May 9, 2017 · In this paper we propose new forward propagation techniques inspired by systems of Ordinary Differential Equations (ODE) that overcome this ...
In this paper, we propose new forward propagation techniques inspired by systems of ordinary differential equations (ODE) that overcome this challenge and lead ...
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Abstract. Deep neural networks have become invaluable tools for supervised machine learning, e.g., classification of text or images.
This paper relates the exploding and vanishing gradient phenomenon to the stability of the discrete ODE and presents several strategies for stabilizing deep ...
May 10, 2017 · Deep neural networks have become invaluable tools for supervised machine learning, e.g., in classification of text or images.
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Dec 2, 2021 · Using this methodology, we found very stable architectures and training configurations that consistently obtain state-of-the-art results in the ...
Dec 17, 2022 · The architectures of deep artificial neural networks (DANNs) are routinely studied to improve their predictive performance.
We present a general stability training method to stabilize deep networks against small input distortions that result from various types of common image ...
Mar 29, 2024 · From artificial neural networks to transformers, explore 8 deep learning architectures every data scientist must know.
Apr 25, 2024 · In this section, we introduce at a high-level two of the most popular supervised deep learning architectures - convolutional neural networks and ...
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