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Found 8 papers, 0 papers with code
On the Sample Complexity of Learning under Geometric Stability
Many supervised learning problems involve high-dimensional data such as images, text, or graphs.
On the Sample Complexity of Learning under Invariance and Geometric Stability
Many supervised learning problems involve high-dimensional data such as images, text, or graphs.
Multilevel Stein variational gradient descent with applications to Bayesian inverse problems
The proposed multilevel Stein variational gradient descent moves most of the iterations to lower, cheaper levels with the aim of requiring only a few iterations on the higher, more expensive levels when compared to the traditional, single-level Stein variational gradient descent variant that uses the highest-level distribution only.
Depth separation beyond radial functions
The first contribution of this paper is to extend such results to a more general class of functions, namely functions with piece-wise oscillatory structure, by building on the proof strategy of (Eldan and Shamir, 2016).
Depth separation for reduced deep networks in nonlinear model reduction: Distilling shock waves in nonlinear hyperbolic problems
Classical reduced models are low-rank approximations using a fixed basis designed to achieve dimensionality reduction of large-scale systems.
Diffusion-based Deep Active Learning
To alleviate the load of data annotation, active deep learning aims to select a minimal set of training points to be labelled which yields maximal model accuracy.
Spurious Valleys in Two-layer Neural Network Optimization Landscapes
Focusing on a class of two-layer neural networks defined by smooth (but generally non-linear) activation functions, we identify a notion of intrinsic dimension and show that it provides necessary and sufficient conditions for the absence of spurious valleys.
Learning the Kernel for Classification and Regression
We investigate a series of learning kernel problems with polynomial combinations of base kernels, which will help us solve regression and classification problems.
