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08/10/2023
Follow Anything: Open-set detection, tracking, and following in real-time
Tracking and following objects of interest is critical to several roboti...
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07/16/2023
Dataset Distillation Meets Provable Subset Selection
Deep learning has grown tremendously over recent years, yielding state-o...
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05/23/2023
On the Size and Approximation Error of Distilled Sets
Dataset Distillation is the task of synthesizing small datasets from lar...
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05/19/2023
AutoCoreset: An Automatic Practical Coreset Construction Framework
A coreset is a tiny weighted subset of an input set, that closely resemb...
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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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09/18/2022
Pruning Neural Networks via Coresets and Convex Geometry: Towards No Assumptions
Pruning is one of the predominant approaches for compressing deep neural...
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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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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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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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01/10/2021
Provably Approximated ICP
The goal of the alignment problem is to align a (given) point cloud P = ...
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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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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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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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06/11/2019