The protocol operates entirely on the forwarding plane but may interact with the routing plane . DFF forwards data packets using a mechanism similar to a " depth-first search " for the destination of a packet . The routing plane may be informed of failures to deliver a packet or loops . Graph search algorithms are very important software components . The paper tries to formally develop two non-recursive graph search algorithms , depth-first search and breadth-first search . This is a very challenge task because non-recursive graph search algorithms have low computing complexity with high logic complexity comparing with recursive graph search algorithms . To address this problem above , implicit logic rules are explored ground on the constraints between every pair of data , and modelled as a Boolean function firstly . Then , the Reduced Ordered Binary Decision Diagram ( ROBDD ) construction and depth-first searching algorithm are performed to verify the satisfiability of the rules . Compared with the original ones , the extracted authenticated logic rules have promising results in the accuracy of data checking , and can further improve the safety of engineering data . A depth-first search produces an independent set , which may or may not be a maximum , in linear time , but the worst case success rate is maybe not high enough to make it really interesting . What may make depth-first searching in split-by-edges trees interesting , though , is the pronounced oscillation of its success rate along the graph size axis . We show how to minimize this problem using behavior restrictions at system modeling , and a partial-order reduction method . Additionally , the algorithm proposed for finding a feasible schedule uses a depth-first search based method . Therefore , states are only generated if strictly necessary . Abstract A segment-based analysis system is developed to analyse the vulnerability of a water distribution network ( WDN ) . Valve-enclosed segments of a WDN can be efficiently identified by the system using a proposed depth-first search-based algorithm and are illustrated by a Web-based geographic information system . Critical segments , whose failures can significantly impact downstream water supply , are identified by another proposed identification algorithm . First , we find all frequent patterns of length two in the database . Second , for each pattern found in the first phase , we recursively generate frequent closed patterns by a frequent pattern tree in a depth-first search manner . Since the PCP-Miner does not generate unnecessary candidates , it is more efficient and scalable than the modified Apriori , SASMiner and MaxGeo . We propose a novel algorithm , TSP-algorithm ( Temporal Subgraph Patterns algorithm ) to mine the patterns which contain temporal information and forms a connective subgraph . The proposed method recursively grows the patterns in a depth-first search manner . Since the TSP-algorithm only needs to scan the database once and does not generate unnecessary candidates , the experiment results show that the TSP-algorithm outperforms the modified Apriori on time-efficiency and memory usage in both synthetic and real datasets . We develop an effective timing-driven ( TD ) incremental routing algorithm TIDE for ASIC circuits that addresses the dual goals of time-efficiency , and slack satisfaction coupled with effective optimizations . There are three main novelties in our approach : ( i ) a technique for locally determining slack satisfaction of the entire routing tree when either a new pin is added to the tree or an interconnect in it is re-routed - this technique is used in both the global and detailed routing phases ; ( ii ) an interval-intersection and tree-truncation algorithm , used in global routing , for quickly determining a near-minimum-length slack-satisfying interconnection of a pin to a partial routing tree ; ( iii ) a depth-first-search process , used in detailed routing , that allows new nets to bump and re-route existing nets in a controlled manner in order to obtain better optimized designs . Experimental results show that within the constraint of routing all nets in only two metal layers , TIDE succeeds in routing more than 94 % of ECO-generated nets , and also that its failure rate is 7 and 6.7 times less than that of the TD versions of previous incremental routers Standard ( Std ) and Ripup & Reroute ( R & R ) , respectively . A Coding Unit ( CU ) quad tree concept is introduced to HEVC to improve the coding efficiency . Each CU node in quad tree will be traversed by depth first search process to find the best Coding Tree Unit ( CTU ) partition . Although this quad tree search process can obtain the best CTU partition , it is very time consuming , especially in interframe coding . Following the model introduced by Aguech et al. ( Probab Eng Inf Sci 21:133–141 , 2007 ) , the weighted depth of a node in a labelled rooted tree is the sum of all labels on the path connecting the node to the root . We analyse weighted depths of nodes with given labels , the last inserted node , nodes ordered as visited by the depth first search process , the weighted path length and the weighted Wiener index in a random binary search tree . We establish three regimes of nodes depending on whether the second-order behaviour of their weighted depths follows from fluctuations of the keys on the path , the depth of the nodes or both . According to the rigorous requirements for real-time performance and conditional resource of the micro-satellite , inference engine of expert system is designed based on fault tree . It designs forward direction inference algorithm based on breadth-first search , and reverse direction inference algorithm based on depth-first search search . Time complexity and space complexity of two algorithms are both linear . In this way , we connect segments of adjacent locations as well as scales . Finally , we find connected segments with a depth - first search ( DFS ) algorithm and combine them into whole words . Our main contribution is the novel segment - linking detection method . Using multiresolution analysis in wavelet theory , the classical nonlinear design problem is transformed into a robust design problem foràpproximately linear ' models with orthonormal wavelet basis on the design space S as regressors . The minimax approach is used to construct designs which are robust against small departures from the ¯nite wavelet representation of the general nonlinear model . We ¯nd that the D-optimal design obtained by Herzberg and Traves ( 1994 ) is also G- , Q-and A-optimal ( in the classical sense ) if the Haar wavelet basis is used in the approximation . We present a novel dynamic network interdiction model that accounts for interactions between an interdictor deploying resources on arcs in a digraph and an evader traversing the network from a designated source to a known terminus , wherein the agents may modify strategies in selected subsequent periods according to respective decision and implementation cycles . For the resulting minimax model , we develop a reformulation that facilitates a direct solution procedure using commercial software or via a proposed alternating heuristic . We examine certain related stability and convergence issues , demonstrate special convergence cases , and provide insights into the computational performance of the solution procedures . Minimax Objective Functions . In this paper we consider one of the basic problems in scheduling and project management : scheduling on parallel identical machines . We present a solution framework for a number of scheduling problems in which the goal is to find a feasible schedule that minimizes some objective function of the minimax type on a set of parallel , identical machines , subject to release dates , deadlines , and/or generalized precedence constraints . Our framework is based on column generation . Although column generation has been successfully applied to many parallel machine scheduling problems with objective functions of the minsum type , the number of applications for minimax objective functions is still small . We determine a lower bound on the objective function in the following way . Parameters include electrical quantities , geometrical dimensions , and frequency . It is shown how gradients related to wave-based least pth and minimax objective functions can be computed . A comparison with a method which avoids the need for analysis of adjoint networks is made . We then show that introducing a parameter per pair of alternatives which specifies the minimum number of partial preferences where this pair of alternatives must be comparable makes the related computational task of manipulative elicitation \NPC for all common voting rules including a class of scoring rules which includes the plurality , $ k$-approval , $ k$-veto , veto , and Borda voting rules , maximin , Copeland$^\alpha$ for every $ \alpha\in[0,1]$ , and simplified Bucklin voting rules . Hence , in this work , we discover a fundamental vulnerability in using minimax regret based approach in partial preferential setting and propose a novel way to tackle it . This paper studies the problem of training RBF network and selecting centers under multiplicative noise . We devise a noise resistant training algorithm based on the alternating direction method of multipliers ( ADMM ) framework and the minimax concave penalty ( MCP ) function . Our algorithm first uses all training samples to create the RBF nodes . By pruning the ensemble , it is possible to generate a simpler ensemble with fewer base learners but a higher accuracy . In this article , we propose the minimax concave penalty ( MCP ) function to prune an AdaBoost ensemble to simplify the model and improve its accuracy simultaneously . The MCP penalty function is compared with LASSO and SCAD in terms of performance in pruning the ensemble . In our formulation , we first define a fault tolerant objective function based on all input vectors from the training samples . We then introduce the minimax concave penalty ( MCP ) function , which is an approximation of $ \ell _ {0}$ -norm , into the objective function . The MCP term is able to force some unimportant RBF weights to zero . A technique for converting the constrained nonlinear optimization problem encountered in the design of weighted minimax quadrature mirror filters into an iterative unconstrained nonlinear optimization problem is presented . This renders the design of weighted minimax quadrature mirror filters possible . The technique is very efficient , typically taking about seven iterations to converge . Abstract A problem of the state prediction and condition-based maintenance of complex engineering systems is considered . An approach to solving this problem is based on the construction of the special minimax and robust algorithms , which can be used in the case when inspection data are incomplete and insufficient . The method for individual robust prediction based on the extremal properties of Karlin polynomials and the ideas of minimax estimation is proposed . A combination of Petri nets is used for high-level abstraction of the controlled processes . A minimax algebraic representation for restricted decision-free Petri nets is used as the modeling mechanism to combine both abstraction levels to produce a " multi-model " of the FMS . The model can then be used to analyze system performance.<<ETX > > The suggested approach adapts to the sensor measurements using a compact , and conceptually simple non-parametric transformation density estimation . Simulation results are provided , which illustrate the improvement of the presented approach over classical robust or non-robust estimation procedures , e.g. Huber 's minimax estimator or the least-squares estimator . This paper investigates the potential of minimax-cost and minimax-regret approaches for centralized & determinative transmission expansion planning under uncertainty regarding market-based generation expansion , particularly when the times needed to implement transmission facilities are longer than those required to build renewable generators . The approaches can deal with non-probabilistic uncertainties in variables representing key drivers for generation investors ' decisions , and reduce exposure to high regrets if adverse scenarios materialize , at the expense of the costs needed to bestow the transmission grid with flexibility to cope with uncertainties in market-based generation expansion . One of the most important issues in scaling up reinforcement learning for practical problems is how to represent and store cost-to-go functions with more compact representations than lookup tables . We address the issue of combining the simple function approximation method-state aggregation with minimax-based reinforcement learning algorithms and present the convergence theory for online Q-hat-learning with state aggregation . Some empirical results are also included . For categorical we use the softmax nonlinearity to represent while we treat the posterior of continuous as a factored Gaussian . Given this structure , the minimax game for the Bidirectional - InfoGAN ( BInfoGAN ) is then where determines the strength of the impact of the mutual information criterion and is set to in all our experiments . section : Experiments Goodfellow et al . [ reference ] introduced the Generative Adversarial Networks ( GAN ) , a framework for training deep generative models using a minimax game . The goal is to learn a generator distribution P G ( x ) that matches the real data distribution P data ( x ) . Similar mutual information inspired objectives have been considered before in the context of clustering [ reference ][ reference ][ reference ] . Therefore , we propose to solve the following information - regularized minimax game : section : Variational Mutual Information Maximization All these generative models are trained by maximizing ( the lower bound of ) training data likelihood , which , as mentioned by , suffers from the difficulty of approximating intractable probabilistic computations . proposed an alternative training methodology to generative models , i.e. GANs , where the training procedure is a minimax game between a generative model and a discriminative model . This framework bypasses the difficulty of maximum likelihood learning and has gained striking successes in natural image generation . This study focuses on image generation tasks using convolutional neural networks ( CNN ) for both and . Several loss functions have been proposed to quantify the difference of the scores between real and generated samples : and , including the minimax loss and non - saturating loss ( ) , hinge loss ( ) , Wasserstein loss ( ) and maximum mean discrepancy ( MMD ) ( ) ( see Appendix [ reference ] for more details ) . Let be the support of distribution ; let , be the parameters of the generator and discriminator respectively . To prove that GANs trained using the minimax loss and gradient updates is locally stable at the equilibrium point , made the following assumption : theorem : ( ) . and ∈∀x⁢S ( PX ), =⁢DθD* ( x ) 0 . For loss functions like minimax and Wasserstein , may be interpreted as how plausible a sample is real . Thus at equilibrium , it may be reasonable to assume all real and generated samples are equally plausible . MCTS averages over these approximation errors , which therefore tend to cancel out when evaluating a large subtree . In contrast , alpha - beta search computes an explicit minimax , which propagates the biggest approximation errors to the root of the subtree . Using MCTS may allow AlphaZero to effectively combine its neural network representations with a powerful , domain - independent search . However , this objective can be problematic , since early on during training the discriminator converges quickly , causing the gradient to vanish . When training GANs , rather than directly using the minimax loss , it is typical to train the generator with the standard loss function with inverted labels . This splits the optimization into two independent objectives , one for the generator and one for the discriminator , where remains unchanged , but becomes : This objective has the same fixed - point properties as the minimax loss but provides stronger gradients to the target mapping . section : ABSTRACT Generative adversarial networks ( GANs ) are a framework for producing a generative model by way of a two - player minimax game . In this paper , we propose the Generative Multi - Adversarial Network ( GMAN ) , a framework that extends GANs to multiple discriminators . whereD is some convex combination of D i ( see Appendix A.5 ) . section : USING THE ORIGINAL MINIMAX OBJECTIVE To illustrate the effect the softmax has on training , observe that the component of AM sof t ( V , 0 ) relevant to generator training can be rewritten as section : Introduction A classic Generative Adversarial Net ( GAN ) learns a discriminator and a generator by playing a two - player minimax game to generate samples from a data distribution . The discriminator is trained to distinguish real samples from those generated by the generator , and it in turn guides the generator to produce realistic samples that can fool the discriminator . These latent distribution alignment approaches were further improved through the use of domain adversarial objectives whereby a domain classifier is trained to distinguish between the source and target representations while the domain representation is learned so as to maximize the error of the domain classifier . The representation is optimized using the standard minimax objective ganin_icml15 , the symmetric confusion objective tzeng_iccv15 , or the inverted label objective tzeng_cvpr17 . Each of these objectives is related to the literature on generative adversarial networks goodfellow_nips14 and follow - up work for improved training procedures for these networks salimans_arxiv16 , arjovsky_arxiv17 . The core difference is that these previous formulations are originally derived from maximum - likelihood estimation , and thus the minimax optimization is only implicit . In contrast , with an explicit minimax formulation we can develop a better understanding of the induced solution . For example , the global bias λ * suggests that there exists more than one stable equilibrium the optimal discriminator can actually reach . An important benchmark for hillclimbing algorithms on pseudo-Boolean functions $ f : \{0,1\}^n \to \mathbb{R}$ are ( strictly ) montone functions , on which a surprising number of hillclimbers fail to be efficient . For example , the $ (1 + 1)$-Evolutionary Algorithm is a standard hillclimber which flips each bit independently with probability $ c/n$ in each round . Perhaps surprisingly , this algorithm shows a phase transition : it optimizes any monotone pseudo-boolean function in quasilinear time if $ c 2.2$. But The rst contribution of this paper is a theoretical comparison of the (1 + 1)-EA evolutionary algorithm to other evolutionary algorithms in the case of so-called monotone reproduction operator , which indicates that the (1 + 1)-EA is an optimal search technique in this setting . After that we study the expected optimization time for the (1 + 1)-EA and show two set covering problem families where it is superior to certain general-purpose exact algorithms . The approach is based on a reinforcement-learning of artificial neural network fed with positions and velocities of the system components . The learning routine is designed on the basis of a 1 + 1 evolutionary scheme with concept of persistence of efficient steps . The performance of the four variants of the algorithm is evaluated on the basis of a simulated 3-DOF system . < inline-formula > < tex-math notation="LaTeX">$k$ < /tex-math></inline-formula > NN search is a fundamental function in image processing , which is useful in many real applications , including image cluster , image classification , and image understanding and analysis in general . In this light , we propose and study a novel collaborative cross-domain < inline-formula > < tex-math notation="LaTeX">$k$ < /tex-math></inline-formula > NN search ( CD-<inline-formula > < tex-math notation="LaTeX">$k$ < /tex-math></inline-formula > NN ) in multidomain space . This model controls spreading activation in a causal network that has rapidly varying connection strengths . It generates diagnostic hypotheses based on the same causal network that would be used by a traditional AI search algorithm . Experimental results are presented that demonstrate that this connectionist model can closely approximate the best diagnostic hypotheses with some causal networks , even when multiple disorders are present simultaneously . Most of the computational burden comes from uni-directional and bi-directional prediction . In this paper , an iterative search strategy is designed to speed up the uni-directional prediction in MVC . It can work with an adaptive search range adjustment through a confidence measure of a loop constraint to obtain both motion and disparity vectors jointly . Branch-and-bound and branch-and-cut use search trees to identify optimal solutions to combinatorial optimization problems . In this paper , we introduce an iterative search strategy which we refer to as cut-and-solve and prove optimality and termination for this method . This search is different from traditional tree search as there is no branching . In the first step , the entire feature set is represented as a weighted graph . In the second step , the features are divided into several clusters using a community detection algorithm and finally in the third step , a novel iterative search strategy based on node centrality is developed to select the final subset of features . The proposed feature selection method offers two major advantages : first , our method groups features into different clusters based on their similarities , in which the features in the same cluster are similar to each other , and to obtain the reduced redundancy set , the final subset of features is selected from different clusters . The standard algorithm to find the best possibble move is cubic , both in its worst and average time complexity . Since TSP instances of interest can have thousands of nodes , up to now it has been impossible to use the 3-OPT local search on anything other than quite small instances . In this paper we describe an alternative algorithm whose average complexity appears to be quadratic and which allowed us to use 3-OPT on instances with several thousand nodes . From the experimental analysis , it was found that ACS works best for solving the dynamic TSP compared to the other five ant algorithms . When coupled with a local search technique , a significant improvement can be seen for ACS using the 3-opt local search algorithm . Lastly , it was also found that different optimal parameter settings were required for solving the different sizes of the dynamic TSP problems . Suggests that a logical strategy might be to put core content on CD-ROM , while supplementing it with Internet based access to topical and real time content . The key benchmark as far as performance is concerned will be to have a product built around a fast search engine that offers simultaneous search of multiple databases stored on one or more servers and where retrieval of data allows instant analysis and easy view , copy , export and print . Concludes with useful advice on implementing a successful strategy based on these requirements . These efforts are frequently limited to exploring known patterns . In this work , we will discuss the implementation of a new , very fast search engine designed primarily for embedding in complex search algorithms ( i.e. functional annotation of new structures ) . The unrestricted nature of the search should be helpful in uncovering hidden effectors . Even compared to the single-indexed DHT ( sDHT ) application , the mDHT solution outperforms by reducing the data retrieval time by 24.5–48.6 % . The multi-level-indexed DHT algorithm presented in this paper contributes a key technique to developing a fast search engine to the extra-large scale of data on the cloud storage architecture . This paper introduces the combination of NoSQL database HBase and enterprise search platform Solr so as to tackle the problem of the secondary index function with fast query . In order to verify the ... They have to be well designed , easy to use , understandable , meaningful , and approachable . In this article , we present a new approach to visualize huge amount of data , based on a Bayesian suggestion algorithm and the widely used enterprise search platform Solr . We demonstrate how the proposed Bayesian suggestion algorithm became a key ingredient in a big data scenario , where generally a query can generate so many results that the user can be confused . They have to be well designed , easy to use , understandable , meaningful , and approachable . In this article we present a new approach to visualize huge amount of data , based on a Bayesian suggestion algorithm and the widely used enterprise search platform Solr . We demonstrate how the proposed Bayesian suggestion algorithm became a key ingredient in a big data scenario , where generally a query can generate so many results that the user can be confused . Although the exited unstructured P2P resource search algorithm can enhance the performance of resource searching , but it will generate lot of P2P traffic and will consume a huge network bandwidth . Consider the messages of Internet service provider ( ISP ) and the clustering of P2P networks , the article proposed an on-demand resource search algorithm which based on the ISP and the similarity of resource character . We develop an effective hybrid fruit fly optimization algorithm ( HFOA ) that applies two vectors to represent individuals and presents routing and scheduling neighborhood structures . An IG-based local search procedure is embedded to enhance the exploitation ability of the proposed algorithm . Two decoding heuristics considering the problem characteristics are developed . This paper presents a search-space-Reduced optimal SBP approach ( RSBP ) for a rigid body . This reduced space is found by a sparse search tree , which is enabled by a Metric Function ( MF ) built on a neural network . The offline-learnt MF estimates the minimum traveling cost between any two nodes in a fixed small workspace with various obstacles . The user is made aware of strategic aspects of searching that allow him to fully exploit all information resources and services . The assistant comes fully integrated into the Okapi search engine of Torii . Torii integrates also iCite . This new algorithm is implemented to remove multiple peaks by accumulating the autocorrelation peaks of the two signals coherently . In order to reduce the search realm of the code phase , a method “ Pre-detection + Sub-search ” was introduced in this new algorithm . The simulation results show that the proposed algorithm can not only reduce the search realm of the code phase significantly but also improve the acquisition sensitivity to some extend . Since P2P systems maintain large and dynamic sets of peers , the search protocol is desired to be scalable and adaptive . In this paper we propose a new P2P search protocol , called Adaptive Randomized Search Protocol ( ARSP ) , which is an efficient extension of the Quorum-Based Protocol for Searching Objects in Peer-to-Peer Networks . ARSP minimizes the communication cost of the protocol by popularity-based index dissemination of objects . The approach taken by GoogLeNet for detection is similar to the R - CNN by , but is augmented with the Inception model as the region classifier . Additionally , the region proposal step is improved by combining the Selective Search approach with multi - box predictions for higher object bounding box recall . In order to cut down the number of false positives , the superpixel size was increased by . In general , it is very difficult to ask a single classifier to perform uniformly well over all IoU levels . At inference , since the majority of the hypotheses produced by a proposal detector , e.g. RPN [ reference ] or selective search [ reference ] , have low quality , the detector must be more discriminant for lower quality hypotheses . A standard compromise between these conflicting requirements is to settle on u = 0.5 . While it is much faster than R - CNN , it still falls short of real - time and takes a significant accuracy hit from not having good proposals . Fast R - CNN speeds up the classification stage of R - CNN but it still relies on selective search which can take around 2 seconds per image to generate bounding box proposals . Thus it has high mAP but at fps it is still far from real - time . [ reference ] for details ) . Our results show that MIXER with a simple greedy search achieves much better accuracy compared to the baselines on all the three tasks . In addition we show that MIXER with greedy search is even more accurate than the cross entropy model augmented with beam search at inference time as a post - processing step . However , the number of potential candidate systems is too large to allow an all - out combination , both for practical reasons and due to overfitting issues . Instead , we perform a greedy search , starting with the single best system , and successively adding additional systems , to find a small set of systems that are maximally complementary . The RT - 02 Switchboard set was used for this search procedure . AlphaZero uses a markedly different approach that averages over the position evaluations within a subtree , rather than computing the minimax evaluation of that subtree . However , chess programs using traditional MCTS were much weaker than alpha - beta search programs , ; while alpha - beta programs based on neural networks have previously been unable to compete with faster , handcrafted evaluation functions . AlphaZero evaluates positions using non - linear function approximation based on a deep neural network , rather than the linear function approximation used in typical chess programs . These attributes were further used to define various test optimization objectives together with test engineers from our industrial partner . Search based techniques were then used for optimizing test by defining a novel fitness function based on the identified objectives and empirically evaluated it with four search algorithms : Alternating Variable Method ( AVM ) , Genetic Algorithm ( GA ) , ( 1 + 1 ) Evolutionary Algorithm ( EA ) and Greedy Algorithm . Additionally , we used Random Search as the comparative baseline .
The protocol operates entirely on the forwarding plane but may interact with the routing plane . DFF forwards data packets using a mechanism similar to a " depth-first search " for the destination of a packet . The routing plane may be informed of failures to deliver a packet or loops .
Graph search algorithms are very important software components . The paper tries to formally develop two non-recursive graph search algorithms , depth-first search and breadth-first search . This is a very challenge task because non-recursive graph search algorithms have low computing complexity with high logic complexity comparing with recursive graph search algorithms .
To address this problem above , implicit logic rules are explored ground on the constraints between every pair of data , and modelled as a Boolean function firstly . Then , the Reduced Ordered Binary Decision Diagram ( ROBDD ) construction and depth-first searching algorithm are performed to verify the satisfiability of the rules . Compared with the original ones , the extracted authenticated logic rules have promising results in the accuracy of data checking , and can further improve the safety of engineering data .
A depth-first search produces an independent set , which may or may not be a maximum , in linear time , but the worst case success rate is maybe not high enough to make it really interesting . What may make depth-first searching in split-by-edges trees interesting , though , is the pronounced oscillation of its success rate along the graph size axis .
We show how to minimize this problem using behavior restrictions at system modeling , and a partial-order reduction method . Additionally , the algorithm proposed for finding a feasible schedule uses a depth-first search based method . Therefore , states are only generated if strictly necessary .
Abstract A segment-based analysis system is developed to analyse the vulnerability of a water distribution network ( WDN ) . Valve-enclosed segments of a WDN can be efficiently identified by the system using a proposed depth-first search-based algorithm and are illustrated by a Web-based geographic information system . Critical segments , whose failures can significantly impact downstream water supply , are identified by another proposed identification algorithm .
First , we find all frequent patterns of length two in the database . Second , for each pattern found in the first phase , we recursively generate frequent closed patterns by a frequent pattern tree in a depth-first search manner . Since the PCP-Miner does not generate unnecessary candidates , it is more efficient and scalable than the modified Apriori , SASMiner and MaxGeo .
We propose a novel algorithm , TSP-algorithm ( Temporal Subgraph Patterns algorithm ) to mine the patterns which contain temporal information and forms a connective subgraph . The proposed method recursively grows the patterns in a depth-first search manner . Since the TSP-algorithm only needs to scan the database once and does not generate unnecessary candidates , the experiment results show that the TSP-algorithm outperforms the modified Apriori on time-efficiency and memory usage in both synthetic and real datasets .
We develop an effective timing-driven ( TD ) incremental routing algorithm TIDE for ASIC circuits that addresses the dual goals of time-efficiency , and slack satisfaction coupled with effective optimizations . There are three main novelties in our approach : ( i ) a technique for locally determining slack satisfaction of the entire routing tree when either a new pin is added to the tree or an interconnect in it is re-routed - this technique is used in both the global and detailed routing phases ; ( ii ) an interval-intersection and tree-truncation algorithm , used in global routing , for quickly determining a near-minimum-length slack-satisfying interconnection of a pin to a partial routing tree ; ( iii ) a depth-first-search process , used in detailed routing , that allows new nets to bump and re-route existing nets in a controlled manner in order to obtain better optimized designs . Experimental results show that within the constraint of routing all nets in only two metal layers , TIDE succeeds in routing more than 94 % of ECO-generated nets , and also that its failure rate is 7 and 6.7 times less than that of the TD versions of previous incremental routers Standard ( Std ) and Ripup & Reroute ( R & R ) , respectively .
A Coding Unit ( CU ) quad tree concept is introduced to HEVC to improve the coding efficiency . Each CU node in quad tree will be traversed by depth first search process to find the best Coding Tree Unit ( CTU ) partition . Although this quad tree search process can obtain the best CTU partition , it is very time consuming , especially in interframe coding .
Following the model introduced by Aguech et al. ( Probab Eng Inf Sci 21:133–141 , 2007 ) , the weighted depth of a node in a labelled rooted tree is the sum of all labels on the path connecting the node to the root . We analyse weighted depths of nodes with given labels , the last inserted node , nodes ordered as visited by the depth first search process , the weighted path length and the weighted Wiener index in a random binary search tree . We establish three regimes of nodes depending on whether the second-order behaviour of their weighted depths follows from fluctuations of the keys on the path , the depth of the nodes or both .
According to the rigorous requirements for real-time performance and conditional resource of the micro-satellite , inference engine of expert system is designed based on fault tree . It designs forward direction inference algorithm based on breadth-first search , and reverse direction inference algorithm based on depth-first search search . Time complexity and space complexity of two algorithms are both linear .
In this way , we connect segments of adjacent locations as well as scales . Finally , we find connected segments with a depth - first search ( DFS ) algorithm and combine them into whole words . Our main contribution is the novel segment - linking detection method .
Using multiresolution analysis in wavelet theory , the classical nonlinear design problem is transformed into a robust design problem foràpproximately linear ' models with orthonormal wavelet basis on the design space S as regressors . The minimax approach is used to construct designs which are robust against small departures from the ¯nite wavelet representation of the general nonlinear model . We ¯nd that the D-optimal design obtained by Herzberg and Traves ( 1994 ) is also G- , Q-and A-optimal ( in the classical sense ) if the Haar wavelet basis is used in the approximation .
We present a novel dynamic network interdiction model that accounts for interactions between an interdictor deploying resources on arcs in a digraph and an evader traversing the network from a designated source to a known terminus , wherein the agents may modify strategies in selected subsequent periods according to respective decision and implementation cycles . For the resulting minimax model , we develop a reformulation that facilitates a direct solution procedure using commercial software or via a proposed alternating heuristic . We examine certain related stability and convergence issues , demonstrate special convergence cases , and provide insights into the computational performance of the solution procedures .
Minimax Objective Functions . In this paper we consider one of the basic problems in scheduling and project management : scheduling on parallel identical machines . We present a solution framework for a number of scheduling problems in which the goal is to find a feasible schedule that minimizes some objective function of the minimax type on a set of parallel , identical machines , subject to release dates , deadlines , and/or generalized precedence constraints . Our framework is based on column generation . Although column generation has been successfully applied to many parallel machine scheduling problems with objective functions of the minsum type , the number of applications for minimax objective functions is still small . We determine a lower bound on the objective function in the following way .
Parameters include electrical quantities , geometrical dimensions , and frequency . It is shown how gradients related to wave-based least pth and minimax objective functions can be computed . A comparison with a method which avoids the need for analysis of adjoint networks is made .
We then show that introducing a parameter per pair of alternatives which specifies the minimum number of partial preferences where this pair of alternatives must be comparable makes the related computational task of manipulative elicitation \NPC for all common voting rules including a class of scoring rules which includes the plurality , $ k$-approval , $ k$-veto , veto , and Borda voting rules , maximin , Copeland$^\alpha$ for every $ \alpha\in[0,1]$ , and simplified Bucklin voting rules . Hence , in this work , we discover a fundamental vulnerability in using minimax regret based approach in partial preferential setting and propose a novel way to tackle it .
This paper studies the problem of training RBF network and selecting centers under multiplicative noise . We devise a noise resistant training algorithm based on the alternating direction method of multipliers ( ADMM ) framework and the minimax concave penalty ( MCP ) function . Our algorithm first uses all training samples to create the RBF nodes .
By pruning the ensemble , it is possible to generate a simpler ensemble with fewer base learners but a higher accuracy . In this article , we propose the minimax concave penalty ( MCP ) function to prune an AdaBoost ensemble to simplify the model and improve its accuracy simultaneously . The MCP penalty function is compared with LASSO and SCAD in terms of performance in pruning the ensemble .
In our formulation , we first define a fault tolerant objective function based on all input vectors from the training samples . We then introduce the minimax concave penalty ( MCP ) function , which is an approximation of $ \ell _ {0}$ -norm , into the objective function . The MCP term is able to force some unimportant RBF weights to zero .
A technique for converting the constrained nonlinear optimization problem encountered in the design of weighted minimax quadrature mirror filters into an iterative unconstrained nonlinear optimization problem is presented . This renders the design of weighted minimax quadrature mirror filters possible . The technique is very efficient , typically taking about seven iterations to converge .
Abstract A problem of the state prediction and condition-based maintenance of complex engineering systems is considered . An approach to solving this problem is based on the construction of the special minimax and robust algorithms , which can be used in the case when inspection data are incomplete and insufficient . The method for individual robust prediction based on the extremal properties of Karlin polynomials and the ideas of minimax estimation is proposed .
A combination of Petri nets is used for high-level abstraction of the controlled processes . A minimax algebraic representation for restricted decision-free Petri nets is used as the modeling mechanism to combine both abstraction levels to produce a " multi-model " of the FMS . The model can then be used to analyze system performance.<<ETX > >
The suggested approach adapts to the sensor measurements using a compact , and conceptually simple non-parametric transformation density estimation . Simulation results are provided , which illustrate the improvement of the presented approach over classical robust or non-robust estimation procedures , e.g. Huber 's minimax estimator or the least-squares estimator .
This paper investigates the potential of minimax-cost and minimax-regret approaches for centralized & determinative transmission expansion planning under uncertainty regarding market-based generation expansion , particularly when the times needed to implement transmission facilities are longer than those required to build renewable generators . The approaches can deal with non-probabilistic uncertainties in variables representing key drivers for generation investors ' decisions , and reduce exposure to high regrets if adverse scenarios materialize , at the expense of the costs needed to bestow the transmission grid with flexibility to cope with uncertainties in market-based generation expansion .
One of the most important issues in scaling up reinforcement learning for practical problems is how to represent and store cost-to-go functions with more compact representations than lookup tables . We address the issue of combining the simple function approximation method-state aggregation with minimax-based reinforcement learning algorithms and present the convergence theory for online Q-hat-learning with state aggregation . Some empirical results are also included .
For categorical we use the softmax nonlinearity to represent while we treat the posterior of continuous as a factored Gaussian . Given this structure , the minimax game for the Bidirectional - InfoGAN ( BInfoGAN ) is then where determines the strength of the impact of the mutual information criterion and is set to in all our experiments . section : Experiments
Goodfellow et al . [ reference ] introduced the Generative Adversarial Networks ( GAN ) , a framework for training deep generative models using a minimax game . The goal is to learn a generator distribution P G ( x ) that matches the real data distribution P data ( x ) .
Similar mutual information inspired objectives have been considered before in the context of clustering [ reference ][ reference ][ reference ] . Therefore , we propose to solve the following information - regularized minimax game : section : Variational Mutual Information Maximization
All these generative models are trained by maximizing ( the lower bound of ) training data likelihood , which , as mentioned by , suffers from the difficulty of approximating intractable probabilistic computations . proposed an alternative training methodology to generative models , i.e. GANs , where the training procedure is a minimax game between a generative model and a discriminative model . This framework bypasses the difficulty of maximum likelihood learning and has gained striking successes in natural image generation .
This study focuses on image generation tasks using convolutional neural networks ( CNN ) for both and . Several loss functions have been proposed to quantify the difference of the scores between real and generated samples : and , including the minimax loss and non - saturating loss ( ) , hinge loss ( ) , Wasserstein loss ( ) and maximum mean discrepancy ( MMD ) ( ) ( see Appendix [ reference ] for more details ) .
Let be the support of distribution ; let , be the parameters of the generator and discriminator respectively . To prove that GANs trained using the minimax loss and gradient updates is locally stable at the equilibrium point , made the following assumption : theorem : ( ) . and ∈∀x⁢S ( PX ), =⁢DθD* ( x ) 0 . For loss functions like minimax and Wasserstein , may be interpreted as how plausible a sample is real . Thus at equilibrium , it may be reasonable to assume all real and generated samples are equally plausible .
MCTS averages over these approximation errors , which therefore tend to cancel out when evaluating a large subtree . In contrast , alpha - beta search computes an explicit minimax , which propagates the biggest approximation errors to the root of the subtree . Using MCTS may allow AlphaZero to effectively combine its neural network representations with a powerful , domain - independent search .
However , this objective can be problematic , since early on during training the discriminator converges quickly , causing the gradient to vanish . When training GANs , rather than directly using the minimax loss , it is typical to train the generator with the standard loss function with inverted labels . This splits the optimization into two independent objectives , one for the generator and one for the discriminator , where remains unchanged , but becomes : This objective has the same fixed - point properties as the minimax loss but provides stronger gradients to the target mapping .
section : ABSTRACT Generative adversarial networks ( GANs ) are a framework for producing a generative model by way of a two - player minimax game . In this paper , we propose the Generative Multi - Adversarial Network ( GMAN ) , a framework that extends GANs to multiple discriminators .
whereD is some convex combination of D i ( see Appendix A.5 ) . section : USING THE ORIGINAL MINIMAX OBJECTIVE To illustrate the effect the softmax has on training , observe that the component of AM sof t ( V , 0 ) relevant to generator training can be rewritten as
section : Introduction A classic Generative Adversarial Net ( GAN ) learns a discriminator and a generator by playing a two - player minimax game to generate samples from a data distribution . The discriminator is trained to distinguish real samples from those generated by the generator , and it in turn guides the generator to produce realistic samples that can fool the discriminator .
These latent distribution alignment approaches were further improved through the use of domain adversarial objectives whereby a domain classifier is trained to distinguish between the source and target representations while the domain representation is learned so as to maximize the error of the domain classifier . The representation is optimized using the standard minimax objective ganin_icml15 , the symmetric confusion objective tzeng_iccv15 , or the inverted label objective tzeng_cvpr17 . Each of these objectives is related to the literature on generative adversarial networks goodfellow_nips14 and follow - up work for improved training procedures for these networks salimans_arxiv16 , arjovsky_arxiv17 .
The core difference is that these previous formulations are originally derived from maximum - likelihood estimation , and thus the minimax optimization is only implicit . In contrast , with an explicit minimax formulation we can develop a better understanding of the induced solution . For example , the global bias λ * suggests that there exists more than one stable equilibrium the optimal discriminator can actually reach .
An important benchmark for hillclimbing algorithms on pseudo-Boolean functions $ f : \{0,1\}^n \to \mathbb{R}$ are ( strictly ) montone functions , on which a surprising number of hillclimbers fail to be efficient . For example , the $ (1 + 1)$-Evolutionary Algorithm is a standard hillclimber which flips each bit independently with probability $ c/n$ in each round . Perhaps surprisingly , this algorithm shows a phase transition : it optimizes any monotone pseudo-boolean function in quasilinear time if $ c 2.2$. But
The rst contribution of this paper is a theoretical comparison of the (1 + 1)-EA evolutionary algorithm to other evolutionary algorithms in the case of so-called monotone reproduction operator , which indicates that the (1 + 1)-EA is an optimal search technique in this setting . After that we study the expected optimization time for the (1 + 1)-EA and show two set covering problem families where it is superior to certain general-purpose exact algorithms .
The approach is based on a reinforcement-learning of artificial neural network fed with positions and velocities of the system components . The learning routine is designed on the basis of a 1 + 1 evolutionary scheme with concept of persistence of efficient steps . The performance of the four variants of the algorithm is evaluated on the basis of a simulated 3-DOF system .
< inline-formula > < tex-math notation="LaTeX">$k$ < /tex-math></inline-formula > NN search is a fundamental function in image processing , which is useful in many real applications , including image cluster , image classification , and image understanding and analysis in general . In this light , we propose and study a novel collaborative cross-domain < inline-formula > < tex-math notation="LaTeX">$k$ < /tex-math></inline-formula > NN search ( CD-<inline-formula > < tex-math notation="LaTeX">$k$ < /tex-math></inline-formula > NN ) in multidomain space .
This model controls spreading activation in a causal network that has rapidly varying connection strengths . It generates diagnostic hypotheses based on the same causal network that would be used by a traditional AI search algorithm . Experimental results are presented that demonstrate that this connectionist model can closely approximate the best diagnostic hypotheses with some causal networks , even when multiple disorders are present simultaneously .
Most of the computational burden comes from uni-directional and bi-directional prediction . In this paper , an iterative search strategy is designed to speed up the uni-directional prediction in MVC . It can work with an adaptive search range adjustment through a confidence measure of a loop constraint to obtain both motion and disparity vectors jointly .
Branch-and-bound and branch-and-cut use search trees to identify optimal solutions to combinatorial optimization problems . In this paper , we introduce an iterative search strategy which we refer to as cut-and-solve and prove optimality and termination for this method . This search is different from traditional tree search as there is no branching .
In the first step , the entire feature set is represented as a weighted graph . In the second step , the features are divided into several clusters using a community detection algorithm and finally in the third step , a novel iterative search strategy based on node centrality is developed to select the final subset of features . The proposed feature selection method offers two major advantages : first , our method groups features into different clusters based on their similarities , in which the features in the same cluster are similar to each other , and to obtain the reduced redundancy set , the final subset of features is selected from different clusters .
The standard algorithm to find the best possibble move is cubic , both in its worst and average time complexity . Since TSP instances of interest can have thousands of nodes , up to now it has been impossible to use the 3-OPT local search on anything other than quite small instances . In this paper we describe an alternative algorithm whose average complexity appears to be quadratic and which allowed us to use 3-OPT on instances with several thousand nodes .
From the experimental analysis , it was found that ACS works best for solving the dynamic TSP compared to the other five ant algorithms . When coupled with a local search technique , a significant improvement can be seen for ACS using the 3-opt local search algorithm . Lastly , it was also found that different optimal parameter settings were required for solving the different sizes of the dynamic TSP problems .
Suggests that a logical strategy might be to put core content on CD-ROM , while supplementing it with Internet based access to topical and real time content . The key benchmark as far as performance is concerned will be to have a product built around a fast search engine that offers simultaneous search of multiple databases stored on one or more servers and where retrieval of data allows instant analysis and easy view , copy , export and print . Concludes with useful advice on implementing a successful strategy based on these requirements .
These efforts are frequently limited to exploring known patterns . In this work , we will discuss the implementation of a new , very fast search engine designed primarily for embedding in complex search algorithms ( i.e. functional annotation of new structures ) . The unrestricted nature of the search should be helpful in uncovering hidden effectors .
Even compared to the single-indexed DHT ( sDHT ) application , the mDHT solution outperforms by reducing the data retrieval time by 24.5–48.6 % . The multi-level-indexed DHT algorithm presented in this paper contributes a key technique to developing a fast search engine to the extra-large scale of data on the cloud storage architecture .
This paper introduces the combination of NoSQL database HBase and enterprise search platform Solr so as to tackle the problem of the secondary index function with fast query . In order to verify the ...
They have to be well designed , easy to use , understandable , meaningful , and approachable . In this article , we present a new approach to visualize huge amount of data , based on a Bayesian suggestion algorithm and the widely used enterprise search platform Solr . We demonstrate how the proposed Bayesian suggestion algorithm became a key ingredient in a big data scenario , where generally a query can generate so many results that the user can be confused .
They have to be well designed , easy to use , understandable , meaningful , and approachable . In this article we present a new approach to visualize huge amount of data , based on a Bayesian suggestion algorithm and the widely used enterprise search platform Solr . We demonstrate how the proposed Bayesian suggestion algorithm became a key ingredient in a big data scenario , where generally a query can generate so many results that the user can be confused .
Although the exited unstructured P2P resource search algorithm can enhance the performance of resource searching , but it will generate lot of P2P traffic and will consume a huge network bandwidth . Consider the messages of Internet service provider ( ISP ) and the clustering of P2P networks , the article proposed an on-demand resource search algorithm which based on the ISP and the similarity of resource character .
We develop an effective hybrid fruit fly optimization algorithm ( HFOA ) that applies two vectors to represent individuals and presents routing and scheduling neighborhood structures . An IG-based local search procedure is embedded to enhance the exploitation ability of the proposed algorithm . Two decoding heuristics considering the problem characteristics are developed .
This paper presents a search-space-Reduced optimal SBP approach ( RSBP ) for a rigid body . This reduced space is found by a sparse search tree , which is enabled by a Metric Function ( MF ) built on a neural network . The offline-learnt MF estimates the minimum traveling cost between any two nodes in a fixed small workspace with various obstacles .
The user is made aware of strategic aspects of searching that allow him to fully exploit all information resources and services . The assistant comes fully integrated into the Okapi search engine of Torii . Torii integrates also iCite .
This new algorithm is implemented to remove multiple peaks by accumulating the autocorrelation peaks of the two signals coherently . In order to reduce the search realm of the code phase , a method “ Pre-detection + Sub-search ” was introduced in this new algorithm . The simulation results show that the proposed algorithm can not only reduce the search realm of the code phase significantly but also improve the acquisition sensitivity to some extend .
Since P2P systems maintain large and dynamic sets of peers , the search protocol is desired to be scalable and adaptive . In this paper we propose a new P2P search protocol , called Adaptive Randomized Search Protocol ( ARSP ) , which is an efficient extension of the Quorum-Based Protocol for Searching Objects in Peer-to-Peer Networks . ARSP minimizes the communication cost of the protocol by popularity-based index dissemination of objects .
The approach taken by GoogLeNet for detection is similar to the R - CNN by , but is augmented with the Inception model as the region classifier . Additionally , the region proposal step is improved by combining the Selective Search approach with multi - box predictions for higher object bounding box recall . In order to cut down the number of false positives , the superpixel size was increased by .
In general , it is very difficult to ask a single classifier to perform uniformly well over all IoU levels . At inference , since the majority of the hypotheses produced by a proposal detector , e.g. RPN [ reference ] or selective search [ reference ] , have low quality , the detector must be more discriminant for lower quality hypotheses . A standard compromise between these conflicting requirements is to settle on u = 0.5 .
While it is much faster than R - CNN , it still falls short of real - time and takes a significant accuracy hit from not having good proposals . Fast R - CNN speeds up the classification stage of R - CNN but it still relies on selective search which can take around 2 seconds per image to generate bounding box proposals . Thus it has high mAP but at fps it is still far from real - time .
[ reference ] for details ) . Our results show that MIXER with a simple greedy search achieves much better accuracy compared to the baselines on all the three tasks . In addition we show that MIXER with greedy search is even more accurate than the cross entropy model augmented with beam search at inference time as a post - processing step .
However , the number of potential candidate systems is too large to allow an all - out combination , both for practical reasons and due to overfitting issues . Instead , we perform a greedy search , starting with the single best system , and successively adding additional systems , to find a small set of systems that are maximally complementary . The RT - 02 Switchboard set was used for this search procedure .
AlphaZero uses a markedly different approach that averages over the position evaluations within a subtree , rather than computing the minimax evaluation of that subtree . However , chess programs using traditional MCTS were much weaker than alpha - beta search programs , ; while alpha - beta programs based on neural networks have previously been unable to compete with faster , handcrafted evaluation functions . AlphaZero evaluates positions using non - linear function approximation based on a deep neural network , rather than the linear function approximation used in typical chess programs .
These attributes were further used to define various test optimization objectives together with test engineers from our industrial partner . Search based techniques were then used for optimizing test by defining a novel fitness function based on the identified objectives and empirically evaluated it with four search algorithms : Alternating Variable Method ( AVM ) , Genetic Algorithm ( GA ) , ( 1 + 1 ) Evolutionary Algorithm ( EA ) and Greedy Algorithm . Additionally , we used Random Search as the comparative baseline .
Depth-First Forwarding (DFF) in Unreliable Networks Research on Formal Development of Non-recursive Algorithms of Graph Search Satisfiability verification of engineering data safety rules of balise based on ROBDD Depth-first search in split-by-edges trees A modeling methodology and pre-run-time scheduling for embedded real-time software Segment-based vulnerability analysis system for a water distribution network Mining frequent closed patterns in pointset databases Mining Temporal Subgraph Patterns in Heterogeneous Information Networks Efficient Timing-Driven Incremental Routing for VLSI Circuits Using DFS and Localized Slack-Satisfaction Computations Fast Coding Unit Depth Decision Algorithm for Interframe Coding in HEVC On Weighted Depths in Random Binary Search Trees Design of Inference Engine for Expert System Based on Fault Tree Detecting Oriented Text in Natural Images by Linking Segments Robust designs for wavelet approximations of nonlinear models A Dynamic Network Interdiction Problem Parallel Machine Scheduling Through Column Generation: Minimax Objective Functions Wave Sensitivities of Networks Manipulative Elicitation - A New Attack on Elections with Incomplete Preferences MCP Based Noise Resistant Algorithm for Training RBF Networks and Selecting Centers A two-stage minimax concave penalty based method in pruned AdaBoost ensemble An $\ell_0$ -Norm-Based Centers Selection for Failure Tolerant RBF Networks The design of weighted minimax quadrature mirror filters Failure Prevention Based on Parameters Estimation and Prediction Multi-faceted modelling of flexible manufacturing systems using a minimax algebraic Petri net representation Robust semiparametric amplitude estimation of sinusoidal signals: The multi-sensor case Minimax approaches to optimal transmission expansion planning considering non-probabilistic scenarios of market-based renewable generation capacity additions Minimax-based reinforcement learning with state aggregation Inferencing based on unsupervised learning of disentangled representations InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient Improving MMD-GAN Training with Repulsive Loss Function Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm Adversarial Discriminative Domain Adaptation Generative Multi-Adversarial Networks Loss-Sensitive Generative Adversarial Networks on Lipschitz Densities CyCADA: Cycle-Consistent Adversarial Domain Adaptation Calibrating Energy-based Generative Adversarial Networks When Does Hillclimbing Fail on Monotone Functions: An entropy compression argument A Study on Performance of the (1+1)-Evolutionary Algorithm Reinforcement learning for vibration suppression of an unknown system Collaborative Cross-Domain $k$ NN Search for Remote Sensing Image Processing A connectionist approach to diagnostic problem solving using causal networks Iterative search strategy with selective bi-directional prediction for low complexity multiview video coding Cut-and-solve: An iterative search strategy for combinatorial optimization problems A graph theoretic approach for unsupervised feature selection Speeding-up the Exploration of the \(3\)-OPT Neighborhood for the TSP Ant Colony Optimization in Dynamic Environments Retrieval technology and the information explosion Fast pattern recognition of protein three dimensional features using a bit-pattern approach as a prescreen. mDHT: a multi-level-indexed DHT algorithm to extra-large-scale data retrieval on HDFS/Hadoop architecture Implementation of secondary index on cloud computing NoSQL database in big data environment Enhancing Big Data Exploration with Faceted Browsing Big data exploration with faceted browsing Resource search optimization of P2P based on ISP and resource character A Hybrid Fruit Fly Optimization Algorithm for the Realistic Hybrid Flowshop Rescheduling Problem in Steelmaking Systems A Scalable Sampling-Based Optimal Path Planning Approach via Search Space Reduction TORII: Access to Digital Research Community A Fast Acquisition Algorithm for L1C Based on L1CA and L1C Combined Detection An adaptive randomized search protocol in peer-to-peer systems Going deeper with convolutions Cascade R-CNN: Delving Into High Quality Object Detection You Only Look Once: Unified, Real-Time Object Detection Sequence Level Training with Recurrent Neural Networks The microsoft 2016 conversational speech recognition system Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm Test Optimization using Weight Based Search Algorithms in a Maritime Application
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SOFL+MSVL ITSC SBAC-PAD SocialCom/PASSAT DATE DCC 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ESA ICONIP (2) ESANN NIPS AAAI ICLR ArXiv 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ICLR International Journal of Computer Vision ICML ICLR FOGA IMECS HPCS SAC 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ICLR 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) ArXiv
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