Benjamin Coleman | DeepAI

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Bernhard Schölkopf
271 publications
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73 publications
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60 publications
Alex Smola
37 publications
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29 publications
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Gary Becigneul
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Tharun Medini
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Aditya Desai
9 publications

∙ 08/29/2023

CAPS: A Practical Partition Index for Filtered Similarity Search

With the surging popularity of approximate near-neighbor search (ANNS), ...

0 Gaurav Gupta, et al. ∙

∙ 05/26/2023

CARAMEL: A Succinct Read-Only Lookup Table via Compressed Static Functions

Lookup tables are a fundamental structure in many data processing and sy...

0 Benjamin Coleman, et al. ∙

∙ 03/30/2023

BOLT: An Automated Deep Learning Framework for Training and Deploying Large-Scale Neural Networks on Commodity CPU Hardware

Efficient large-scale neural network training and inference on commodity...

5 Nicholas Meisburger, et al. ∙

∙ 10/27/2022

DESSERT: An Efficient Algorithm for Vector Set Search with Vector Set Queries

We study the problem of vector set search with vector set queries. This ...

0 Joshua Engels, et al. ∙

∙ 06/22/2021

Practical Near Neighbor Search via Group Testing

We present a new algorithm for the approximate near neighbor problem tha...

0 Joshua Engels, et al. ∙

∙ 06/21/2021

Efficient Inference via Universal LSH Kernel

Large machine learning models achieve unprecedented performance on vario...

1 Zichang Liu, et al. ∙

∙ 04/07/2021

Graph Reordering for Cache-Efficient Near Neighbor Search

Graph search is one of the most successful algorithmic trends in near ne...

0 Benjamin Coleman, et al. ∙

∙ 02/24/2021

Density Sketches for Sampling and Estimation

We introduce Density sketches (DS): a succinct online summary of the dat...

5 Aditya Desai, et al. ∙

∙ 07/21/2020

Bloom Origami Assays: Practical Group Testing

We study the problem usually referred to as group testing in the context...

0 Louis Abraham, et al. ∙

∙ 06/25/2020

STORM: Foundations of End-to-End Empirical Risk Minimization on the Edge

Empirical risk minimization is perhaps the most influential idea in stat...

6 Benjamin Coleman, et al. ∙

∙ 06/16/2020

A One-Pass Private Sketch for Most Machine Learning Tasks

Differential privacy (DP) is a compelling privacy definition that explai...

1 Benjamin Coleman, et al. ∙

∙ 12/04/2019

Sub-linear RACE Sketches for Approximate Kernel Density Estimation on Streaming Data

Kernel density estimation is a simple and effective method that lies at ...

0 Benjamin Coleman, et al. ∙

∙ 10/07/2019

RAMBO: Repeated And Merged Bloom Filter for Multiple Set Membership Testing (MSMT) in Sub-linear time

Approximate set membership is a common problem with wide applications in...

0 Gaurav Gupta, et al. ∙

∙ 02/18/2019

RACE: Sub-Linear Memory Sketches for Approximate Near-Neighbor Search on Streaming Data

We demonstrate the first possibility of a sub-linear memory sketch for s...

0 Benjamin Coleman, et al. ∙

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