49 citations · 137 across the 4 of their papers we have counts for
11 papers
Explicit regularization and implicit bias in deep network classifiers trained with the square loss
Tomaso Poggio, Qianli Liao
Deep ReLU networks trained with the square loss have been observed to perform well in classification tasks. We provide here a theoretical justification based on analysis of the ass…
Hierarchically Compositional Tasks and Deep Convolutional Networks
Arturo Deza, Qianli Liao, Andrzej Banburski +1
The main success stories of deep learning, starting with ImageNet, depend on deep convolutional networks, which on certain tasks perform significantly better than traditional shall…
Theoretical Issues in Deep Networks: Approximation, Optimization and Generalization
Tomaso Poggio, Andrzej Banburski, Qianli Liao
While deep learning is successful in a number of applications, it is not yet well understood theoretically. A satisfactory theoretical characterization of deep learning however, is…
Theory III: Dynamics and Generalization in Deep Networks
Andrzej Banburski, Qianli Liao, Brando Miranda +4
The key to generalization is controlling the complexity of the network. However, there is no obvious control of complexity -- such as an explicit regularization term -- in the trai…
Biologically-plausible learning algorithms can scale to large datasets
Will Xiao, Honglin Chen, Qianli Liao +1
The backpropagation (BP) algorithm is often thought to be biologically implausible in the brain. One of the main reasons is that BP requires symmetric weight matrices in the feedfo…
A Surprising Linear Relationship Predicts Test Performance in Deep Networks
Qianli Liao, Brando Miranda, Andrzej Banburski +2
Given two networks with the same training loss on a dataset, when would they have drastically different test losses and errors? Better understanding of this question of generalizat…