258 citations · 935 across the 92 of their papers we have counts for
7 papers · 1 filter
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…
Random Offset Block Embedding Array (ROBE) for CriteoTB Benchmark MLPerf DLRM Model : 1000 Compression and 3.1 Faster Inference
Aditya Desai, Li Chou, Anshumali Shrivastava
Deep learning for recommendation data is one of the most pervasive and challenging AI workload in recent times. State-of-the-art recommendation models are one of the largest models…
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…
Semantically Constrained Memory Allocation (SCMA) for Embedding in Efficient Recommendation Systems
Aditya Desai, Yanzhou Pan, Kuangyuan Sun +2
Deep learning-based models are utilized to achieve state-of-the-art performance for recommendation systems. A key challenge for these models is to work with millions of categorical…
Climbing the WOL: Training for Cheaper Inference
Zichang Liu, Zhaozhuo Xu, Alan Ji +3
Efficient inference for wide output layers (WOLs) is an essential yet challenging task in large scale machine learning. Most approaches reduce this problem to approximate maximum i…
SSH (Sketch, Shingle, & Hash) for Indexing Massive-Scale Time Series
Chen Luo, Anshumali Shrivastava
Similarity search on time series is a frequent operation in large-scale data-driven applications. Sophisticated similarity measures are standard for time series matching, as they a…