Paper Group ANR 14
Preference at First Sight. Fast model selection by limiting SVM training times. Bayesian generalized fused lasso modeling via NEG distribution. Deformably Registering and Annotating Whole CLARITY Brains to an Atlas via Masked LDDMM. Linguistically Regularized LSTMs for Sentiment Classification. Sequential Person Recognition in Photo Albums with a R …
Preference at First Sight
| Title | Preference at First Sight |
| Authors | Chanjuan Liu |
| Abstract | We consider decision-making and game scenarios in which an agent is limited by his/her computational ability to foresee all the available moves towards the future - that is, we study scenarios with short sight. We focus on how short sight affects the logical properties of decision making in multi-agent settings. We start with single-agent sequential decision making (SSDM) processes, modeling them by a new structure of “preference-sight trees”. Using this model, we first explore the relation between a new natural solution concept of Sight-Compatible Backward Induction (SCBI) and the histories produced by classical Backward Induction (BI). In particular, we find necessary and sufficient conditions for the two analyses to be equivalent. Next, we study whether larger sight always contributes to better outcomes. Then we develop a simple logical special-purpose language to formally express some key properties of our preference-sight models. Lastly, we show how short-sight SSDM scenarios call for substantial enrichments of existing fixed-point logics that have been developed for the classical BI solution concept. We also discuss changes in earlier modal logics expressing “surface reasoning” about best actions in the presence of short sight. Our analysis may point the way to logical and computational analysis of more realistic game models. |
| Tasks | Decision Making |
| Published | 2016-06-24 |
| URL | http://arxiv.org/abs/1606.07524v1 |
| http://arxiv.org/pdf/1606.07524v1.pdf | |
| PWC | https://paperswithcode.com/paper/preference-at-first-sight |
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Fast model selection by limiting SVM training times
| Title | Fast model selection by limiting SVM training times |
| Authors | Aydin Demircioglu, Daniel Horn, Tobias Glasmachers, Bernd Bischl, Claus Weihs |
| Abstract | Kernelized Support Vector Machines (SVMs) are among the best performing supervised learning methods. But for optimal predictive performance, time-consuming parameter tuning is crucial, which impedes application. To tackle this problem, the classic model selection procedure based on grid-search and cross-validation was refined, e.g. by data subsampling and direct search heuristics. Here we focus on a different aspect, the stopping criterion for SVM training. We show that by limiting the training time given to the SVM solver during parameter tuning we can reduce model selection times by an order of magnitude. |
| Tasks | Model Selection |
| Published | 2016-02-10 |
| URL | http://arxiv.org/abs/1602.03368v1 |
| http://arxiv.org/pdf/1602.03368v1.pdf | |
| PWC | https://paperswithcode.com/paper/fast-model-selection-by-limiting-svm-training |
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Bayesian generalized fused lasso modeling via NEG distribution
| Title | Bayesian generalized fused lasso modeling via NEG distribution |
| Authors | Kaito Shimamura, Masao Ueki, Shuichi Kawano, Sadanori Konishi |
| Abstract | The fused lasso penalizes a loss function by the $L_1$ norm for both the regression coefficients and their successive differences to encourage sparsity of both. In this paper, we propose a Bayesian generalized fused lasso modeling based on a normal-exponential-gamma (NEG) prior distribution. The NEG prior is assumed into the difference of successive regression coefficients. The proposed method enables us to construct a more versatile sparse model than the ordinary fused lasso by using a flexible regularization term. We also propose a sparse fused algorithm to produce exact sparse solutions. Simulation studies and real data analyses show that the proposed method has superior performance to the ordinary fused lasso. |
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| Published | 2016-02-16 |
| URL | http://arxiv.org/abs/1602.04910v1 |
| http://arxiv.org/pdf/1602.04910v1.pdf | |
| PWC | https://paperswithcode.com/paper/bayesian-generalized-fused-lasso-modeling-via |
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Deformably Registering and Annotating Whole CLARITY Brains to an Atlas via Masked LDDMM
| Title | Deformably Registering and Annotating Whole CLARITY Brains to an Atlas via Masked LDDMM |
| Authors | Kwame S. Kutten, Joshua T. Vogelstein, Nicolas Charon, Li Ye, Karl Deisseroth, Michael I. Miller |
| Abstract | The CLARITY method renders brains optically transparent to enable high-resolution imaging in the structurally intact brain. Anatomically annotating CLARITY brains is necessary for discovering which regions contain signals of interest. Manually annotating whole-brain, terabyte CLARITY images is difficult, time-consuming, subjective, and error-prone. Automatically registering CLARITY images to a pre-annotated brain atlas offers a solution, but is difficult for several reasons. Removal of the brain from the skull and subsequent storage and processing cause variable non-rigid deformations, thus compounding inter-subject anatomical variability. Additionally, the signal in CLARITY images arises from various biochemical contrast agents which only sparsely label brain structures. This sparse labeling challenges the most commonly used registration algorithms that need to match image histogram statistics to the more densely labeled histological brain atlases. The standard method is a multiscale Mutual Information B-spline algorithm that dynamically generates an average template as an intermediate registration target. We determined that this method performs poorly when registering CLARITY brains to the Allen Institute’s Mouse Reference Atlas (ARA), because the image histogram statistics are poorly matched. Therefore, we developed a method (Mask-LDDMM) for registering CLARITY images, that automatically find the brain boundary and learns the optimal deformation between the brain and atlas masks. Using Mask-LDDMM without an average template provided better results than the standard approach when registering CLARITY brains to the ARA. The LDDMM pipelines developed here provide a fast automated way to anatomically annotate CLARITY images. Our code is available as open source software at http://NeuroData.io. |
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| Published | 2016-05-06 |
| URL | http://arxiv.org/abs/1605.02060v1 |
| http://arxiv.org/pdf/1605.02060v1.pdf | |
| PWC | https://paperswithcode.com/paper/deformably-registering-and-annotating-whole |
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Linguistically Regularized LSTMs for Sentiment Classification
| Title | Linguistically Regularized LSTMs for Sentiment Classification |
| Authors | Qiao Qian, Minlie Huang, Jinhao Lei, Xiaoyan Zhu |
| Abstract | Sentiment understanding has been a long-term goal of AI in the past decades. This paper deals with sentence-level sentiment classification. Though a variety of neural network models have been proposed very recently, however, previous models either depend on expensive phrase-level annotation, whose performance drops substantially when trained with only sentence-level annotation; or do not fully employ linguistic resources (e.g., sentiment lexicons, negation words, intensity words), thus not being able to produce linguistically coherent representations. In this paper, we propose simple models trained with sentence-level annotation, but also attempt to generating linguistically coherent representations by employing regularizers that model the linguistic role of sentiment lexicons, negation words, and intensity words. Results show that our models are effective to capture the sentiment shifting effect of sentiment, negation, and intensity words, while still obtain competitive results without sacrificing the models’ simplicity. |
| Tasks | Sentiment Analysis |
| Published | 2016-11-12 |
| URL | http://arxiv.org/abs/1611.03949v2 |
| http://arxiv.org/pdf/1611.03949v2.pdf | |
| PWC | https://paperswithcode.com/paper/linguistically-regularized-lstms-for |
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Sequential Person Recognition in Photo Albums with a Recurrent Network
| Title | Sequential Person Recognition in Photo Albums with a Recurrent Network |
| Authors | Yao Li, Guosheng Lin, Bohan Zhuang, Lingqiao Liu, Chunhua Shen, Anton van den Hengel |
| Abstract | Recognizing the identities of people in everyday photos is still a very challenging problem for machine vision, due to non-frontal faces, changes in clothing, location, lighting and similar. Recent studies have shown that rich relational information between people in the same photo can help in recognizing their identities. In this work, we propose to model the relational information between people as a sequence prediction task. At the core of our work is a novel recurrent network architecture, in which relational information between instances’ labels and appearance are modeled jointly. In addition to relational cues, scene context is incorporated in our sequence prediction model with no additional cost. In this sense, our approach is a unified framework for modeling both contextual cues and visual appearance of person instances. Our model is trained end-to-end with a sequence of annotated instances in a photo as inputs, and a sequence of corresponding labels as targets. We demonstrate that this simple but elegant formulation achieves state-of-the-art performance on the newly released People In Photo Albums (PIPA) dataset. |
| Tasks | Person Recognition |
| Published | 2016-11-30 |
| URL | http://arxiv.org/abs/1611.09967v1 |
| http://arxiv.org/pdf/1611.09967v1.pdf | |
| PWC | https://paperswithcode.com/paper/sequential-person-recognition-in-photo-albums |
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Cortical Computation via Iterative Constructions
| Title | Cortical Computation via Iterative Constructions |
| Authors | Christos Papadimitrou, Samantha Petti, Santosh Vempala |
| Abstract | We study Boolean functions of an arbitrary number of input variables that can be realized by simple iterative constructions based on constant-size primitives. This restricted type of construction needs little global coordination or control and thus is a candidate for neurally feasible computation. Valiant’s construction of a majority function can be realized in this manner and, as we show, can be generalized to any uniform threshold function. We study the rate of convergence, finding that while linear convergence to the correct function can be achieved for any threshold using a fixed set of primitives, for quadratic convergence, the size of the primitives must grow as the threshold approaches 0 or 1. We also study finite realizations of this process and the learnability of the functions realized. We show that the constructions realized are accurate outside a small interval near the target threshold, where the size of the construction grows as the inverse square of the interval width. This phenomenon, that errors are higher closer to thresholds (and thresholds closer to the boundary are harder to represent), is a well-known cognitive finding. |
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| Published | 2016-02-26 |
| URL | http://arxiv.org/abs/1602.08357v2 |
| http://arxiv.org/pdf/1602.08357v2.pdf | |
| PWC | https://paperswithcode.com/paper/cortical-computation-via-iterative |
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Efficient Global Point Cloud Alignment using Bayesian Nonparametric Mixtures
| Title | Efficient Global Point Cloud Alignment using Bayesian Nonparametric Mixtures |
| Authors | Julian Straub, Trevor Campbell, Jonathan P. How, John W. Fisher III |
| Abstract | Point cloud alignment is a common problem in computer vision and robotics, with applications ranging from 3D object recognition to reconstruction. We propose a novel approach to the alignment problem that utilizes Bayesian nonparametrics to describe the point cloud and surface normal densities, and branch and bound (BB) optimization to recover the relative transformation. BB uses a novel, refinable, near-uniform tessellation of rotation space using 4D tetrahedra, leading to more efficient optimization compared to the common axis-angle tessellation. We provide objective function bounds for pruning given the proposed tessellation, and prove that BB converges to the optimum of the cost function along with providing its computational complexity. Finally, we empirically demonstrate the efficiency of the proposed approach as well as its robustness to real-world conditions such as missing data and partial overlap. |
| Tasks | 3D Object Recognition, Object Recognition |
| Published | 2016-03-15 |
| URL | http://arxiv.org/abs/1603.04868v3 |
| http://arxiv.org/pdf/1603.04868v3.pdf | |
| PWC | https://paperswithcode.com/paper/efficient-global-point-cloud-alignment-using |
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Active Learning On Weighted Graphs Using Adaptive And Non-adaptive Approaches
| Title | Active Learning On Weighted Graphs Using Adaptive And Non-adaptive Approaches |
| Authors | Eyal En Gad, Akshay Gadde, A. Salman Avestimehr, Antonio Ortega |
| Abstract | This paper studies graph-based active learning, where the goal is to reconstruct a binary signal defined on the nodes of a weighted graph, by sampling it on a small subset of the nodes. A new sampling algorithm is proposed, which sequentially selects the graph nodes to be sampled, based on an aggressive search for the boundary of the signal over the graph. The algorithm generalizes a recent method for sampling nodes in unweighted graphs. The generalization improves the sampling performance using the information gained from the available graph weights. An analysis of the number of samples required by the proposed algorithm is provided, and the gain over the unweighted method is further demonstrated in simulations. Additionally, the proposed method is compared with an alternative state of-the-art method, which is based on the graph’s spectral properties. It is shown that the proposed method significantly outperforms the spectral sampling method, if the signal needs to be predicted with high accuracy. On the other hand, if a higher level of inaccuracy is tolerable, then the spectral method outperforms the proposed aggressive search method. Consequently, we propose a hybrid method, which is shown to combine the advantages of both approaches. |
| Tasks | Active Learning |
| Published | 2016-05-18 |
| URL | http://arxiv.org/abs/1605.05710v1 |
| http://arxiv.org/pdf/1605.05710v1.pdf | |
| PWC | https://paperswithcode.com/paper/active-learning-on-weighted-graphs-using |
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Using Reinforcement Learning to Validate Empirical Game-Theoretic Analysis: A Continuous Double Auction Study
| Title | Using Reinforcement Learning to Validate Empirical Game-Theoretic Analysis: A Continuous Double Auction Study |
| Authors | Mason Wright |
| Abstract | Empirical game-theoretic analysis (EGTA) has recently been applied successfully to analyze the behavior of large numbers of competing traders in a continuous double auction market. Multiagent simulation methods like EGTA are useful for studying complex strategic environments like a stock market, where it is not feasible to solve analytically for the rational behavior of each agent. A weakness of simulation-based methods in strategic settings, however, is that it is typically impossible to prove that the strategy profile assigned to the simulated agents is stable, as in a Nash equilibrium. I propose using reinforcement learning to analyze the regret of supposed Nash-equilibrium strategy profiles found by EGTA. I have developed a new library of reinforcement learning tools, which I have integrated into an extended version of the market simulator from our prior work. I provide evidence for the effectiveness of our library methods, both on a suite of benchmark problems from the literature, and on non-equilibrium strategy profiles in our market environment. Finally, I use our new reinforcement learning tools to provide evidence that the equilibria found by EGTA in our recent continuous double auction study are likely to have only negligible regret, even with respect to an extended strategy space. |
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| Published | 2016-04-22 |
| URL | http://arxiv.org/abs/1604.06710v1 |
| http://arxiv.org/pdf/1604.06710v1.pdf | |
| PWC | https://paperswithcode.com/paper/using-reinforcement-learning-to-validate |
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Personalized Federated Search at LinkedIn
| Title | Personalized Federated Search at LinkedIn |
| Authors | Dhruv Arya, Viet Ha-Thuc, Shakti Sinha |
| Abstract | LinkedIn has grown to become a platform hosting diverse sources of information ranging from member profiles, jobs, professional groups, slideshows etc. Given the existence of multiple sources, when a member issues a query like “software engineer”, the member could look for software engineer profiles, jobs or professional groups. To tackle this problem, we exploit a data-driven approach that extracts searcher intents from their profile data and recent activities at a large scale. The intents such as job seeking, hiring, content consuming are used to construct features to personalize federated search experience. We tested the approach on the LinkedIn homepage and A/B tests show significant improvements in member engagement. As of writing this paper, the approach powers all of federated search on LinkedIn homepage. |
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| Published | 2016-02-16 |
| URL | http://arxiv.org/abs/1602.04924v1 |
| http://arxiv.org/pdf/1602.04924v1.pdf | |
| PWC | https://paperswithcode.com/paper/personalized-federated-search-at-linkedin |
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Personalized Expertise Search at LinkedIn
| Title | Personalized Expertise Search at LinkedIn |
| Authors | Viet Ha-Thuc, Ganesh Venkataraman, Mario Rodriguez, Shakti Sinha, Senthil Sundaram, Lin Guo |
| Abstract | LinkedIn is the largest professional network with more than 350 million members. As the member base increases, searching for experts becomes more and more challenging. In this paper, we propose an approach to address the problem of personalized expertise search on LinkedIn, particularly for exploratory search queries containing {\it skills}. In the offline phase, we introduce a collaborative filtering approach based on matrix factorization. Our approach estimates expertise scores for both the skills that members list on their profiles as well as the skills they are likely to have but do not explicitly list. In the online phase (at query time) we use expertise scores on these skills as a feature in combination with other features to rank the results. To learn the personalized ranking function, we propose a heuristic to extract training data from search logs while handling position and sample selection biases. We tested our models on two products - LinkedIn homepage and LinkedIn recruiter. A/B tests showed significant improvements in click through rates - 31% for CTR@1 for recruiter (18% for homepage) as well as downstream messages sent from search - 37% for recruiter (20% for homepage). As of writing this paper, these models serve nearly all live traffic for skills search on LinkedIn homepage as well as LinkedIn recruiter. |
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| Published | 2016-02-15 |
| URL | http://arxiv.org/abs/1602.04572v1 |
| http://arxiv.org/pdf/1602.04572v1.pdf | |
| PWC | https://paperswithcode.com/paper/personalized-expertise-search-at-linkedin |
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Generalization Bounds for Weighted Automata
| Title | Generalization Bounds for Weighted Automata |
| Authors | Borja Balle, Mehryar Mohri |
| Abstract | This paper studies the problem of learning weighted automata from a finite labeled training sample. We consider several general families of weighted automata defined in terms of three different measures: the norm of an automaton’s weights, the norm of the function computed by an automaton, or the norm of the corresponding Hankel matrix. We present new data-dependent generalization guarantees for learning weighted automata expressed in terms of the Rademacher complexity of these families. We further present upper bounds on these Rademacher complexities, which reveal key new data-dependent terms related to the complexity of learning weighted automata. |
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| Published | 2016-10-25 |
| URL | http://arxiv.org/abs/1610.07883v1 |
| http://arxiv.org/pdf/1610.07883v1.pdf | |
| PWC | https://paperswithcode.com/paper/generalization-bounds-for-weighted-automata |
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A Multi-Modal Graph-Based Semi-Supervised Pipeline for Predicting Cancer Survival
| Title | A Multi-Modal Graph-Based Semi-Supervised Pipeline for Predicting Cancer Survival |
| Authors | Hamid Reza Hassanzadeh, John H. Phan, May D. Wang |
| Abstract | Cancer survival prediction is an active area of research that can help prevent unnecessary therapies and improve patient’s quality of life. Gene expression profiling is being widely used in cancer studies to discover informative biomarkers that aid predict different clinical endpoint prediction. We use multiple modalities of data derived from RNA deep-sequencing (RNA-seq) to predict survival of cancer patients. Despite the wealth of information available in expression profiles of cancer tumors, fulfilling the aforementioned objective remains a big challenge, for the most part, due to the paucity of data samples compared to the high dimension of the expression profiles. As such, analysis of transcriptomic data modalities calls for state-of-the-art big-data analytics techniques that can maximally use all the available data to discover the relevant information hidden within a significant amount of noise. In this paper, we propose a pipeline that predicts cancer patients’ survival by exploiting the structure of the input (manifold learning) and by leveraging the unlabeled samples using Laplacian support vector machines, a graph-based semi supervised learning (GSSL) paradigm. We show that under certain circumstances, no single modality per se will result in the best accuracy and by fusing different models together via a stacked generalization strategy, we may boost the accuracy synergistically. We apply our approach to two cancer datasets and present promising results. We maintain that a similar pipeline can be used for predictive tasks where labeled samples are expensive to acquire. |
| Tasks | |
| Published | 2016-11-17 |
| URL | http://arxiv.org/abs/1611.05751v1 |
| http://arxiv.org/pdf/1611.05751v1.pdf | |
| PWC | https://paperswithcode.com/paper/a-multi-modal-graph-based-semi-supervised |
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Learning conditional independence structure for high-dimensional uncorrelated vector processes
| Title | Learning conditional independence structure for high-dimensional uncorrelated vector processes |
| Authors | Nguyen Tran Quang, Alexander Jung |
| Abstract | We formulate and analyze a graphical model selection method for inferring the conditional independence graph of a high-dimensional nonstationary Gaussian random process (time series) from a finite-length observation. The observed process samples are assumed uncorrelated over time and having a time-varying marginal distribution. The selection method is based on testing conditional variances obtained for small subsets of process components. This allows to cope with the high-dimensional regime, where the sample size can be (drastically) smaller than the process dimension. We characterize the required sample size such that the proposed selection method is successful with high probability. |
| Tasks | Model Selection, Time Series |
| Published | 2016-09-13 |
| URL | http://arxiv.org/abs/1609.03772v1 |
| http://arxiv.org/pdf/1609.03772v1.pdf | |
| PWC | https://paperswithcode.com/paper/learning-conditional-independence-structure |
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