Studies have shown that the people depicted in image search results tend to be of majority groups with respect to socially salient attributes such as gender or race. This skew goes beyond that which already exists in ...
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Studies have shown that the people depicted in image search results tend to be of majority groups with respect to socially salient attributes such as gender or race. This skew goes beyond that which already exists in the world-i.e., the search results for images of people are more imbalanced than the ground truth would suggest. For example, Kay et al. showed that although 28% of CEOs in the U.S. are women, only 10% of the top 100 results for "CEO"in Google Image Search are women. Similar observations abound across search terms and across socially salient attributes. Most existing approaches to correct for this kind of bias assume that the images of people include labels denoting the relevant socially salient attributes. These labels are explicitly used to either change the dataset, adjust the training of the algorithm, and/or in the execution of the method. However, such labels are often unknown. Further, using machine learning techniques to infer these labels may often not be possible within acceptable accuracy ranges and may not be desirable due to the additional biases this process could incur. As observed in prior work, alternate approaches consider the diversity of image features, which often do not translate to images of visibly diverse people. We develop a novel approach that takes as input a visibly diverse control set of images of people and uses this set as part of a procedure to select a set of images of people in response to a query. The goal is to have a resulting set that is more visibly diverse in a manner that emulates the diversity depicted in the control set. It accomplishes this by evaluating the similarity of the images selected by a black-box algorithm with the images in the diversity control set, and incorporating this "diversity score"into the final selection process. Importantly, this approach does not require images to be labelled at any point; effectively, it gives a way to implicitly diversify the set of images selected. We provide two var
Reversible data hiding in encrypted image (RDH-EI) is a hot topic of data hiding in recent years. Most RDH-EI algorithms do not reach desirable embedding rate and their computational costs are not suitable for real-ti...
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We propose a highly parallelizable Newton-type method for nonlinear model predictive control by exploiting the particular structure of the associated Karush-Kuhn-Tucker conditions. These equations are approximately de...
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We develop an efficient parallel distributed algorithm for matrix completion, named NOMAD (Non-locking, stOchastic Multi-machine algorithm for Asynchronous and Decentralized matrix completion). NOMAD is a decentralize...
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We develop an efficient parallel distributed algorithm for matrix completion, named NOMAD (Non-locking, stOchastic Multi-machine algorithm for Asynchronous and Decentralized matrix completion). NOMAD is a decentralized algorithm with non-blocking communication between processors. One of the key features of NOMAD is that the ownership of a variable is asynchronously transferred between processors in a decentralized fashion. As a consequence it is a lock-free parallel algorithm. In spite of being asynchronous, the variable updates of NOMAD are serializable, that is, there is an equivalent update ordering in a serial implementation. NOMAD outperforms synchronous algorithms which require explicit bulk synchronization after every iteration: our extensive empirical evaluation shows that not only does our algorithm perform well in distributed setting on commodity hardware, but also outperforms stateof-the-art algorithms on a HPC cluster both in multi-core and distributed memory settings.
We propose a new scalable algorithm that can compute Personalized PageRank (PPR) very quickly. The Power method is a state-of-the-art algorithm for computing exact PPR;however, it requires many iterations. Thus reduci...
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We propose a new scalable algorithm that can compute Personalized PageRank (PPR) very quickly. The Power method is a state-of-the-art algorithm for computing exact PPR;however, it requires many iterations. Thus reducing the number of iterations is the main challenge. We achieve this by exploiting graph structures of web graphs and social networks. The convergence of our algorithm is very fast. In fact, it requires up to 7.5 times fewer iterations than the Power method and is up to five times faster in actual computation time. To the best of our knowledge, this is the first time to use graph structures explicitly to solve PPR quickly. Our contributions can be summarized as follows. 1. We provide an algorithm for computing a tree decomposition, which is more efficient and scalable than any previous algorithm. 2. Using the above algorithm, we can obtain a cor e-tree decomposition of any web graph and social network. This allows us to decompose a web graph and a social network into (1) the core, which behaves like an expander graph, and (2) a small tree-width graph, which behaves like a tree in an algorithmic sense. 3. We apply a direct method to the small tree-width graph to construct an LU decomposition. 4. Building on the LU decomposition and using it as preconditoner,we apply GMRES method (a state-of-theart advanced iterative method) to compute PPR for whole web graphs and social networks.
State of the art methods for state estimation and perception make use of least-squares optimization methods to perform efficient inference on noisy sensor data. Much of this efficiency is achieved by using sparse matr...
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ISBN:
(纸本)9781467317375
State of the art methods for state estimation and perception make use of least-squares optimization methods to perform efficient inference on noisy sensor data. Much of this efficiency is achieved by using sparse matrix factorization methods. The sparsity structure of the underlying matrix factorization which makes these optimization methods tractable is highly dependent on the choice of variable reordering;but there has been no systematic evaluation of reordering methods in the SLAM community. In this paper we evaluate the performance of various reordering techniques on benchmark SLAM data sets and provide definitive recommendations based on our results. We also compare these state of the art algorithms against our simple and easy to implement algorithm which achieves comparable performance. Finally, we provide empirical evidence that few gains remain with respect to variants of minimum degree ordering.
This survey paper contains a general characterization of stationary and instationary linear iterative one-step methods solving over- or underdetermined linear algebraic systems, techniques for convergence acceleration...
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This survey paper contains a general characterization of stationary and instationary linear iterative one-step methods solving over- or underdetermined linear algebraic systems, techniques for convergence acceleration, and some generalizations of the Kaczmarz (art) and the De la Garza methods. Among others the question is answered, under what conditions an iteration process yields a generalized matrix inverse of the type A − , A (1,2) , A (1,2,3) , A (1,2,4) , A + , respectively, and under what conditions the resulting limit vector is a minimum-norm-solution, a least-squares-solution or a solution of the initial linear system.
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