Now showing items 1-5 of 5
Can a biologically-plausible hierarchy e ectively replace face detection, alignment, and recognition pipelines?
(Center for Brains, Minds and Machines (CBMM), arXiv, 2014-03-27)
The standard approach to unconstrained face recognition in natural photographs is via a detection, alignment, recognition pipeline. While that approach has achieved impressive results, there are several reasons to be ...
Biologically-plausible learning algorithms can scale to large datasets
(Center for Brains, Minds and Machines (CBMM), arXiv.org, 2018-11-08)
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 feedforward and feedback pathways. To address ...
Theoretical Issues in Deep Networks
(Center for Brains, Minds and Machines (CBMM), 2019-08-17)
While deep learning is successful in a number of applications, it is not yet well understood theoretically. A theoretical characterization of deep learning should answer questions about their approximation power, the ...
Hierarchically Local Tasks and Deep Convolutional Networks
(Center for Brains, Minds and Machines (CBMM), 2020-06-24)
The main success stories of deep learning, starting with ImageNet, depend on convolutional networks, which on certain tasks perform significantly better than traditional shallow classifiers, such as support vector machines. ...
Implicit dynamic regularization in deep networks
(Center for Brains, Minds and Machines (CBMM), 2020-08-17)
Square loss has been observed to perform well in classification tasks, at least as well as crossentropy. However, a theoretical justification is lacking. Here we develop a theoretical analysis for the square loss that also ...