pwc-archive/papers-with-abstracts huggingface dataset:
Deep Spatio-Temporal Architectures and Learning for Protein Structure Prediction
Authors: Pietro D. Lena, Ken Nagata, Pierre F. Baldi
Date: 2012-12-01 00:00:00
Proceeding: NeurIPS 2012 12
Tasks: BIG-bench Machine Learning, Prediction, Protein Folding, Protein Structure Prediction

Residue-residue contact prediction is a fundamental problem in protein structure prediction. Hower, despite considerable research efforts, contact prediction methods are still largely unreliable. Here we introduce a novel deep machine-learning architecture which consists of a multidimensional stack of learning modules. For contact prediction, the idea is implemented as a three-dimensional stack of Neural Networks NN^k_{ij}, where i and j index the spatial coordinates of the contact map and k indexes ''time''. The temporal dimension is introduced to capture the fact that protein folding is not an instantaneous process, but rather a progressive refinement. Networks at level k in the stack can be trained in supervised fashion to refine the predictions produced by the previous level, hence addressing the problem of vanishing gradients, typical of deep architectures. Increased accuracy and generalization capabilities of this approach are established by rigorous comparison with other classical machine learning approaches for contact prediction. The deep approach leads to an accuracy for difficult long-range contacts of about 30%, roughly 10% above the state-of-the-art. Many variations in the architectures and the training algorithms are possible, leaving room for further improvements. Furthermore, the approach is applicable to other problems with strong underlying spatial and temporal components.
Assessing Blinding in Clinical Trials
Authors: Ognjen Arandjelovic
Date: 2012-12-01 00:00:00
Proceeding: NeurIPS 2012 12

The interaction between the patient's expected outcome of an intervention and the inherent effects of that intervention can have extraordinary effects. Thus in clinical trials an effort is made to conceal the nature of the administered intervention from the participants in the trial i.e. to blind it. Yet, in practice perfect blinding is impossible to ensure or even verify. The current standard is follow up the trial with an auxiliary questionnaire, which allows trial participants to express their belief concerning the assigned intervention and which is used to compute a measure of the extent of blinding in the trial. If the estimated extent of blinding exceeds a threshold the trial is deemed sufficiently blinded; otherwise, the trial is deemed to have failed. In this paper we make several important contributions. Firstly, we identify a series of fundamental problems of the aforesaid practice and discuss them in context of the most commonly used blinding measures. Secondly, motivated by the highlighted problems, we formulate a novel method for handling imperfectly blinded trials. We too adopt a post-trial feedback questionnaire but interpret the collected data using an original approach, fundamentally different from those previously proposed. Unlike previous approaches, ours is void of any ad hoc free parameters, is robust to small changes in auxiliary data and is not predicated on any strong assumptions used to interpret participants' feedback.
Scalable nonconvex inexact proximal splitting
Authors: Suvrit Sra
Date: 2012-12-01 00:00:00
Proceeding: NeurIPS 2012 12

We study large-scale, nonsmooth, nonconconvex optimization problems. In particular, we focus on nonconvex problems with \emph{composite} objectives. This class of problems includes the extensively studied convex, composite objective problems as a special case. To tackle composite nonconvex problems, we introduce a powerful new framework based on asymptotically \emph{nonvanishing} errors, avoiding the common convenient assumption of eventually vanishing errors. Within our framework we derive both batch and incremental nonconvex proximal splitting algorithms. To our knowledge, our framework is first to develop and analyze incremental \emph{nonconvex} proximal-splitting algorithms, even if we disregard the ability to handle nonvanishing errors. We illustrate our theoretical framework by showing how it applies to difficult large-scale, nonsmooth, and nonconvex problems.
Learning to Discover Social Circles in Ego Networks
Authors: Jure Leskovec, Julian J. McAuley
Date: 2012-12-01 00:00:00
Proceeding: NeurIPS 2012 12
Tasks: Clustering, Node Clustering

Our personal social networks are big and cluttered, and currently there is no good way to organize them. Social networking sites allow users to manually categorize their friends into social circles (e.g. `circles' on Google+, and `lists' on Facebook and Twitter), however they are laborious to construct and must be updated whenever a user's network grows. We define a novel machine learning task of identifying users' social circles. We pose the problem as a node clustering problem on a user's ego-network, a network of connections between her friends. We develop a model for detecting circles that combines network structure as well as user profile information. For each circle we learn its members and the circle-specific user profile similarity metric. Modeling node membership to multiple circles allows us to detect overlapping as well as hierarchically nested circles. Experiments show that our model accurately identifies circles on a diverse set of data from Facebook, Google+, and Twitter for all of which we obtain hand-labeled ground-truth data.
Majorization for CRFs and Latent Likelihoods
Authors: Tony Jebara, Anna Choromanska
Date: 2012-12-01 00:00:00
Proceeding: NeurIPS 2012 12

The partition function plays a key role in probabilistic modeling including conditional random fields, graphical models, and maximum likelihood estimation. To optimize partition functions, this article introduces a quadratic variational upper bound. This inequality facilitates majorization methods: optimization of complicated functions through the iterative solution of simpler sub-problems. Such bounds remain efficient to compute even when the partition function involves a graphical model (with small tree-width) or in latent likelihood settings. For large-scale problems, low-rank versions of the bound are provided and outperform LBFGS as well as first-order methods. Several learning applications are shown and reduce to fast and convergent update rules. Experimental results show advantages over state-of-the-art optimization methods.
Ensemble weighted kernel estimators for multivariate entropy estimation
Authors: Kumar Sricharan, Alfred O. Hero
Date: 2012-12-01 00:00:00
Proceeding: NeurIPS 2012 12

The problem of estimation of entropy functionals of probability densities has received much attention in the information theory, machine learning and statistics communities. Kernel density plug-in estimators are simple, easy to implement and widely used for estimation of entropy. However, kernel plug-in estimators suffer from the curse of dimensionality, wherein the MSE rate of convergence is glacially slow - of order  $O(T^{-{\gamma}/{d}})$, where $T$ is the number of samples, and $\gamma>0$ is a rate parameter. In this paper, it is shown that for sufficiently smooth densities, an ensemble of kernel plug-in estimators can be combined via a weighted convex combination, such that the resulting weighted estimator has a superior parametric MSE rate of convergence of order $O(T^{-1})$. Furthermore, it is shown that these optimal weights can be determined by solving a convex optimization problem which does not require training data or knowledge of the underlying density, and therefore can be performed offline. This novel result is remarkable in that, while each of the individual kernel plug-in estimators belonging to the ensemble suffer from the curse of dimensionality, by appropriate ensemble averaging we can achieve parametric convergence rates.
Efficient high dimensional maximum entropy modeling via symmetric partition functions
Authors: Paul Vernaza, Drew Bagnell
Date: 2012-12-01 00:00:00
Proceeding: NeurIPS 2012 12
Tasks: Vocal Bursts Intensity Prediction

The application of the maximum entropy principle to sequence   modeling has been popularized by methods such as Conditional Random   Fields (CRFs).  However, these approaches are generally limited to   modeling paths in discrete spaces of low dimensionality.  We   consider the problem of modeling distributions over paths in   continuous spaces of high dimensionality---a problem for which   inference is generally intractable.  Our main contribution is to   show that maximum entropy modeling of high-dimensional, continuous   paths is tractable as long as the constrained features    possess a certain kind of low dimensional structure.   In this case, we show that the associated {\em partition function} is   symmetric and that this symmetry can be exploited to compute the   partition function efficiently in a compressed form.  Empirical   results are given showing an application of our method to maximum   entropy modeling of high dimensional human motion capture data.
Discriminatively Trained Sparse Code Gradients for Contour Detection
Authors: Ren Xiaofeng, Liefeng Bo
Date: 2012-12-01 00:00:00
Proceeding: NeurIPS 2012 12
Tasks: Contour Detection, Dictionary Learning, Image Segmentation, Object Recognition, Semantic Segmentation

Finding contours in natural images is a fundamental problem that serves as the basis of many tasks such as image segmentation and object recognition. At the core of contour detection technologies are a set of hand-designed gradient features, used by most existing approaches including the state-of-the-art Global Pb (gPb) operator.  In this work, we show that contour detection accuracy can be significantly improved by computing Sparse Code Gradients (SCG), which measure contrast using patch representations automatically learned through sparse coding.  We use K-SVD and Orthogonal Matching Pursuit for efficient dictionary learning and encoding, and use multi-scale pooling and power transforms to code oriented local neighborhoods before computing gradients and applying linear SVM. By extracting rich representations from pixels and avoiding collapsing them prematurely, Sparse Code Gradients effectively learn how to measure local contrasts and find contours. We improve the F-measure metric on the BSDS500 benchmark to 0.74 (up from 0.71 of gPb contours).  Moreover, our learning approach can easily adapt to novel sensor data such as Kinect-style RGB-D cameras: Sparse Code Gradients on depth images and surface normals lead to promising contour detection using depth and depth+color, as verified on the NYU Depth Dataset.  Our work combines the concept of oriented gradients with sparse representation and opens up future possibilities for learning contour detection and segmentation.
Analyzing 3D Objects in Cluttered Images
Authors: Mohsen Hejrati, Deva Ramanan
Date: 2012-12-01 00:00:00
Proceeding: NeurIPS 2012 12
Tasks: 3D Shape Reconstruction, Viewpoint Estimation

We present an approach to detecting and analyzing the 3D configuration of objects in real-world images with heavy occlusion and clutter. We focus on the application of finding and analyzing cars. We do so with a two-stage model; the first stage reasons about 2D shape and appearance variation due to within-class variation(station wagons look different than sedans) and changes in viewpoint. Rather than using a view-based model, we describe a compositional representation that models a large number of effective views and shapes using a small number of local view-based templates. We use this model to propose candidate detections and 2D estimates of shape. These estimates are then refined by our second stage, using an explicit 3D model of shape and viewpoint. We use a morphable model to capture 3D within-class variation, and use a weak-perspective camera model to capture viewpoint. We learn all model parameters from 2D annotations. We demonstrate state-of-the-art accuracy for detection, viewpoint estimation, and 3D shape reconstruction on challenging images from the PASCAL VOC 2011 dataset.
3D Object Detection and Viewpoint Estimation with a Deformable 3D Cuboid Model
Authors: Sanja Fidler, Sven Dickinson, Raquel Urtasun
Date: 2012-12-01 00:00:00
Proceeding: NeurIPS 2012 12
Tasks: 3D Object Detection, Object, object-detection, Object Detection, Viewpoint Estimation

This paper addresses the problem of category-level 3D object detection. Given a monocular image, our aim is to localize the objects  in 3D by enclosing them with tight  oriented 3D bounding boxes. We propose a novel approach that extends the well-acclaimed deformable part-based model[Felz.] to reason in 3D. Our model represents an object class as a deformable 3D cuboid composed of faces and parts, which are both allowed to deform with respect to their anchors on the 3D box. We model the appearance of each face in fronto-parallel coordinates, thus effectively factoring out the appearance variation induced by viewpoint. Our model reasons about face visibility patters called aspects. We train the cuboid model jointly and discriminatively and share weights across all aspects to attain efficiency. Inference then entails sliding and rotating the box in 3D and scoring object hypotheses. While for inference we discretize the search space, the variables are  continuous in our model. We demonstrate the effectiveness of our approach in indoor and outdoor scenarios, and show that our approach outperforms the state-of-the-art in both 2D[Felz09] and 3D object detection[Hedau12].
Structured Learning of Gaussian Graphical Models
Authors: Karthik Mohan, Mike Chung, Seungyeop Han, Daniela Witten, Su-In Lee, Maryam Fazel
Date: 2012-12-01 00:00:00
Proceeding: NeurIPS 2012 12

We consider estimation of multiple high-dimensional Gaussian graphical models corresponding to a single set of nodes under several distinct conditions. We assume that most aspects of the networks are shared, but that there are some structured differences between them. Specifically, the network differences are generated from node perturbations: a few nodes are perturbed across networks, and most or all edges stemming from such nodes differ between networks. This corresponds to a simple model for the mechanism underlying many cancers, in which the gene regulatory network is disrupted  due to the aberrant activity of a few specific genes. We propose to solve this problem using the structured joint graphical lasso, a convex optimization problem that is based upon the use of a novel symmetric overlap norm penalty, which we solve using  an alternating directions method of multipliers algorithm. Our proposal is illustrated on synthetic data and on an application to brain cancer gene expression data.
A Polylog Pivot Steps Simplex Algorithm for Classification
Authors: Elad Hazan, Zohar Karnin
Date: 2012-12-01 00:00:00
Proceeding: NeurIPS 2012 12
Tasks: Classification, General Classification

We present a simplex algorithm for linear programming in a linear classification formulation. The paramount complexity parameter in linear classification problems is called the margin. We prove that for margin values of practical interest our simplex variant performs a polylogarithmic number of pivot steps in the worst case, and its overall running time is near linear. This is in contrast to general linear programming, for which no sub-polynomial pivot rule is known.
Shifting Weights: Adapting Object Detectors from Image to Video
Authors: Kevin Tang, Vignesh Ramanathan, Li Fei-Fei, Daphne Koller
Date: 2012-12-01 00:00:00
Proceeding: NeurIPS 2012 12
Tasks: Domain Adaptation, Event Detection, Object, Unsupervised Domain Adaptation

Typical object detectors trained on images perform poorly on video, as there is a clear distinction in domain between the two types of data. In this paper, we tackle the problem of adapting object detectors learned from images to work well on videos. We treat the problem as one of unsupervised domain adaptation, in which we are given labeled data from the source domain (image), but only unlabeled data from the target domain (video). Our approach, self-paced domain adaptation, seeks to iteratively adapt the detector by re-training the detector with automatically discovered target domain examples, starting with the easiest first. At each iteration, the algorithm adapts by considering an increased number of target domain examples, and a decreased number of source domain examples. To discover target domain examples from the vast amount of video data, we introduce a simple, robust approach that scores trajectory tracks instead of bounding boxes. We also show how rich and expressive features specific to the target domain can be incorporated under the same framework. We show promising results on the 2011 TRECVID Multimedia Event Detection and LabelMe Video datasets that illustrate the benefit of our approach to adapt object detectors to video.
A Scalable CUR Matrix Decomposition Algorithm: Lower Time Complexity and Tighter Bound
Authors: Shusen Wang, Zhihua Zhang
Date: 2012-12-01 00:00:00
Proceeding: NeurIPS 2012 12

The CUR matrix decomposition is an important extension of Nyström approximation to a general matrix. It approximates any data matrix in terms of a small number of its columns and rows. In this paper we propose a novel randomized CUR algorithm with an expected relative-error bound. The proposed algorithm has the advantages over the existing relative-error CUR algorithms that it possesses tighter theoretical bound and lower time complexity, and that it can avoid maintaining the whole data matrix in main memory. Finally, experiments on several real-world datasets demonstrate significant improvement over the existing relative-error algorithms.