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Define B(u) to be the number of edges that connect u to nodes at distance iâ1 from v. In other words, B(u) is the number of edges that connect u to the connected ...
matrix trace norm, matrix Frobenius norm, l1 norm, and inner-product operator, respectively. Anchor Graphs. In the discr
Mar 8, 2011 - 2 School of Computer Science, University of Electronic Science and ... known paradigms, such as virus sign
host and port scanning for compiling lists of un-secure servers to be used as attack ... However, processing data in a streaming fashion for network monitoring that .... application (i.e., source-destination pairs of all DNS traffic), and many others
Qiang Wang, Zhiyuan Guo, Gang Liu, Jun Guo. Beijing University of Posts and Telecommunications. Email:[email protected]. ABSTRACT.
College of Computer & Information Science. Northeastern University ... A hash table is an in-memory data structure t
Mar 3, 2016 - 44â100 Gliwice, Poland, email: [email protected]. 1 ..... For r â {3,4,5} we experimentally determined constants cr, such that if n = crm then the expected number of .... Addison-Wesley, Reading, Mass., 1991. [GS89].
since the amount of storage available is limited, the .... use of the Domain Name System (DNS) to support .... In a second experiment, we mapped 1500 names.
and (iii) the application scalability of our scheme for content copy tracing and ... including fingerprinting, digital signature, and passive/non-invasive ...
Nov 7, 2014 - explosive growth of the internet, a huge amount of data have been ... its fast query speed and low storage
Jan 21, 2015 - suitable one-way function for quantum digital signature protocol from [9]. ... of words of length k over a finite alphabet Σ. In the paper we let X ...
Dec 16, 2016 - 3 IBM Almaden Research Center, Almaden, USA. 4 Computer ... CV] 16 Dec 2016 ..... call Curves varying the code size(16, 32, 48 and 64 bits).
Jul 17, 2016 - Bloom filters. Therefore, improving the performance of hashing algo- rithms would have a great impact in a wide range of bioinformatics tools.
This paper presents regular hashing functions. Used in conjunction with differential computation process, regular hashing functions enable searching time of ...
Oct 1, 2016 - Jie Feng, Svebor Karaman, I-Hong Jhuo, Shih-Fu Chang. Columbia University. New York, NY, USA. {jf2776, sk4089, sc250}@columbia.edu, ...
Dec 2, 2014 - For MNIST and CIFAR-10, the whole dataset is split into a test set with 1, 000 samples and a training set with all remaining samples.
Linear Hashing with Separators-A Dynamic. Hashing Scheme Achieving One-Access. Retrieval. PER-AKE LARSON. University of Waterloo. A new dynamic ...
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cal analysis on the recognition rate of the proposed hashing approach. Experiments ... of the images as well as the larg
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evictions that the insertion procedure can tolerate without generating some sort of failure. We find this variant simplest to handle. Pagh and Rodler [11] show that ...
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Conjugate gradient for fine-tuning. ⢠Reconstructing the code words. ⢠Replacing the stochastic binary values of hid
Semantic Hashing Presented by : Ali Vashaee IFT725 Neural network With Hugo larochelle Hinton, Salakhutdinov 2009
Outline • • • • •
What is semantic hashing Unlabeled data Multilevel autoencoder Results Conclusion
Semantic hashing
Document retrieval • Word-count vector • If we computes document similarity directly in the word-count space, which can be slow for large vocabularies.
Document Retrieval • • • •
N= size of the document corpus V= latent variable size LSA: O(Vlog(N)) with kdtree Semantic Hashing : is not dependent on the size of the document corpus. And linear of the size of the short list that it will produce.
Deep auto-encoder
Word count vector
First layer RBM • ‘‘Constrained Poisson Model” that is used for modeling word-count vectors.
The constrained Poisson model
is the bias of the conditional Poisson model for word i, and bj is the bias of feature j.
RBM
Unrolling Word count vector
Word count vector
Fine Tuning • Conjugate gradient for fine-tuning. • Reconstructing the code words. • Replacing the stochastic binary values of hidden units with probability. • Code layer close to binary . • Deterministic noise to force fine tuning to find the binary codes in top layer.
Activities of 128 bit code
• After fine tuning the array codes are threshold to get binary values.
Training • Pretraining: Mini-baches with 100 cases. • Greedy pretrained with 50 epochs. • The weights were initialized with small random values sampled from a zero-mean normal distribution with variance 0.01. • For fine-tuning : conjugate gradients. • After fine-tuning, the codes were thresholded to produce binary code vectors.
Hashing • Hashing : a shortlist of similar documents in a time that is independent of the size of the document collection and linear.
Semantic Hashing Gaurman et al
Screen clipping taken: 11/28/2012, 6:36 PM
Hamming distance • Very little memory is needed for codes. • Fast to find the hamming distance between binary codes.
Experimental Results
• Using a 2000 word counts vector. • Two text datasets: 20-newsgroups and Reuters corpus Volume I (RCV1-v2). • 2000-500-500 (128 ,30 )
Class structure of documents • Semantic hashing with 128 bit code:
20 Newsgroups
• 3.6 ms to search through 1 million documents using 128-bit codes. • The same search takes 72 ms for 128-dimensional LSA
Reuters RCV2
Image retrieval Torralba et al
• nearest neighbors from a database of 12,900,000 images
