pwc-archive/papers-with-abstracts huggingface dataset:
Sparse Inertial Poser: Automatic 3D Human Pose Estimation from Sparse IMUs
Authors: Timo von Marcard, Bodo Rosenhahn, Michael J. Black, Gerard Pons-Moll
Date: 2017-03-23 00:00:00
Tasks: 3D Human Pose Estimation, Pose Estimation

We address the problem of making human motion capture in the wild more
practical by using a small set of inertial sensors attached to the body. Since
the problem is heavily under-constrained, previous methods either use a large
number of sensors, which is intrusive, or they require additional video input.
We take a different approach and constrain the problem by: (i) making use of a
realistic statistical body model that includes anthropometric constraints and
(ii) using a joint optimization framework to fit the model to orientation and
acceleration measurements over multiple frames. The resulting tracker Sparse
Inertial Poser (SIP) enables 3D human pose estimation using only 6 sensors
(attached to the wrists, lower legs, back and head) and works for arbitrary
human motions. Experiments on the recently released TNT15 dataset show that,
using the same number of sensors, SIP achieves higher accuracy than the dataset
baseline without using any video data. We further demonstrate the effectiveness
of SIP on newly recorded challenging motions in outdoor scenarios such as
climbing or jumping over a wall.
Semi-Supervised Sparse Representation Based Classification for Face Recognition with Insufficient Labeled Samples
Authors: Yuan Gao, Jiayi Ma, Alan L. Yuille
Date: 2016-09-12 00:00:00
Tasks: Face Recognition, General Classification, Sparse Representation-based Classification

This paper addresses the problem of face recognition when there is only few,
or even only a single, labeled examples of the face that we wish to recognize.
Moreover, these examples are typically corrupted by nuisance variables, both
linear (i.e., additive nuisance variables such as bad lighting, wearing of
glasses) and non-linear (i.e., non-additive pixel-wise nuisance variables such
as expression changes). The small number of labeled examples means that it is
hard to remove these nuisance variables between the training and testing faces
to obtain good recognition performance. To address the problem we propose a
method called Semi-Supervised Sparse Representation based Classification
(S$^3$RC). This is based on recent work on sparsity where faces are represented
in terms of two dictionaries: a gallery dictionary consisting of one or more
examples of each person, and a variation dictionary representing linear
nuisance variables (e.g., different lighting conditions, different glasses).
The main idea is that (i) we use the variation dictionary to characterize the
linear nuisance variables via the sparsity framework, then (ii) prototype face
images are estimated as a gallery dictionary via a Gaussian Mixture Model
(GMM), with mixed labeled and unlabeled samples in a semi-supervised manner, to
deal with the non-linear nuisance variations between labeled and unlabeled
samples. We have done experiments with insufficient labeled samples, even when
there is only a single labeled sample per person. Our results on the AR,
Multi-PIE, CAS-PEAL, and LFW databases demonstrate that the proposed method is
able to deliver significantly improved performance over existing methods.
Deep Direct Regression for Multi-Oriented Scene Text Detection
Authors: Wenhao He, Xu-Yao Zhang, Fei Yin, Cheng-Lin Liu
Date: 2017-03-24 00:00:00
Proceeding: ICCV 2017 10
Tasks: Multi-Oriented Scene Text Detection, object-detection, Object Detection, regression, Scene Text Detection, Text Detection

In this paper, we first provide a new perspective to divide existing high
performance object detection methods into direct and indirect regressions.
Direct regression performs boundary regression by predicting the offsets from a
given point, while indirect regression predicts the offsets from some bounding
box proposals. Then we analyze the drawbacks of the indirect regression, which
the recent state-of-the-art detection structures like Faster-RCNN and SSD
follows, for multi-oriented scene text detection, and point out the potential
superiority of direct regression. To verify this point of view, we propose a
deep direct regression based method for multi-oriented scene text detection.
Our detection framework is simple and effective with a fully convolutional
network and one-step post processing. The fully convolutional network is
optimized in an end-to-end way and has bi-task outputs where one is pixel-wise
classification between text and non-text, and the other is direct regression to
determine the vertex coordinates of quadrilateral text boundaries. The proposed
method is particularly beneficial for localizing incidental scene texts. On the
ICDAR2015 Incidental Scene Text benchmark, our method achieves the F1-measure
of 81%, which is a new state-of-the-art and significantly outperforms previous
approaches. On other standard datasets with focused scene texts, our method
also reaches the state-of-the-art performance.
Experimental Identification of Hard Data Sets for Classification and Feature Selection Methods with Insights on Method Selection
Authors: Cuiju Luan, Guozhu Dong
Date: 2017-03-24 00:00:00
Tasks: Classification, feature selection, General Classification

The paper reports an experimentally identified list of benchmark data sets
that are hard for representative classification and feature selection methods.
This was done after systematically evaluating a total of 48 combinations of
methods, involving eight state-of-the-art classification algorithms and six
commonly used feature selection methods, on 129 data sets from the UCI
repository (some data sets with known high classification accuracy were
excluded). In this paper, a data set for classification is called hard if none
of the 48 combinations can achieve an AUC over 0.8 and none of them can achieve
an F-Measure value over 0.8; it is called easy otherwise. A total of 15 out of
the 129 data sets were found to be hard in that sense. This paper also compares
the performance of different methods, and it produces rankings of
classification methods, separately on the hard data sets and on the easy data
sets. This paper is the first to rank methods separately for hard data sets and
for easy data sets. It turns out that the classifier rankings resulting from
our experiments are somehow different from those in the literature and hence
they offer new insights on method selection. It should be noted that the Random
Forest method remains to be the best in all groups of experiments.
A Nonconvex Splitting Method for Symmetric Nonnegative Matrix Factorization: Convergence Analysis and Optimality
Authors: Songtao Lu, Mingyi Hong, Zhengdao Wang
Date: 2017-03-24 00:00:00
Tasks: Clustering, Community Detection, Image Segmentation, Semantic Segmentation

Symmetric nonnegative matrix factorization (SymNMF) has important
applications in data analytics problems such as document clustering, community
detection and image segmentation. In this paper, we propose a novel nonconvex
variable splitting method for solving SymNMF. The proposed algorithm is
guaranteed to converge to the set of Karush-Kuhn-Tucker (KKT) points of the
nonconvex SymNMF problem. Furthermore, it achieves a global sublinear
convergence rate. We also show that the algorithm can be efficiently
implemented in parallel. Further, sufficient conditions are provided which
guarantee the global and local optimality of the obtained solutions. Extensive
numerical results performed on both synthetic and real data sets suggest that
the proposed algorithm converges quickly to a local minimum solution.
Supervisor Synthesis of POMDP based on Automata Learning
Authors: Xiaobin Zhang, Bo Wu, Hai Lin
Date: 2017-03-24 00:00:00

As a general and thus popular model for autonomous systems, partially
observable Markov decision process (POMDP) can capture uncertainties from
different sources like sensing noises, actuation errors, and uncertain
environments. However, its comprehensiveness makes the planning and control in
POMDP difficult. Traditional POMDP planning problems target to find the optimal
policy to maximize the expectation of accumulated rewards. But for safety
critical applications, guarantees of system performance described by formal
specifications are desired, which motivates us to consider formal methods to
synthesize supervisor for POMDP. With system specifications given by
Probabilistic Computation Tree Logic (PCTL), we propose a supervisory control
framework with a type of deterministic finite automata (DFA), za-DFA, as the
controller form. While the existing work mainly relies on optimization
techniques to learn fixed-size finite state controllers (FSCs), we develop an
$L^*$ learning based algorithm to determine both space and transitions of
za-DFA. Membership queries and different oracles for conjectures are defined.
The learning algorithm is sound and complete. An example is given in detailed
steps to illustrate the supervisor synthesis algorithm.
Perception Driven Texture Generation
Authors: Yanhai Gan, Huifang Chi, Ying Gao, Jun Liu, Guoqiang Zhong, Junyu Dong
Date: 2017-03-24 00:00:00
Tasks: Texture Synthesis

This paper investigates a novel task of generating texture images from
perceptual descriptions. Previous work on texture generation focused on either
synthesis from examples or generation from procedural models. Generating
textures from perceptual attributes have not been well studied yet. Meanwhile,
perceptual attributes, such as directionality, regularity and roughness are
important factors for human observers to describe a texture. In this paper, we
propose a joint deep network model that combines adversarial training and
perceptual feature regression for texture generation, while only random noise
and user-defined perceptual attributes are required as input. In this model, a
preliminary trained convolutional neural network is essentially integrated with
the adversarial framework, which can drive the generated textures to possess
given perceptual attributes. An important aspect of the proposed model is that,
if we change one of the input perceptual features, the corresponding appearance
of the generated textures will also be changed. We design several experiments
to validate the effectiveness of the proposed method. The results show that the
proposed method can produce high quality texture images with desired perceptual
properties.
\$1 Today or \$2 Tomorrow? The Answer is in Your Facebook Likes
Authors: Tao Ding, Warren K. Bickel, SHimei Pan
Date: 2017-03-22 00:00:00
Tasks: Decision Making

In economics and psychology, delay discounting is often used to characterize
how individuals choose between a smaller immediate reward and a larger delayed
reward. People with higher delay discounting rate (DDR) often choose smaller
but more immediate rewards (a "today person"). In contrast, people with a lower
discounting rate often choose a larger future rewards (a "tomorrow person").
Since the ability to modulate the desire of immediate gratification for long
term rewards plays an important role in our decision-making, the lower
discounting rate often predicts better social, academic and health outcomes. In
contrast, the higher discounting rate is often associated with problematic
behaviors such as alcohol/drug abuse, pathological gambling and credit card
default. Thus, research on understanding and moderating delay discounting has
the potential to produce substantial societal benefits.
The Dependence of Machine Learning on Electronic Medical Record Quality
Authors: Long Ho, David Ledbetter, Melissa Aczon, Randall Wetzel
Date: 2017-03-23 00:00:00
Tasks: BIG-bench Machine Learning, ICU Mortality

There is growing interest in applying machine learning methods to Electronic
Medical Records (EMR). Across different institutions, however, EMR quality can
vary widely. This work investigated the impact of this disparity on the
performance of three advanced machine learning algorithms: logistic regression,
multilayer perceptron, and recurrent neural network. The EMR disparity was
emulated using different permutations of the EMR collected at Children's
Hospital Los Angeles (CHLA) Pediatric Intensive Care Unit (PICU) and
Cardiothoracic Intensive Care Unit (CTICU). The algorithms were trained using
patients from the PICU to predict in-ICU mortality for patients in a held out
set of PICU and CTICU patients. The disparate patient populations between the
PICU and CTICU provide an estimate of generalization errors across different
ICUs. We quantified and evaluated the generalization of these algorithms on
varying EMR size, input types, and fidelity of data.
On the Robustness of Convolutional Neural Networks to Internal Architecture and Weight Perturbations
Authors: Nicholas Cheney, Martin Schrimpf, Gabriel Kreiman
Date: 2017-03-23 00:00:00

Deep convolutional neural networks are generally regarded as robust function
approximators. So far, this intuition is based on perturbations to external
stimuli such as the images to be classified. Here we explore the robustness of
convolutional neural networks to perturbations to the internal weights and
architecture of the network itself. We show that convolutional networks are
surprisingly robust to a number of internal perturbations in the higher
convolutional layers but the bottom convolutional layers are much more fragile.
For instance, Alexnet shows less than a 30% decrease in classification
performance when randomly removing over 70% of weight connections in the top
convolutional or dense layers but performance is almost at chance with the same
perturbation in the first convolutional layer. Finally, we suggest further
investigations which could continue to inform the robustness of convolutional
networks to internal perturbations.
TokTrack: A Complete Token Provenance and Change Tracking Dataset for the English Wikipedia
Authors: Fabian Flöck, Kenan Erdogan, Maribel Acosta
Date: 2017-03-23 00:00:00
Tasks: Articles

We present a dataset that contains every instance of all tokens (~ words)
ever written in undeleted, non-redirect English Wikipedia articles until
October 2016, in total 13,545,349,787 instances. Each token is annotated with
(i) the article revision it was originally created in, and (ii) lists with all
the revisions in which the token was ever deleted and (potentially) re-added
and re-deleted from its article, enabling a complete and straightforward
tracking of its history. This data would be exceedingly hard to create by an
average potential user as it is (i) very expensive to compute and as (ii)
accurately tracking the history of each token in revisioned documents is a
non-trivial task. Adapting a state-of-the-art algorithm, we have produced a
dataset that allows for a range of analyses and metrics, already popular in
research and going beyond, to be generated on complete-Wikipedia scale;
ensuring quality and allowing researchers to forego expensive text-comparison
computation, which so far has hindered scalable usage. We show how this data
enables, on token-level, computation of provenance, measuring survival of
content over time, very detailed conflict metrics, and fine-grained
interactions of editors like partial reverts, re-additions and other metrics,
in the process gaining several novel insights.
Semi-Automatic Segmentation and Ultrasonic Characterization of Solid Breast Lesions
Authors: Mohammad Saad Billah, Tahmida Binte Mahmud
Date: 2017-03-23 00:00:00
Tasks: Segmentation

Characterization of breast lesions is an essential prerequisite to detect
breast cancer in an early stage. Automatic segmentation makes this
categorization method robust by freeing it from subjectivity and human error.
Both spectral and morphometric features are successfully used for
differentiating between benign and malignant breast lesions. In this thesis, we
used empirical mode decomposition method for semi-automatic segmentation.
Sonographic features like ehcogenicity, heterogeneity, FNPA, margin definition,
Hurst coefficient, compactness, roundness, aspect ratio, convexity, solidity,
form factor were calculated to be used as our characterization parameters. All
of these parameters did not give desired comparative results. But some of them
namely echogenicity, heterogeneity, margin definition, aspect ratio and
convexity gave good results and were used for characterization.
Recurrent Topic-Transition GAN for Visual Paragraph Generation
Authors: Xiaodan Liang, Zhiting Hu, Hao Zhang, Chuang Gan, Eric P. Xing
Date: 2017-03-21 00:00:00
Proceeding: ICCV 2017 10
Tasks: Generative Adversarial Network, Image Description, Image Paragraph Captioning, Sentence

A natural image usually conveys rich semantic content and can be viewed from
different angles. Existing image description methods are largely restricted by
small sets of biased visual paragraph annotations, and fail to cover rich
underlying semantics. In this paper, we investigate a semi-supervised paragraph
generative framework that is able to synthesize diverse and semantically
coherent paragraph descriptions by reasoning over local semantic regions and
exploiting linguistic knowledge. The proposed Recurrent Topic-Transition
Generative Adversarial Network (RTT-GAN) builds an adversarial framework
between a structured paragraph generator and multi-level paragraph
discriminators. The paragraph generator generates sentences recurrently by
incorporating region-based visual and language attention mechanisms at each
step. The quality of generated paragraph sentences is assessed by multi-level
adversarial discriminators from two aspects, namely, plausibility at sentence
level and topic-transition coherence at paragraph level. The joint adversarial
training of RTT-GAN drives the model to generate realistic paragraphs with
smooth logical transition between sentence topics. Extensive quantitative
experiments on image and video paragraph datasets demonstrate the effectiveness
of our RTT-GAN in both supervised and semi-supervised settings. Qualitative
results on telling diverse stories for an image also verify the
interpretability of RTT-GAN.
Classification and regression using an outer approximation projection-gradient method
Authors: Michel Barlaud, Wafa Belhajali, Patrick L. Combettes, Lionel Fillatre
Date: 2015-06-06 00:00:00
Tasks: Classification, feature selection, General Classification, regression

This paper deals with sparse feature selection and grouping for
classification and regression. The classification or regression problems under
consideration consists in minimizing a convex empirical risk function subject
to an $\ell^1$ constraint, a pairwise $\ell^\infty$ constraint, or a pairwise
$\ell^1$ constraint. Existing work, such as the Lasso formulation, has focused
mainly on Lagrangian penalty approximations, which often require ad hoc or
computationally expensive procedures to determine the penalization parameter.
We depart from this approach and address the constrained problem directly via a
splitting method. The structure of the method is that of the classical
gradient-projection algorithm, which alternates a gradient step on the
objective and a projection step onto the lower level set modeling the
constraint. The novelty of our approach is that the projection step is
implemented via an outer approximation scheme in which the constraint set is
approximated by a sequence of simple convex sets consisting of the intersection
of two half-spaces. Convergence of the iterates generated by the algorithm is
established for a general smooth convex minimization problem with inequality
constraints. Experiments on both synthetic and biological data show that our
method outperforms penalty methods.
Recursive Regression with Neural Networks: Approximating the HJI PDE Solution
Authors: Vicenç Rubies Royo, Claire Tomlin
Date: 2016-11-08 00:00:00
Tasks: regression

The majority of methods used to compute approximations to the
Hamilton-Jacobi-Isaacs partial differential equation (HJI PDE) rely on the
discretization of the state space to perform dynamic programming updates. This
type of approach is known to suffer from the curse of dimensionality due to the
exponential growth in grid points with the state dimension. In this work we
present an approximate dynamic programming algorithm that computes an
approximation of the solution of the HJI PDE by alternating between solving a
regression problem and solving a minimax problem using a feedforward neural
network as the function approximator. We find that this method requires less
memory to run and to store the approximation than traditional gridding methods,
and we test it on a few systems of two, three and six dimensions.
Adjusting for Dropout Variance in Batch Normalization and Weight Initialization
Authors: Dan Hendrycks, Kevin Gimpel
Date: 2016-07-08 00:00:00
Tasks: Data Augmentation

We show how to adjust for the variance introduced by dropout with corrections
to weight initialization and Batch Normalization, yielding higher accuracy.
Though dropout can preserve the expected input to a neuron between train and
test, the variance of the input differs. We thus propose a new weight
initialization by correcting for the influence of dropout rates and an
arbitrary nonlinearity's influence on variance through simple corrective
scalars. Since Batch Normalization trained with dropout estimates the variance
of a layer's incoming distribution with some inputs dropped, the variance also
differs between train and test. After training a network with Batch
Normalization and dropout, we simply update Batch Normalization's variance
moving averages with dropout off and obtain state of the art on CIFAR-10 and
CIFAR-100 without data augmentation.
A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks
Authors: Dan Hendrycks, Kevin Gimpel
Date: 2016-10-07 00:00:00
Tasks: Anomaly Detection, Automatic Speech Recognition, Automatic Speech Recognition (ASR), Out-of-Distribution Detection, Speech Recognition

We consider the two related problems of detecting if an example is
misclassified or out-of-distribution. We present a simple baseline that
utilizes probabilities from softmax distributions. Correctly classified
examples tend to have greater maximum softmax probabilities than erroneously
classified and out-of-distribution examples, allowing for their detection. We
assess performance by defining several tasks in computer vision, natural
language processing, and automatic speech recognition, showing the
effectiveness of this baseline across all. We then show the baseline can
sometimes be surpassed, demonstrating the room for future research on these
underexplored detection tasks.
Fairness Constraints: Mechanisms for Fair Classification
Authors: Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rodriguez, Krishna P. Gummadi
Date: 2015-07-19 00:00:00
Tasks: Classification, Decision Making, Fairness, General Classification

Algorithmic decision making systems are ubiquitous across a wide variety of
online as well as offline services. These systems rely on complex learning
methods and vast amounts of data to optimize the service functionality,
satisfaction of the end user and profitability. However, there is a growing
concern that these automated decisions can lead, even in the absence of intent,
to a lack of fairness, i.e., their outcomes can disproportionately hurt (or,
benefit) particular groups of people sharing one or more sensitive attributes
(e.g., race, sex). In this paper, we introduce a flexible mechanism to design
fair classifiers by leveraging a novel intuitive measure of decision boundary
(un)fairness. We instantiate this mechanism with two well-known classifiers,
logistic regression and support vector machines, and show on real-world data
that our mechanism allows for a fine-grained control on the degree of fairness,
often at a small cost in terms of accuracy.