Deep Neural Networks

By Mehrdad Mahdavi. 2023

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Quoc V. Le
2. David R. So
3. Chen Liang
4. Esteban Real
5. Greg Yang
6. Yoshua Bengio
7. Barret Zoph
8. Ruslan Salakhutdinov

Citations in the corpus

• AutoML-Zero: Evolving Machine Learning Algorithms From Scratch (2020, methodology): “AutoML has utilized a wide array of paradigms, including growing networks neuron-by-neuron (Stanley & Miikkulainen, 2002), hyperparameter optimization (Snoek et al., 2012; Loshchilov & Hutter, 2016; Jaderberg et al., 2017) and, neural architecture search (Zoph & Le, 2016; Real et al., 2017).”
• Freeze and Chaos for DNNs: an NTK view of Batch Normalization, Checkerboard and Boundary Effects (2019, background): “[17] Lee, Jae Hoon, Bahri, Yasaman, Novak, Roman, Schoenholz, Samuel S.”
• Tree-structured Parzen estimator: Understanding its algorithm components and their roles for better empirical performance (2023, background): “…(Schneider et al., 2020), material discovery (Xue et al., 2016; Li et al., 2017a; Vahid et al., 2018), financial applications (Gonzalvez et al., 2019), and hyperparameter optimization (HPO) of machine learning algorithms (Loshchilov & Hutter, 2016; Chen et al., 2018; Feurer & Hutter, 2019).”
• A Survey on Evolutionary Neural Architecture Search (2020, unknown): “GP [62], [111], [112], [115], [149], [150] [151], [152] [43] [153] ES [123] [44], [45], [57], [154]–[156] [41] [139], [157]”