標(biāo)題: Titlebook: Recent Trends in Learning From Data; Tutorials from the I Luca Oneto,Nicolò Navarin,Davide Anguita Book 2020 The Editor(s) (if applicable) [打印本頁(yè)] 作者: 技巧 時(shí)間: 2025-3-21 17:54
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書(shū)目名稱Recent Trends in Learning From Data讀者反饋學(xué)科排名
作者: Bronchial-Tubes 時(shí)間: 2025-3-21 23:34
Introduction,e International Neural Network Society, with the aim of representing an international meeting for researchers and other professionals in Big Data, Deep Learning and related areas. This book collects the tutorials presented at the conference which cover most of the recent trends in learning from data作者: 人工制品 時(shí)間: 2025-3-22 02:18 作者: bypass 時(shí)間: 2025-3-22 04:35
Deep Randomized Neural Networks,ic fashion. Typical examples of such systems consist of multi-layered neural network architectures where the connections to the hidden layer(s) are left untrained after initialization. Limiting the training algorithms to operate on a reduced set of weights inherently characterizes the class of Rando作者: FRET 時(shí)間: 2025-3-22 12:26 作者: Hyaluronic-Acid 時(shí)間: 2025-3-22 16:18
Deep Learning for Graphs, a whole range of complex data representations, including hierarchical structures, graphs and networks, and giving special attention to recent deep learning models for graphs. While we provide a general introduction to the field, we explicitly focus on the neural network paradigm showing how, across作者: Allowance 時(shí)間: 2025-3-22 17:35
Limitations of Shallow Networks,pplications till the recent renewal of interest in deep architectures. Experimental evidence and successful applications of deep networks pose theoretical questions asking: When and why are deep networks better than shallow ones? This chapter presents some probabilistic and constructive results on l作者: 失眠癥 時(shí)間: 2025-3-22 21:31
Fairness in Machine Learning,out the ethical issues that may arise from the adoption of these technologies. ML fairness is a recently established area of machine learning that studies how to ensure that biases in the data and model inaccuracies do not lead to models that treat individuals unfavorably on the basis of characteris作者: 船員 時(shí)間: 2025-3-23 03:45
Online Continual Learning on Sequences,usly encountered training samples. Learning continually in a single data pass is crucial for agents and robots operating in changing environments and required to acquire, fine-tune, and transfer increasingly complex representations from non-i.i.d. input distributions. Machine learning models that ad作者: 鴕鳥(niǎo) 時(shí)間: 2025-3-23 08:07
Book 2020er advanced neural networks, deep architectures, and supervised and reinforcement machine learning models. They describe important theoretical concepts, presenting in detail all the necessary mathematical formalizations, and offer essential guidance on their use in current big data research.?作者: 橫截,橫斷 時(shí)間: 2025-3-23 13:18 作者: OREX 時(shí)間: 2025-3-23 16:04
Deep Learning for Graphs, the years, these models have been extended to the adaptive processing of incrementally more complex classes of structured data. The ultimate aim is to show how to cope with the fundamental issue of learning adaptive representations for samples with varying size and topology.作者: Cabg318 時(shí)間: 2025-3-23 19:46
1860-949X learning models. They describe important theoretical concepts, presenting in detail all the necessary mathematical formalizations, and offer essential guidance on their use in current big data research.?978-3-030-43885-2978-3-030-43883-8Series ISSN 1860-949X Series E-ISSN 1860-9503 作者: inchoate 時(shí)間: 2025-3-24 00:10 作者: hemophilia 時(shí)間: 2025-3-24 05:22 作者: 喃喃訴苦 時(shí)間: 2025-3-24 10:11 作者: 阻止 時(shí)間: 2025-3-24 10:53
Paolo Ferragina,Giorgio Vinciguerra. Instead, Hay suggests that dance artists use language as a tool for both sensing micromovements in their bodies developed through years of dance training and for instructing audiences, producers, and critics on the significant place of language in dance.作者: STELL 時(shí)間: 2025-3-24 17:47 作者: vibrant 時(shí)間: 2025-3-24 22:19
Ilya Kisil,Giuseppe G. Calvi,Bruno Scalzo Dees,Danilo P. Mandich (2.?=?256). As we shall see in subsequent chapters, however, one does not always carry out (that is, “run”) each possible combination; nevertheless, the principle that fewer levels per factor allows a larger number of factors to be studied still holds.作者: phase-2-enzyme 時(shí)間: 2025-3-24 23:22
Davide Bacciu,Alessio Michelih (2.?=?256). As we shall see in subsequent chapters, however, one does not always carry out (that is, “run”) each possible combination; nevertheless, the principle that fewer levels per factor allows a larger number of factors to be studied still holds.作者: BRIEF 時(shí)間: 2025-3-25 06:37
Věra K?rkováh (2.?=?256). As we shall see in subsequent chapters, however, one does not always carry out (that is, “run”) each possible combination; nevertheless, the principle that fewer levels per factor allows a larger number of factors to be studied still holds.作者: DALLY 時(shí)間: 2025-3-25 09:25 作者: Grasping 時(shí)間: 2025-3-25 12:06
German I. Parisi,Vincenzo Lomonacoh (2.?=?256). As we shall see in subsequent chapters, however, one does not always carry out (that is, “run”) each possible combination; nevertheless, the principle that fewer levels per factor allows a larger number of factors to be studied still holds.作者: 典型 時(shí)間: 2025-3-25 17:39
Deep Randomized Neural Networks,f neural architectures (e.g. before training of the hidden layers’ connections). In recent years, the study of Randomized Neural Networks has been extended towards deep architectures, opening new research directions to the design of effective yet extremely efficient deep learning models in vectorial作者: Vital-Signs 時(shí)間: 2025-3-25 20:58 作者: Compassionate 時(shí)間: 2025-3-26 03:43 作者: aviator 時(shí)間: 2025-3-26 08:01 作者: 軍火 時(shí)間: 2025-3-26 10:14
Luca Oneto,Nicolò Navarin,Davide AnguitaGathers tutorials from the 2019 INNS Big Data and Deep Learning Conference.Describes cutting-edge AI-based tools and applications.Offers essential guidance on the design and analysis of advanced AI-ba作者: corpuscle 時(shí)間: 2025-3-26 13:26
978-3-030-43885-2The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl作者: Bumble 時(shí)間: 2025-3-26 18:33 作者: 審問(wèn),審訊 時(shí)間: 2025-3-26 23:03 作者: 小爭(zhēng)吵 時(shí)間: 2025-3-27 01:38 作者: 繞著哥哥問(wèn) 時(shí)間: 2025-3-27 07:06
Introduction,e International Neural Network Society, with the aim of representing an international meeting for researchers and other professionals in Big Data, Deep Learning and related areas. This book collects the tutorials presented at the conference which cover most of the recent trends in learning from data.作者: comely 時(shí)間: 2025-3-27 10:44 作者: 貨物 時(shí)間: 2025-3-27 16:26
that are involved in the neurotransmitter synthesis. However, we could show in human auricles recently that one main reason for the catecholamine depletion is the distension of the adrenergic ground plexus in the course of hypertrophy of the myocardial muscle cells. At the same time, electron micros作者: CHASE 時(shí)間: 2025-3-27 18:52
Luca Oneto,Nicolò Navarin,Alessandro Sperduti,Davide AnguitaEP; .) is of major importance for efflux of bile salts to the bile and BSEP inhibition frequently provokes drug-induced cholestasis. This chapter describes two assays to determine inhibition of BSEP-mediated bile salt excretion. The first assay uses inside-out membrane vesicles, prepared from BSEP-t作者: Generator 時(shí)間: 2025-3-28 01:05
Paolo Ferragina,Giorgio Vinciguerrasiki and Pichaud [2016] 2019: 95 n. 29). Her choreography comprises written scores that can only be danced to the extent that a performer is willing and able to suspend their habitual dance and performance training, ask questions of themselves and their perception while moving, and release any inten作者: Customary 時(shí)間: 2025-3-28 03:30 作者: 做作 時(shí)間: 2025-3-28 06:57 作者: LAIR 時(shí)間: 2025-3-28 12:06 作者: allergen 時(shí)間: 2025-3-28 17:56 作者: Clumsy 時(shí)間: 2025-3-28 21:44
Luca Oneto,Silvia Chiappaveral chapters, we consider designs in which all factors have two levels. Many experiments are of this type. This is because two is the minimum number of levels a factor can have and still be studied, and by having the . (2), an experiment of a certain size can include the .. After all, an experimen作者: braggadocio 時(shí)間: 2025-3-29 02:51
