220 citations · 425 across the 14 of their papers we have counts for
15 papers
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…
Decoupled Context Processing for Context Augmented Language Modeling
Zonglin Li, Ruiqi Guo, Sanjiv Kumar
Language models can be augmented with a context retriever to incorporate knowledge from large external databases. By leveraging retrieved context, the neural network does not have…
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…
TPU-KNN: K Nearest Neighbor Search at Peak FLOP/s
Felix Chern, Blake Hechtman, Andy Davis +3
This paper presents a novel nearest neighbor search algorithm achieving TPU (Google Tensor Processing Unit) peak performance, outperforming state-of-the-art GPU algorithms with sim…
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…
Privacy and Utility Tradeoff in Approximate Differential Privacy
Quan Geng, Wei Ding, Ruiqi Guo +1
We characterize the minimum noise amplitude and power for noise-adding mechanisms in -differential privacy for single real-valued query function. We derive new lower bounds…
Optimal Noise-Adding Mechanism in Additive Differential Privacy
Quan Geng, Wei Ding, Ruiqi Guo +1
We derive the optimal -differentially private query-output independent noise-adding mechanism for single real-valued query function under a general cost-minimization framew…
Now Playing: Continuous low-power music recognition
Blaise Agüera y Arcas, Beat Gfeller, Ruiqi Guo +8
Existing music recognition applications require a connection to a server that performs the actual recognition. In this paper we present a low-power music recognizer that runs entir…
Efficient Natural Language Response Suggestion for Smart Reply
Matthew Henderson, Rami Al-Rfou, Brian Strope +6
This paper presents a computationally efficient machine-learned method for natural language response suggestion. Feed-forward neural networks using n-gram embedding features encode…
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…
Quantization based Fast Inner Product Search
Ruiqi Guo, Sanjiv Kumar, Krzysztof Choromanski +1
We propose a quantization based approach for fast approximate Maximum Inner Product Search (MIPS). Each database vector is quantized in multiple subspaces via a set of codebooks, l…
Predicting Complete 3D Models of Indoor Scenes
Ruiqi Guo, Chuhang Zou, Derek Hoiem
One major goal of vision is to infer physical models of objects, surfaces, and their layout from sensors. In this paper, we aim to interpret indoor scenes from one RGBD image. Our…
Multi-scale Orderless Pooling of Deep Convolutional Activation Features
Yunchao Gong, Liwei Wang, Ruiqi Guo +1
Deep convolutional neural networks (CNN) have shown their promise as a universal representation for recognition. However, global CNN activations lack geometric invariance, which li…