Deep learning has triggered the current rise of artificial intelligence and is the workhorse of today’s machine intelligence. Numerous success stories have rapidly spread all over science, industry and society,
Geirhos R, Jacobsen JH, Michaelis C et al (2020) Shortcut learning in deep neural networks. Nat Mach Intell 2:665–673. https://doi.org/10.1038/s42256-020-00257-z Article Google Scholar Ghimire S, Deo RC, Raj N, Mi J (2019) Deep learning neural networks trained with MODIS satell...
MachineLearningModel MacroInternal MacroPrivate MacroProtected MacroPublic MacroSealed MacroShortcut MageProduct MagicWand MainMenuControl Makefile MakefileApplication MakeSameHeight ManageCounterSets ManifestFile ManualTest ManyToMany MapInternal MapItemInternal MapItemPrivate MapItemProtected MapItemPublic MapItemSe...
Image recognition complex plasmas deep learning 1. Introduction Complex plasma, also known as dusty plasma, consists of micrometer-sized solid particles immersed in an ordinary ion-electron plasma [1,2]. Complex plasma widely exists in plasma industry processing [3,4], cosmic space [5,6], and...
With the recent advancements in deep learning (DL), many DL-based I2I methods have been proposed and adapted to the medical imaging field, demonstrating promising performance. In general, prior I2I methods can be summarized into two classes: Generative Adversarial Network (GAN) and Diffusion Model...
In some implementations, the ASR model can be an on device and/or streaming ASR model. For example, the system can use a variety of ASR models trained to generate a text representation of a spoken utterance including a deep neural network, a recurrent neural network (RNN), a long short-...
Deep learning has triggered the current rise of artificial intelligence and is the workhorse of today's machine intelligence. Numerous success stories have rapidly spread all over science, industry and society, but its limitations have only recently come into focus. In this perspective we seek to ...
Shortcut learning occurs when a deep neural network overly relies on spurious correlations in the training dataset in order to solve downstream tasks. Prior works have shown how this impairs the compositional generalization capability of deep learning models. To address this problem, we propose a ...
Optical neural systemsOptical signalsOn-chip photonic neural networks (PNNs) have recently emerged as an attractive hardware accelerator for deep learning applications. However, deep PNNs with higher inference complexity are harder to train due to gradient vanishing and exploding problems. In this work...
Residual Network (ResNet) is undoubtedly a milestone in deep learning. ResNet is equipped with shortcut connections between layers, and exhibits efficient training using simple first order algorithms. Despite of the great empirical success, the reason behind is far from being well understood. In ...