Search Results for author:
Found 11 papers, 2 papers with code
Adversarial Robustness is at Odds with Lazy Training
Recent works show that adversarial examples exist for random neural networks [Daniely and Schacham, 2020] and that these examples can be found using a single step of gradient ascent [Bubeck et al., 2021].
On Convergence and Generalization of Dropout Training
We study dropout in two-layer neural networks with rectified linear unit (ReLU) activations.
Dropout: Explicit Forms and Capacity Control
In deep learning, we show that the data-dependent regularizer due to dropout directly controls the Rademacher complexity of the underlying class of deep neural networks.
On Dropout and Nuclear Norm Regularization
We give a formal and complete characterization of the explicit regularizer induced by dropout in deep linear networks with squared loss.
Streaming Kernel PCA with \tilde{O}(\sqrt{n}) Random Features
We study the statistical and computational aspects of kernel principal component analysis using random Fourier features and show that under mild assumptions, $O(\sqrt{n} \log n)$ features suffices to achieve $O(1/\epsilon^2)$ sample complexity.
Streaming Kernel PCA with $\tilde{O}(\sqrt{n})$ Random Features
We study the statistical and computational aspects of kernel principal component analysis using random Fourier features and show that under mild assumptions, $O(\sqrt{n} \log n)$ features suffices to achieve $O(1/\epsilon^2)$ sample complexity.
Stochastic PCA with $\ell_2$ and $\ell_1$ Regularization
We revisit convex relaxation based methods for stochastic optimization of principal component analysis (PCA).
Streaming Principal Component Analysis in Noisy Setting
We study streaming algorithms for principal component analysis (PCA) in noisy settings.
On the Implicit Bias of Dropout
Algorithmic approaches endow deep learning systems with implicit bias that helps them generalize even in over-parametrized settings.
Stochastic Approximation for Canonical Correlation Analysis
We propose novel first-order stochastic approximation algorithms for canonical correlation analysis (CCA).
Understanding Deep Neural Networks with Rectified Linear Units
In this paper we investigate the family of functions representable by deep neural networks (DNN) with rectified linear units (ReLU).
