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615,452
Depthfirst IterativeDeepening: An Optimal Admissible Tree Search
 Artificial Intelligence
, 1985
"... The complexities of various search algorithms are considered in terms of time, space, and cost of solution path. It is known that breadthfirst search requires too much space and depthfirst search can use too much time and doesn't always find a cheapest path. A depthfirst iteratiwdeepening a ..."
Abstract

Cited by 527 (24 self)
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The complexities of various search algorithms are considered in terms of time, space, and cost of solution path. It is known that breadthfirst search requires too much space and depthfirst search can use too much time and doesn't always find a cheapest path. A depthfirst iteratiw
Depth first search and linear graph algorithms
 SIAM JOURNAL ON COMPUTING
, 1972
"... The value of depthfirst search or "backtracking" as a technique for solving problems is illustrated by two examples. An improved version of an algorithm for finding the strongly connected components of a directed graph and ar algorithm for finding the biconnected components of an undirect ..."
Abstract

Cited by 1406 (19 self)
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The value of depthfirst search or "backtracking" as a technique for solving problems is illustrated by two examples. An improved version of an algorithm for finding the strongly connected components of a directed graph and ar algorithm for finding the biconnected components
The anatomy of a largescale hypertextual web search engine.
 Comput. Netw. ISDN Syst.,
, 1998
"... Abstract In this paper, we present Google, a prototype of a largescale search engine which makes heavy use of the structure present in hypertext. Google is designed to crawl and index the Web efficiently and produce much more satisfying search results than existing systems. The prototype with a fu ..."
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Cited by 4673 (5 self)
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an indepth description of our largescale web search engine the first such detailed public description we know of to date. Apart from the problems of scaling traditional search techniques to data of this magnitude, there are new technical challenges involved with using the additional information
gSpan: GraphBased Substructure Pattern Mining
, 2002
"... We investigate new approaches for frequent graphbased pattern mining in graph datasets and propose a novel algorithm called gSpan (graphbased Substructure pattern mining) , which discovers frequent substructures without candidate generation. gSpan builds a new lexicographic order among graphs, and ..."
Abstract

Cited by 650 (34 self)
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, and maps each graph to a unique minimum DFS code as its canonical label. Based on this lexicographic order, gSpan adopts the depthfirst search strategy to mine frequent connected subgraphs efficiently. Our performance study shows that gSpan substantially outperforms previous algorithms, sometimes
Taming the computational complexity of combinatorial auctions: Optimal and approximate approaches
, 1999
"... In combinatorial auctions, multiple goods are sold simultaneously and bidders may bid for arbitrary combinations of goods. Determining the outcome of such an auction is an optimization problem that is NPcomplete in the general case. We propose two methods of overcoming this apparent intractability. ..."
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Cited by 288 (9 self)
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. The first method, which is guaranteed to be optimal, reduces running time by structuring the search space so that a modified depthfirst search usually avoids even considering allocations that contain conflicting bids. Caching and pruning are also used to speed searching. Our second method is a heuristic
Fully homomorphic encryption using ideal lattices
 In Proc. STOC
, 2009
"... We propose a fully homomorphic encryption scheme – i.e., a scheme that allows one to evaluate circuits over encrypted data without being able to decrypt. Our solution comes in three steps. First, we provide a general result – that, to construct an encryption scheme that permits evaluation of arbitra ..."
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Cited by 663 (17 self)
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the former. In the final step, we show how to modify the scheme to reduce the depth of the decryption circuit, and thereby obtain a bootstrappable encryption scheme, without reducing the depth that the scheme can evaluate. Abstractly, we accomplish this by enabling the encrypter to start the decryption
A learning algorithm for Boltzmann machines
 Cognitive Science
, 1985
"... The computotionol power of massively parallel networks of simple processing elements resides in the communication bandwidth provided by the hardware connections between elements. These connections con allow a significant fraction of the knowledge of the system to be applied to an instance of a probl ..."
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Cited by 584 (13 self)
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problem in o very short time. One kind of computation for which massively porollel networks appear to be well suited is large constraint satisfaction searches, but to use the connections efficiently two conditions must be met: First, a search technique that is suitable for parallel networks must be found
Articulated body motion capture by annealed particle filtering
 In IEEE Conf. on Computer Vision and Pattern Recognition
, 2000
"... The main challenge in articulated body motion tracking is the large number of degrees of freedom (around 30) to be recovered. Search algorithms, either deterministic or stochastic, that search such a space without constraint, fall foul of exponential computational complexity. One approach is to intr ..."
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Cited by 494 (4 self)
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special preparation of subjects or restrictive assumptions. The principal contribution of this paper is the development of a modified particle filter for search in high dimensional configuration spaces. It uses a continuation principle, based on annealing, to introduce the influence of narrow peaks
Fast texture synthesis using treestructured vector quantization
, 2000
"... Figure 1: Our texture generation process takes an example texture patch (left) and a random noise (middle) as input, and modifies this random noise to make it look like the given example texture. The synthesized texture (right) can be of arbitrary size, and is perceived as very similar to the given ..."
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Cited by 561 (12 self)
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Figure 1: Our texture generation process takes an example texture patch (left) and a random noise (middle) as input, and modifies this random noise to make it look like the given example texture. The synthesized texture (right) can be of arbitrary size, and is perceived as very similar to the given
Threedimensional object recognition from single twodimensional images
 Artificial Intelligence
, 1987
"... A computer vision system has been implemented that can recognize threedimensional objects from unknown viewpoints in single grayscale images. Unlike most other approaches, the recognition is accomplished without any attempt to reconstruct depth information bottomup from the visual input. Instead, ..."
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Cited by 484 (7 self)
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A computer vision system has been implemented that can recognize threedimensional objects from unknown viewpoints in single grayscale images. Unlike most other approaches, the recognition is accomplished without any attempt to reconstruct depth information bottomup from the visual input. Instead
Results 1  10
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