A new approach to interdomain routing based on secure multi-party computation

By Debayan Gupta, Aaron Segal, Aurojit Panda, Gil Segev, Michael Schapira, Joan Feigenbaum, Jennifer Rexford, Scott Shenker. 2012

2,191 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Privacy & Security

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Citations in the corpus

Aggregating Local Image Descriptors into Compact Codes (2012, methodology): “[18], the query x is not converted to a code: There is no approximation error on the query side.”
Product Quantization for Nearest Neighbor Search (2011, methodology): “The motivation of using the Hamming space in [20], [17], [19] is to compute distances efﬁciently.”
Unbounded cache model for online language modeling with open vocabulary (2017, neutral): “Several works have built on this initial binarization technique, such as spectral hashing [54], or Iterative Quantization (ITQ) [19].”
Geometric Deep Learning on Graphs and Manifolds Using Mixture Model CNNs (2016, methodology): “ing data as low-dimensional manifolds is the core of manifold learning techniques such as Laplacian eigenmaps [3] for non-linear dimensionality reduction, spectral clustering [34] or spectral hashing [57]. Signal processing on graphs. More recent works tried to generalize signal processing methods t”
FastText.zip: Compressing text classification models (2016, background): “Many subsequent works have improved this initial binarization technique, such as spectal hashing (Weiss et al., 2009), or Iterative Quantization (ITQ) (Gong & Lazebnik, 2011), which learns a rotation matrix minimizing the quantization loss of the binarization.”
Analyzing the Performance of Multilayer Neural Networks for Object Recognition (2014, background): “Such codes are important for practical applications in large-scale image retrieval and mobile image analysis [8,24].”
Compressing Deep Convolutional Networks using Vector Quantization (2014, methodology): “Many learning-based binarization or product quantization methods are available, such as Spectral Hashing (Weiss et al., 2008), Iterative Quantization (Gong et al., 2012), and Catesian k means (Norouzi & Fleet, 2013), among others.”
Learning Discrete Representations via Information Maximizing Self-Augmented Training (2017, methodology): “, 2013; Norouzi & Blei, 2011) and non-linear (Xu et al., 2004; Kulis & Darrell, 2009; Ng et al., 2001; Weiss et al., 2009).”
Barnes-Hut-SNE (2013, background): “Most of these studies focus on speeding up nearest-neighbor searches using space-partitioning (metric) trees ( e.g. , B-trees [2], cover trees [3], and vantage-point trees [31]) or using locality sensitive hashing approaches ( e.g. , [12, 29]).”
Inductive Hashing on Manifolds (2013, methodology): “, spectral relaxation [33]), Y can be easily obtained by spectral decomposition on the Laplcaian matrix L = D −W, where D = diag(W1) and 1 is the vector with all ones.”
LDAHash: Improved Matching with Smaller Descriptors (2012, methodology): “In particular, similarity-sensitive hashing (SSH) or locality-sensitive hashing (LSH) [9], [10], [14], [11], [12] algorithms seek to find an efficient binary representation of high-dimensional data maintaining their similarity in the new space.”
NASH: Toward End-to-End Neural Architecture for Generative Semantic Hashing (2018, background): “, 2004), Stack Restricted Boltzmann Machines (S-RBM) (Salakhutdinov and Hinton, 2009), Spectral Hashing (SpH) (Weiss et al., 2009), Self-taught Hashing (STH) (Zhang et al.”
Supervised Discrete Hashing (2015, methodology): “Examples include Spectral Hashing [38], PCAH [34], AGH [21], IMH [29], etc.”
Efficient Object Category Recognition Using Classemes (2010, methodology): “Although a number of systems satisfy these desiderata for object instance or place recognition [18,9] or for whole scene recognition [26], we argue that no existing system has addressed these requirements in the context of object category recognition.”
Minimal Loss Hashing for Compact Binary Codes (2011, methodology): “We also use a synthetic dataset comprising uniformly sampled points from a 10D hypercube (Weiss et al., 2008).”
Feature Learning Based Deep Supervised Hashing with Pairwise Labels (2015, methodology): “• Unsupervised hashing methods with hand-crafted features, including SH [33] and ITQ [3].”
Fast Matching of Binary Features (2012, background): “[10] formalize the requirements for good codes and introduce a 2012 Ninth Conference on Computer and Robot Vision”
Learning Discriminative Binary Codes for Large-scale Cross-modal Retrieval (2017, background): “To tackle the efficiency challenge, several promising approaches [12], [13], [14], [15] introduced unimodal hashing techniques [16], [17], [18], [19], [20], [21], [22] to cross-modal retrieval.”
Binary Generative Adversarial Networks for Image Retrieval (2017, methodology): “Speciﬁcally, we compare with four non-deep hashing methods (iterative quantization (ITQ) hashing [10], spectral hashing (SH) [46], Locality Sensitive Hashing (LSH) [5] and spherical hashing [15]), and two deep hashing methods (DeepBit [28] and Deep Hashing (DH) [7]).”