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Recurrent neural networks as versatile tools of neuroscience research
O Barak - Current opinion in neurobiology, 2017 - Elsevier
Highlights•Recurrent neural networks (RNNs) are powerful models of neural systems.•RNNs
can be either designed or trained to perform a task.•In both cases, low dimensional …
can be either designed or trained to perform a task.•In both cases, low dimensional …
Complete dictionary recovery over the sphere I: Overview and the geometric picture
We consider the problem of recovering a complete (ie, square and invertible) matrix A 0,
from Y∈ R n× p with Y= A 0 X 0, provided X 0 is sufficiently sparse. This recovery problem is …
from Y∈ R n× p with Y= A 0 X 0, provided X 0 is sufficiently sparse. This recovery problem is …
Towards understanding ensemble, knowledge distillation and self-distillation in deep learning
We formally study how ensemble of deep learning models can improve test accuracy, and
how the superior performance of ensemble can be distilled into a single model using …
how the superior performance of ensemble can be distilled into a single model using …
A convergence theory for deep learning via over-parameterization
Deep neural networks (DNNs) have demonstrated dominating performance in many fields;
since AlexNet, networks used in practice are going wider and deeper. On the theoretical …
since AlexNet, networks used in practice are going wider and deeper. On the theoretical …
Fine-grained analysis of optimization and generalization for overparameterized two-layer neural networks
Recent works have cast some light on the mystery of why deep nets fit any data and
generalize despite being very overparametrized. This paper analyzes training and …
generalize despite being very overparametrized. This paper analyzes training and …
Gradient descent finds global minima of deep neural networks
Gradient descent finds a global minimum in training deep neural networks despite the
objective function being non-convex. The current paper proves gradient descent achieves …
objective function being non-convex. The current paper proves gradient descent achieves …
Learning and generalization in overparameterized neural networks, going beyond two layers
Learning and Generalization in Overparameterized Neural Networks, Going Beyond Two Layers
Page 1 Learning and Generalization in Overparameterized Neural Networks, Going Beyond Two …
Page 1 Learning and Generalization in Overparameterized Neural Networks, Going Beyond Two …