Paper Group ANR 422
A Projective Simulation Scheme for Partially-Observable Multi-Agent Systems. Distance for Functional Data Clustering Based on Smoothing Parameter Commutation. Learning Simple Auctions. Estimating Probability Distributions using “Dirac” Kernels (via Rademacher-Walsh Polynomial Basis Functions). Sparse Signal Subspace Decomposition Based on Adaptive …
A Projective Simulation Scheme for Partially-Observable Multi-Agent Systems
| Title | A Projective Simulation Scheme for Partially-Observable Multi-Agent Systems |
| Authors | Rasoul Kheiri |
| Abstract | We introduce a kind of partial observability to the projective simulation (PS) learning method. It is done by adding a belief projection operator and an observability parameter to the original framework of the efficiency of the PS model. I provide theoretical formulations, network representations, and situated scenarios derived from the invasion toy problem as a starting point for some multi-agent PS models. |
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| Published | 2016-10-29 |
| URL | https://arxiv.org/abs/1610.09372v4 |
| https://arxiv.org/pdf/1610.09372v4.pdf | |
| PWC | https://paperswithcode.com/paper/a-projective-simulation-scheme-for-a |
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Distance for Functional Data Clustering Based on Smoothing Parameter Commutation
| Title | Distance for Functional Data Clustering Based on Smoothing Parameter Commutation |
| Authors | ShengLi Tzeng, Christian Hennig, Yu-Fen Li, Chien-Ju Lin |
| Abstract | We propose a novel method to determine the dissimilarity between subjects for functional data clustering. Spline smoothing or interpolation is common to deal with data of such type. Instead of estimating the best-representing curve for each subject as fixed during clustering, we measure the dissimilarity between subjects based on varying curve estimates with commutation of smoothing parameters pair-by-pair (of subjects). The intuitions are that smoothing parameters of smoothing splines reflect inverse signal-to-noise ratios and that applying an identical smoothing parameter the smoothed curves for two similar subjects are expected to be close. The effectiveness of our proposal is shown through simulations comparing to other dissimilarity measures. It also has several pragmatic advantages. First, missing values or irregular time points can be handled directly, thanks to the nature of smoothing splines. Second, conventional clustering method based on dissimilarity can be employed straightforward, and the dissimilarity also serves as a useful tool for outlier detection. Third, the implementation is almost handy since subroutines for smoothing splines and numerical integration are widely available. Fourth, the computational complexity does not increase and is parallel with that in calculating Euclidean distance between curves estimated by smoothing splines. |
| Tasks | Outlier Detection |
| Published | 2016-04-10 |
| URL | http://arxiv.org/abs/1604.02668v1 |
| http://arxiv.org/pdf/1604.02668v1.pdf | |
| PWC | https://paperswithcode.com/paper/distance-for-functional-data-clustering-based |
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Learning Simple Auctions
| Title | Learning Simple Auctions |
| Authors | Jamie Morgenstern, Tim Roughgarden |
| Abstract | We present a general framework for proving polynomial sample complexity bounds for the problem of learning from samples the best auction in a class of “simple” auctions. Our framework captures all of the most prominent examples of “simple” auctions, including anonymous and non-anonymous item and bundle pricings, with either a single or multiple buyers. The technique we propose is to break the analysis of auctions into two natural pieces. First, one shows that the set of allocation rules have large amounts of structure; second, fixing an allocation on a sample, one shows that the set of auctions agreeing with this allocation on that sample have revenue functions with low dimensionality. Our results effectively imply that whenever it’s possible to compute a near-optimal simple auction with a known prior, it is also possible to compute such an auction with an unknown prior (given a polynomial number of samples). |
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| Published | 2016-04-11 |
| URL | http://arxiv.org/abs/1604.03171v1 |
| http://arxiv.org/pdf/1604.03171v1.pdf | |
| PWC | https://paperswithcode.com/paper/learning-simple-auctions |
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Estimating Probability Distributions using “Dirac” Kernels (via Rademacher-Walsh Polynomial Basis Functions)
| Title | Estimating Probability Distributions using “Dirac” Kernels (via Rademacher-Walsh Polynomial Basis Functions) |
| Authors | Hamse Y. Mussa, Avid M. Afzal |
| Abstract | In many applications (in particular information systems, such as pattern recognition, machine learning, cheminformatics, bioinformatics to name but a few) the assessment of uncertainty is essential - i.e., the estimation of the underlying probability distribution function. More often than not, the form of this function is unknown and it becomes necessary to non-parametrically construct/estimate it from a given sample. One of the methods of choice to non-parametrically estimate the unknown probability distribution function for a given random variable (defined on binary space) has been the expansion of the estimation function in Rademacher-Walsh Polynomial basis functions. In this paper we demonstrate that the expansion of the probability distribution function estimation in Rademacher-Walsh Polynomial basis functions is equivalent to the expansion of the function estimation in a set of “Dirac kernel” functions. The latter approach can ameliorate the computational bottleneck and notational awkwardness often associated with the Rademacher-Walsh Polynomial basis functions approach, in particular when the binary input space is large. |
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| Published | 2016-09-23 |
| URL | http://arxiv.org/abs/1609.07333v1 |
| http://arxiv.org/pdf/1609.07333v1.pdf | |
| PWC | https://paperswithcode.com/paper/estimating-probability-distributions-using |
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Sparse Signal Subspace Decomposition Based on Adaptive Over-complete Dictionary
| Title | Sparse Signal Subspace Decomposition Based on Adaptive Over-complete Dictionary |
| Authors | Hong Sun, Chengwei Sang, Didier Le Ruyet |
| Abstract | This paper proposes a subspace decomposition method based on an over-complete dictionary in sparse representation, called “Sparse Signal Subspace Decomposition” (or 3SD) method. This method makes use of a novel criterion based on the occurrence frequency of atoms of the dictionary over the data set. This criterion, well adapted to subspace-decomposition over a dependent basis set, adequately re ects the intrinsic characteristic of regularity of the signal. The 3SD method combines variance, sparsity and component frequency criteria into an unified framework. It takes benefits from using an over-complete dictionary which preserves details and from subspace decomposition which rejects strong noise. The 3SD method is very simple with a linear retrieval operation. It does not require any prior knowledge on distributions or parameters. When applied to image denoising, it demonstrates high performances both at preserving fine details and suppressing strong noise. |
| Tasks | Denoising, Image Denoising |
| Published | 2016-10-27 |
| URL | http://arxiv.org/abs/1610.08813v1 |
| http://arxiv.org/pdf/1610.08813v1.pdf | |
| PWC | https://paperswithcode.com/paper/sparse-signal-subspace-decomposition-based-on |
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Robust video object tracking via Bayesian model averaging based feature fusion
| Title | Robust video object tracking via Bayesian model averaging based feature fusion |
| Authors | Yi Dai, Bin Liu |
| Abstract | In this article, we are concerned with tracking an object of interest in video stream. We propose an algorithm that is robust against occlusion, the presence of confusing colors, abrupt changes in the object feature space and changes in object size. We develop the algorithm within a Bayesian modeling framework. The state space model is used for capturing the temporal correlation in the sequence of frame images by modeling the underlying dynamics of the tracking system. The Bayesian model averaging (BMA) strategy is proposed for fusing multi-clue information in the observations. Any number of object features are allowed to be involved in the proposed framework. Every feature represents one source of information to be fused and is associated with an observation model. The state inference is performed by employing the particle filter methods. In comparison with related approaches, the BMA based tracker is shown to have robustness, expressivity, and comprehensibility. |
| Tasks | Object Tracking, Video Object Tracking |
| Published | 2016-04-02 |
| URL | http://arxiv.org/abs/1604.00475v3 |
| http://arxiv.org/pdf/1604.00475v3.pdf | |
| PWC | https://paperswithcode.com/paper/robust-video-object-tracking-via-bayesian |
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Dealing with Range Anxiety in Mean Estimation via Statistical Queries
| Title | Dealing with Range Anxiety in Mean Estimation via Statistical Queries |
| Authors | Vitaly Feldman |
| Abstract | We give algorithms for estimating the expectation of a given real-valued function $\phi:X\to {\bf R}$ on a sample drawn randomly from some unknown distribution $D$ over domain $X$, namely ${\bf E}{{\bf x}\sim D}[\phi({\bf x})]$. Our algorithms work in two well-studied models of restricted access to data samples. The first one is the statistical query (SQ) model in which an algorithm has access to an SQ oracle for the input distribution $D$ over $X$ instead of i.i.d. samples from $D$. Given a query function $\phi:X \to [0,1]$, the oracle returns an estimate of ${\bf E}{{\bf x}\sim D}[\phi({\bf x})]$ within some tolerance $\tau$. The second, is a model in which only a single bit is communicated from each sample. In both of these models the error obtained using a naive implementation would scale polynomially with the range of the random variable $\phi({\bf x})$ (which might even be infinite). In contrast, without restrictions on access to data the expected error scales with the standard deviation of $\phi({\bf x})$. Here we give a simple algorithm whose error scales linearly in standard deviation of $\phi({\bf x})$ and logarithmically with an upper bound on the second moment of $\phi({\bf x})$. As corollaries, we obtain algorithms for high dimensional mean estimation and stochastic convex optimization in these models that work in more general settings than previously known solutions. |
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| Published | 2016-11-20 |
| URL | http://arxiv.org/abs/1611.06475v2 |
| http://arxiv.org/pdf/1611.06475v2.pdf | |
| PWC | https://paperswithcode.com/paper/dealing-with-range-anxiety-in-mean-estimation |
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Calculus of the exponent of Kurdyka-Łojasiewicz inequality and its applications to linear convergence of first-order methods
| Title | Calculus of the exponent of Kurdyka-Łojasiewicz inequality and its applications to linear convergence of first-order methods |
| Authors | Guoyin Li, Ting Kei Pong |
| Abstract | In this paper, we study the Kurdyka-{\L}ojasiewicz (KL) exponent, an important quantity for analyzing the convergence rate of first-order methods. Specifically, we develop various calculus rules to deduce the KL exponent of new (possibly nonconvex and nonsmooth) functions formed from functions with known KL exponents. In addition, we show that the well-studied Luo-Tseng error bound together with a mild assumption on the separation of stationary values implies that the KL exponent is $\frac12$. The Luo-Tseng error bound is known to hold for a large class of concrete structured optimization problems, and thus we deduce the KL exponent of a large class of functions whose exponents were previously unknown. Building upon this and the calculus rules, we are then able to show that for many convex or nonconvex optimization models for applications such as sparse recovery, their objective function’s KL exponent is $\frac12$. This includes the least squares problem with smoothly clipped absolute deviation (SCAD) regularization or minimax concave penalty (MCP) regularization and the logistic regression problem with $\ell_1$ regularization. Since many existing local convergence rate analysis for first-order methods in the nonconvex scenario relies on the KL exponent, our results enable us to obtain explicit convergence rate for various first-order methods when they are applied to a large variety of practical optimization models. Finally, we further illustrate how our results can be applied to establishing local linear convergence of the proximal gradient algorithm and the inertial proximal algorithm with constant step-sizes for some specific models that arise in sparse recovery. |
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| Published | 2016-02-09 |
| URL | http://arxiv.org/abs/1602.02915v5 |
| http://arxiv.org/pdf/1602.02915v5.pdf | |
| PWC | https://paperswithcode.com/paper/calculus-of-the-exponent-of-kurdyka |
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On-the-fly Network Pruning for Object Detection
| Title | On-the-fly Network Pruning for Object Detection |
| Authors | Marc Masana, Joost van de Weijer, Andrew D. Bagdanov |
| Abstract | Object detection with deep neural networks is often performed by passing a few thousand candidate bounding boxes through a deep neural network for each image. These bounding boxes are highly correlated since they originate from the same image. In this paper we investigate how to exploit feature occurrence at the image scale to prune the neural network which is subsequently applied to all bounding boxes. We show that removing units which have near-zero activation in the image allows us to significantly reduce the number of parameters in the network. Results on the PASCAL 2007 Object Detection Challenge demonstrate that up to 40% of units in some fully-connected layers can be entirely eliminated with little change in the detection result. |
| Tasks | Network Pruning, Object Detection |
| Published | 2016-05-11 |
| URL | http://arxiv.org/abs/1605.03477v1 |
| http://arxiv.org/pdf/1605.03477v1.pdf | |
| PWC | https://paperswithcode.com/paper/on-the-fly-network-pruning-for-object |
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Are Elephants Bigger than Butterflies? Reasoning about Sizes of Objects
| Title | Are Elephants Bigger than Butterflies? Reasoning about Sizes of Objects |
| Authors | Hessam Bagherinezhad, Hannaneh Hajishirzi, Yejin Choi, Ali Farhadi |
| Abstract | Human vision greatly benefits from the information about sizes of objects. The role of size in several visual reasoning tasks has been thoroughly explored in human perception and cognition. However, the impact of the information about sizes of objects is yet to be determined in AI. We postulate that this is mainly attributed to the lack of a comprehensive repository of size information. In this paper, we introduce a method to automatically infer object sizes, leveraging visual and textual information from web. By maximizing the joint likelihood of textual and visual observations, our method learns reliable relative size estimates, with no explicit human supervision. We introduce the relative size dataset and show that our method outperforms competitive textual and visual baselines in reasoning about size comparisons. |
| Tasks | Visual Reasoning |
| Published | 2016-02-02 |
| URL | http://arxiv.org/abs/1602.00753v1 |
| http://arxiv.org/pdf/1602.00753v1.pdf | |
| PWC | https://paperswithcode.com/paper/are-elephants-bigger-than-butterflies |
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Towards Building an RGBD-M Scanner
| Title | Towards Building an RGBD-M Scanner |
| Authors | Zhe Wu, Sai-Kit Yeung, Ping Tan |
| Abstract | We present a portable device to capture both shape and reflectance of an indoor scene. Consisting of a Kinect, an IR camera and several IR LEDs, our device allows the user to acquire data in a similar way as he/she scans with a single Kinect. Scene geometry is reconstructed by KinectFusion. To estimate reflectance from incomplete and noisy observations, 3D vertices of the same material are identified by our material segmentation propagation algorithm. Then BRDF observations at these vertices are merged into a more complete and accurate BRDF for the material. Effectiveness of our device is demonstrated by quality results on real-world scenes. |
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| Published | 2016-03-12 |
| URL | http://arxiv.org/abs/1603.03875v1 |
| http://arxiv.org/pdf/1603.03875v1.pdf | |
| PWC | https://paperswithcode.com/paper/towards-building-an-rgbd-m-scanner |
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Big Models for Big Data using Multi objective averaged one dependence estimators
| Title | Big Models for Big Data using Multi objective averaged one dependence estimators |
| Authors | Mrutyunjaya Panda |
| Abstract | Even though, many researchers tried to explore the various possibilities on multi objective feature selection, still it is yet to be explored with best of its capabilities in data mining applications rather than going for developing new ones. In this paper, multi-objective evolutionary algorithm ENORA is used to select the features in a multi-class classification problem. The fusion of AnDE (averaged n-dependence estimators) with n=1, a variant of naive Bayes with efficient feature selection by ENORA is performed in order to obtain a fast hybrid classifier which can effectively learn from big data. This method aims at solving the problem of finding optimal feature subset from full data which at present still remains to be a difficult problem. The efficacy of the obtained classifier is extensively evaluated with a range of most popular 21 real world dataset, ranging from small to big. The results obtained are encouraging in terms of time, Root mean square error, zero-one loss and classification accuracy. |
| Tasks | Feature Selection |
| Published | 2016-10-25 |
| URL | http://arxiv.org/abs/1610.07752v1 |
| http://arxiv.org/pdf/1610.07752v1.pdf | |
| PWC | https://paperswithcode.com/paper/big-models-for-big-data-using-multi-objective |
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Expanding Subjective Lexicons for Social Media Mining with Embedding Subspaces
| Title | Expanding Subjective Lexicons for Social Media Mining with Embedding Subspaces |
| Authors | Silvio Amir, Rámon Astudillo, Wang Ling, Paula C. Carvalho, Mário J. Silva |
| Abstract | Recent approaches for sentiment lexicon induction have capitalized on pre-trained word embeddings that capture latent semantic properties. However, embeddings obtained by optimizing performance of a given task (e.g. predicting contextual words) are sub-optimal for other applications. In this paper, we address this problem by exploiting task-specific representations, induced via embedding sub-space projection. This allows us to expand lexicons describing multiple semantic properties. For each property, our model jointly learns suitable representations and the concomitant predictor. Experiments conducted over multiple subjective lexicons, show that our model outperforms previous work and other baselines; even in low training data regimes. Furthermore, lexicon-based sentiment classifiers built on top of our lexicons outperform similar resources and yield performances comparable to those of supervised models. |
| Tasks | Word Embeddings |
| Published | 2016-12-31 |
| URL | http://arxiv.org/abs/1701.00145v2 |
| http://arxiv.org/pdf/1701.00145v2.pdf | |
| PWC | https://paperswithcode.com/paper/expanding-subjective-lexicons-for-social |
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Getting More Out Of Syntax with PropS
| Title | Getting More Out Of Syntax with PropS |
| Authors | Gabriel Stanovsky, Jessica Ficler, Ido Dagan, Yoav Goldberg |
| Abstract | Semantic NLP applications often rely on dependency trees to recognize major elements of the proposition structure of sentences. Yet, while much semantic structure is indeed expressed by syntax, many phenomena are not easily read out of dependency trees, often leading to further ad-hoc heuristic post-processing or to information loss. To directly address the needs of semantic applications, we present PropS – an output representation designed to explicitly and uniformly express much of the proposition structure which is implied from syntax, and an associated tool for extracting it from dependency trees. |
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| Published | 2016-03-04 |
| URL | http://arxiv.org/abs/1603.01648v1 |
| http://arxiv.org/pdf/1603.01648v1.pdf | |
| PWC | https://paperswithcode.com/paper/getting-more-out-of-syntax-with-props |
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Data driven estimation of Laplace-Beltrami operator
| Title | Data driven estimation of Laplace-Beltrami operator |
| Authors | Frédéric Chazal, Ilaria Giulini, Bertrand Michel |
| Abstract | Approximations of Laplace-Beltrami operators on manifolds through graph Lapla-cians have become popular tools in data analysis and machine learning. These discretized operators usually depend on bandwidth parameters whose tuning remains a theoretical and practical problem. In this paper, we address this problem for the unnormalized graph Laplacian by establishing an oracle inequality that opens the door to a well-founded data-driven procedure for the bandwidth selection. Our approach relies on recent results by Lacour and Massart [LM15] on the so-called Lepski’s method. |
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| Published | 2016-12-30 |
| URL | http://arxiv.org/abs/1612.09434v1 |
| http://arxiv.org/pdf/1612.09434v1.pdf | |
| PWC | https://paperswithcode.com/paper/data-driven-estimation-of-laplace-beltrami |
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