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Found 15 papers, 3 papers with code
Newton-PnP: Real-time Visual Navigation for Autonomous Toy-Drones
The Perspective-n-Point problem aims to estimate the relative pose between a calibrated monocular camera and a known 3D model, by aligning pairs of 2D captured image points to their corresponding 3D points in the model.
Introduction to Coresets: Approximated Mean
The survey may help guide new researchers unfamiliar with the field, and introduce them to the very basic foundations of coresets, through a simple, yet fundamental, problem.
Unsupervised High-Fidelity Facial Texture Generation and Reconstruction
In this paper, we propose a novel unified pipeline for both tasks, generation of both geometry and texture, and recovery of high-fidelity texture.
Coresets for Decision Trees of Signals
Its regression or classification loss to a given matrix $D$ of $N$ entries (labels) is the sum of squared differences over every label in $D$ and its assigned label by $t$.
Provably Approximated ICP
A harder version is the \emph{registration problem}, where the correspondence is unknown, and the minimum is also over all possible correspondence functions from $P$ to $Q$.
Provably Approximated Point Cloud Registration
A harder version is the registration problem, where the correspondence is unknown, and the minimum is also over all possible correspondence functions from P to Q. Algorithms such as the Iterative Closest Point (ICP) and its variants were suggested for these problems, but none yield a provable non-trivial approximation for the global optimum.
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 $\mathbb{R}^d$.
Introduction to Coresets: Accurate Coresets
A coreset (or core-set) of an input set is its small summation, such that solving a problem on the coreset as its input, provably yields the same result as solving the same problem on the original (full) set, for a given family of problems (models, classifiers, loss functions).
Fast and Accurate Least-Mean-Squares Solvers
Least-mean squares (LMS) solvers such as Linear / Ridge / Lasso-Regression, SVD and Elastic-Net not only solve fundamental machine learning problems, but are also the building blocks in a variety of other methods, such as decision trees and matrix factorizations.
Provable Approximations for Constrained $\ell_p$ Regression
The $\ell_p$ linear regression problem is to minimize $f(x)=||Ax-b||_p$ over $x\in\mathbb{R}^d$, where $A\in\mathbb{R}^{n\times d}$, $b\in \mathbb{R}^n$, and $p>0$.
Aligning Points to Lines: Provable Approximations
This problem is non-trivial even if $z=1$ and the matching $\pi$ is given.
Generic Coreset for Scalable Learning of Monotonic Kernels: Logistic Regression, Sigmoid and more
Coreset (or core-set) is a small weighted \emph{subset} $Q$ of an input set $P$ with respect to a given \emph{monotonic} function $f:\mathbb{R}\to\mathbb{R}$ that \emph{provably} approximates its fitting loss $\sum_{p\in P}f(p\cdot x)$ to \emph{any} given $x\in\mathbb{R}^d$.
CoBe -- Coded Beacons for Localization, Object Tracking, and SLAM Augmentation
This paper presents a novel beacon light coding protocol, which enables fast and accurate identification of the beacons in an image.
Coresets for Kinematic Data: From Theorems to Real-Time Systems
By maintaining such a coreset for kinematic (moving) set of $n$ points, we can run pose-estimation algorithms, such as Kabsch or PnP, on the small coresets, instead of the $n$ points, in real-time using weak devices, while obtaining the same results.
