Paper Group ANR 738
Re-evaluating the need for Modelling Term-Dependence in Text Classification Problems. Estimating Individual Treatment Effect in Observational Data Using Random Forest Methods. On Nesting Monte Carlo Estimators. Attended End-to-end Architecture for Age Estimation from Facial Expression Videos. DeformNet: Free-Form Deformation Network for 3D Shape Re …
Re-evaluating the need for Modelling Term-Dependence in Text Classification Problems
| Title | Re-evaluating the need for Modelling Term-Dependence in Text Classification Problems |
| Authors | Sounak Banerjee, Prasenjit Majumder, Mandar Mitra |
| Abstract | A substantial amount of research has been carried out in developing machine learning algorithms that account for term dependence in text classification. These algorithms offer acceptable performance in most cases but they are associated with a substantial cost. They require significantly greater resources to operate. This paper argues against the justification of the higher costs of these algorithms, based on their performance in text classification problems. In order to prove the conjecture, the performance of one of the best dependence models is compared to several well established algorithms in text classification. A very specific collection of datasets have been designed, which would best reflect the disparity in the nature of text data, that are present in real world applications. The results show that even one of the best term dependence models, performs decent at best when compared to other independence models. Coupled with their substantially greater requirement for hardware resources for operation, this makes them an impractical choice for being used in real world scenarios. |
| Tasks | Text Classification |
| Published | 2017-10-25 |
| URL | http://arxiv.org/abs/1710.09085v1 |
| http://arxiv.org/pdf/1710.09085v1.pdf | |
| PWC | https://paperswithcode.com/paper/re-evaluating-the-need-for-modelling-term |
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Estimating Individual Treatment Effect in Observational Data Using Random Forest Methods
| Title | Estimating Individual Treatment Effect in Observational Data Using Random Forest Methods |
| Authors | Min Lu, Saad Sadiq, Daniel J. Feaster, Hemant Ishwaran |
| Abstract | Estimation of individual treatment effect in observational data is complicated due to the challenges of confounding and selection bias. A useful inferential framework to address this is the counterfactual (potential outcomes) model which takes the hypothetical stance of asking what if an individual had received both treatments. Making use of random forests (RF) within the counterfactual framework we estimate individual treatment effects by directly modeling the response. We find accurate estimation of individual treatment effects is possible even in complex heterogeneous settings but that the type of RF approach plays an important role in accuracy. Methods designed to be adaptive to confounding, when used in parallel with out-of-sample estimation, do best. One method found to be especially promising is counterfactual synthetic forests. We illustrate this new methodology by applying it to a large comparative effectiveness trial, Project Aware, in order to explore the role drug use plays in sexual risk. The analysis reveals important connections between risky behavior, drug usage, and sexual risk. |
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| Published | 2017-01-19 |
| URL | http://arxiv.org/abs/1701.05306v2 |
| http://arxiv.org/pdf/1701.05306v2.pdf | |
| PWC | https://paperswithcode.com/paper/estimating-individual-treatment-effect-in |
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On Nesting Monte Carlo Estimators
| Title | On Nesting Monte Carlo Estimators |
| Authors | Tom Rainforth, Robert Cornish, Hongseok Yang, Andrew Warrington, Frank Wood |
| Abstract | Many problems in machine learning and statistics involve nested expectations and thus do not permit conventional Monte Carlo (MC) estimation. For such problems, one must nest estimators, such that terms in an outer estimator themselves involve calculation of a separate, nested, estimation. We investigate the statistical implications of nesting MC estimators, including cases of multiple levels of nesting, and establish the conditions under which they converge. We derive corresponding rates of convergence and provide empirical evidence that these rates are observed in practice. We further establish a number of pitfalls that can arise from naive nesting of MC estimators, provide guidelines about how these can be avoided, and lay out novel methods for reformulating certain classes of nested expectation problems into single expectations, leading to improved convergence rates. We demonstrate the applicability of our work by using our results to develop a new estimator for discrete Bayesian experimental design problems and derive error bounds for a class of variational objectives. |
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| Published | 2017-09-18 |
| URL | http://arxiv.org/abs/1709.06181v4 |
| http://arxiv.org/pdf/1709.06181v4.pdf | |
| PWC | https://paperswithcode.com/paper/on-nesting-monte-carlo-estimators |
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Attended End-to-end Architecture for Age Estimation from Facial Expression Videos
| Title | Attended End-to-end Architecture for Age Estimation from Facial Expression Videos |
| Authors | Wenjie Pei, Hamdi Dibeklioğlu, Tadas Baltrušaitis, David M. J. Tax |
| Abstract | The main challenges of age estimation from facial expression videos lie not only in the modeling of the static facial appearance, but also in the capturing of the temporal facial dynamics. Traditional techniques to this problem focus on constructing handcrafted features to explore the discriminative information contained in facial appearance and dynamics separately. This relies on sophisticated feature-refinement and framework-design. In this paper, we present an end-to-end architecture for age estimation, called Spatially-Indexed Attention Model (SIAM), which is able to simultaneously learn both the appearance and dynamics of age from raw videos of facial expressions. Specifically, we employ convolutional neural networks to extract effective latent appearance representations and feed them into recurrent networks to model the temporal dynamics. More importantly, we propose to leverage attention models for salience detection in both the spatial domain for each single image and the temporal domain for the whole video as well. We design a specific spatially-indexed attention mechanism among the convolutional layers to extract the salient facial regions in each individual image, and a temporal attention layer to assign attention weights to each frame. This two-pronged approach not only improves the performance by allowing the model to focus on informative frames and facial areas, but it also offers an interpretable correspondence between the spatial facial regions as well as temporal frames, and the task of age estimation. We demonstrate the strong performance of our model in experiments on a large, gender-balanced database with 400 subjects with ages spanning from 8 to 76 years. Experiments reveal that our model exhibits significant superiority over the state-of-the-art methods given sufficient training data. |
| Tasks | Age Estimation |
| Published | 2017-11-23 |
| URL | https://arxiv.org/abs/1711.08690v2 |
| https://arxiv.org/pdf/1711.08690v2.pdf | |
| PWC | https://paperswithcode.com/paper/attended-end-to-end-architecture-for-age |
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DeformNet: Free-Form Deformation Network for 3D Shape Reconstruction from a Single Image
| Title | DeformNet: Free-Form Deformation Network for 3D Shape Reconstruction from a Single Image |
| Authors | Andrey Kurenkov, Jingwei Ji, Animesh Garg, Viraj Mehta, JunYoung Gwak, Christopher Choy, Silvio Savarese |
| Abstract | 3D reconstruction from a single image is a key problem in multiple applications ranging from robotic manipulation to augmented reality. Prior methods have tackled this problem through generative models which predict 3D reconstructions as voxels or point clouds. However, these methods can be computationally expensive and miss fine details. We introduce a new differentiable layer for 3D data deformation and use it in DeformNet to learn a model for 3D reconstruction-through-deformation. DeformNet takes an image input, searches the nearest shape template from a database, and deforms the template to match the query image. We evaluate our approach on the ShapeNet dataset and show that - (a) the Free-Form Deformation layer is a powerful new building block for Deep Learning models that manipulate 3D data (b) DeformNet uses this FFD layer combined with shape retrieval for smooth and detail-preserving 3D reconstruction of qualitatively plausible point clouds with respect to a single query image (c) compared to other state-of-the-art 3D reconstruction methods, DeformNet quantitatively matches or outperforms their benchmarks by significant margins. For more information, visit: https://deformnet-site.github.io/DeformNet-website/ . |
| Tasks | 3D Reconstruction |
| Published | 2017-08-11 |
| URL | http://arxiv.org/abs/1708.04672v1 |
| http://arxiv.org/pdf/1708.04672v1.pdf | |
| PWC | https://paperswithcode.com/paper/deformnet-free-form-deformation-network-for |
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Multi-dueling Bandits with Dependent Arms
| Title | Multi-dueling Bandits with Dependent Arms |
| Authors | Yanan Sui, Vincent Zhuang, Joel W. Burdick, Yisong Yue |
| Abstract | The dueling bandits problem is an online learning framework for learning from pairwise preference feedback, and is particularly well-suited for modeling settings that elicit subjective or implicit human feedback. In this paper, we study the problem of multi-dueling bandits with dependent arms, which extends the original dueling bandits setting by simultaneously dueling multiple arms as well as modeling dependencies between arms. These extensions capture key characteristics found in many real-world applications, and allow for the opportunity to develop significantly more efficient algorithms than were possible in the original setting. We propose the \selfsparring algorithm, which reduces the multi-dueling bandits problem to a conventional bandit setting that can be solved using a stochastic bandit algorithm such as Thompson Sampling, and can naturally model dependencies using a Gaussian process prior. We present a no-regret analysis for multi-dueling setting, and demonstrate the effectiveness of our algorithm empirically on a wide range of simulation settings. |
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| Published | 2017-04-29 |
| URL | http://arxiv.org/abs/1705.00253v1 |
| http://arxiv.org/pdf/1705.00253v1.pdf | |
| PWC | https://paperswithcode.com/paper/multi-dueling-bandits-with-dependent-arms |
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Learning to Extract Semantic Structure from Documents Using Multimodal Fully Convolutional Neural Network
| Title | Learning to Extract Semantic Structure from Documents Using Multimodal Fully Convolutional Neural Network |
| Authors | Xiao Yang, Ersin Yumer, Paul Asente, Mike Kraley, Daniel Kifer, C. Lee Giles |
| Abstract | We present an end-to-end, multimodal, fully convolutional network for extracting semantic structures from document images. We consider document semantic structure extraction as a pixel-wise segmentation task, and propose a unified model that classifies pixels based not only on their visual appearance, as in the traditional page segmentation task, but also on the content of underlying text. Moreover, we propose an efficient synthetic document generation process that we use to generate pretraining data for our network. Once the network is trained on a large set of synthetic documents, we fine-tune the network on unlabeled real documents using a semi-supervised approach. We systematically study the optimum network architecture and show that both our multimodal approach and the synthetic data pretraining significantly boost the performance. |
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| Published | 2017-06-07 |
| URL | http://arxiv.org/abs/1706.02337v1 |
| http://arxiv.org/pdf/1706.02337v1.pdf | |
| PWC | https://paperswithcode.com/paper/learning-to-extract-semantic-structure-from |
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PPR-FCN: Weakly Supervised Visual Relation Detection via Parallel Pairwise R-FCN
| Title | PPR-FCN: Weakly Supervised Visual Relation Detection via Parallel Pairwise R-FCN |
| Authors | Hanwang Zhang, Zawlin Kyaw, Jinyang Yu, Shih-Fu Chang |
| Abstract | We aim to tackle a novel vision task called Weakly Supervised Visual Relation Detection (WSVRD) to detect “subject-predicate-object” relations in an image with object relation groundtruths available only at the image level. This is motivated by the fact that it is extremely expensive to label the combinatorial relations between objects at the instance level. Compared to the extensively studied problem, Weakly Supervised Object Detection (WSOD), WSVRD is more challenging as it needs to examine a large set of regions pairs, which is computationally prohibitive and more likely stuck in a local optimal solution such as those involving wrong spatial context. To this end, we present a Parallel, Pairwise Region-based, Fully Convolutional Network (PPR-FCN) for WSVRD. It uses a parallel FCN architecture that simultaneously performs pair selection and classification of single regions and region pairs for object and relation detection, while sharing almost all computation shared over the entire image. In particular, we propose a novel position-role-sensitive score map with pairwise RoI pooling to efficiently capture the crucial context associated with a pair of objects. We demonstrate the superiority of PPR-FCN over all baselines in solving the WSVRD challenge by using results of extensive experiments over two visual relation benchmarks. |
| Tasks | Object Detection, Weakly Supervised Object Detection |
| Published | 2017-08-07 |
| URL | http://arxiv.org/abs/1708.01956v1 |
| http://arxiv.org/pdf/1708.01956v1.pdf | |
| PWC | https://paperswithcode.com/paper/ppr-fcn-weakly-supervised-visual-relation |
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Stochastic Planning and Lifted Inference
| Title | Stochastic Planning and Lifted Inference |
| Authors | Roni Khardon, Scott Sanner |
| Abstract | Lifted probabilistic inference (Poole, 2003) and symbolic dynamic programming for lifted stochastic planning (Boutilier et al, 2001) were introduced around the same time as algorithmic efforts to use abstraction in stochastic systems. Over the years, these ideas evolved into two distinct lines of research, each supported by a rich literature. Lifted probabilistic inference focused on efficient arithmetic operations on template-based graphical models under a finite domain assumption while symbolic dynamic programming focused on supporting sequential decision-making in rich quantified logical action models and on open domain reasoning. Given their common motivation but different focal points, both lines of research have yielded highly complementary innovations. In this chapter, we aim to help close the gap between these two research areas by providing an overview of lifted stochastic planning from the perspective of probabilistic inference, showing strong connections to other chapters in this book. This also allows us to define Generalized Lifted Inference as a paradigm that unifies these areas and elucidates open problems for future research that can benefit both lifted inference and stochastic planning. |
| Tasks | Decision Making |
| Published | 2017-01-04 |
| URL | http://arxiv.org/abs/1701.01048v1 |
| http://arxiv.org/pdf/1701.01048v1.pdf | |
| PWC | https://paperswithcode.com/paper/stochastic-planning-and-lifted-inference |
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Learning from Noisy Label Distributions
| Title | Learning from Noisy Label Distributions |
| Authors | Yuya Yoshikawa |
| Abstract | In this paper, we consider a novel machine learning problem, that is, learning a classifier from noisy label distributions. In this problem, each instance with a feature vector belongs to at least one group. Then, instead of the true label of each instance, we observe the label distribution of the instances associated with a group, where the label distribution is distorted by an unknown noise. Our goals are to (1) estimate the true label of each instance, and (2) learn a classifier that predicts the true label of a new instance. We propose a probabilistic model that considers true label distributions of groups and parameters that represent the noise as hidden variables. The model can be learned based on a variational Bayesian method. In numerical experiments, we show that the proposed model outperforms existing methods in terms of the estimation of the true labels of instances. |
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| Published | 2017-08-11 |
| URL | http://arxiv.org/abs/1708.04529v1 |
| http://arxiv.org/pdf/1708.04529v1.pdf | |
| PWC | https://paperswithcode.com/paper/learning-from-noisy-label-distributions |
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Deep Temporal-Recurrent-Replicated-Softmax for Topical Trends over Time
| Title | Deep Temporal-Recurrent-Replicated-Softmax for Topical Trends over Time |
| Authors | Pankaj Gupta, Subburam Rajaram, Hinrich Schütze, Bernt Andrassy |
| Abstract | Dynamic topic modeling facilitates the identification of topical trends over time in temporal collections of unstructured documents. We introduce a novel unsupervised neural dynamic topic model named as Recurrent Neural Network-Replicated Softmax Model (RNNRSM), where the discovered topics at each time influence the topic discovery in the subsequent time steps. We account for the temporal ordering of documents by explicitly modeling a joint distribution of latent topical dependencies over time, using distributional estimators with temporal recurrent connections. Applying RNN-RSM to 19 years of articles on NLP research, we demonstrate that compared to state-of-the art topic models, RNNRSM shows better generalization, topic interpretation, evolution and trends. We also introduce a metric (named as SPAN) to quantify the capability of dynamic topic model to capture word evolution in topics over time. |
| Tasks | Topic Models |
| Published | 2017-11-15 |
| URL | http://arxiv.org/abs/1711.05626v2 |
| http://arxiv.org/pdf/1711.05626v2.pdf | |
| PWC | https://paperswithcode.com/paper/deep-temporal-recurrent-replicated-softmax |
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On the Robustness of Semantic Segmentation Models to Adversarial Attacks
| Title | On the Robustness of Semantic Segmentation Models to Adversarial Attacks |
| Authors | Anurag Arnab, Ondrej Miksik, Philip H. S. Torr |
| Abstract | Deep Neural Networks (DNNs) have demonstrated exceptional performance on most recognition tasks such as image classification and segmentation. However, they have also been shown to be vulnerable to adversarial examples. This phenomenon has recently attracted a lot of attention but it has not been extensively studied on multiple, large-scale datasets and structured prediction tasks such as semantic segmentation which often require more specialised networks with additional components such as CRFs, dilated convolutions, skip-connections and multiscale processing. In this paper, we present what to our knowledge is the first rigorous evaluation of adversarial attacks on modern semantic segmentation models, using two large-scale datasets. We analyse the effect of different network architectures, model capacity and multiscale processing, and show that many observations made on the task of classification do not always transfer to this more complex task. Furthermore, we show how mean-field inference in deep structured models, multiscale processing (and more generally, input transformations) naturally implement recently proposed adversarial defenses. Our observations will aid future efforts in understanding and defending against adversarial examples. Moreover, in the shorter term, we show how to effectively benchmark robustness and show which segmentation models should currently be preferred in safety-critical applications due to their inherent robustness. |
| Tasks | Image Classification, Semantic Segmentation, Structured Prediction |
| Published | 2017-11-27 |
| URL | http://arxiv.org/abs/1711.09856v3 |
| http://arxiv.org/pdf/1711.09856v3.pdf | |
| PWC | https://paperswithcode.com/paper/on-the-robustness-of-semantic-segmentation |
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Optimal Threshold Design for Quanta Image Sensor
| Title | Optimal Threshold Design for Quanta Image Sensor |
| Authors | Omar A. Elgendy, Stanley H. Chan |
| Abstract | Quanta Image Sensor (QIS) is a binary imaging device envisioned to be the next generation image sensor after CCD and CMOS. Equipped with a massive number of single photon detectors, the sensor has a threshold $q$ above which the number of arriving photons will trigger a binary response “1”, or “0” otherwise. Existing methods in the device literature typically assume that $q=1$ uniformly. We argue that a spatially varying threshold can significantly improve the signal-to-noise ratio of the reconstructed image. In this paper, we present an optimal threshold design framework. We make two contributions. First, we derive a set of oracle results to theoretically inform the maximally achievable performance. We show that the oracle threshold should match exactly with the underlying pixel intensity. Second, we show that around the oracle threshold there exists a set of thresholds that give asymptotically unbiased reconstructions. The asymptotic unbiasedness has a phase transition behavior which allows us to develop a practical threshold update scheme using a bisection method. Experimentally, the new threshold design method achieves better rate of convergence than existing methods. |
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| Published | 2017-04-12 |
| URL | http://arxiv.org/abs/1704.03886v2 |
| http://arxiv.org/pdf/1704.03886v2.pdf | |
| PWC | https://paperswithcode.com/paper/optimal-threshold-design-for-quanta-image |
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Warmstarting of Model-based Algorithm Configuration
| Title | Warmstarting of Model-based Algorithm Configuration |
| Authors | Marius Lindauer, Frank Hutter |
| Abstract | The performance of many hard combinatorial problem solvers depends strongly on their parameter settings, and since manual parameter tuning is both tedious and suboptimal the AI community has recently developed several algorithm configuration (AC) methods to automatically address this problem. While all existing AC methods start the configuration process of an algorithm A from scratch for each new type of benchmark instances, here we propose to exploit information about A’s performance on previous benchmarks in order to warmstart its configuration on new types of benchmarks. We introduce two complementary ways in which we can exploit this information to warmstart AC methods based on a predictive model. Experiments for optimizing a very flexible modern SAT solver on twelve different instance sets show that our methods often yield substantial speedups over existing AC methods (up to 165-fold) and can also find substantially better configurations given the same compute budget. |
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| Published | 2017-09-14 |
| URL | http://arxiv.org/abs/1709.04636v3 |
| http://arxiv.org/pdf/1709.04636v3.pdf | |
| PWC | https://paperswithcode.com/paper/warmstarting-of-model-based-algorithm |
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Random Forest regression for manifold-valued responses
| Title | Random Forest regression for manifold-valued responses |
| Authors | Dimosthenis Tsagkrasoulis, Giovanni Montana |
| Abstract | An increasing array of biomedical and computer vision applications requires the predictive modeling of complex data, for example images and shapes. The main challenge when predicting such objects lies in the fact that they do not comply to the assumptions of Euclidean geometry. Rather, they occupy non-linear spaces, a.k.a. manifolds, where it is difficult to define concepts such as coordinates, vectors and expected values. In this work, we construct a non-parametric predictive methodology for manifold-valued objects, based on a distance modification of the Random Forest algorithm. Our method is versatile and can be applied both in cases where the response space is a well-defined manifold, but also when such knowledge is not available. Model fitting and prediction phases only require the definition of a suitable distance function for the observed responses. We validate our methodology using simulations and apply it on a series of illustrative image completion applications, showcasing superior predictive performance, compared to various established regression methods. |
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| Published | 2017-01-29 |
| URL | http://arxiv.org/abs/1701.08381v2 |
| http://arxiv.org/pdf/1701.08381v2.pdf | |
| PWC | https://paperswithcode.com/paper/random-forest-regression-for-manifold-valued |
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