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03/09/2023
Provable Data Subset Selection For Efficient Neural Network Training
Radial basis function neural networks (RBFNN) are well-known for their c...
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09/21/2022
Deep Learning on Home Drone: Searching for the Optimal Architecture
We suggest the first system that runs real-time semantic segmentation vi...
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03/08/2022
New Coresets for Projective Clustering and Applications
(j,k)-projective clustering is the natural generalization of the family ...
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03/08/2022
Obstacle Aware Sampling for Path Planning
Many path planning algorithms are based on sampling the state space. Whi...
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03/06/2022
Coresets for Data Discretization and Sine Wave Fitting
In the monitoring problem, the input is an unbounded stream P=p_1,p_2⋯ o...
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03/05/2022
Newton-PnP: Real-time Visual Navigation for Autonomous Toy-Drones
The Perspective-n-Point problem aims to estimate the relative pose betwe...
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11/04/2021
Introduction to Coresets: Approximated Mean
A strong coreset for the mean queries of a set P in ℝ^d is a small weigh...
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11/04/2021
A Unified Approach to Coreset Learning
Coreset of a given dataset and loss function is usually a small weighed ...
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10/07/2021
Coresets for Decision Trees of Signals
A k-decision tree t (or k-tree) is a recursive partition of a matrix (2D...
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07/23/2021
Compressing Neural Networks: Towards Determining the Optimal Layer-wise Decomposition
We present a novel global compression framework for deep neural networks...
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04/06/2021
Low-Regret Active learning
We develop an online learning algorithm for identifying unlabeled data p...
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01/10/2021
Provably Approximated ICP
The goal of the alignment problem is to align a (given) point cloud P = ...
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11/26/2020
Faster Projective Clustering Approximation of Big Data
In projective clustering we are given a set of n points in R^d and wish ...
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11/18/2020
Introduction to Core-sets: an Updated Survey
In optimization or machine learning problems we are given a set of items...
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10/08/2020
Deep Learning Meets Projective Clustering
A common approach for compressing NLP networks is to encode the embeddin...
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09/11/2020
Compressed Deep Networks: Goodbye SVD, Hello Robust Low-Rank Approximation
A common technique for compressing a neural network is to compute the k-...
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06/09/2020
Coresets for Near-Convex Functions
Coreset is usually a small weighted subset of n input points in R^d, tha...
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06/09/2020
Faster PAC Learning and Smaller Coresets via Smoothed Analysis
PAC-learning usually aims to compute a small subset (ε-sample/net) from ...
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03/09/2020
Sets Clustering
The input to the sets-k-means problem is an integer k≥ 1 and a set P={P_...
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02/15/2020
On Coresets for Support Vector Machines
We present an efficient coreset construction algorithm for large-scale S...
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02/15/2020
Sparse Coresets for SVD on Infinite Streams
In streaming Singular Value Decomposition (SVD), d-dimensional rows of a...
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11/18/2019
Provable Filter Pruning for Efficient Neural Networks
We present a provable, sampling-based approach for generating compact Co...
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10/19/2019
Introduction to Coresets: Accurate Coresets
A coreset (or core-set) of an input set is its small summation, such tha...
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10/11/2019
SiPPing Neural Networks: Sensitivity-informed Provable Pruning of Neural Networks
We introduce a pruning algorithm that provably sparsifies the parameters...
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07/09/2019
On Activation Function Coresets for Network Pruning
Model compression provides a means to efficiently deploy deep neural net...
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07/02/2019
Tight Sensitivity Bounds For Smaller Coresets
An ε-coreset for Least-Mean-Squares (LMS) of a matrix A∈R^n× d is a smal...
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06/12/2019
Coresets for Gaussian Mixture Models of Any Shape
An ε-coreset for a given set D of n points, is usually a small weighted ...
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06/11/2019
Fast and Accurate Least-Mean-Squares Solvers
Least-mean squares (LMS) solvers such as Linear / Ridge / Lasso-Regressi...
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03/16/2019
k-Means Clustering of Lines for Big Data
The k-means for lines is a set of k centers (points) that minimizes the ...
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02/27/2019
Provable Approximations for Constrained ℓ_p Regression
The ℓ_p linear regression problem is to minimize f(x)=||Ax-b||_p over x∈...
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01/02/2019
Real-Time EEG Classification via Coresets for BCI Applications
A brain-computer interface (BCI) based on the motor imagery (MI) paradig...
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07/23/2018
Minimizing Sum of Non-Convex but Piecewise log-Lipschitz Functions using Coresets
We suggest a new optimization technique for minimizing the sum ∑_i=1^n f...
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07/12/2018
Turning Big data into tiny data: Constant-size coresets for k-means, PCA and projective clustering
We develop and analyze a method to reduce the size of a very large set o...
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04/15/2018
Data-Dependent Coresets for Compressing Neural Networks with Applications to Generalization Bounds
The deployment of state-of-the-art neural networks containing millions o...
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02/21/2018
Coresets For Monotonic Functions with Applications to Deep Learning
Coreset (or core-set) in this paper is a small weighted subset Q of the ...
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01/22/2018
Scalable Secure Computation of Statistical Functions with Applications to k-Nearest Neighbors
Given a set S of n d-dimensional points, the k-nearest neighbors (KNN) i...
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03/23/2017
Training Mixture Models at Scale via Coresets
How can we train a statistical mixture model on a massive data set? In t...
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11/30/2015