5 citations · 19 across the 17 of their papers we have counts for
18 papers
Adaptive Sampling for Deep Learning via Efficient Nonparametric Proxies
Shabnam Daghaghi, Benjamin Coleman, Benito Geordie +1
Data sampling is an effective method to improve the training speed of neural networks, with recent results demonstrating that it can even break the neural scaling laws. These resul…
CAPS: A Practical Partition Index for Filtered Similarity Search
Gaurav Gupta, Jonah Yi, Benjamin Coleman +3
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 c…
CARAMEL: A Succinct Read-Only Lookup Table via Compressed Static Functions
Benjamin Coleman, David Torres Ramos, Vihan Lakshman +2
Lookup tables are a fundamental structure in many data processing and systems applications. Examples include tokenized text in NLP, quantized embedding collections in recommendatio…
Unified Embedding: Battle-Tested Feature Representations for Web-Scale ML Systems
Benjamin Coleman, Wang-Cheng Kang, Matthew Fahrbach +4
Learning high-quality feature embeddings efficiently and effectively is critical for the performance of web-scale machine learning systems. A typical model ingests hundreds of feat…
BOLT: An Automated Deep Learning Framework for Training and Deploying Large-Scale Search and Recommendation Models on Commodity CPU Hardware
Nicholas Meisburger, Vihan Lakshman, Benito Geordie +9
Efficient large-scale neural network training and inference on commodity CPU hardware is of immense practical significance in democratizing deep learning (DL) capabilities. Present…
DESSERT: An Efficient Algorithm for Vector Set Search with Vector Set Queries
Joshua Engels, Benjamin Coleman, Vihan Lakshman +1
We study the problem of with . This task is analogous to traditional near-neighbor search, with the exception that both th…