Anshumali Shrivastava

research

∙ 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. ∙

research

∙ 05/30/2023

Algorithmic Foundations of Inexact Computing

Inexact computing also referred to as approximate computing is a style o...

0 John Augustine, et al. ∙

research

∙ 05/26/2023

Scissorhands: Exploiting the Persistence of Importance Hypothesis for LLM KV Cache Compression at Test Time

Large language models(LLMs) have sparked a new wave of exciting AI appli...

2 Zichang Liu, et al. ∙

research

∙ 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. ∙

research

∙ 05/17/2023

Compress, Then Prompt: Improving Accuracy-Efficiency Trade-off of LLM Inference with Transferable Prompt

Large Language Models (LLMs), armed with billions of parameters, exhibit...

9 Zhaozhuo Xu, et al. ∙

research

∙ 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. ∙

research

∙ 03/10/2023

A Theoretical Analysis Of Nearest Neighbor Search On Approximate Near Neighbor Graph

Graph-based algorithms have demonstrated state-of-the-art performance in...

1 Anshumali Shrivastava, et al. ∙

research

∙ 12/29/2022

Learning Multimodal Data Augmentation in Feature Space

The ability to jointly learn from multiple modalities, such as text, aud...

14 Zichang Liu, et al. ∙

research

∙ 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. ∙

research

∙ 10/04/2022

Coordinated Science Laboratory 70th Anniversary Symposium: The Future of Computing

In 2021, the Coordinated Science Laboratory CSL, an Interdisciplinary Re...

0 Klara Nahrstedt, et al. ∙

research

∙ 07/21/2022

The trade-offs of model size in large recommendation models : A 10000 × compressed criteo-tb DLRM model (100 GB parameters to mere 10MB)

Embedding tables dominate industrial-scale recommendation model sizes, u...

7 Aditya Desai, et al. ∙

research

∙ 07/21/2022

Efficient model compression with Random Operation Access Specific Tile (ROAST) hashing

Advancements in deep learning are often associated with increasing model...

3 Aditya Desai, et al. ∙

research

∙ 04/18/2022

Learning to Retrieve Relevant Experiences for Motion Planning

Recent work has demonstrated that motion planners' performance can be si...

0 Constantinos Chamzas, et al. ∙

research

∙ 01/29/2022

Distributed SLIDE: Enabling Training Large Neural Networks on Low Bandwidth and Simple CPU-Clusters via Model Parallelism and Sparsity

More than 70 of these idle compute are cheap CPUs with few cores that ar...

22 Minghao Yan, et al. ∙

research

∙ 11/30/2021

Breaking the Linear Iteration Cost Barrier for Some Well-known Conditional Gradient Methods Using MaxIP Data-structures

Conditional gradient methods (CGM) are widely used in modern machine lea...

0 Anshumali Shrivastava, et al. ∙

research

∙ 11/12/2021

Satellite Images and Deep Learning to Identify Discrepancy in Mailing Addresses with Applications to Census 2020 in Houston

The accuracy and completeness of population estimation would significant...

0 Zhaozhuo Xu, et al. ∙

research

∙ 10/23/2021

Federated Multiple Label Hashing (FedMLH): Communication Efficient Federated Learning on Extreme Classification Tasks

Federated learning enables many local devices to train a deep learning m...

3 Zhenwei Dai, et al. ∙

research

∙ 08/04/2021

Random Offset Block Embedding Array (ROBE) for CriteoTB Benchmark MLPerf DLRM Model : 1000× Compression and 2.7× Faster Inference

Deep learning for recommendation data is the one of the most pervasive a...

1 Aditya Desai, et al. ∙

research

∙ 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. ∙

research

∙ 06/21/2021

Efficient Inference via Universal LSH Kernel

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

1 Zichang Liu, et al. ∙

research

∙ 06/15/2021

PairConnect: A Compute-Efficient MLP Alternative to Attention

Transformer models have demonstrated superior performance in natural lan...

21 Zhaozhuo Xu, et al. ∙

research

∙ 06/11/2021

Efficient and Less Centralized Federated Learning

With the rapid growth in mobile computing, massive amounts of data and c...

0 Li Chou, et al. ∙

research

∙ 05/18/2021

Sublinear Least-Squares Value Iteration via Locality Sensitive Hashing

We present the first provable Least-Squares Value Iteration (LSVI) algor...

9 Anshumali Shrivastava, et al. ∙

research

∙ 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. ∙

research

∙ 03/17/2021

IRLI: Iterative Re-partitioning for Learning to Index

Neural models have transformed the fundamental information retrieval pro...

8 Gaurav Gupta, et al. ∙

research

∙ 03/06/2021

Accelerating SLIDE Deep Learning on Modern CPUs: Vectorization, Quantizations, Memory Optimizations, and More

Deep learning implementations on CPUs (Central Processing Units) are gai...

0 Shabnam Daghaghi, et al. ∙

research

∙ 02/26/2021

Beyond Convolutions: A Novel Deep Learning Approach for Raw Seismic Data Ingestion

Traditional seismic processing workflows (SPW) are expensive, requiring ...

17 Zhaozhuo Xu, et al. ∙

research

∙ 02/24/2021

Semantically Constrained Memory Allocation (SCMA) for Embedding in Efficient Recommendation Systems

Deep learning-based models are utilized to achieve state-of-the-art perf...

0 Aditya Desai, et al. ∙

research

∙ 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. ∙

research

∙ 12/31/2020

A Constant-time Adaptive Negative Sampling

Softmax classifiers with a very large number of classes naturally occur ...

13 Shabnam Daghaghi, et al. ∙

research

∙ 10/29/2020

Active Sampling Count Sketch (ASCS) for Online Sparse Estimation of a Trillion Scale Covariance Matrix

Estimating and storing the covariance (or correlation) matrix of high-di...

0 Zhenwei Dai, et al. ∙

research

∙ 10/29/2020

Learning Sampling Distributions Using Local 3D Workspace Decompositions for Motion Planning in High Dimensions

Earlier work has shown that reusing experience from prior motion plannin...

0 Constantinos Chamzas, et al. ∙

research

∙ 08/30/2020

SOLAR: Sparse Orthogonal Learned and Random Embeddings

Dense embedding models are commonly deployed in commercial search engine...

11 Tharun Medini, et al. ∙

research

∙ 08/05/2020

Distributed Tera-Scale Similarity Search with MPI: Provably Efficient Similarity Search over billions without a Single Distance Computation

We present SLASH (Sketched LocAlity Sensitive Hashing), an MPI (Message ...

0 Nicholas Meisburger, et al. ∙

research

∙ 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. ∙

research

∙ 07/02/2020

Climbing the WOL: Training for Cheaper Inference

Efficient inference for wide output layers (WOLs) is an essential yet ch...

0 Zichang Liu, et al. ∙

research

∙ 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. ∙

research

∙ 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. ∙

research

∙ 06/08/2020

Privacy Adversarial Network: Representation Learning for Mobile Data Privacy

The remarkable success of machine learning has fostered a growing number...

32 Sicong Liu, et al. ∙

research

∙ 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. ∙

research

∙ 12/04/2019

Angular Visual Hardness

Although convolutional neural networks (CNNs) are inspired by the mechan...

66 Beidi Chen, et al. ∙

research

∙ 12/02/2019

FourierSAT: A Fourier Expansion-Based Algebraic Framework for Solving Hybrid Boolean Constraints

The Boolean SATisfiability problem (SAT) is of central importance in com...

0 Anastasios Kyrillidis, et al. ∙

research

∙ 10/30/2019

Lsh-sampling Breaks the Computation Chicken-and-egg Loop in Adaptive Stochastic Gradient Estimation

Stochastic Gradient Descent or SGD is the most popular optimization algo...

14 Beidi Chen, et al. ∙

research

∙ 10/28/2019

Extreme Classification in Log Memory using Count-Min Sketch: A Case Study of Amazon Search with 50M Products

In the last decade, it has been shown that many hard AI tasks, especiall...

22 Tharun Medini, et al. ∙

research

∙ 10/21/2019

Adaptive Learned Bloom Filter (Ada-BF): Efficient Utilization of the Classifier

Recent work suggests improving the performance of Bloom filter by incorp...

0 Zhenwei Dai, et al. ∙

research

∙ 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. ∙

research

∙ 09/10/2019

Semantic Similarity Based Softmax Classifier for Zero-Shot Learning

Zero-Shot Learning (ZSL) is a classification task where we do not have e...

1 Shabnam Daghaghi, et al. ∙

research

∙ 08/23/2019

Revisiting Consistent Hashing with Bounded Loads

Dynamic load balancing lies at the heart of distributed caching. Here, t...

0 John Chen, et al. ∙

research

∙ 03/20/2019

Using Local Experiences for Global Motion Planning

Sampling-based planners are effective in many real-world applications su...

12 Constantinos Chamzas, et al. ∙

research

∙ 03/07/2019

SLIDE : In Defense of Smart Algorithms over Hardware Acceleration for Large-Scale Deep Learning Systems

Deep Learning (DL) algorithms are the central focus of modern machine le...

0 Beidi Chen, et al. ∙

Anshumali Shrivastava

Featured Co-authors