21 citations · 50 across the 7 of their papers we have counts for
6 papers · 1 filter
Automating Nearest Neighbor Search Configuration with Constrained Optimization
Philip Sun, Ruiqi Guo, Sanjiv Kumar
The approximate nearest neighbor (ANN) search problem is fundamental to efficiently serving many real-world machine learning applications. A number of techniques have been develope…
The Lazy Neuron Phenomenon: On Emergence of Activation Sparsity in Transformers
Zonglin Li, Chong You, Srinadh Bhojanapalli +8
This paper studies the curious phenomenon for machine learning models with Transformer architectures that their activation maps are sparse. By activation map we refer to the interm…
Accelerating Large-Scale Inference with Anisotropic Vector Quantization
Ruiqi Guo, Philip Sun, Erik Lindgren +4
Quantization based techniques are the current state-of-the-art for scaling maximum inner product search to massive databases. Traditional approaches to quantization aim to minimize…
Local Orthogonal Decomposition for Maximum Inner Product Search
Xiang Wu, Ruiqi Guo, Sanjiv Kumar +1
Inverted file and asymmetric distance computation (IVFADC) have been successfully applied to approximate nearest neighbor search and subsequently maximum inner product search. In s…
Efficient Inner Product Approximation in Hybrid Spaces
Xiang Wu, Ruiqi Guo, David Simcha +2
Many emerging use cases of data mining and machine learning operate on large datasets with data from heterogeneous sources, specifically with both sparse and dense components. For…
Stochastic Generative Hashing
Bo Dai, Ruiqi Guo, Sanjiv Kumar +2
Learning-based binary hashing has become a powerful paradigm for fast search and retrieval in massive databases. However, due to the requirement of discrete outputs for the hash fu…