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Found 17 papers, 4 papers with code
Sub-linear Memory Sketches for Near Neighbor Search on Streaming Data with RACE
We present the first sublinear memory sketch that can be queried to find the nearest neighbors in a dataset.
Adaptive Sampling for Deep Learning via Efficient Nonparametric Proxies
To address this problem, we propose a novel sampling distribution based on nonparametric kernel regression that learns an effective importance score as the neural network trains.
CAPS: A Practical Partition Index for Filtered Similarity Search
With the surging popularity of approximate near-neighbor search (ANNS), driven by advances in neural representation learning, the ability to serve queries accompanied by a set of constraints has become an area of intense interest.
CARAMEL: A Succinct Read-Only Lookup Table via Compressed Static Functions
Lookup tables are a fundamental structure in many data processing and systems applications.
Unified Embedding: Battle-Tested Feature Representations for Web-Scale ML Systems
Learning high-quality feature embeddings efficiently and effectively is critical for the performance of web-scale machine learning systems.
BOLT: An Automated Deep Learning Framework for Training and Deploying Large-Scale Search and Recommendation Models on Commodity CPU Hardware
Efficient large-scale neural network training and inference on commodity CPU hardware is of immense practical significance in democratizing deep learning (DL) capabilities.
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STORM: Sketch Toward Online Risk Minimization
To this end, we propose STORM, an online sketching-based method for empirical risk minimization.
Efficient Inference via Universal LSH Kernel
Large machine learning models achieve unprecedented performance on various tasks and have evolved as the go-to technique.
Density Sketches for Sampling and Estimation
We introduce Density sketches (DS): a succinct online summary of the data distribution.
Bloom Origami Assays: Practical Group Testing
Group testing is a well-studied problem with several appealing solutions, but recent biological studies impose practical constraints for COVID-19 that are incompatible with traditional methods.
STORM: Foundations of End-to-End Empirical Risk Minimization on the Edge
To this end, we propose STORM, an online sketch for empirical risk minimization.
A One-Pass Private Sketch for Most Machine Learning Tasks
Existing methods for DP kernel density estimation scale poorly, often exponentially slower with an increase in dimensions.
Sub-linear RACE Sketches for Approximate Kernel Density Estimation on Streaming Data
We evaluate our method on real-world high-dimensional datasets and show that our sketch achieves 10x better compression compared to competing methods.
Fast Processing and Querying of 170TB of Genomics Data via a Repeated And Merged BloOm Filter (RAMBO)
Interestingly, it is a count-min sketch type arrangement of a membership testing utility (Bloom Filter in our case).
RAMBO: Repeated And Merged Bloom Filter for Multiple Set Membership Testing (MSMT) in Sub-linear time
A simple array of Bloom Filters can achieve that.
Sub-linear Memory Sketches for Near Neighbor Search on Streaming Data
We present the first sublinear memory sketch that can be queried to find the nearest neighbors in a dataset.
