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“The new machine learning models we call ‘CfC’s’ [closed-form Continuous-time] replace the differential equation defining the computation of the neuron with a closed form approximation ...
Innovations in explainable AI are revolutionizing the ability of companies like Wells Fargo to improve their ability to understand why ... citing several kinds of models used for machine learning ...
For many years, sequence modelling and generation was done by using plain recurrent neural networks (RNNs). An well-cited early example was the Elman network (1990). In theory, the information from one token can propagate arbitrarily far down the sequence, but in practice the vanishing-gradient problem leaves the model's state at the end of a long sentence without precise, extractable ...
She has led several efforts to encourage work on societal good applications in machine learning, including editing the Special Issue on Machine Learning for Science and Society in the Machine Learning journal , and organizing the American Statistical Association's report "Discovery with Data: Leveraging Statistics with Computer Science to ...
Luca Longo is an Italian computer scientist specializing in Explainable artificial intelligence, Deep Learning and Argumentation theory with research in the areas of Human performance modeling.
During the deep learning era, attention mechanism was developed solve similar problems in encoding-decoding. [1]In machine translation, the seq2seq model, as it was proposed in 2014, [16] would encode an input text into a fixed-length vector, which would then be decoded into an output text.
Extreme learning machines are feedforward neural networks for classification, regression, clustering, sparse approximation, compression and feature learning with a single layer or multiple layers of hidden nodes, where the parameters of hidden nodes (not just the weights connecting inputs to hidden nodes) need to be tuned.
AI-assisted virtualization software uses AI-related technology such as machine learning, deep learning, and neural networks to attempt to make more accurate predictions and decisions regarding the management of virtual environments.