16 citations · 22 across the 4 of their papers we have counts for
9 papers
Distributed SLIDE: Enabling Training Large Neural Networks on Low Bandwidth and Simple CPU-Clusters via Model Parallelism and Sparsity
Minghao Yan, Nicholas Meisburger, Tharun Medini +1
More than 70% of cloud computing is paid for but sits idle. A large fraction of these idle compute are cheap CPUs with few cores that are not utilized during the less busy hours. T…
IRLI: Iterative Re-partitioning for Learning to Index
Gaurav Gupta, Tharun Medini, Anshumali Shrivastava +1
Neural models have transformed the fundamental information retrieval problem of mapping a query to a giant set of items. However, the need for efficient and low latency inference f…
A Tale of Two Efficient and Informative Negative Sampling Distributions
Shabnam Daghaghi, Tharun Medini, Nicholas Meisburger +3
Softmax classifiers with a very large number of classes naturally occur in many applications such as natural language processing and information retrieval. The calculation of full…
SOLAR: Sparse Orthogonal Learned and Random Embeddings
Tharun Medini, Beidi Chen, Anshumali Shrivastava
Dense embedding models are commonly deployed in commercial search engines, wherein all the document vectors are pre-computed, and near-neighbor search (NNS) is performed with the q…
Extreme Classification in Log Memory using Count-Min Sketch: A Case Study of Amazon Search with 50M Products
Tharun Medini, Qixuan Huang, Yiqiu Wang +2
In the last decade, it has been shown that many hard AI tasks, especially in NLP, can be naturally modeled as extreme classification problems leading to improved precision. However…
RAMBO: Repeated And Merged BloOm Filter for Ultra-fast Multiple Set Membership Testing (MSMT) on Large-Scale Data
Gaurav Gupta, Minghao Yan, Benjamin Coleman +4
Multiple Set Membership Testing (MSMT) is a well-known problem in a variety of search and query applications. Given a dataset of K different sets and a query q, it aims to find all…