no code implementations • 17 Jan 2024 • Loay Mualem, Murad Tukan, Moran Fledman
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.
no code implementations • 20 Dec 2023 • Murad Tukan, Fares Fares, Yotam Grufinkle, Ido Talmor, Loay Mualem, Vladimir Braverman, Dan Feldman
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.
no code implementations • 16 Jul 2023 • Murad Tukan, Alaa Maalouf, Margarita Osadchy
Deep learning has grown tremendously over recent years, yielding state-of-the-art results in various fields.
no code implementations • 23 May 2023 • Alaa Maalouf, Murad Tukan, Noel Loo, Ramin Hasani, Mathias Lechner, Daniela Rus
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?
1 code implementation • 19 May 2023 • Alaa Maalouf, Murad Tukan, Vladimir Braverman, Daniela Rus
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.
1 code implementation • 9 Mar 2023 • Murad Tukan, Samson Zhou, Alaa Maalouf, Daniela Rus, Vladimir Braverman, Dan Feldman
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.
1 code implementation • 10 Jan 2023 • Murad Tukan, Eli Biton, Roee Diamant
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.
no code implementations • 18 Sep 2022 • Murad Tukan, Loay Mualem, Alaa Maalouf
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.
1 code implementation • 8 Mar 2022 • Murad Tukan, Xuan Wu, Samson Zhou, Vladimir Braverman, Dan Feldman
$(j, k)$-projective clustering is the natural generalization of the family of $k$-clustering and $j$-subspace clustering problems.
no code implementations • 8 Mar 2022 • Murad Tukan, Alaa Maalouf, Dan Feldman, Roi Poranne
While this approach is very simple, it can become costly when the obstacles are unknown, since samples hitting these obstacles are wasted.
no code implementations • 6 Mar 2022 • Alaa Maalouf, Murad Tukan, Eric Price, Daniel Kane, Dan Feldman
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.
no code implementations • 11 Sep 2020 • Murad Tukan, Alaa Maalouf, Matan Weksler, Dan Feldman
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 $O(nd)$.
no code implementations • NeurIPS 2020 • Murad Tukan, Alaa Maalouf, Dan Feldman
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.).
no code implementations • 9 Jun 2020 • Alaa Maalouf, Ibrahim Jubran, Murad Tukan, Dan Feldman
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 $\varepsilon\in(0, 1)$.
no code implementations • ICML 2020 • Ibrahim Jubran, Murad Tukan, Alaa Maalouf, Dan Feldman
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 $\mathbb{R}^d$.
no code implementations • 15 Feb 2020 • Murad Tukan, Cenk Baykal, Dan Feldman, Daniela Rus
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.
no code implementations • ICLR 2018 • Cenk Baykal, Murad Tukan, Dan Feldman, Daniela Rus
Support Vector Machines (SVMs) are one of the most popular algorithms for classification and regression analysis.