49 citations · 54 across the 3 of their papers we have counts for
4 papers
The Foes of Neural Network's Data Efficiency Among Unnecessary Input Dimensions
Vanessa D'Amario, Sanjana Srivastava, Tomotake Sasaki +1
Datasets often contain input dimensions that are unnecessary to predict the output label, e.g. background in object recognition, which lead to more trainable parameters. Deep Neura…
Minimal Images in Deep Neural Networks: Fragile Object Recognition in Natural Images
Sanjana Srivastava, Guy Ben-Yosef, Xavier Boix
The human ability to recognize objects is impaired when the object is not shown in full. "Minimal images" are the smallest regions of an image that remain recognizable for humans.…
Theory IIIb: Generalization in Deep Networks
Tomaso Poggio, Qianli Liao, Brando Miranda +3
A main puzzle of deep neural networks (DNNs) revolves around the apparent absence of "overfitting", defined in this paper as follows: the expected error does not get worse when inc…
Theory of Deep Learning III: explaining the non-overfitting puzzle
Tomaso Poggio, Kenji Kawaguchi, Qianli Liao +5
A main puzzle of deep networks revolves around the absence of overfitting despite large overparametrization and despite the large capacity demonstrated by zero training error on ra…