Results 41 to 50 of about 370,356 (314)
Theoretical foundations of deep learning
, 2022 S.69-96In this chapter, the authors will derive the theoretical foundations of deep neural network architectures. In contrast to shallow neural topologies, deep neural networks comprise more than one hidden layer of neurons.
Brüggenwirth, S., Wagner, S.
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Deep Randomized Neural Networks [PDF]
, 2020 Randomized Neural Networks explore the behavior of neural systems where the majority of connections are fixed, either in a stochastic or a deterministic 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.
Gallicchio C., Scardapane S.
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Expressivity of Deep Neural Networks
, 2022 This review paper will appear as a book chapter in the book "Theory of Deep Learning" by Cambridge University ...
Ingo Gühring +2 more
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Deep Learning and Music Adversaries [PDF]
, 2015 OA Monitor ExerciseOA Monitor ExerciseAn {\em adversary} is essentially an algorithm intent on making a classification system perform in some particular way given an input, e.g., increase the probability of a false negative.
STURM, BLT +5 more
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, 2021 In recent years, the use of deep learning (neural network) potential energy surface (NNPES) in molecular dynamics simulation has experienced explosive growth as it can be as accurate as quantum chemistry methods while being as efficient as classical ...
John ZH, Zhang, Mingyuan, Xu, Tong, Zhu
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A deep gated recurrent neural network for petroleum production forecasting [PDF]
, 2020 Forecasting of oil production plays a vital role in petroleum engineering and contributes to supporting engineers in the management of petroleum reservoirs.
Abir Jaafar Hussain +3 more
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Deep Neural Network or Dermatologist? [PDF]
, 2019 Deep learning techniques have proven high accuracy for identifying melanoma in digitised dermoscopic images. A strength is that these methods are not constrained by features that are pre-defined by human semantics. A down-side is that it is difficult to understand the rationale of the model predictions and to identify potential failure modes. This is a
Kyle Young +4 more
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A hybrid quantum–classical neural network with deep residual learning [PDF]
, 2021 Inspired by the success of classical neural networks, there has been tremendous effort to develop classical effective neural networks into quantum concept.
Zhao, Guoying +4 more
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Search for deep graph neural networks
Information Sciences, 2023 Current GNN-oriented NAS methods focus on the search for different layer aggregate components with shallow and simple architectures, which are limited by the 'over-smooth' problem. To further explore the benefits from structural diversity and depth of GNN architectures, we propose a GNN generation pipeline with a novel two-stage search space, which ...
Guosheng Feng +2 more
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