Open AccessProceedings Article
Ranking via Robust Binary Classification
Hyokun Yun,Parameswaran Raman,S. V. N. Vishwanathan +2 more
- 08 Dec 2014
- Vol. 27, pp 2582-2590
TL;DR: It is shown that RoBiRank can be efficiently parallelized across a large number of machines; for a task that requires 386,133 x 49,824, 519 pairwise interactions between items to be ranked, Ro biRank finds solutions that are of dramatically higher quality than that can be found by a state-of-the-art competitor algorithm, given the same amount of wall-clock time for computation.
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Abstract: We propose RoBiRank, a ranking algorithm that is motivated by observing a close connection between evaluation metrics for learning to rank and loss functions for robust classification. It shows competitive performance on standard benchmark datasets against a number of other representative algorithms in the literature. We also discuss extensions of RoBiRank to large scale problems where explicit feature vectors and scores are not given. We show that RoBiRank can be efficiently parallelized across a large number of machines; for a task that requires 386,133 x 49,824, 519 pairwise interactions between items to be ranked, RoBiRank finds solutions that are of dramatically higher quality than that can be found by a state-of-the-art competitor algorithm, given the same amount of wall-clock time for computation.
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Deep Ranking for Person Re-identification via Joint Representation Learning
TL;DR: Zhang et al. as mentioned in this paper proposed a unified deep ranking framework that jointly tackles both of these key components to maximize their strengths, and the ranking model is solved with a deep convolutional neural network (CNN) that builds the relation between input image pairs and their similarity scores through joint representation learning directly from raw image pixels.
Deep Ranking for Person Re-Identification via Joint Representation Learning
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References
•Book
Introduction to Information Retrieval
Christopher D. Manning,Prabhakar Raghavan,Hinrich Schütze +2 more
- 01 Jan 2008
TL;DR: In this article, the authors present an up-to-date treatment of all aspects of the design and implementation of systems for gathering, indexing, and searching documents; methods for evaluating systems; and an introduction to the use of machine learning methods on text collections.
A Stochastic Approximation Method
Herbert Robbins,Sutton Monro +1 more
TL;DR: In this article, a method for making successive experiments at levels x1, x2, ··· in such a way that xn will tend to θ in probability is presented.
Robust Stochastic Approximation Approach to Stochastic Programming
TL;DR: It is intended to demonstrate that a properly modified SA approach can be competitive and even significantly outperform the SAA method for a certain class of convex stochastic problems.
2.9K
•Book
Numerical Optimization (Springer Series in Operations Research and Financial Engineering)
Jorge Nocedal,Stephen J. Wright +1 more
- 28 Apr 2000
TL;DR: Numerical optimization presents a graduate text, in continuous presents, that talks extensively about algorithmic performance and thinking, and about mathematical optimization in understanding of initiative.
2.4K
•Proceedings Article
The Tradeoffs of Large Scale Learning
Olivier Bousquet,Léon Bottou +1 more
- 03 Dec 2007
TL;DR: This contribution develops a theoretical framework that takes into account the effect of approximate optimization on learning algorithms and shows distinct tradeoffs for the case of small-scale and large-scale learning problems.
1.8K