ImageNet is an image dataset organized according to the WordNet hierarchy. Each meaningful concept in WordNet, possibly described by multiple words or word phrases, is called a ”synonym set” or ”synset”. There are more than 100,000 synsets in WordNet, majority of them are nouns (80,000+). In ImageNet, we aim to provide on average 1000 images to illustrate each synset. Images of each concept are quality-controlled and human-annotated. In its completion, we hope ImageNet will offer tens of millions of cleanly sorted images for most of the concepts in the WordNet hierarchy.

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  1. Andrew Engel, Zhichao Wang, Anand D. Sarwate, Sutanay Choudhury, Tony Chiang: TorchNTK: A Library for Calculation of Neural Tangent Kernels of PyTorch Models (2022) arXiv
  2. Aricioğlu, Burak; Uzun, Süleyman; Kaçar, Sezgin: Deep learning based classification of time series of Chen and Rössler chaotic systems over their graphic images (2022)
  3. Avazov, Kuldoshbay; Abdusalomov, Akmalbek; Mukhiddinov, Mukhriddin; Baratov, Nodirbek; Makhmudov, Fazliddin; Cho, Young Im: An improvement for the automatic classification method for ultrasound images used on CNN (2022)
  4. Badreddine, Samy; d’Avila Garcez, Artur; Serafini, Luciano; Spranger, Michael: Logic tensor networks (2022)
  5. Basir, Shamsulhaq; Senocak, Inanc: Physics and equality constrained artificial neural networks: application to forward and inverse problems with multi-fidelity data fusion (2022)
  6. Bihlo, Alex; Popovych, Roman O.: Physics-informed neural networks for the shallow-water equations on the sphere (2022)
  7. Biswas, A.; Tian, J.; Ulusoy, S.: Error estimates for deep learning methods in fluid dynamics (2022)
  8. Boob, Digvijay; Dey, Santanu S.; Lan, Guanghui: Complexity of training ReLU neural network (2022)
  9. Bos, Thijs; Schmidt-Hieber, Johannes: Convergence rates of deep ReLU networks for multiclass classification (2022)
  10. Boute, Robert N.; Gijsbrechts, Joren; van Jaarsveld, Willem; Vanvuchelen, Nathalie: Deep reinforcement learning for inventory control: a roadmap (2022)
  11. Cai, HanQin; McKenzie, Daniel; Yin, Wotao; Zhang, Zhenliang: Zeroth-order regularized optimization (ZORO): approximately sparse gradients and adaptive sampling (2022)
  12. Cai, Zhiqiang; Chen, Jingshuang; Liu, Min: Self-adaptive deep neural network: numerical approximation to functions and PDEs (2022)
  13. Cao, Jiezhang; Li, Jincheng; Hu, Xiping; Wu, Xiangmiao; Tan, Mingkui: Towards interpreting deep neural networks via layer behavior understanding (2022)
  14. Caragea, Andrei; Lee, Dae Gwan; Maly, Johannes; Pfander, Götz; Voigtlaender, Felix: Quantitative approximation results for complex-valued neural networks (2022)
  15. Chen, Qipin; Hao, Wenrui; He, Juncai: A weight initialization based on the linear product structure for neural networks (2022)
  16. Chrupała, Grzegorz: Visually grounded models of spoken language: a survey of datasets, architectures and evaluation techniques (2022)
  17. Dang, Wei-Dong; Lv, Dong-Mei; Li, Ru-Mei; Rui, Lin-Ge; Yang, Zhuo-Yi; Ma, Chao; Gao, Zhong-Ke: Multilayer network-based CNN model for emotion recognition (2022)
  18. Dash, Tirtharaj; Srinivasan, Ashwin; Baskar, A.: Inclusion of domain-knowledge into GNNs using mode-directed inverse entailment (2022)
  19. Daubechies, I.; DeVore, R.; Foucart, S.; Hanin, B.; Petrova, G.: Nonlinear approximation and (Deep) ReLU networks (2022)
  20. Duru, Cihat; Alemdar, Hande; Baran, Ozgur Ugras: A deep learning approach for the transonic flow field predictions around airfoils (2022)

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