Anshumali Shrivastava

research

∙ 04/18/2022

Learning to Retrieve Relevant Experiences for Motion Planning

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

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...

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...

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...

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...

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...

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...

Joshua Engels, et al. ∙

research

∙ 06/21/2021

Efficient Inference via Universal LSH Kernel

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

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...

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...

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...

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...

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...

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...

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 ...

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...

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...

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 ...

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...

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...

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...

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 ...

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...

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...

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...

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...

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...

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 ...

Benjamin Coleman, et al. ∙

research

∙ 12/04/2019

Angular Visual Hardness

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

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...

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...

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...

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...

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...

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...

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...

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...

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...

Beidi Chen, et al. ∙

research

∙ 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...

Benjamin Coleman, et al. ∙

research

∙ 02/01/2019

Compressing Gradient Optimizers via Count-Sketches

Many popular first-order optimization methods (e.g., Momentum, AdaGrad, ...

Ryan Spring, et al. ∙

research

∙ 01/25/2019

Better accuracy with quantified privacy: representations learned via reconstructive adversarial network

The remarkable success of machine learning, especially deep learning, ha...

Sicong Liu, et al. ∙

research

∙ 10/11/2018

Probabilistic Blocking with An Application to the Syrian Conflict

Entity resolution seeks to merge databases as to remove duplicate entrie...

Rebecca C. Steorts, et al. ∙

research

∙ 10/09/2018

Extreme Classification in Log Memory

We present Merged-Averaged Classifiers via Hashing (MACH) for K-classifi...

Qixuan Huang, et al. ∙

research

∙ 06/12/2018

MISSION: Ultra Large-Scale Feature Selection using Count-Sketches

Feature selection is an important challenge in machine learning. It play...

Amirali Aghazadeh, et al. ∙

research

∙ 02/21/2018

Scaling-up Split-Merge MCMC with Locality Sensitive Sampling (LSS)

Split-Merge MCMC (Monte Carlo Markov Chain) is one of the essential and ...

Chen Luo, et al. ∙

research

∙ 10/07/2017

Unique Entity Estimation with Application to the Syrian Conflict

Entity resolution identifies and removes duplicate entities in large, no...

Beidi Chen, et al. ∙

research

∙ 09/04/2017

FLASH: Randomized Algorithms Accelerated over CPU-GPU for Ultra-High Dimensional Similarity Search

We present FLASH ( Fast LSH Algorithm for Similarity search accelerat...

Yiqiu Wang, et al. ∙

research

∙ 08/25/2017

Accelerating Dependency Graph Learning from Heterogeneous Categorical Event Streams via Knowledge Transfer

Dependency graph, as a heterogeneous graph representing the intrinsic re...

Chen Luo, et al. ∙

research

∙ 06/20/2017

Arrays of (locality-sensitive) Count Estimators (ACE): High-Speed Anomaly Detection via Cache Lookups

Anomaly detection is one of the frequent and important subroutines deplo...

Chen Luo, et al. ∙

research

∙ 03/15/2017

A New Unbiased and Efficient Class of LSH-Based Samplers and Estimators for Partition Function Computation in Log-Linear Models

Log-linear models are arguably the most successful class of graphical mo...

Ryan Spring, et al. ∙

Anshumali Shrivastava

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