Titlebook: Recent Trends in Learning From Data; Tutorials from the I Luca Oneto,Nicolò Navarin,Davide Anguita Book 2020 The Editor(s) (if applicable)

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Bronchial-Tubes

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

人工制品

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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

FRET

Hyaluronic-Acid

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

Allowance

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

失眠症

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

船员

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

鸵鸟

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.