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Pegasos: Primal Estimated subgradient solver for SVM
"... We describe and analyze a simple and effective stochastic subgradient descent algorithm for solving the optimization problem cast by Support Vector Machines (SVM). We prove that the number of iterations required to obtain a solution of accuracy ɛ is Õ(1/ɛ), where each iteration operates on a singl ..."
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Cited by 542 (20 self)
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linear kernels while working solely on the primal objective function, though in this case the runtime does depend linearly on the training set size. Our algorithm is particularly well suited for large text classification problems, where we demonstrate an orderofmagnitude speedup over previous SVM learning
GPU Based Computation of the Structural Tensor for RealTime Figure Detection
"... In this paper we present a realtime realization of the method of detection of local structures in images of predefined orientation. The method is based on an analysis of the structural tensor computed in monochrome and color images. Thanks to the GPU implementation of the lowlevel feature detectio ..."
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detection an orderofmagnitude speedup was achieved compared to the software implementation. The method can be used for realtime detection of solid objects in HDTV streams as shown by many examples.
Scaling Averagereward Reinforcement Learning for Product Delivery
"... Reinforcement learning in realworld domains suffers from three curses of dimensionality: explosions in state space and action space, and high stochasticity. We give partial solutions to each of these curses that provide orderofmagnitude speedups in execution time over standard approaches. We demo ..."
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Reinforcement learning in realworld domains suffers from three curses of dimensionality: explosions in state space and action space, and high stochasticity. We give partial solutions to each of these curses that provide orderofmagnitude speedups in execution time over standard approaches. We
FPGAbased Acceleration of CHARMMpotential Minimization*†
"... Energy minimization is an important step in molecular modeling, with applications in molecular docking and in mapping binding sites. Minimization involves repeated evaluation of various bonded and nonbonded energies of a protein complex. It is a computationally expensive process, with runtimes typi ..."
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typically being many hours on a desktop system. In the current article, we present acceleration of the energy evaluation phase of minimization using Field Programmable Gate Arrays. We project a multiple ordersofmagnitude speedup over a single CPU core and a factor of 8 speedup over our previous
BranchandCheck: A Hybrid Framework Integrating Mixed Integer Programming and Constraint Logic Programming
, 2001
"... We present BranchandCheck, a hybrid framework integrating Mixed Integer Programming and Constraint Logic Programming, which encapsulates the traditional Benders Decomposition and BranchandBound as special cases. In particular we describe its relation to Benders and the use of nogoods and linear ..."
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Cited by 37 (0 self)
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relaxations. We give two examples of how problems can be modelled and solved using BranchandCheck and present computational results demonstrating more than orderofmagnitude speedup compared to previous approaches. We also mention important future research issues such as hierarchical, dynamic
Efficient weight learning for Markov logic networks
 In Proceedings of the Eleventh European Conference on Principles and Practice of Knowledge Discovery in Databases
, 2007
"... Abstract. Markov logic networks (MLNs) combine Markov networks and firstorder logic, and are a powerful and increasingly popular representation for statistical relational learning. The stateoftheart method for discriminative learning of MLN weights is the voted perceptron algorithm, which is ess ..."
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Cited by 87 (7 self)
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alternatives, from perweight learning rates to secondorder methods. In particular, we focus on two approaches that avoid computing the partition function: diagonal Newton and scaled conjugate gradient. In experiments on standard SRL datasets, we obtain orderofmagnitude speedups, or more accurate models
Automating efficient rammodel secure computation
 in IEEE Symposium on Security and Privacy
, 2014
"... Abstract—RAMmodel secure computation addresses the inherent limitations of circuitmodel secure computation considered in almost all previous work. Here, we describe the first automated approach for RAMmodel secure computation in the semihonest model. We define an intermediate representation cal ..."
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Cited by 19 (8 self)
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called SCVM and a corresponding type system suited for RAMmodel secure computation. Leveraging compiletime optimizations, our approach achieves orderofmagnitude speedups compared to both circuitmodel secure computation and the stateofart RAMmodel secure computation. I.
PAPER—Accelerating Parallel Evaluations of ROCS
"... Abstract: Modern graphics processing units (GPUs) are flexibly programmable and have peak computational throughput significantly faster than conventional CPUs. Herein, we describe the design and implementation of PAPER, an opensource implementation of Gaussian molecular shape overlay for NVIDIA GP ..."
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Cited by 4 (4 self)
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GPUs. We demonstrate one to two orderofmagnitude speedups on highend commodity GPU hardware relative to a reference CPU implementation of the shape overlay algorithm and speedups of over one order of magnitude relative to the commercial OpenEye ROCS package. In addition, we describe errors incurred
A scaled conjugate gradient algorithm for fast supervised learning
 NEURAL NETWORKS
, 1993
"... A supervised learning algorithm (Scaled Conjugate Gradient, SCG) with superlinear convergence rate is introduced. The algorithm is based upon a class of optimization techniques well known in numerical analysis as the Conjugate Gradient Methods. SCG uses second order information from the neural netwo ..."
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Cited by 451 (0 self)
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FletcherGoldfarbShanno memoryless quasiNewton algorithm (BFGS) [1]. SCG yields a speedup of at least an order of magnitude relative to BP. The speedup depends on the convergence criterion, i.e., the bigger demand for reduction in error the bigger the speedup. SCG is fully automated including no user dependent parameters
Fast approximate nearest neighbors with automatic algorithm configuration
 In VISAPP International Conference on Computer Vision Theory and Applications
, 2009
"... nearestneighbors search, randomized kdtrees, hierarchical kmeans tree, clustering. For many computer vision problems, the most time consuming component consists of nearest neighbor matching in highdimensional spaces. There are no known exact algorithms for solving these highdimensional problems ..."
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Cited by 455 (2 self)
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that implements these approaches. This library provides about one order of magnitude improvement in query time over the best previously available software and provides fully automated parameter selection. 1
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