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Found 17 papers, 4 papers with code
Bridging the Gap Between General and Down-Closed Convex Sets in Submodular Maximization
In this work, we suggest novel offline and online algorithms that provably provide such an interpolation based on a natural decomposition of the convex body constraint into two distinct convex bodies: a down-closed convex body and a general convex body.
ORBSLAM3-Enhanced Autonomous Toy Drones: Pioneering Indoor Exploration
In response to this formidable challenge, we introduce a real-time autonomous indoor exploration system tailored for drones equipped with a monocular \emph{RGB} camera.
Dataset Distillation Meets Provable Subset Selection
Deep learning has grown tremendously over recent years, yielding state-of-the-art results in various fields.
On the Size and Approximation Error of Distilled Sets
Despite significant empirical progress in recent years, there is little understanding of the theoretical limitations/guarantees of dataset distillation, specifically, what excess risk is achieved by distillation compared to the original dataset, and how large are distilled datasets?
AutoCoreset: An Automatic Practical Coreset Construction Framework
A coreset is a tiny weighted subset of an input set, that closely resembles the loss function, with respect to a certain set of queries.
Provable Data Subset Selection For Efficient Neural Network Training
In this paper, we introduce the first algorithm to construct coresets for \emph{RBFNNs}, i. e., small weighted subsets that approximate the loss of the input data on any radial basis function network and thus approximate any function defined by an \emph{RBFNN} on the larger input data.
An Efficient Drifters Deployment Strategy to Evaluate Water Current Velocity Fields
In this paper, we consider a common approach for water current prediction that uses Lagrangian floaters for water current prediction by interpolating the trajectory of the elements to reflect the velocity field.
Pruning Neural Networks via Coresets and Convex Geometry: Towards No Assumptions
Lately, coresets (provable data summarizations) were leveraged for pruning DNNs, adding the advantage of theoretical guarantees on the trade-off between the compression rate and the approximation error.
Obstacle Aware Sampling for Path Planning
While this approach is very simple, it can become costly when the obstacles are unknown, since samples hitting these obstacles are wasted.
New Coresets for Projective Clustering and Applications
$(j, k)$-projective clustering is the natural generalization of the family of $k$-clustering and $j$-subspace clustering problems.
Coresets for Data Discretization and Sine Wave Fitting
The goal (e. g., for anomaly detection) is to approximate the $n$ points received so far in $P$ by a single frequency $\sin$, e. g. $\min_{c\in C}cost(P, c)+\lambda(c)$, where $cost(P, c)=\sum_{i=1}^n \sin^2(\frac{2\pi}{N} p_ic)$, $C\subseteq [N]$ is a feasible set of solutions, and $\lambda$ is a given regularization function.
Compressed Deep Networks: Goodbye SVD, Hello Robust Low-Rank Approximation
Here, $d$ is the number of the neurons in the layer, $n$ is the number in the next one, and $A_{k, 2}$ can be stored in $O((n+d)k)$ memory instead of $O(nd)$.
Coresets for Near-Convex Functions
Coreset is usually a small weighted subset of $n$ input points in $\mathbb{R}^d$, that provably approximates their loss function for a given set of queries (models, classifiers, etc.).
Faster PAC Learning and Smaller Coresets via Smoothed Analysis
PAC-learning usually aims to compute a small subset ($\varepsilon$-sample/net) from $n$ items, that provably approximates a given loss function for every query (model, classifier, hypothesis) from a given set of queries, up to an additive error $\varepsilon\in(0, 1)$.
Sets Clustering
The input to the \emph{sets-$k$-means} problem is an integer $k\geq 1$ and a set $\mathcal{P}=\{P_1,\cdots, P_n\}$ of sets in $\mathbb{R}^d$.
On Coresets for Support Vector Machines
A coreset is a small, representative subset of the original data points such that a models trained on the coreset are provably competitive with those trained on the original data set.
Small Coresets to Represent Large Training Data for Support Vector Machines
Support Vector Machines (SVMs) are one of the most popular algorithms for classification and regression analysis.
