year-old guitarist Michael is the figurative flag-bearer of learning anew in the digital age. Despite decades of experience making multitrack recordings at home and professionally, Michael found himself in unfamiliar territory when first encountering the DAW Ableton Live. Leaning on skeuomorphic design cues and refusing to be bound by the learning approaches that characterize “digital immigrants,” Michael clicked his way through frustration to discover the din of his dreams. Without the aid of a teacher in any sense of the word, Michael matter-of-factly summarized his learning approach: “I didn’t have anybody tutoring me and I didn’t have any help files, so I just had to figure it out for myself.” His music-making processes exemplify how the quest for a specific sound (timbre) is foundational in DIY home recording.
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Book chapters on the topic "Multitask learning"

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Caruana, Rich. "Multitask Learning." In Learning to Learn, 95–133. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5529-2_5.

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Dekel, Ofer, Philip M. Long, and Yoram Singer. "Online Multitask Learning." In Learning Theory, 453–67. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11776420_34.

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Frasca, Marco, Giuliano Grossi, and Giorgio Valentini. "Multitask Hopfield Networks." In Machine Learning and Knowledge Discovery in Databases, 349–65. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-46147-8_21.

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Sun, Shiliang, Liang Mao, Ziang Dong, and Lidan Wu. "Multiview Transfer Learning and Multitask Learning." In Multiview Machine Learning, 85–104. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-3029-2_7.

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Gupta, Abhishek, and Yew-Soon Ong. "Multitask Knowledge Transfer Across Problems." In Adaptation, Learning, and Optimization, 83–92. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-02729-2_6.

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Mao, Chengzhi, Amogh Gupta, Vikram Nitin, Baishakhi Ray, Shuran Song, Junfeng Yang, and Carl Vondrick. "Multitask Learning Strengthens Adversarial Robustness." In Computer Vision – ECCV 2020, 158–74. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58536-5_10.

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Dimitrakakis, Christos, and Constantin A. Rothkopf. "Bayesian Multitask Inverse Reinforcement Learning." In Lecture Notes in Computer Science, 273–84. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29946-9_27.

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Mattick, Alexander, Martin Mayr, Andreas Maier, and Vincent Christlein. "Is Multitask Learning Always Better?" In Document Analysis Systems, 674–87. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-06555-2_45.

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Gönen, Mehmet, Melih Kandemir, and Samuel Kaski. "Multitask Learning Using Regularized Multiple Kernel Learning." In Neural Information Processing, 500–509. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24958-7_58.

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Kamath, Uday, John Liu, and James Whitaker. "Transfer Learning: Scenarios, Self-Taught Learning, and Multitask Learning." In Deep Learning for NLP and Speech Recognition, 463–93. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-14596-5_10.

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Conference papers on the topic "Multitask learning"

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Murugesan, Keerthiram, and Jaime Carbonell. "Self-Paced Multitask Learning with Shared Knowledge." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/351.

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This paper introduces self-paced task selection to multitask learning, where instances from more closely related tasks are selected in a progression of easier-to-harder tasks, to emulate an effective human education strategy, but applied to multitask machine learning. We develop the mathematical foundation for the approach based on iterative selection of the most appropriate task, learning the task parameters, and updating the shared knowledge, optimizing a new bi-convex loss function. This proposed method applies quite generally, including to multitask feature learning, multitask learning with alternating structure optimization, etc. Results show that in each of the above formulations self-paced (easier-to-harder) task selection outperforms the baseline version of these methods in all the experiments.
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Suresh, Harini, Jen J. Gong, and John V. Guttag. "Learning Tasks for Multitask Learning." In KDD '18: The 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3219819.3219930.

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Horowitz, Roberto, and Perry Li. "Multitask Robot Learning Control." In 1992 American Control Conference. IEEE, 1992. http://dx.doi.org/10.23919/acc.1992.4792615.

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Li, Rui, Fenglong Ma, Wenjun Jiang, and Jing Gao. "Online Federated Multitask Learning." In 2019 IEEE International Conference on Big Data (Big Data). IEEE, 2019. http://dx.doi.org/10.1109/bigdata47090.2019.9006060.

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Chaplot, Devendra Singh, Lisa Lee, Ruslan Salakhutdinov, Devi Parikh, and Dhruv Batra. "Embodied Multimodal Multitask 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.2020/338.

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Visually-grounded embodied language learning models have recently shown to be effective at learning multiple multimodal tasks such as following navigational instructions and answering questions. In this paper, we address two key limitations of these models, (a) the inability to transfer the grounded knowledge across different tasks and (b) the inability to transfer to new words and concepts not seen during training using only a few examples. We propose a multitask model which facilitates knowledge transfer across tasks by disentangling the knowledge of words and visual attributes in the intermediate representations. We create scenarios and datasets to quantify cross-task knowledge transfer and show that the proposed model outperforms a range of baselines in simulated 3D environments. We also show that this disentanglement of representations makes our model modular and interpretable which allows for transfer to instructions containing new concepts.
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Hao, Shuji, Peilin Zhao, Yong Liu, Steven C. H. Hoi, and Chunyan Miao. "Online Multitask Relative Similarity Learning." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/253.

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Relative similarity learning~(RSL) aims to learn similarity functions from data with relative constraints. Most previous algorithms developed for RSL are batch-based learning approaches which suffer from poor scalability when dealing with real-world data arriving sequentially. These methods are often designed to learn a single similarity function for a specific task. Therefore, they may be sub-optimal to solve multiple task learning problems. To overcome these limitations, we propose a scalable RSL framework named OMTRSL (Online Multi-Task Relative Similarity Learning). Specifically, we first develop a simple yet effective online learning algorithm for multi-task relative similarity learning. Then, we also propose an active learning algorithm to save the labeling cost. The proposed algorithms not only enjoy theoretical guarantee, but also show high efficacy and efficiency in extensive experiments on real-world datasets.
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Zheng, Zishuo, Yadong Wei, Zixu Zhao, Xindi Wu, Zhengcheng Li, and Pengju Ren. "Multitask Learning With Enhanced Modules." In 2018 IEEE 23rd International Conference on Digital Signal Processing (DSP). IEEE, 2018. http://dx.doi.org/10.1109/icdsp.2018.8631696.

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Donini, Michele, David Martinez-Rego, Martin Goodson, John Shawe-Taylor, and Massimiliano Pontil. "Distributed variance regularized Multitask Learning." In 2016 International Joint Conference on Neural Networks (IJCNN). IEEE, 2016. http://dx.doi.org/10.1109/ijcnn.2016.7727594.

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Makelberge, Julie, and Andrew D. Ker. "Exploring multitask learning for steganalysis." In IS&T/SPIE Electronic Imaging, edited by Adnan M. Alattar, Nasir D. Memon, and Chad D. Heitzenrater. SPIE, 2013. http://dx.doi.org/10.1117/12.2004261.

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Sanabria, Ramon, and Florian Metze. "Hierarchical Multitask Learning With CTC." In 2018 IEEE Spoken Language Technology Workshop (SLT). IEEE, 2018. http://dx.doi.org/10.1109/slt.2018.8639530.

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Reports on the topic "Multitask learning"

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Patwa, B., P. L. St-Charles, G. Bellefleur, and B. Rousseau. Predictive models for first arrivals on seismic reflection data, Manitoba, New Brunswick, and Ontario. Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/329758.

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First arrivals are the primary waves picked and analyzed by seismologists to infer properties of the subsurface. Here we try to solve a problem in a small subsection of the seismic processing workflow: first break picking of seismic reflection data. We formulate this problem as an image segmentation task. Data is preprocessed, cleaned from outliers and extrapolated to make the training of deep learning models feasible. We use Fully Convolutional Networks (specifically UNets) to train initial models and explore their performance with losses, layer depths, and the number of classes. We propose to use residual connections to improve each UNet block and residual paths to solve the semantic gap between UNet encoder and decoder which improves the performance of the model. Adding spatial information as an extra channel helped increase the RMSE performance of the first break predictions. Other techniques like data augmentation, multitask loss, and normalization methods, were further explored to evaluate model improvement.
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