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08/29/2023
CAPS: A Practical Partition Index for Filtered Similarity Search
With the surging popularity of approximate near-neighbor search (ANNS), ...
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05/30/2023
Algorithmic Foundations of Inexact Computing
Inexact computing also referred to as approximate computing is a style o...
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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...
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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...
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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...
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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...
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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...
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12/29/2022
Learning Multimodal Data Augmentation in Feature Space
The ability to jointly learn from multiple modalities, such as text, aud...
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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 ...
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10/04/2022
Coordinated Science Laboratory 70th Anniversary Symposium: The Future of Computing
In 2021, the Coordinated Science Laboratory CSL, an Interdisciplinary Re...
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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...
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07/21/2022
Efficient model compression with Random Operation Access Specific Tile (ROAST) hashing
Advancements in deep learning are often associated with increasing model...
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04/18/2022
Learning to Retrieve Relevant Experiences for Motion Planning
Recent work has demonstrated that motion planners' performance can be si...
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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...
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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...
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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...
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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...
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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...
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06/22/2021
Practical Near Neighbor Search via Group Testing
We present a new algorithm for the approximate near neighbor problem tha...
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06/21/2021
Efficient Inference via Universal LSH Kernel
Large machine learning models achieve unprecedented performance on vario...
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06/15/2021
PairConnect: A Compute-Efficient MLP Alternative to Attention
Transformer models have demonstrated superior performance in natural lan...
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06/11/2021
Efficient and Less Centralized Federated Learning
With the rapid growth in mobile computing, massive amounts of data and c...
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05/18/2021
Sublinear Least-Squares Value Iteration via Locality Sensitive Hashing
We present the first provable Least-Squares Value Iteration (LSVI) algor...
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04/07/2021
Graph Reordering for Cache-Efficient Near Neighbor Search
Graph search is one of the most successful algorithmic trends in near ne...
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03/17/2021
IRLI: Iterative Re-partitioning for Learning to Index
Neural models have transformed the fundamental information retrieval pro...
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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...
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02/26/2021
Beyond Convolutions: A Novel Deep Learning Approach for Raw Seismic Data Ingestion
Traditional seismic processing workflows (SPW) are expensive, requiring ...
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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...
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02/24/2021
Density Sketches for Sampling and Estimation
We introduce Density sketches (DS): a succinct online summary of the dat...
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12/31/2020
A Constant-time Adaptive Negative Sampling
Softmax classifiers with a very large number of classes naturally occur ...
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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...
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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...
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08/30/2020
SOLAR: Sparse Orthogonal Learned and Random Embeddings
Dense embedding models are commonly deployed in commercial search engine...
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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 ...
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07/21/2020
Bloom Origami Assays: Practical Group Testing
We study the problem usually referred to as group testing in the context...
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07/02/2020
Climbing the WOL: Training for Cheaper Inference
Efficient inference for wide output layers (WOLs) is an essential yet ch...
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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...
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06/16/2020
A One-Pass Private Sketch for Most Machine Learning Tasks
Differential privacy (DP) is a compelling privacy definition that explai...
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06/08/2020
Privacy Adversarial Network: Representation Learning for Mobile Data Privacy
The remarkable success of machine learning has fostered a growing number...
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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 ...
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12/04/2019
Angular Visual Hardness
Although convolutional neural networks (CNNs) are inspired by the mechan...
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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...
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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...
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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...
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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...
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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...
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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...
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08/23/2019
Revisiting Consistent Hashing with Bounded Loads
Dynamic load balancing lies at the heart of distributed caching. Here, t...
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03/20/2019
Using Local Experiences for Global Motion Planning
Sampling-based planners are effective in many real-world applications su...
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03/07/2019
