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Found 41 papers, 8 papers with code
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.
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.
Deep Learning on Home Drone: Searching for the Optimal Architecture
We suggest the first system that runs real-time semantic segmentation via deep learning on a weak micro-computer such as the Raspberry Pi Zero v2 (whose price was \$15) attached to a toy-drone.
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.
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.
A Unified Approach to Coreset Learning
Our approach offers a new definition of coreset, which is a natural relaxation of the standard definition and aims at approximating the \emph{average} loss of the original data over the queries.
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 $t$.
Compressing Neural Networks: Towards Determining the Optimal Layer-wise Decomposition
We present a novel global compression framework for deep neural networks that automatically analyzes each layer to identify the optimal per-layer compression ratio, while simultaneously achieving the desired overall compression.
Low-Regret Active learning
We develop an online learning algorithm for identifying unlabeled data points that are most informative for training (i. e., active learning).
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.
Introduction to Core-sets: an Updated Survey
In optimization or machine learning problems we are given a set of items, usually points in some metric space, and the goal is to minimize or maximize an objective function over some space of candidate solutions.
Deep Learning Meets Projective Clustering
Based on this approach, we provide a novel architecture that replaces the original embedding layer by a set of $k$ small layers that operate in parallel and are then recombined with a single fully-connected layer.
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 $O(nd)$.
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 $\varepsilon\in(0, 1)$.
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.).
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.
Provable Filter Pruning for Efficient Neural Networks
We present a provable, sampling-based approach for generating compact Convolutional Neural Networks (CNNs) by identifying and removing redundant filters from an over-parameterized network.
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).
SiPPing Neural Networks: Sensitivity-informed Provable Pruning of Neural Networks
We introduce a pruning algorithm that provably sparsifies the parameters of a trained model in a way that approximately preserves the model's predictive accuracy.
Data-Independent Neural Pruning via Coresets
We propose the first efficient, data-independent neural pruning algorithm with a provable trade-off between its compression rate and the approximation error for any future test sample.
Tight Sensitivity Bounds For Smaller Coresets
With high probability, non-uniform sampling based on upper bounds on what is known as importance or sensitivity of each row in $A$ yields a coreset.
Coresets for Gaussian Mixture Models of Any Shape
For example, for any input set $D$ whose coordinates are integers in $[-n^{100}, n^{100}]$ and any fixed $k, d\geq 1$, the coreset size is $(\log n)^{O(1)}/\varepsilon^2$, and can be computed in time near-linear in $n$, with high probability.
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 $p>0$.
Real-Time EEG Classification via Coresets for BCI Applications
We suggest an algorithm that maintains the representation such coreset tailored to handle the EEG signal which enables: (i) real time and continuous computation of the Common Spatial Pattern (CSP) feature extraction method on a coreset representation of the signal (instead on the signal itself) , (ii) improvement of the CSP algorithm efficiency with provable guarantees by applying CSP algorithm on the coreset, and (iii) real time addition of the data trials (EEG data windows) to the coreset.
Aligning Points to Lines: Provable Approximations
This problem is non-trivial even if $z=1$ and the matching $\pi$ is given.
Data-Dependent Coresets for Compressing Neural Networks with Applications to Generalization Bounds
We present an efficient coresets-based neural network compression algorithm that sparsifies the parameters of a trained fully-connected neural network in a manner that provably approximates the network's output.
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 $x\in\mathbb{R}^d$.
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.
Coresets for Vector Summarization with Applications to Network Graphs
We provide a deterministic data summarization algorithm that approximates the mean $\bar{p}=\frac{1}{n}\sum_{p\in P} p$ of a set $P$ of $n$ vectors in $\REAL^d$, by a weighted mean $\tilde{p}$ of a \emph{subset} of $O(1/\eps)$ vectors, i. e., independent of both $n$ and $d$.
Training Gaussian Mixture Models at Scale via Coresets
In this work we show how to construct coresets for mixtures of Gaussians.
Dimensionality Reduction of Massive Sparse Datasets Using Coresets
An open practical problem has been to compute a non-trivial approximation to the PCA of very large but sparse databases such as the Wikipedia document-term matrix in a reasonable time.
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.
Coresets for k-Segmentation of Streaming Data
We consider the problem of computing optimal segmentation of such signals by k-piecewise linear function, using only one pass over the data by maintaining a coreset for the signal.
Scalable Training of Mixture Models via Coresets
In this paper, we show how to construct coresets for mixtures of Gaussians and natural generalizations.
A Unified Framework for Approximating and Clustering Data
In the $k$-clustering variant, each $x\in X$ is a tuple of $k$ shapes, and $f(x)$ is the distance from $p$ to its closest shape in $x$.
