Search Results for author:
Found 23 papers, 5 papers with code
SOAR: Improved Indexing for Approximate Nearest Neighbor Search
This paper introduces SOAR: Spilling with Orthogonality-Amplified Residuals, a novel data indexing technique for approximate nearest neighbor (ANN) search.
ReST meets ReAct: Self-Improvement for Multi-Step Reasoning LLM Agent
Answering complex natural language questions often necessitates multi-step reasoning and integrating external information.
Ranked #1 on
Question Answering
on Bamboogle
Automating Nearest Neighbor Search Configuration with Constrained Optimization
The approximate nearest neighbor (ANN) search problem is fundamental to efficiently serving many real-world machine learning applications.
The Lazy Neuron Phenomenon: On Emergence of Activation Sparsity in Transformers
This paper studies the curious phenomenon for machine learning models with Transformer architectures that their activation maps are sparse.
Decoupled Context Processing for Context Augmented Language Modeling
Language models can be augmented with a context retriever to incorporate knowledge from large external databases.
Deep learning for non-parameterized MEMS structural design
With reasonable training, our deep learning neural network becomes a high-speed, high-accuracy calculator: it can identify the flexural mode frequency and the quality factor 4. 6 × 10 times and 2. 6 × 10 times faster, respectively, than conventional numerical simulation packages, with good accuracies of 98. 8 ± 1. 6% and 96. 8 ± 3. 1%, respectively.
TPU-KNN: K Nearest Neighbor Search at Peak FLOP/s
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 similar level of recall.
Efficient Training of Retrieval Models using Negative Cache
These models are typically trained by optimizing the model parameters to score relevant positive" pairs higher than the irrelevantnegative" ones.
New Loss Functions for Fast Maximum Inner Product Search
In this work, we focus directly on minimizing error in inner product approximation and derive a new class of quantization loss functions.
Accelerating Large-Scale Inference with Anisotropic Vector Quantization
Based on the observation that for a given query, the database points that have the largest inner products are more relevant, we develop a family of anisotropic quantization loss functions.
Local Orthogonal Decomposition for Maximum Inner Product Search
More specifically, we decompose a residual vector locally into two orthogonal components and perform uniform quantization and multiscale quantization to each component respectively.
Efficient Inner Product Approximation in Hybrid Spaces
In this paper, we propose a technique that approximates the inner product computation in hybrid vectors, leading to substantial speedup in search while maintaining high accuracy.
Privacy and Utility Tradeoff in Approximate Differential Privacy
We show that the multiplicative gap of the lower bounds and upper bounds goes to zero in various high privacy regimes, proving the tightness of the lower and upper bounds and thus establishing the optimality of the truncated Laplacian mechanism.
Optimal Noise-Adding Mechanism in Additive Differential Privacy
We derive the optimal $(0, \delta)$-differentially private query-output independent noise-adding mechanism for single real-valued query function under a general cost-minimization framework.
Multiscale Quantization for Fast Similarity Search
We propose a multiscale quantization approach for fast similarity search on large, high-dimensional datasets.
Now Playing: Continuous low-power music recognition
To reduce battery consumption, a small music detector runs continuously on the mobile device's DSP chip and wakes up the main application processor only when it is confident that music is present.
Complete 3D Scene Parsing from an RGBD Image
In this paper, we aim to interpret indoor scenes from one RGBD image.
Efficient Natural Language Response Suggestion for Smart Reply
This paper presents a computationally efficient machine-learned method for natural language response suggestion.
Stochastic Generative Hashing
Learning-based binary hashing has become a powerful paradigm for fast search and retrieval in massive databases.
Fast Orthogonal Projection Based on Kronecker Product
We propose a family of structured matrices to speed up orthogonal projections for high-dimensional data commonly seen in computer vision applications.
Quantization based Fast Inner Product Search
We propose a quantization based approach for fast approximate Maximum Inner Product Search (MIPS).
Predicting Complete 3D Models of Indoor Scenes
One major goal of vision is to infer physical models of objects, surfaces, and their layout from sensors.
Multi-scale Orderless Pooling of Deep Convolutional Activation Features
Deep convolutional neural networks (CNN) have shown their promise as a universal representation for recognition.
