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Quantized Convolutional Neural Networks for Mobile Devices
TLDR
This paper proposes an efficient framework, namely Quantized CNN, to simultaneously speed-up the computation and reduce the storage and memory overhead of CNN models. Expand
Extremely Low Bit Neural Network: Squeeze the Last Bit Out with ADMM
TLDR
This paper focuses on compressing and accelerating deep models with network weights represented by very small numbers of bits, referred to as extremely low bit neural network, and proposes to solve this problem using extragradient and iterative quantization algorithms that lead to considerably faster convergency compared to conventional optimization methods. Expand
Online sketching hashing
TLDR
A novel approach to handle these two problems simultaneously based on the idea of data sketching, which can learn hash functions in an online fashion, while needs rather low computational complexity and storage space. Expand
Fast and Accurate Image Matching with Cascade Hashing for 3D Reconstruction
TLDR
The proposed Cascade Hashing method is designed to be three-layer structure: hashing lookup, hashing remapping, and hashing ranking, which is demonstrated to be less sensitive to noise. Expand
Learning Binary Codes with Bagging PCA
TLDR
This paper attempts to leverage the bootstrap sampling idea and integrate it with PCA, resulting in a new projection method called BaggingPCA, in order to learn effective binary codes, closely connected with the core idea of LSH. Expand
Hashing for Distributed Data
TLDR
A novel hashing model to learn hash functions in a distributed setting is developed as a set of subproblems with consensus constraints that can be analytically solved in parallel on the distributed compute nodes. Expand
Quantized CNN: A Unified Approach to Accelerate and Compress Convolutional Networks
TLDR
A quantized CNN is presented, a unified approach to accelerate and compress convolutional networks, guided by minimizing the approximation error of individual layer’s response, both fully connected and Convolutional layers are carefully quantized. Expand
Item group based pairwise preference learning for personalized ranking
TLDR
A novel PRIGP(Personalized Ranking with Item Group based Pairwise preference learning) algorithm is proposed to integrate item based pairwise preference and item group basedpairwise preference into the same framework. Expand
Semi-supervised multi-graph hashing for scalable similarity search
TLDR
A semi-supervised Multi-Graph Hashing (MGH) framework is proposed that can effectively integrate the multiple modalities with optimized weights in a multi-graph learning scheme and can be more effective for fast similarity search. Expand
Supervised Hashing with Soft Constraints
TLDR
A general framework for supervised hashing is presented which does not toughly require a dissimilar pair to have maximum Hamming distance, and a soft constraint which can be viewed as a regularization to avoid over-fitting is utilized. Expand
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