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Found 11 papers, 5 papers with code
Flexible numerical optimization with ensmallen
The library provides a fast and flexible C++ framework for mathematical optimization of arbitrary user-supplied functions.
On Coresets for Regularized Loss Minimization
Our main result is that if the regularizer's effect does not become negligible as the norm of the hypothesis scales, and as the data scales, then a uniform sample of modest size is with high probability a coreset.
Functional Aggregate Queries with Additive Inequalities
This new width is sandwiched between the submodular and the fractional hypertree widths.
ensmallen: a flexible C++ library for efficient function optimization
We present ensmallen, a fast and flexible C++ library for mathematical optimization of arbitrary user-supplied functions, which can be applied to many machine learning problems.
Detecting DGA domains with recurrent neural networks and side information
Our experiments show the model is capable of effectively identifying domains generated by difficult DGA families.
mlpack 3: a fast, flexible machine learning library
In the past several years, the field of machine learning has seen an explosion of interest and excitement, with hundreds or thousands of algorithms developed for different tasks every year.
A generic and fast C++ optimization framework
The development of the mlpack C++ machine learning library (http://www. mlpack. org/) has required the design and implementation of a flexible, robust optimization system that is able to solve the types of arbitrary optimization problems that may arise all throughout machine learning problems.
Designing and building the mlpack open-source machine learning library
mlpack is an open-source C++ machine learning library with an emphasis on speed and flexibility.
Detecting Adversarial Samples from Artifacts
Deep neural networks (DNNs) are powerful nonlinear architectures that are known to be robust to random perturbations of the input.
Dual-tree $k$-means with bounded iteration runtime
k-means is a widely used clustering algorithm, but for $k$ clusters and a dataset size of $N$, each iteration of Lloyd's algorithm costs $O(kN)$ time.
Plug-and-play dual-tree algorithm runtime analysis
In this paper, we present a problem-independent runtime guarantee for any dual-tree algorithm using the cover tree, separating out the problem-dependent and the problem-independent elements.
