Relevant bibliographies by topics / AutoDL

Academic literature on the topic 'AutoDL'

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Published: 9 March 2023
Last updated: 10 March 2023

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Journal articles on the topic "AutoDL"

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Bang, Chang Seok, Hyun Lim, Hae Min Jeong, and Sung Hyeon Hwang. "Use of Endoscopic Images in the Prediction of Submucosal Invasion of Gastric Neoplasms: Automated Deep Learning Model Development and Usability Study." Journal of Medical Internet Research 23, no. 4 (April 15, 2021): e25167. http://dx.doi.org/10.2196/25167.

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Background In a previous study, we examined the use of deep learning models to classify the invasion depth (mucosa-confined versus submucosa-invaded) of gastric neoplasms using endoscopic images. The external test accuracy reached 77.3%. However, model establishment is labor intense, requiring high performance. Automated deep learning (AutoDL) models, which enable fast searching of optimal neural architectures and hyperparameters without complex coding, have been developed. Objective The objective of this study was to establish AutoDL models to classify the invasion depth of gastric neoplasms. Additionally, endoscopist–artificial intelligence interactions were explored. Methods The same 2899 endoscopic images that were employed to establish the previous model were used. A prospective multicenter validation using 206 and 1597 novel images was conducted. The primary outcome was external test accuracy. Neuro-T, Create ML Image Classifier, and AutoML Vision were used in establishing the models. Three doctors with different levels of endoscopy expertise were asked to classify the invasion depth of gastric neoplasms for each image without AutoDL support, with faulty AutoDL support, and with best performance AutoDL support in sequence. Results The Neuro-T–based model reached 89.3% (95% CI 85.1%-93.5%) external test accuracy. For the model establishment time, Create ML Image Classifier showed the fastest time of 13 minutes while reaching 82.0% (95% CI 76.8%-87.2%) external test accuracy. While the expert endoscopist's decisions were not influenced by AutoDL, the faulty AutoDL misled the endoscopy trainee and the general physician. However, this was corrected by the support of the best performance AutoDL model. The trainee gained the most benefit from the AutoDL support. Conclusions AutoDL is deemed useful for the on-site establishment of customized deep learning models. An inexperienced endoscopist with at least a certain level of expertise can benefit from AutoDL support.
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Chen, Yi-Wei, Qingquan Song, and Xia Hu. "Techniques for Automated Machine Learning." ACM SIGKDD Explorations Newsletter 22, no. 2 (January 17, 2021): 35–50. http://dx.doi.org/10.1145/3447556.3447567.

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Automated machine learning (AutoML) aims to find optimal machine learning solutions automatically given a problem description, its task type, and datasets. It could release the burden of data scientists from the multifarious manual tuning process and enable the access of domain experts to the off-the-shelf machine learning solutions without extensive experience. In this paper, we portray AutoML as a bi-level optimization problem, where one problem is nested within another to search the optimum in the search space, and review the current developments of AutoML in terms of three categories, automated feature engineering (AutoFE), automated model and hyperparameter tuning (AutoMHT), and automated deep learning (AutoDL). Stateof- the-art techniques in the three categories are presented. The iterative solver is proposed to generalize AutoML techniques. We summarize popular AutoML frameworks and conclude with current open challenges of AutoML.
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Tuggener, Lukas, Mohammadreza Amirian, Fernando Benites, Pius von Däniken, Prakhar Gupta, Frank-Peter Schilling, and Thilo Stadelmann. "Design Patterns for Resource-Constrained Automated Deep-Learning Methods." AI 1, no. 4 (November 6, 2020): 510–38. http://dx.doi.org/10.3390/ai1040031.

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We present an extensive evaluation of a wide variety of promising design patterns for automated deep-learning (AutoDL) methods, organized according to the problem categories of the 2019 AutoDL challenges, which set the task of optimizing both model accuracy and search efficiency under tight time and computing constraints. We propose structured empirical evaluations as the most promising avenue to obtain design principles for deep-learning systems due to the absence of strong theoretical support. From these evaluations, we distill relevant patterns which give rise to neural network design recommendations. In particular, we establish (a) that very wide fully connected layers learn meaningful features faster; we illustrate (b) how the lack of pretraining in audio processing can be compensated by architecture search; we show (c) that in text processing deep-learning-based methods only pull ahead of traditional methods for short text lengths with less than a thousand characters under tight resource limitations; and lastly we present (d) evidence that in very data- and computing-constrained settings, hyperparameter tuning of more traditional machine-learning methods outperforms deep-learning systems.
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Zimmer, Lucas, Marius Lindauer, and Frank Hutter. "Auto-Pytorch: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL." IEEE Transactions on Pattern Analysis and Machine Intelligence 43, no. 9 (September 1, 2021): 3079–90. http://dx.doi.org/10.1109/tpami.2021.3067763.

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Liu, Zhengying, Adrien Pavao, Zhen Xu, Sergio Escalera, Fabio Ferreira, Isabelle Guyon, Sirui Hong, et al. "Winning Solutions and Post-Challenge Analyses of the ChaLearn AutoDL Challenge 2019." IEEE Transactions on Pattern Analysis and Machine Intelligence 43, no. 9 (September 1, 2021): 3108–25. http://dx.doi.org/10.1109/tpami.2021.3075372.

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Chen, Xu, and Brett Wujek. "AutoDAL: Distributed Active Learning with Automatic Hyperparameter Selection." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 3537–44. http://dx.doi.org/10.1609/aaai.v34i04.5759.

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Automated machine learning (AutoML) strives to establish an appropriate machine learning model for any dataset automatically with minimal human intervention. Although extensive research has been conducted on AutoML, most of it has focused on supervised learning. Research of automated semi-supervised learning and active learning algorithms is still limited. Implementation becomes more challenging when the algorithm is designed for a distributed computing environment. With this as motivation, we propose a novel automated learning system for distributed active learning (AutoDAL) to address these challenges. First, automated graph-based semi-supervised learning is conducted by aggregating the proposed cost functions from different compute nodes in a distributed manner. Subsequently, automated active learning is addressed by jointly optimizing hyperparameters in both the classification and query selection stages leveraging the graph loss minimization and entropy regularization. Moreover, we propose an efficient distributed active learning algorithm which is scalable for big data by first partitioning the unlabeled data and replicating the labeled data to different worker nodes in the classification stage, and then aggregating the data in the controller in the query selection stage. The proposed AutoDAL algorithm is applied to multiple benchmark datasets and a real-world electrocardiogram (ECG) dataset for classification. We demonstrate that the proposed AutoDAL algorithm is capable of achieving significantly better performance compared to several state-of-the-art AutoML approaches and active learning algorithms.
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Parker-Holder, Jack, Raghu Rajan, Xingyou Song, André Biedenkapp, Yingjie Miao, Theresa Eimer, Baohe Zhang, et al. "Automated Reinforcement Learning (AutoRL): A Survey and Open Problems." Journal of Artificial Intelligence Research 74 (June 1, 2022): 517–68. http://dx.doi.org/10.1613/jair.1.13596.

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The combination of Reinforcement Learning (RL) with deep learning has led to a series of impressive feats, with many believing (deep) RL provides a path towards generally capable agents. However, the success of RL agents is often highly sensitive to design choices in the training process, which may require tedious and error-prone manual tuning. This makes it challenging to use RL for new problems and also limits its full potential. In many other areas of machine learning, AutoML has shown that it is possible to automate such design choices, and AutoML has also yielded promising initial results when applied to RL. However, Automated Reinforcement Learning (AutoRL) involves not only standard applications of AutoML but also includes additional challenges unique to RL, that naturally produce a different set of methods. As such, AutoRL has been emerging as an important area of research in RL, providing promise in a variety of applications from RNA design to playing games, such as Go. Given the diversity of methods and environments considered in RL, much of the research has been conducted in distinct subfields, ranging from meta-learning to evolution. In this survey, we seek to unify the field of AutoRL, provide a common taxonomy, discuss each area in detail and pose open problems of interest to researchers going forward.
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Cao, Longbing. "Beyond AutoML: Mindful and Actionable AI and AutoAI With Mind and Action." IEEE Intelligent Systems 37, no. 5 (September 1, 2022): 6–18. http://dx.doi.org/10.1109/mis.2022.3207860.

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Lan, Hai, Yuanjia Zhang, Zhifeng Bao, Yu Dong, Dongxu Huang, Liu Tang, and Jian Zhang. "AutoDI." Proceedings of the VLDB Endowment 15, no. 12 (August 2022): 3626–29. http://dx.doi.org/10.14778/3554821.3554860.

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Manual analysis on plan regression is both labor-intensive and inefficient for a large query plan and numerous queries. In this paper, we demonstrate AutoDI, an automatic detection and inference tool that has been developed to investigate why a sub-optimal plan is obtained by analyzing two different plans of the same query. AutoDI consists of two main modules, Difference Finder and Inference. The former aims to find where the two plans are different, and the latter tries to obtain the reasons why the differences come out. In our demonstration, we use a real plan regression in TiDB to show how AutoDI works.
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Yakovlev, Anatoly, Hesam Fathi Moghadam, Ali Moharrer, Jingxiao Cai, Nikan Chavoshi, Venkatanathan Varadarajan, Sandeep R. Agrawal, et al. "Oracle AutoML." Proceedings of the VLDB Endowment 13, no. 12 (August 2020): 3166–80. http://dx.doi.org/10.14778/3415478.3415542.

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Dissertations / Theses on the topic "AutoDL"

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PEREGO, RICCARDO. "Automated Deep Learning through Constrained Bayesian Optimization." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2021. http://hdl.handle.net/10281/314922.

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In un mondo sempre più tecnologico e interconnesso, la quantità di dati è in continua crescita e, di conseguenza, anche gli algoritmi di decision-making sono in continua evoluzione per adattarsi ad essi. Una delle principali fonti di questa grande quantità di dati è l'Internet of Things, in cui miliardi di sensori si scambiano informazioni attraverso la rete per svolgere vari tipi di attività come il monitoraggio industriale e medico. Negli ultimi anni lo sviluppo tecnologico ha permesso di definire nuove architetture hardware ad alte prestazioni per i sensori, detti microcontrollori, che hanno permesso la creazione di un nuovo tipo di calcolo decentralizzato denominato Edge Computing. Questo nuovo paradigma di calcolo ha permesso ai sensori di eseguire algoritmi di decision-making per prendere decisioni immediate e locali invece di trasferire i dati su un server centrale di elaborazione. Per supportare l'Edge Computing, la comunità di ricerca ha iniziato a sviluppare nuove tecniche avanzate per gestire in modo efficiente le limitate risorse su questi dispositivi per l'applicazione dei più avanzati modelli di Machine Learning, in particolare le Deep Neural Network. Automated Machine Learning è una branca del campo del Machine Learning che mira a divulgare la potenza del Machine Learning ai non esperti, oltre a supportare in modo efficiente i data scientists nella progettazione delle proprie pipeline di analisi dei dati. L'adozione del Automated Machine Learning ha reso possibile lo sviluppo quasi automatico di modelli sempre più performanti. Tuttavia, con l'avvento dell'Edge Computing, è nata una specializzazione del Machine Learning, definita come Tiny Machine Learning (Tiny ML), ovvero l'applicazione di algoritmi di Machine Learning su dispositivi con risorse hardware limitate. Questa tesi si occupa principalmente dell'applicabilità del Automated Machine Learning per generare modelli accurati che devono essere anche implementabili su dispositivi minuscoli, in particolare i microcontrollori. Più specificamente, l'approccio proposto è volto a massimizzare le prestazioni delle Reti Neurali Profonde e a soddisfare i vincoli associati alle limitate risorse hardware, comprese le batterie, dei microcontrollori. Grazie ad una stretta collaborazione con STMicroelectronics, azienda leader nella progettazione, produzione e vendita di microcontrollori, è stato possibile sviluppare un nuovo framework di Automated Machine Learning che si occupa dei vincoli black-box relativi all’impossibilità di implementare una Deep Neural Network su questi piccoli dispositivi, ampiamente adottato nelle applicazioni dell'internet degli oggetti. L'applicazione su due casi d'uso reali forniti da STMicroelectronics (ad esempio, Human Activity Recognition e Image Recognition) ha dimostrato che il nuovo approccio proposto è in grado di trovare in modo efficiente configurazioni per reti neurali profonde accurate e implementabili, aumentandone l'accuratezza rispetto ai modelli di base e riducendo drasticamente le risorse hardware necessarie per farle funzionare su un microcontrollore (cioè, una riduzione di oltre il 90%). L'approccio è stato anche confrontato con una delle soluzioni AutoML all'avanguardia per valutare la sua capacità di superare i problemi che attualmente limitano l'ampia applicazione di AutoML nel campo del Tiny ML. Infine, questa tesi di dottorato suggerisce interessanti e stimolanti direzioni di ricerca per aumentare ulteriormente l'applicabilità dell'approccio proposto, integrando i risultati di ricerche recenti e innovative (ad esempio, weakly defined search spaces, Meta-Learning, Multi-objective and Multi-Information Source optimization).
In an increasingly technological and interconnected world, the amount of data is continuously growing, and as a consequence, decision-making algorithms are also continually evolving to adapt to it. One of the major sources of this vast amount of data is the Internet of Things, in which billions of sensors exchange information over the network to perform various types of activities such as industrial and medical monitoring. In recent years, technological development has made it possible to define new high-performance hardware architectures for sensors, called Microcontrollers, which enabled the creation of a new kind of decentralized computing named Edge Computing. This new computing paradigm allowed sensors to run decision-making algorithm at the edge in order to take immediate and local decisions instead of transferring the data on central server processing. To support Edge Computing, the research community started developing new advanced techniques to efficiently manage the limited resources on these devices for applying the most advanced Machine Learning models, especially the Deep Neural Networks. Automated Machine Learning is a branch of the Machine Learning field aimed at disclosing the power of Machine Learning to non-experts as well as efficiently supporting data scientists in designing their own data analysis pipelines. The adoption of Automated Machine Learning has made it possible to develop increasingly high-performance models almost automatically. However, with the advent of Edge Computing, a specialization of Machine Learning, defined as Tiny Machine Learning (Tiny ML), has been arising, that is, the application of Machine Learning algorithms on devices having limited hardware resources. This thesis mainly addresses the applicability of Automated Machine Learning to generate accurate models which must be also deployable on tiny devices, specifically Microcontroller Units. More specifically, the proposed approach is aimed at maximizing the performances of Deep Neural Networks while satisfying the constraints associated to the limited hardware resources, including batteries, of Microcontrollers. Thanks to a close collaboration with STMicroelectronics, a leading company for design, production and sale of microcontrollers, it was possible to develop a novel Automated Machine Learning framework that deals with the black-box constraints related to the deployability of a Deep Neural Network on these tiny devices, widely adopted in IoT applications. The application on two real-life use cases provided by STMicroelectronics (i.e., Human Activity Recognition and Image Recognition) proved that the novel proposed approach can efficiently find out configurations for accurate and deployable Deep Neural Networks, increasing their accuracy against baseline models while drastically reducing hardware required to run them on a microcontroller (i.e., a reduction of more than 90\%). The approach was also compared against one of the state-of-the-art AutoML solutions in order to evaluate its capability to overcome the issues which currently limit the wide application of AutoML in the tiny ML field. Finally, this PhD thesis suggests interesting and challenging research directions to further increase the applicability of the proposed approach by integrating recent and innovative research results (e.g., weakly defined search spaces, Meta-Learning, Multi-objective and Multi-Information Source optimization).
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Forien, Nicolas. "Autour de la criticité auto-organisée." Thesis, université Paris-Saclay, 2020. http://www.theses.fr/2020UPASM012.

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De nombreux modèles physiques présentent un phénomène appelé transition de phase : il existe un point critique ou une courbe critique dans l'espace des paramètres qui sépare deux régimes distincts caractérisés par des propriétés macroscopiques très différentes. Le comportement de ces systèmes au point critique est particulièrement intéressant et fait apparaître des lois d'échelle qui sont souvent communes à tout un ensemble de systèmes très différents. Les physiciens Per Bak, Chao Tang et Kurt Wiesenfeld ont remarqué que ces comportements "critiques" sont étonnamment communs dans la nature, alors qu'ils ne devraient survenir que lorsque les paramètres se trouvent être précisément ajustés au point critique. Pour expliquer cela, ils ont montré que certains systèmes ont tendance à être naturellement attirés vers des points ou des régimes critiques. Ces systèmes présentent un phénomène appelé "criticité auto-organisée". Cette thèse porte sur la construction de plusieurs modèles simples présentant ce phénomène. Pour cela, nous partons d'un modèle présentant une transition de phase et nous le modifions pour forcer un comportement "auto-critique". Nous étudions notamment une variante d'un modèle construit par Matthias Gorny à partir du modèle d'Ising Curie-Weiss généralisé, en passant d'un modèle de type champ moyen à une interaction limitée à une certaine portée. Nous construisons également quelques modèles de criticité auto-organisée à partir de la percolation Bernoulli dans des boîtes finies, ainsi qu'à partir du modèle d'Ising en dimension 2
Many models in physics present a phenomenon called phase transition: there is a critical point or a critical curve in the parameter space separating two distinct regions characterized by very different macroscopic properties. In such systems, the behaviour at the critical point is of particular interest and presents some scaling laws which appear to be universal across a wide range of different systems. The physicists Per Bak, Chao Tang and Kurt Wiesenfeld pointed out that these "critical" features are very common in nature, although they should only appear when the parameters happen to be finely tuned to the critical point. To explain this, they showed that some systems tend to be naturally attracted by critical points or critical regimes. This phenomenon is called "self-organized criticality". The goal of this thesis is to construct several simple models which present this phenomenon. To achieve this, we consider a model with a phase transition and we modify it in order to obtain a "self-critical" behaviour. We study a modification of a model constructed by Matthias Gorny from the generalized Ising Curie-Weiss model, in which the mean-field Hamiltonian is replaced by a long-range interaction. Several other models of self-organized criticality are constructed from Bernoulli percolation in finite boxes, and from the planar Ising model
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Pietracola, Annamaria. "Une rencontre autour des blessures auto-infligées à l'infirmerie scolaire." Poitiers, 2011. http://www.theses.fr/2011POIT5015.

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Ces dernières années, des recherches montrent que la souffrance psychique à l’adolescence s’exprime au travers d’un mal-être qui touche le corps sous la forme de blessures auto-infligées. Un grand nombre de jeunes ne s’adresse pas tout de suite à des services hospitaliers, ou à des professionnels de santé. Ils préfèrent rester longtemps dans l’ombre. En dépit de cela, nous avons constaté une importante augmentation de jeunes qui se rendent à l’infirmerie scolaire. A partir de l’hypothèse de l’inconscient métapsychologique, peut-on supposer que, pour les adolescents qui s’y rendent, l’infirmerie scolaire représente autre chose qu’un lieu de soins médicaux ordinaire ? L’analyse d’entretiens cliniques menés auprès d’adolescents en souffrance, montre que l’infirmerie scolaire est utilisée comme « un refuge », un lieu où s’abriter, face aux angoisses pubertaires difficilement représentables. Le psychologue qui propose ces entretiens est alors investi comme nouvel objet de soutien ; il complète le Moi fragile du jeune sujet en lui permettant de mieux se distancier des premiers investissements parentaux. Parfois également, l’infirmerie peut se transformer en un lieu de support identificatoire où l’adulte est alors investi en tant que sujet séparé et différencié. Ce faisant, l’adolescent peut commencer à penser à soi, articulant les affects et les représentations, et s’inscrire dans un processus de subjectivation
In recent years, research shows that psychological distress in adolescence is expressed through a weakness that affects the body in the form of self-cutting. Many young people do not consult hospital services or health professionals immediately. They prefer to stay hidden in the shadow for a long time. Nevertheless, we have noticed that the number of young people who go to the school infirmary is increasing. From the hypothesis of the unconsciousness metapsychology, could school sickbay mean something else for teenagers who use it? The analysis of clinical interviews conducted with adolescents suffering, shows that school nursing has become "a refuge”, a place to shelter against the anxieties of non-representable puberty. Thus, the psychologist becomes a new object which supports and completes the weak self of the young patient, since it enables him to make a distance between his parents and himself. Sometimes the school infirmary can be turned into a helpful place because the adult is seen as a separate and different subject. Then, the teenager can start thinking about himself, playing with the affects and the representations before adopting a subjective process
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Pimentel, Victor Costa de Andrade. "Plataforma embarcada de reconhecimento autom?tico da fala para o aux?lio de pessoas com mobilidade reduzida." reponame:Repositório Institucional do IFPB, 2016. http://repositorio.ifpb.edu.br/jspui/handle/177683/236.

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A busca por maior independ?ncia e autonomia para as pessoas com defici?ncia tem se apresentado como um fator decisivo ao proporcionar uma melhoria na qualidade de vida desses indiv?duos atrav?s do uso de tecnologias assistivas. A fala se constitui na mais b?sica, comum e eficiente forma de comunica??o entre os seres humanos, de modo que a entrada de comandos por voz pode ser uma alternativa para que pessoas com mobilidade reduzida, e que tenham preservada boa capacidade das habilidades da fala, realizem o controle do computador ou outros dispositivos. O objetivo deste trabalho consiste no desenvolvimento de uma interface de comandos por voz, atrav?s do reconhecimento autom?tico da fala, que seja facilmente adaptada e incorporada a sistemas e ferramentas de aux?lio ao controle do ambiente dom?stico (dom?tica). Com esse intuito, foram executadas duas abordagens de desenvolvimento. A primeira consistiu de um experimento piloto realizado com o intuito de formar uma base inicial de conhecimento no desenvolvimento de aplica??es utilizando o reconhecimento de comandos por voz. Esta etapa baseou-se na utiliza??o de um m?dulo de hardware espec?fico, que recebe os comando de voz diretamente atrav?s de um microfone, constituindo-se de um sistema dependente de locutor capaz de reconhecer comandos de palavras isoladas para o controle das luzes de umLED RGB. J? a segunda abordagem, integra componentes de hardware aberto e software livre e de c?digo aberto, sendo os comandos de voz fornecidos ao sistema atrav?s de um smartphone configurado com softphone VoIP (Voz sobre IP). Nesse ?ltimo caso, o softphone, ent?o, se registra no servidor de comunica??o Asterisk, que implementa uma central telef?nica com unidade de resposta aud?vel (URA). Integrada ao servidor, est? a ferramenta de reconhecimento da fala, Julius. Esses componentes est?o embarcados na plataforma Beaglebone Black, de baixo custo. O sistema ? dependente de locutor e capaz de reconhecer frases com tr?s palavras para o controle da ilumina??o, televis?o e acesso a portas de umambiente dom?stico hipot?tico constitu?do de sala, cozinha, quarto, banheiro e ?rea externa. Os resultados obtidos a partir dos testes realizados indicam taxas de acerto de 95,9% e 94,77% para as interfaces desenvolvidas na primeira e segunda abordagens, respectivamente. Esses ?ndices sugeremque ? vi?vel o emprego dos m?dulos de reconhecimento desenvolvidos na implementa??o de solu??es de tecnologias assistivas.
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Santos, Sandro Roberto dos. "O direito de autor na obra musical: desequilíbrio do contrato e os novos rumos da proteção autoral." Universidade de São Paulo, 2009. http://www.teses.usp.br/teses/disponiveis/2/2131/tde-04082016-094412/.

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Trabalhando com a linha de pesquisa \"Direito moral e patrimonial de autor\" propõe-se um estudo sobre o Direito de Autor na obra musical, abordando mais especificamente as relações contratuais entre autores e empresas de gravação e distribuição, quais sejam: produtores fonográficos (gravadoras) e editoras musicais. O presente trabalho é dividido em três partes. A primeira trata da propriedade intelectual sobre obras imateriais, sua evolução histórica e os conceitos da doutrina autoralista, substrato intelectual necessário para compreensão do problema de pesquisa: o desequilíbrio contratual e o dano autoral. A segunda parte analisa a questão proposta sob o ponto de vista do Direito Civil e da Constituição Federal brasileira, apresentando propostas de solução pela via judicial. A terceira e última parte cumpre a tarefa de apresentar propostas preventivas ao problema apresentado, seja quanto à prática contratual seja na atualização da legislação autoral brasileira.
Trabalhando com a linha de pesquisa \"Direito moral e patrimonial de autor\" propõe-se um estudo sobre o Direito de Autor na obra musical, abordando mais especificamente as relações contratuais entre autores e empresas de gravação e distribuição, quais sejam: produtores fonográficos (gravadoras) e editoras musicais. O presente trabalho é dividido em três partes. A primeira trata da propriedade intelectual sobre obras imateriais, sua evolução histórica e os conceitos da doutrina autoralista, substrato intelectual necessário para compreensão do problema de pesquisa: o desequilíbrio contratual e o dano autoral. A segunda parte analisa a questão proposta sob o ponto de vista do Direito Civil e da Constituição Federal brasileira, apresentando propostas de solução pela via judicial. A terceira e última parte cumpre a tarefa de apresentar propostas preventivas ao problema apresentado, seja quanto à prática contratual seja na atualização da legislação autoral brasileira.
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Aldor, Edvard, and Daniel Helle. "Interweaving AutoML and Data Science Method." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-302504.

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The advent of automated machine learning (AutoML) tools promises to democratize machine learning to non-experts in the field. These tools certainly effect the process of applying machine learning for non-experts, but it is not clear how data scientists will be able to draw benefit from having access to automated machine learning systems. This bachelor thesis is an inquiry into how an application of one such AutoML system, Google Cloud AutoML, can improve upon commercial state of the art ensemble models. Specifically, inferences about unimportant features were examined and removed from an ensemble model, weighted between eXtreme Gradient Boosting and Random Forest Regressor, based on a feature importance graph produced by Google Cloud AutoML tables. The results showed a positive impact overall on the error rates of mean absolute error and root mean square error. While it was not of statistical significance, the method for feature selection showed a trend of increasing its performance on the error metrics. While the method was not proved to be statistically significant it nonetheless showed promise as a technique for reducing model complexity by decreasing dimensionality.
Automatiserad maskininlärning (AutoML) yrkar för att tillgängliggöra maskininlärning och demokratisera tekniken för icke-experter. AutoMLs nytta för maskininlärningsexperter är dock inte uppenbar; teknikens användbarhet för experter är också ämnet som kommer att behandlas i denna avhandling. För att undersöka ifall AutoML kan bistå professionellt gångbara maskininlärningsmodeller prövas en metod för borttagning av prediktorer. Specifikt undersöks det om man kan dra slutsatser från AutoML genom att ta bort oberoende prediktorer som rankas att vara sämst på en AutoML-generad rankningslista. Rankningslistan bedömer prediktorernas värden i termer av hur viktiga de anses vara enligt Google Cloud AutoML. Resultatet från undersökningen visade en positiv påverkan på både mean absolute error och root mean square error. Trots att resultaten inte uppnådde statistisk signifikans, gav metoden upphov till en positiv trend av minskat fel. Utöver det konstaterades det att metoden kunde reducera modellkomplexitet genom att reducera dimensionalitet utan att kunna bevisas leda till en statistiskt signifikant ökning av fel.
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Avilés, Arnillas Alexsandra Susana, Garcia Calderon Renzo David Balbin, Negrón Claudia Alejandra Hurtado, and Boza Ingrid Julieta Salazar. "Proyecto Empresarial Autoke." Bachelor's thesis, Universidad Peruana de Ciencias Aplicadas (UPC), 2017. http://hdl.handle.net/10757/623429.

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Autoke es un aplicativo para Smartphones que contará con un portal web informativo, la propuesta de valor del presente proyecto empresarial implica ofrecer un servicio online completo al automovilista por medio de un App, que le permita rápidamente contactarse con el taller de mecánica más cercano ante una necesidad de servicio. Para esto se ofrecerá a los talleres de mecánica un servicio de membresía que permita promocionar y publicitar sus servicios en el App de la empresa, y publicitar a través de enlaces sus servicios estableciendo conexión con los clientes tan solo al realizar un clic en nuestra aplicación.
Trabajo de investigación
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Tetewsky, Avram Ross Jeff Soltz Arnold Vaughn Norman Anszperger Jan O'Brien Chris Graham Dave Craig Doug Lozow Jeff. "Making sense of inter-signal corrections : accounting for GPS satellite calibration parameters in legacy and modernized ionosphere correction algorithms /." [Eugene, Ore. : Gibbons Media & Research], 2009. http://www.insidegnss.com/auto/julyaug09-tetewsky-final.pdf.

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"Author biographies are available in the expanded on-line version of this article [http://www.insidegnss.com/auto/julyaug09-tetewsky-final.pdf]"
"July/August 2009." Web site title: Making Sense of GPS Inter-Signal Corrections : Satellite Calibration Parameters in Legacy and Modernized Ionosphere Correction Algorithms.
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Wild, Klaus. "Wahrnehmungsorientierte Schulentwicklung innere Schulentwicklung unter Berücksichtigung der Wahrnehmung von Schulqualität durch Lehrkräfte." Winzer Duschl, 2006. http://www.verlag-duschl.de/detail.php?table=autor&id=92.

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Salomon, Damien. "Croissance, propriétés optiques et intégration d'hétérostructures radiales InGaN/GaN autour de fils auto-assemblés de GaN crûs sur saphir et silicium." Thesis, Grenoble, 2013. http://www.theses.fr/2013GRENY052.

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Ce travail est consacré à la réalisation de diodes électroluminescentes visibles à base de fils de GaN crûs sur Si(111) par épitaxie en phase vapeur de précurseurs organo-métalliques. Nous cherchons en particulier à comprendre les mécanismes de croissance des fils de GaN et les propriétés structurales et optiques de puits quantiques InGaN/GaN cœur/coquille déposés autour de ceux-ci. La croissance de fils orientés le long de l'axe -c sur saphir est dans un premier temps détaillée et expliquée. Nous montrons que l'injection de silane pendant la croissance des fils permet de former une couche de passivation de SiNx autour de ceux-ci. L'arrêt de l'injection de silane après quelques dizaines de secondes ne modifie pas la géométrie fil et ce procédé peut donc être utilisé pour contrôler le positionnement le long du fil de la zone de dépôts des puits quantiques InGaN/GaN. Ce procédé est ensuite transféré sur substrat Si(111) grâce au dépôt préalable d'une fine couche tampon d'AlN sur le substrat. Le dépôt de puits quantiques InGaN/GaN sur les facettes non-polaires m de ces fils et l'influence de différents paramètres de croissance sur leur émission de lumière sont étudiés. Nous montrons notamment l'existence de plusieurs familles des puits quantiques dans les fils dont les longueurs d'onde d'émission ont pu être indexées à l'aide de cartographies de cathodoluminescence. La concentration en indium des puits quantiques déposés a été estimée en comparant les énergies d'émissions des puits à des simulations utilisant la théorie k.p dans l'approximations 8 bandes pour les électrons et les trous et est comprise entre 8 et 24%. Enfin, des structures LED complètes ont été déposées sur les fils de GaN par MOVPE et une électroluminescence bleue à 450 nm à température ambiante est mesurée sur des fils uniques et sur des assemblées de fils sur silicium
This work reports on the realization by metal organic vapor phase epitaxy of visible light emitting diodes based on GaN wires grown on Si(111) with a focus on understanding the wires growth mechanisms and the properties of InGaN/GaN core/shell heterostructures grown around them. First we report the MOVPE growth of –c oriented GaN wires on sapphire. We demonstrate that the injection of silane during the growth induces the formation of a SiNx passivation layer around the GaN wires, preventing the lateral expansion. The silane flow can be stopped after a certain time without modifying the wire geometry. This phenomenon is used to control the position of the InGaN/GaN multiple quantum well shells along the wires. The wire growth on sapphire has then been transferred to silicon substrate thanks to the deposition of a thin AlN buffer layer prior to the wire growth. The deposition of InGaN/GaN core/shell heterostructures on the non-polar m-plane side facets of the wires and the influence of different growth parameters on the light emission properties of the quantum wells are then studied. Several types of quantum wells grown on different facets of the wire surface are observed. These different families emit light at different wavelengths that have been indexed thanks to cathodoluminescence mapping. The indium concentration in the quantum wells deposited is estimated between 8 and 24 %, depending on the growth conditions. This estimation has been made by comparing the emission wavelength of the quantum well to the recombination energy of electrons and wells simulated using the 8x8 band k.p theory for electron and hole masses. Finally, complete LED structures have been deposited on GaN wires by MOVPE and blue electroluminescence at 450 nm has been measured on single wires and assemblies of wires on Si(111)
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Books on the topic "AutoDL"

1

Autogol. Bogotá: Alfaguara, 2009.

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Accascina, Guido. Automi. Guidonia (Roma): Iacobellieditore, 2020.

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Trojanow, Ilija. Autopol. München: DTV Premium, 1997.

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Sanches, Hércoles Tecino. Legislação autoral. São Paulo: Editora LTr, 1999.

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Autori & opere. Sesto San Giovanni: Edizioni del Foglio clandestino, 2016.

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La autora. [Aguadulce?]: Confluencias Editorial, 2022.

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Canali, Giuseppina Gagliardi. Carissimo autore--. [L'Aquila]: Edizioni del Gallo cedrone, 1988.

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Dias, Maurício Cozer. Direito autoral. Campinas, SP: LZN Editora, 2002.

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Biteus, Jonas. Fault isolation in distributed embedded systems. Linko ping, Sue cia: Linko pings Universitet. Department of Computer and Information Science, 2007.

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Pašić, Milutin. Autori i dela. Požega: SVITAK izdavačka radionica, 1995.

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Book chapters on the topic "AutoDL"

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Walsh, Barry. "AutoML, AutoAI, and the Rise of NoLo UIs." In Productionizing AI, 187–209. Berkeley, CA: Apress, 2022. http://dx.doi.org/10.1007/978-1-4842-8817-7_6.

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Agrawal, Tanay. "Optuna and AutoML." In Hyperparameter Optimization in Machine Learning, 109–29. Berkeley, CA: Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-6579-6_5.

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Viveros Vigoya, Mara. "AUTORA." In El oxímoron de las clases medias negras, 221–24. Bielefeld, Germany: Bielefeld University Press / transcript Verlag, 2022. http://dx.doi.org/10.14361/9783839459089-013.

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Gigena, Andrea Ivanna. "Autora." In Afrontar las crisis desde América Latina, 199–204. Bielefeld, Germany: Bielefeld University Press / transcript Verlag, 2023. http://dx.doi.org/10.14361/9783839459119-011.

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Jiao, Wencong, Tao Li, Guoqiang Zhong, and Li-Na Wang. "AutoML for DenseNet Compression." In Neural Information Processing, 151–61. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-36718-3_13.

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Bisong, Ekaba. "Google AutoML: Cloud Vision." In Building Machine Learning and Deep Learning Models on Google Cloud Platform, 581–98. Berkeley, CA: Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-4470-8_42.

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Weber, Robert, and Peter Seeberg. "AutoML und Guided Analytics." In KI in der Industrie, 37–44. München: Carl Hanser Verlag GmbH & Co. KG, 2020. http://dx.doi.org/10.3139/9783446464278.003.

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Zhou, Kaixiong, Zirui Liu, Keyu Duan, and Xia Hu. "Graph Neural Networks: AutoML." In Graph Neural Networks: Foundations, Frontiers, and Applications, 371–89. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-6054-2_17.

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de Luca, Aldo, and Flavio D’Alessandro. "Automi finiti." In Teoria degli Automi Finiti, 117–78. Milano: Springer Milan, 2013. http://dx.doi.org/10.1007/978-88-470-5474-5_4.

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Manderscheid, Katharina. "Selbstfahrende Wende oder automobile Kontinuität? Überlegungen zu Technologie, Innovation und sozialem Wandel." In AVENUE21. Politische und planerische Aspekte der automatisierten Mobilität, 27–40. Berlin, Heidelberg: Springer Berlin Heidelberg, 2021. http://dx.doi.org/10.1007/978-3-662-63354-0_4.

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ZusammenfassungDas Automobil gehört zu den zentralen Kennzeichen moderner industrialisierter Gesellschaften und symbolisiert gesellschaftlichen Fortschritt und individuellen Wohlstand. In den Ländern der westlichen Welt nehmen die Anzahl privater Autos bezogen auf die EinwohnerInnen sowie die täglich mit dem Auto zurückgelegten Distanzen immer noch zu. Im Jahr 2018 gab es in Deutschland 568 Personenkraftfahrzeuge pro 1 000 EinwohnerInnen (Umweltbundesamt 2019b) und im Durchschnitt legte vor der Corona-Pandemie1 jede Person 29 von täglich 39 Kilometern mit dem Auto als FahrerIn oder BeifahrerIn zurück (Nobis/Kuhnimhof 2018: 46).
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Conference papers on the topic "AutoDL"

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Li, Yaliang, Zhen Wang, Bolin Ding, and Ce Zhang. "AutoML." In KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3447548.3470827.

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Poenaru-Olaru, Lorena. "AutoML." In Middleware '21: 22nd International Middleware Conference. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3491087.3493674.

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Dos Santos Silva, Lucas Nildaimon, Lucas Cardoso Silva, Fernando Rezende Zagatti, Bruno Silva Sette, Helena De Medeiros Caseli, Daniel Lucredio, and Diego Furtado Silva. "CurL-AutoML: Curriculum Learning-based AutoML." In 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE, 2021. http://dx.doi.org/10.1109/icmla52953.2021.00127.

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T. Cordeiro Ottoni, Lara, André Luiz C. Ottoni, Marcos S. de Oliveira, and Erivelton G. Nepomuceno. "AutoRL-TSP: Sistema de Aprendizado por Reforço Automatizado para o Problema do Caixeiro Viajante." In Congresso Brasileiro de Automática - 2020. sbabra, 2020. http://dx.doi.org/10.48011/asba.v2i1.1658.

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O AutoML (Aprendizado de Máquina Automatizado) tem como objetivo desenvolver técnicas para automatizar todo o processo de aprendizagem de máquina, de forma a obter um sistema que se adeque as condições do problema. Nesse sentido, um dos aspectos relevantes de sistemas de AutoML é a definição das configurações iniciais da simulação, que podem influenciar consideravelmente no resultado final do aprendizado. No entanto, a literatura carece de trabalhos que apresentem métodos de AutoML para aplicações de Aprendizado por Reforço (AR) em problemas de otimização combinatória. Dessa forma, o objetivo deste trabalho é propor um sistema de AR Automatizado (AutoRL-TSP) para ajuste automático de parâmetros do AR para aplicação no Problema do Caixeiro Viajante. Para isso, são apresentados os módulos do AutoRL-TSP: sistema de AR, base de conhecimento, estruturas de otimização e recomendação. Os resultados apresentam os \textit{rankings} de recomendação de parâmetros de acordo com a instância (TSPLIB) e o método de otimização simulado. Em geral, ao adotar a base de conhecimento e o algoritmo Variable Neighborhood Search, o AutoRL-TSP alcançou os melhores resultados.
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Drozdal, Jaimie, Justin Weisz, Dakuo Wang, Gaurav Dass, Bingsheng Yao, Changruo Zhao, Michael Muller, Lin Ju, and Hui Su. "Trust in AutoML." In IUI '20: 25th International Conference on Intelligent User Interfaces. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3377325.3377501.

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Escalante, Hugo, Jorge Madrid, and Eduardo Morales. "ChaLearn AutoML Challenges." In LatinX in AI at Neural Information Processing Systems Conference 2018. Journal of LatinX in AI Research, 2018. http://dx.doi.org/10.52591/lxai201812035.

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Academic challenges comprise effective means to rapidly advance the state of the art and solve problems of practical relevance. We elaborate on the benefits of challenges for the LatinX community and summarize ChaLearn challenges on Autonomous Machine Learning.
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Carey, Alycia, and Nicholas Pattengale. "Privacy-Preserving AutoML." In Proposed for presentation at the Sandia Machine Learning and Deep Learning Workshop held July 19-22, 2021 in ,. US DOE, 2021. http://dx.doi.org/10.2172/1877808.

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Carey, Alycia, and Nicholas Pattengale. "Privacy-Preserving AutoML." In Proposed for presentation at the Virtual New Mexico Student Intern Symposium held July 26-29, 2021 in ,. US DOE, 2021. http://dx.doi.org/10.2172/1881695.

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Hirzel, Martin, Kiran Kate, Parikshit Ram, Avraham Shinnar, and Jason Tsay. "Gradual AutoML using Lale." In KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3534678.3542630.

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Hanussek, Marc, Matthias Blohm, and Maximilien Kintz. "Can AutoML outperform humans? An evaluation on popular OpenML datasets using AutoML Benchmark." In AIRC'20: 2020 2nd International Conference on Artificial Intelligence, Robotics and Control. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3448326.3448353.

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Reports on the topic "AutoDL"

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Qi, Fei, Zhaohui Xia, Gaoyang Tang, Hang Yang, Yu Song, Guangrui Qian, Xiong An, Chunhuan Lin, and Guangming Shi. A Graph-based Evolutionary Algorithm for Automated Machine Learning. Web of Open Science, December 2020. http://dx.doi.org/10.37686/ser.v1i2.77.

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As an emerging field, Automated Machine Learning (AutoML) aims to reduce or eliminate manual operations that require expertise in machine learning. In this paper, a graph-based architecture is employed to represent flexible combinations of ML models, which provides a large searching space compared to tree-based and stacking-based architectures. Based on this, an evolutionary algorithm is proposed to search for the best architecture, where the mutation and heredity operators are the key for architecture evolution. With Bayesian hyper-parameter optimization, the proposed approach can automate the workflow of machine learning. On the PMLB dataset, the proposed approach shows the state-of-the-art performance compared with TPOT, Autostacker, and auto-sklearn. Some of the optimized models are with complex structures which are difficult to obtain in manual design.
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Page, Janie, Rongxin Yin, Mary Piette, and Richard Brown. Exploration of PG&E AutoDR Incentive Options. Final project report. Office of Scientific and Technical Information (OSTI), May 2020. http://dx.doi.org/10.2172/1631675.

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Grant, A. C., and K. D. McAlpine. Chapitre 6: La Marge Continentale Autour de Terre-Neuve. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1990. http://dx.doi.org/10.4095/132707.

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Cardoso, Leonardo, Roberto A. Tenenbaum, Ranny L. X. N. Michalski, Olavo M. Silva, and William D’Andrea Fonseca. Resenha de livros: A edição nº 53 recebe resenhas também dos autores. Revista Acústica e Vibrações, December 2021. http://dx.doi.org/10.55753/aev.v36e53.49.

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Nesta edição da revista, a presente seção conta com cinco resenhas de livros (também chamadas de book reviews). E há uma novidade: as duas primeiras resenhas foram escritas pelos próprios autores dos livros. As outras três resenhas ficaram a cargo dos editores do número 53. Outra novidade é que todas elas estão também disponíveis em língua inglesa (elas começam na página 7). Lembramos que as resenhas são escritas de forma abreviada e concisa, de modo a resumir o conteúdo dos livros (em assuntos relacionados com as diversas ciências que envolvem acústica, vibrações e áudio) e trazer informações acerca dos autores (para contextualizar ainda mais as obras). Para este número trazemos as resenhas dos seguintes livros: Sound-Politics in São Paulo Autor: Leonardo Cardoso | Oxford Press, 2019 Dinâmica Aplicada Autor: Roberto A. Tenenbaum | Editora Manole, 2016 (4 ed.) Acústica nos Edifícios Autor: Jorge Patrício | Publindústria, 2018 (7 ed.) Understanding Acoustics: An Experimentalist’s View of Sound and Vibration Autor: Steven L. Garret | Springer, 2020 Spatial Hearing: The Psychophysics of Human Sound Localization Autor: Jens Blauert | MIT Press, 1996 (Rev. Ed.). Leonardo Cardoso, professor na Texas A&M University, apresenta seu livro sobre política sonora em São Paulo. Roberto Tenenbaum, professor da UFSM, apresenta a quarta edição de sua obra importante na compreensão de Acústica e Vibrações. Em seguida, é apresentado um dos livros de Jorge Patrício, referência portuguesa em Acústica de Edificações. O quarto livro é o “Entendendo a acústica”, de autoria de Steven Garret. Por último, o livro clássico do prof. alemão Jens Blauert, “Áudio espacial”, é apresentado. Esperamos que a leitura das resenhas ofereça as primeiras compreensões/impressões sobre as obras e desperte vontade de conhecê-las por inteiro: uma excelente maneira de ampliar o conhecimento e de se manter atualizado.
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Azuaje Pirela, Michelle. ¡Sonríe! la inteligencia artificial te está “interpretando”. Universidad Autónoma de Chile, May 2021. http://dx.doi.org/10.32457/20.500.12728/9031202111.

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Gran revuelo ha causado en la comunidad académica internacional la reciente publicación de la Dra. Kate Crawford, autora del libro “Atlas of AI” quien en un artículo de Nature advierte que: “la pandemia de la COVID-19 se está utilizando como pretexto para introducir herramientas de inteligencia artificial no probadas en los lugares de trabajo y las escuelas.” Por lo que, además, insiste en que es hora de regular a los sistemas de inteligencia artificial (en adelante IA) que “interpretan” emociones humanas. En efecto, esta preocupación no es algo nuevo. Desde hace algunos años la mencionada autora junto a diversos investigadores y organizaciones protectoras de derechos humanos han venido advirtiendo sobre el incremento en la comercialización y el uso de sistemas de IA que aseguran poder analizar los movimientos faciales, el tono de voz y los patrones del habla, para interpretar emociones humanas.
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Rakotoarivony, Rianja, Pierre Walter, and Charlotte Spira. Protocole de Suivi de la Production Avicole autour du Parc Naturel Makira. Wildlife Conservation Society and Food and Agriculture Organization and Center for International Forestry Research and CIRAD, 2021. http://dx.doi.org/10.19121/2021.report.43080.

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Rakotoarivony, Rianja, Pierre Walter, and Charlotte Spira. Protocole de Suivi de la Production Piscicole autour du Parc Naturel Makira. Wildlife Conservation Society and Food and Agriculture Organization and Center for International Forestry Research and CIRAD, 2021. http://dx.doi.org/10.19121/2021.report.43077.

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Granacki, John, Ivan Hom, and Tauseef Kazi. AUTO: Automatic Script Generation System. Fort Belvoir, VA: Defense Technical Information Center, November 1993. http://dx.doi.org/10.21236/ada285897.

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Dupor, Bill, Yi-Chan Tsai, Rong Li, and Mehkari M. Saif. The 2008 U.S. Auto Market Collapse. Federal Reserve Bank of St. Louis, 2018. http://dx.doi.org/10.20955/wp.2018.019.

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Dupor, Bill, Yi-Chan Tsai, Rong Li, and M. Saif Mehkari. The 2008 U.S. Auto Market Collapse. Federal Reserve Bank of St. Louis, 2020. http://dx.doi.org/10.20955/wp.2020.004.

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