pwc-archive/papers-with-abstracts huggingface dataset:
Explaining Aggregates for Exploratory Analytics
Authors: Fotis Savva, Christos Anagnostopoulos, Peter Triantafillou
Date: 2018-12-29 00:00:00

Analysts wishing to explore multivariate data spaces, typically pose queries involving selection operators, i.e., range or radius queries, which define data subspaces of possible interest and then use aggregation functions, the results of which determine their exploratory analytics interests. However, such aggregate query (AQ) results are simple scalars and as such, convey limited information about the queried subspaces for exploratory analysis. We address this shortcoming aiding analysts to explore and understand data subspaces by contributing a novel explanation mechanism coined XAXA: eXplaining Aggregates for eXploratory Analytics. XAXA's novel AQ explanations are represented using functions obtained by a three-fold joint optimization problem. Explanations assume the form of a set of parametric piecewise-linear functions acquired through a statistical learning model. A key feature of the proposed solution is that model training is performed by only monitoring AQs and their answers on-line. In XAXA, explanations for future AQs can be computed without any database (DB) access and can be used to further explore the queried data subspaces, without issuing any more queries to the DB. We evaluate the explanation accuracy and efficiency of XAXA through theoretically grounded metrics over real-world and synthetic datasets and query workloads.
Rendu basé image avec contraintes sur les gradients
Authors: Grégoire Nieto, Frédéric Devernay, James Crowley
Date: 2018-12-29 00:00:00
Tasks: 3D Reconstruction

Multi-view image-based rendering consists in generating a novel view of a
scene from a set of source views. In general, this works by first doing a
coarse 3D reconstruction of the scene, and then using this reconstruction to
establish correspondences between source and target views, followed by blending
the warped views to get the final image. Unfortunately, discontinuities in the
blending weights, due to scene geometry or camera placement, result in
artifacts in the target view. In this paper, we show how to avoid these
artifacts by imposing additional constraints on the image gradients of the
novel view. We propose a variational framework in which an energy functional is
derived and optimized by iteratively solving a linear system. We demonstrate
this method on several structured and unstructured multi-view datasets, and
show that it numerically outperforms state-of-the-art methods, and eliminates
artifacts that result from visibility discontinuities
Quantized Guided Pruning for Efficient Hardware Implementations of Convolutional Neural Networks
Authors: Ghouthi Boukli Hacene, Vincent Gripon, Matthieu Arzel, Nicolas Farrugia, Yoshua Bengio
Date: 2018-12-29 00:00:00
Tasks: Quantization

Convolutional Neural Networks (CNNs) are state-of-the-art in numerous
computer vision tasks such as object classification and detection. However, the
large amount of parameters they contain leads to a high computational
complexity and strongly limits their usability in budget-constrained devices
such as embedded devices. In this paper, we propose a combination of a new
pruning technique and a quantization scheme that effectively reduce the
complexity and memory usage of convolutional layers of CNNs, and replace the
complex convolutional operation by a low-cost multiplexer. We perform
experiments on the CIFAR10, CIFAR100 and SVHN and show that the proposed method
achieves almost state-of-the-art accuracy, while drastically reducing the
computational and memory footprints. We also propose an efficient hardware
architecture to accelerate CNN operations. The proposed hardware architecture
is a pipeline and accommodates multiple layers working at the same time to
speed up the inference process.
Skeleton Transformer Networks: 3D Human Pose and Skinned Mesh from Single RGB Image
Authors: Yusuke Yoshiyasu, Ryusuke Sagawa, Ko Ayusawa, Akihiko Murai
Date: 2018-12-29 00:00:00
Tasks: 3D Human Pose Estimation

In this paper, we present Skeleton Transformer Networks (SkeletonNet), an
end-to-end framework that can predict not only 3D joint positions but also 3D
angular pose (bone rotations) of a human skeleton from a single color image.
This in turn allows us to generate skinned mesh animations. Here, we propose a
two-step regression approach. The first step regresses bone rotations in order
to obtain an initial solution by considering skeleton structure. The second
step performs refinement based on heatmap regressor using a 3D pose
representation called cross heatmap which stacks heatmaps of xy and zy
coordinates. By training the network using the proposed 3D human pose dataset
that is comprised of images annotated with 3D skeletal angular poses, we showed
that SkeletonNet can predict a full 3D human pose (joint positions and bone
rotations) from a single image in-the-wild.
Attention-Based Capsule Networks with Dynamic Routing for Relation Extraction
Authors: Ningyu Zhang, Shumin Deng, Zhanlin Sun, Xi Chen, Wei zhang, Huajun Chen
Date: 2018-12-29 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Multi-Label Learning, Relation, Relation Extraction

A capsule is a group of neurons, whose activity vector represents the
instantiation parameters of a specific type of entity. In this paper, we
explore the capsule networks used for relation extraction in a multi-instance
multi-label learning framework and propose a novel neural approach based on
capsule networks with attention mechanisms. We evaluate our method with
different benchmarks, and it is demonstrated that our method improves the
precision of the predicted relations. Particularly, we show that capsule
networks improve multiple entity pairs relation extraction.
A Deep Learning based Framework to Detect and Recognize Humans using Contactless Palmprints in the Wild
Authors: Yang Liu, Ajay Kumar
Date: 2018-12-29 00:00:00
Tasks: Triplet

Contactless and online palmprint identfication offers improved user
convenience, hygiene, user-security and is highly desirable in a range of
applications. This technical report details an accurate and generalizable deep
learning-based framework to detect and recognize humans using contactless
palmprint images in the wild. Our network is based on fully convolutional
network that generates deeply learned residual features. We design a
soft-shifted triplet loss function to more effectively learn discriminative
palmprint features. Online palmprint identification also requires a contactless
palm detector, which is adapted and trained from faster-R-CNN architecture, to
detect palmprint region under varying backgrounds. Our reproducible
experimental results on publicly available contactless palmprint databases
suggest that the proposed framework consistently outperforms several classical
and state-of-the-art palmprint recognition methods. More importantly, the model
presented in this report offers superior generalization capability, unlike
other popular methods in the literature, as it does not essentially require
database-specific parameter tuning, which is another key advantage over other
methods in the literature.
Support Vector Guided Softmax Loss for Face Recognition
Authors: Xiaobo Wang, Shuo Wang, Shifeng Zhang, Tianyu Fu, Hailin Shi, Tao Mei
Date: 2018-12-29 00:00:00
Tasks: Face Recognition

Face recognition has witnessed significant progresses due to the advances of
deep convolutional neural networks (CNNs), the central challenge of which, is
feature discrimination. To address it, one group tries to exploit mining-based
strategies (\textit{e.g.}, hard example mining and focal loss) to focus on the
informative examples. The other group devotes to designing margin-based loss
functions (\textit{e.g.}, angular, additive and additive angular margins) to
increase the feature margin from the perspective of ground truth class. Both of
them have been well-verified to learn discriminative features. However, they
suffer from either the ambiguity of hard examples or the lack of discriminative
power of other classes. In this paper, we design a novel loss function, namely
support vector guided softmax loss (SV-Softmax), which adaptively emphasizes
the mis-classified points (support vectors) to guide the discriminative
features learning. So the developed SV-Softmax loss is able to eliminate the
ambiguity of hard examples as well as absorb the discriminative power of other
classes, and thus results in more discrimiantive features. To the best of our
knowledge, this is the first attempt to inherit the advantages of mining-based
and margin-based losses into one framework. Experimental results on several
benchmarks have demonstrated the effectiveness of our approach over
state-of-the-arts.
Meta Reinforcement Learning with Distribution of Exploration Parameters Learned by Evolution Strategies
Authors: Yiming Shen, Kehan Yang, Yufeng Yuan, Simon Cheng Liu
Date: 2018-12-29 00:00:00
Tasks: Meta-Learning, Meta Reinforcement Learning, MuJoCo, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In this paper, we propose a novel meta-learning method in a reinforcement learning setting, based on evolution strategies (ES), exploration in parameter space and deterministic policy gradients. ES methods are easy to parallelize, which is desirable for modern training architectures; however, such methods typically require a huge number of samples for effective training. We use deterministic policy gradients during adaptation and other techniques to compensate for the sample-efficiency problem while maintaining the inherent scalability of ES methods. We demonstrate that our method achieves good results compared to gradient-based meta-learning in high-dimensional control tasks in the MuJoCo simulator. In addition, because of gradient-free methods in the meta-training phase, which do not need information about gradients and policies in adaptation training, we predict and confirm our algorithm performs better in tasks that need multi-step adaptation.
SPI-Optimizer: an integral-Separated PI Controller for Stochastic Optimization
Authors: Dan Wang, Mengqi Ji, Yong Wang, Haoqian Wang, Lu Fang
Date: 2018-12-29 00:00:00
Tasks: Stochastic Optimization

To overcome the oscillation problem in the classical momentum-based
optimizer, recent work associates it with the proportional-integral (PI)
controller, and artificially adds D term producing a PID controller. It
suppresses oscillation with the sacrifice of introducing extra hyper-parameter.
In this paper, we start by analyzing: why momentum-based method oscillates
about the optimal point? and answering that: the fluctuation problem relates to
the lag effect of integral (I) term. Inspired by the conditional integration
idea in classical control society, we propose SPI-Optimizer, an
integral-Separated PI controller based optimizer WITHOUT introducing extra
hyperparameter. It separates momentum term adaptively when the inconsistency of
current and historical gradient direction occurs. Extensive experiments
demonstrate that SPIOptimizer generalizes well on popular network architectures
to eliminate the oscillation, and owns competitive performance with faster
convergence speed (up to 40% epochs reduction ratio ) and more accurate
classification result on MNIST, CIFAR10, and CIFAR100 (up to 27.5% error
reduction ratio) than the state-of-the-art methods.
Annotation-cost Minimization for Medical Image Segmentation using Suggestive Mixed Supervision Fully Convolutional Networks
Authors: Yash Bhalgat, Meet Shah, Suyash Awate
Date: 2018-12-29 00:00:00
Tasks: Image Segmentation, Medical Image Segmentation, Semantic Segmentation

For medical image segmentation, most fully convolutional networks (FCNs) need
strong supervision through a large sample of high-quality dense segmentations,
which is taxing in terms of costs, time and logistics involved. This burden of
annotation can be alleviated by exploiting weak inexpensive annotations such as
bounding boxes and anatomical landmarks. However, it is very difficult to
\textit{a priori} estimate the optimal balance between the number of
annotations needed for each supervision type that leads to maximum performance
with the least annotation cost. To optimize this cost-performance trade off, we
present a budget-based cost-minimization framework in a mixed-supervision
setting via dense segmentations, bounding boxes, and landmarks. We propose a
linear programming (LP) formulation combined with uncertainty and similarity
based ranking strategy to judiciously select samples to be annotated next for
optimal performance. In the results section, we show that our proposed method
achieves comparable performance to state-of-the-art approaches with
significantly reduced cost of annotations.
Monocular 3D Pose Recovery via Nonconvex Sparsity with Theoretical Analysis
Authors: Jianqiao Wangni, Dahua Lin, Ji Liu, Kostas Daniilidis, Jianbo Shi
Date: 2018-12-29 00:00:00

For recovering 3D object poses from 2D images, a prevalent method is to
pre-train an over-complete dictionary $\mathcal D=\{B_i\}_i^D$ of 3D basis
poses. During testing, the detected 2D pose $Y$ is matched to dictionary by $Y
\approx \sum_i M_i B_i$ where $\{M_i\}_i^D=\{c_i \Pi R_i\}$, by estimating the
rotation $R_i$, projection $\Pi$ and sparse combination coefficients $c \in
\mathbb R_{+}^D$. In this paper, we propose non-convex regularization $H(c)$ to
learn coefficients $c$, including novel leaky capped $\ell_1$-norm
regularization (LCNR), \begin{align*} H(c)=\alpha \sum_{i } \min(|c_i|,\tau)+
\beta \sum_{i } \max(| c_i|,\tau), \end{align*} where $0\leq \beta \leq \alpha$
and $0<\tau$ is a certain threshold, so the invalid components smaller than
$\tau$ are composed with larger regularization and other valid components with
smaller regularization. We propose a multi-stage optimizer with convex
relaxation and ADMM. We prove that the estimation error $\mathcal L(l)$ decays
w.r.t. the stages $l$, \begin{align*} Pr\left(\mathcal L(l) < \rho^{l-1}
\mathcal L(0) + \delta \right) \geq 1- \epsilon, \end{align*} where $0< \rho
<1, 0<\delta, 0<\epsilon \ll 1$. Experiments on large 3D human datasets like
H36M are conducted to support our improvement upon previous approaches. To the
best of our knowledge, this is the first theoretical analysis in this line of
research, to understand how the recovery error is affected by fundamental
factors, e.g. dictionary size, observation noises, optimization times. We
characterize the trade-off between speed and accuracy towards real-time
inference in applications.
DeGroot-Friedkin Map in Opinion Dynamics is Mirror Descent
Authors: Abhishek Halder
Date: 2018-12-29 00:00:00

We provide a variational interpretation of the DeGroot-Friedkin map in
opinion dynamics. Specifically, we show that the nonlinear dynamics for the
DeGroot-Friedkin map can be viewed as mirror descent on the standard simplex
with the associated Bregman divergence being equal to the generalized
Kullback-Leibler divergence, i.e., an entropic mirror descent. Our results
reveal that the DeGroot-Friedkin map elicits an individual's social power to be
close to her social influence while minimizing the so called "extropy" -- the
entropy of the complimentary opinion.
3D Convolution on RGB-D Point Clouds for Accurate Model-free Object Pose Estimation
Authors: Zhongang Cai, Cunjun Yu, Quang-Cuong Pham
Date: 2018-12-29 00:00:00
Tasks: Pose Estimation, Robotic Grasping, Translation

The conventional pose estimation of a 3D object usually requires the
knowledge of the 3D model of the object. Even with the recent development in
convolutional neural networks (CNNs), a 3D model is often necessary in the
final estimation. In this paper, we propose a two-stage pipeline that takes in
raw colored point cloud data and estimates an object's translation and rotation
by running 3D convolutions on voxels. The pipeline is simple yet highly
accurate: translation error is reduced to the voxel resolution (around 1 cm)
and rotation error is around 5 degrees. The pipeline is also put to actual
robotic grasping tests where it achieves above 90% success rate for test
objects. Another innovation is that a motion capture system is used to
automatically label the point cloud samples which makes it possible to rapidly
collect a large amount of highly accurate real data for training the neural
networks.
Classification of Functioning, Disability, and Health for Children and Youth: ICF-CY Self Care (SCADI Dataset) Using Predictive Analytics
Authors: Avishek Choudhury, Christopher Greene
Date: 2018-12-29 00:00:00
Tasks: Classification, General Classification

The International Classification of Functioning, Disability, and Health for Children and Youth (ICF-CY) is a scaffold for designating and systematizing data on functioning and disability. It offers a standard semantic and a theoretical foundation for the demarcation and extent of wellbeing and infirmity. The multidimensional layout of ICF-CY comprehends a plethora of information with about 1400 categories making it difficult to analyze. Our research proposes a predictive model that classify self-care problems on Self-Care Activities Dataset based on the ICF- CY. The data used in this study resides 206 attributes of 70 children with motor and physical disability. Our study implements, compare and analyze Random Forest, Support vector machine, Naive Bayes, Hoeffding tree, and Lazy locally weighted learning using two-tailed T-test at 95% confidence interval. Boruta algorithm involved in the study minimizes the data dimensionality to advocate the minimal-optimal set of predictors. Random forest gave the best classification accuracy of 84.75%; root mean squared error of 0.18 and receiver operating characteristic of 0.99. Predictive analytics can simplify the usage of ICF-CY by automating the classification process of disability, functioning, and health.
Learn to Interpret Atari Agents
Authors: Zhao Yang, Song Bai, Li Zhang, Philip H. S. Torr
Date: 2018-12-29 00:00:00
Tasks: Decision Making, Deep Reinforcement Learning, Reinforcement Learning

Deep reinforcement learning (DeepRL) agents surpass human-level performance in many tasks. However, the direct mapping from states to actions makes it hard to interpret the rationale behind the decision-making of the agents. In contrast to previous a-posteriori methods for visualizing DeepRL policies, in this work, we propose to equip the DeepRL model with an innate visualization ability. Our proposed agent, named region-sensitive Rainbow (RS-Rainbow), is an end-to-end trainable network based on the original Rainbow, a powerful deep Q-network agent. It learns important regions in the input domain via an attention module. At inference time, after each forward pass, we can visualize regions that are most important to decision-making by backpropagating gradients from the attention module to the input frames. The incorporation of our proposed module not only improves model interpretability, but leads to performance improvement. Extensive experiments on games from the Atari 2600 suite demonstrate the effectiveness of RS-Rainbow.
End-to-end neural relation extraction using deep biaffine attention
Authors: Dat Quoc Nguyen, Karin Verspoor
Date: 2018-12-29 00:00:00
Tasks: General Classification, Relation, Relation Classification, Relation Extraction

We propose a neural network model for joint extraction of named entities and
relations between them, without any hand-crafted features. The key contribution
of our model is to extend a BiLSTM-CRF-based entity recognition model with a
deep biaffine attention layer to model second-order interactions between latent
features for relation classification, specifically attending to the role of an
entity in a directional relationship. On the benchmark "relation and entity
recognition" dataset CoNLL04, experimental results show that our model
outperforms previous models, producing new state-of-the-art performances.
Weakly-Supervised Hierarchical Text Classification
Authors: Yu Meng, Jiaming Shen, Chao Zhang, Jiawei Han
Date: 2018-12-29 00:00:00
Tasks: Blocking, Classification, Feature Engineering, General Classification, text-classification, Text Classification

Hierarchical text classification, which aims to classify text documents into
a given hierarchy, is an important task in many real-world applications.
Recently, deep neural models are gaining increasing popularity for text
classification due to their expressive power and minimum requirement for
feature engineering. However, applying deep neural networks for hierarchical
text classification remains challenging, because they heavily rely on a large
amount of training data and meanwhile cannot easily determine appropriate
levels of documents in the hierarchical setting. In this paper, we propose a
weakly-supervised neural method for hierarchical text classification. Our
method does not require a large amount of training data but requires only
easy-to-provide weak supervision signals such as a few class-related documents
or keywords. Our method effectively leverages such weak supervision signals to
generate pseudo documents for model pre-training, and then performs
self-training on real unlabeled data to iteratively refine the model. During
the training process, our model features a hierarchical neural structure, which
mimics the given hierarchy and is capable of determining the proper levels for
documents with a blocking mechanism. Experiments on three datasets from
different domains demonstrate the efficacy of our method compared with a
comprehensive set of baselines.
Autoencoder Based Residual Deep Networks for Robust Regression Prediction and Spatiotemporal Estimation
Authors: Lianfa Li, Ying Fang, Jun Wu, Jinfeng Wang
Date: 2018-12-29 00:00:00
Tasks: Imputation, Missing Values, regression

To have a superior generalization, a deep learning neural network often
involves a large size of training sample. With increase of hidden layers in
order to increase learning ability, neural network has potential degradation in
accuracy. Both could seriously limit applicability of deep learning in some
domains particularly involving predictions of continuous variables with a small
size of samples. Inspired by residual convolutional neural network in computer
vision and recent findings of crucial shortcuts in the brains in neuroscience,
we propose an autoencoder-based residual deep network for robust prediction. In
a nested way, we leverage shortcut connections to implement residual mapping
with a balanced structure for efficient propagation of error signals. The novel
method is demonstrated by multiple datasets, imputation of high spatiotemporal
resolution non-randomness missing values of aerosol optical depth, and
spatiotemporal estimation of fine particulate matter <2.5 \mu m, achieving the
cutting edge of accuracy and efficiency. Our approach is also a general-purpose
regression learner to be applicable in diverse domains.
Neural Clustering Processes
Authors: Ari Pakman, Yueqi Wang, Catalin Mitelut, JinHyung Lee, Liam Paninski
Date: 2018-12-28 00:00:00
Proceeding: ICML 2020 1
Tasks: Bayesian Inference, Clustering, Spike Sorting

Probabilistic clustering models (or equivalently, mixture models) are basic building blocks in countless statistical models and involve latent random variables over discrete spaces. For these models, posterior inference methods can be inaccurate and/or very slow. In this work we introduce deep network architectures trained with labeled samples from any generative model of clustered datasets. At test time, the networks generate approximate posterior samples of cluster labels for any new dataset of arbitrary size. We develop two complementary approaches to this task, requiring either O(N) or O(K) network forward passes per dataset, where N is the dataset size and K the number of clusters. Unlike previous approaches, our methods sample the labels of all the data points from a well-defined posterior, and can learn nonparametric Bayesian posteriors since they do not limit the number of mixture components. As a scientific application, we present a novel approach to neural spike sorting for high-density multielectrode arrays.