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Found 9 papers, 0 papers with code
SceneGen: Generative Contextual Scene Augmentation using Scene Graph Priors
Spatial computing experiences are constrained by the real-world surroundings of the user.
On the Multiple Descent of Minimum-Norm Interpolants and Restricted Lower Isometry of Kernels
We study the risk of minimum-norm interpolants of data in Reproducing Kernel Hilbert Spaces.
Near Optimal Stratified Sampling
The performance of a machine learning system is usually evaluated by using i. i. d.\ observations with true labels.
Consistency of Interpolation with Laplace Kernels is a High-Dimensional Phenomenon
We show that minimum-norm interpolation in the Reproducing Kernel Hilbert Space corresponding to the Laplace kernel is not consistent if input dimension is constant.
How Many Samples are Needed to Estimate a Convolutional Neural Network?
We show that for an $m$-dimensional convolutional filter with linear activation acting on a $d$-dimensional input, the sample complexity of achieving population prediction error of $\epsilon$ is $\widetilde{O(m/\epsilon^2)$, whereas the sample-complexity for its FNN counterpart is lower bounded by $\Omega(d/\epsilon^2)$ samples.
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.
Gradient Descent Provably Optimizes Over-parameterized Neural Networks
One of the mysteries in the success of neural networks is randomly initialized first order methods like gradient descent can achieve zero training loss even though the objective function is non-convex and non-smooth.
How Many Samples are Needed to Estimate a Convolutional or Recurrent Neural Network?
It is widely believed that the practical success of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) owes to the fact that CNNs and RNNs use a more compact parametric representation than their Fully-Connected Neural Network (FNN) counterparts, and consequently require fewer training examples to accurately estimate their parameters.
Generalization Bounds of SGLD for Non-convex Learning: Two Theoretical Viewpoints
This is the first algorithm-dependent result with reasonable dependence on aggregated step sizes for non-convex learning, and has important implications to statistical learning aspects of stochastic gradient methods in complicated models such as deep learning.
