pwc-archive/papers-with-abstracts huggingface dataset:
Discovering Features in Sr$_{14}$Cu$_{24}$O$_{41}$ Neutron Single Crystal Diffraction Data by Cluster Analysis
Authors: Yawei Hui, Yaohua Liu, Byung-Hoon Park
Date: 2018-09-13 00:00:00
Tasks: Clustering, Open-Ended Question Answering

To address the SMC'18 data challenge, "Discovering Features in
Sr$_{14}$Cu$_{24}$O$_{41}$", we have used the clustering algorithm "DBSCAN" to
separate the diffuse scattering features from the Bragg peaks, which takes into
account both spatial and photometric information in the dataset during in the
clustering process. We find that, in additional to highly localized Bragg
peaks, there exists broad diffuse scattering patterns consisting of
distinguishable geometries. Besides these two distinctive features, we also
identify a third distinguishable feature submerged in the low signal-to-noise
region in the reciprocal space, whose origin remains an open question.
Statistical Estimation of Ergodic Markov Chain Kernel over Discrete State Space
Authors: Geoffrey Wolfer, Aryeh Kontorovich
Date: 2018-09-13 00:00:00

We investigate the statistical complexity of estimating the parameters of a discrete-state Markov chain kernel from a single long sequence of state observations. In the finite case, we characterize (modulo logarithmic factors) the minimax sample complexity of estimation with respect to the operator infinity norm, while in the countably infinite case, we analyze the problem with respect to a natural entry-wise norm derived from total variation. We show that in both cases, the sample complexity is governed by the mixing properties of the unknown chain, for which, in the finite-state case, there are known finite-sample estimators with fully empirical confidence intervals.
Clipped Matrix Completion: A Remedy for Ceiling Effects
Authors: Takeshi Teshima, Miao Xu, Issei Sato, Masashi Sugiyama
Date: 2018-09-13 00:00:00
Tasks: Matrix Completion, Recommendation Systems

We consider the problem of recovering a low-rank matrix from its clipped
observations. Clipping is conceivable in many scientific areas that obstructs
statistical analyses. On the other hand, matrix completion (MC) methods can
recover a low-rank matrix from various information deficits by using the
principle of low-rank completion. However, the current theoretical guarantees
for low-rank MC do not apply to clipped matrices, as the deficit depends on the
underlying values. Therefore, the feasibility of clipped matrix completion
(CMC) is not trivial. In this paper, we first provide a theoretical guarantee
for the exact recovery of CMC by using a trace-norm minimization algorithm.
Furthermore, we propose practical CMC algorithms by extending ordinary MC
methods. Our extension is to use the squared hinge loss in place of the squared
loss for reducing the penalty of over-estimation on clipped entries. We also
propose a novel regularization term tailored for CMC. It is a combination of
two trace-norm terms, and we theoretically bound the recovery error under the
regularization. We demonstrate the effectiveness of the proposed methods
through experiments using both synthetic and benchmark data for recommendation
systems.
Efficient Graph Cut Optimization for Full CRFs with Quantized Edges
Authors: Olga Veksler
Date: 2018-09-13 00:00:00
Tasks: Semantic Segmentation, Superpixels

Fully connected pairwise Conditional Random Fields (Full-CRF) with Gaussian
edge weights can achieve superior results compared to sparsely connected CRFs.
However, traditional methods for Full-CRFs are too expensive. Previous work
develops efficient approximate optimization based on mean field inference,
which is a local optimization method and can be far from the optimum. We
propose efficient and effective optimization based on graph cuts for Full-CRFs
with quantized edge weights. To quantize edge weights, we partition the image
into superpixels and assume that the weight of an edge between any two pixels
depends only on the superpixels these pixels belong to. Our quantized edge CRF
is an approximation to the Gaussian edge CRF, and gets closer to it as
superpixel size decreases. Being an approximation, our model offers an
intuition about the regularization properties of the Guassian edge Full-CRF.
For efficient inference, we first consider the two-label case and develop an
approximate method based on transforming the original problem into a smaller
domain. Then we handle multi-label CRF by showing how to implement expansion
moves. In both binary and multi-label cases, our solutions have significantly
lower energy compared to that of mean field inference. We also show the
effectiveness of our approach on semantic segmentation task.
Semiparametrical Gaussian Processes Learning of Forward Dynamical Models for Navigating in a Circular Maze
Authors: Diego Romeres, Devesh Jha, Alberto Dalla Libera, William Yerazunis, Daniel Nikovski
Date: 2018-09-13 00:00:00
Tasks: Friction, Gaussian Processes, Navigate

This paper presents a problem of model learning for the purpose of learning
how to navigate a ball to a goal state in a circular maze environment with two
degrees of freedom. The motion of the ball in the maze environment is
influenced by several non-linear effects such as dry friction and contacts,
which are difficult to model physically. We propose a semiparametric model to
estimate the motion dynamics of the ball based on Gaussian Process Regression
equipped with basis functions obtained from physics first principles. The
accuracy of this semiparametric model is shown not only in estimation but also
in prediction at n-steps ahead and its compared with standard algorithms for
model learning. The learned model is then used in a trajectory optimization
algorithm to compute ball trajectories. We propose the system presented in the
paper as a benchmark problem for reinforcement and robot learning, for its
interesting and challenging dynamics and its relative ease of reproducibility.
Sequential Coordination of Deep Models for Learning Visual Arithmetic
Authors: Eric Crawford, Guillaume Rabusseau, Joelle Pineau
Date: 2018-09-13 00:00:00
Proceeding: ICLR 2018 1
Tasks: Reinforcement Learning

Achieving machine intelligence requires a smooth integration of perception
and reasoning, yet models developed to date tend to specialize in one or the
other; sophisticated manipulation of symbols acquired from rich perceptual
spaces has so far proved elusive. Consider a visual arithmetic task, where the
goal is to carry out simple arithmetical algorithms on digits presented under
natural conditions (e.g. hand-written, placed randomly). We propose a
two-tiered architecture for tackling this problem. The lower tier consists of a
heterogeneous collection of information processing modules, which can include
pre-trained deep neural networks for locating and extracting characters from
the image, as well as modules performing symbolic transformations on the
representations extracted by perception. The higher tier consists of a
controller, trained using reinforcement learning, which coordinates the modules
in order to solve the high-level task. For instance, the controller may learn
in what contexts to execute the perceptual networks and what symbolic
transformations to apply to their outputs. The resulting model is able to solve
a variety of tasks in the visual arithmetic domain, and has several advantages
over standard, architecturally homogeneous feedforward networks including
improved sample efficiency.
Synthetic Occlusion Augmentation with Volumetric Heatmaps for the 2018 ECCV PoseTrack Challenge on 3D Human Pose Estimation
Authors: István Sárándi, Timm Linder, Kai O. Arras, Bastian Leibe
Date: 2018-09-13 00:00:00
Tasks: 3D Human Pose Estimation, Data Augmentation, Pose Estimation

In this paper we present our winning entry at the 2018 ECCV PoseTrack
Challenge on 3D human pose estimation. Using a fully-convolutional backbone
architecture, we obtain volumetric heatmaps per body joint, which we convert to
coordinates using soft-argmax. Absolute person center depth is estimated by a
1D heatmap prediction head. The coordinates are back-projected to 3D camera
space, where we minimize the L1 loss. Key to our good results is the training
data augmentation with randomly placed occluders from the Pascal VOC dataset.
In addition to reaching first place in the Challenge, our method also surpasses
the state-of-the-art on the full Human3.6M benchmark among methods that use no
additional pose datasets in training. Code for applying synthetic occlusions is
availabe at https://github.com/isarandi/synthetic-occlusion.
SiftingGAN: Generating and Sifting Labeled Samples to Improve the Remote Sensing Image Scene Classification Baseline in vitro
Authors: Dongao Ma, Ping Tang, Lijun Zhao
Date: 2018-09-13 00:00:00
Tasks: Data Augmentation, General Classification, Scene Classification

Lack of annotated samples greatly restrains the direct application of deep
learning in remote sensing image scene classification. Although researches have
been done to tackle this issue by data augmentation with various image
transformation operations, they are still limited in quantity and diversity.
Recently, the advent of the unsupervised learning based generative adversarial
networks (GANs) bring us a new way to generate augmented samples. However, such
GAN-generated samples are currently only served for training GANs model itself
and for improving the performance of the discriminator in GANs internally (in
vivo). It becomes a question of serious doubt whether the GAN-generated samples
can help better improve the scene classification performance of other deep
learning networks (in vitro), compared with the widely used transformed
samples. To answer this question, this paper proposes a SiftingGAN approach to
generate more numerous, more diverse and more authentic labeled samples for
data augmentation. SiftingGAN extends traditional GAN framework with an
Online-Output method for sample generation, a Generative-Model-Sifting method
for model sifting, and a Labeled-Sample-Discriminating method for sample
sifting. Experiments on the well-known AID dataset demonstrate that the
proposed SiftingGAN method can not only effectively improve the performance of
the scene classification baseline that is achieved without data augmentation,
but also significantly excels the comparison methods based on traditional
geometric/radiometric transformation operations.
Part-based Graph Convolutional Network for Action Recognition
Authors: Kalpit Thakkar, P. J. Narayanan
Date: 2018-09-13 00:00:00
Tasks: Action Recognition, Skeleton Based Action Recognition, Temporal Action Localization

Human actions comprise of joint motion of articulated body parts or
`gestures'. Human skeleton is intuitively represented as a sparse graph with
joints as nodes and natural connections between them as edges. Graph
convolutional networks have been used to recognize actions from skeletal
videos. We introduce a part-based graph convolutional network (PB-GCN) for this
task, inspired by Deformable Part-based Models (DPMs). We divide the skeleton
graph into four subgraphs with joints shared across them and learn a
recognition model using a part-based graph convolutional network. We show that
such a model improves performance of recognition, compared to a model using
entire skeleton graph. Instead of using 3D joint coordinates as node features,
we show that using relative coordinates and temporal displacements boosts
performance. Our model achieves state-of-the-art performance on two challenging
benchmark datasets NTURGB+D and HDM05, for skeletal action recognition.
Sparse Label Smoothing Regularization for Person Re-Identification
Authors: Jean-Paul Ainam, Ke Qin, Guisong Liu, Guangchun Luo
Date: 2018-09-13 00:00:00
Tasks: Clustering, Data Augmentation, Person Re-Identification, Retrieval, Semi-Supervised Person Re-Identification

Person re-identification (re-id) is a cross-camera retrieval task which
establishes a correspondence between images of a person from multiple cameras.
Deep Learning methods have been successfully applied to this problem and have
achieved impressive results. However, these methods require a large amount of
labeled training data. Currently labeled datasets in person re-id are limited
in their scale and manual acquisition of such large-scale datasets from
surveillance cameras is a tedious and labor-intensive task. In this paper, we
propose a framework that performs intelligent data augmentation and assigns
partial smoothing label to generated data. Our approach first exploits the
clustering property of existing person re-id datasets to create groups of
similar objects that model cross-view variations. Each group is then used to
generate realistic images through adversarial training. Our aim is to emphasize
feature similarity between generated samples and the original samples. Finally,
we assign a non-uniform label distribution to the generated samples and define
a regularized loss function for training. The proposed approach tackles two
problems (1) how to efficiently use the generated data and (2) how to address
the over-smoothness problem found in current regularization methods. Extensive
experiments on four larges cale datasets show that our regularization method
significantly improves the Re-ID accuracy compared to existing methods.
Gaussian process classification using posterior linearisation
Authors: Ángel F. García-Fernández, Filip Tronarp, Simo Särkkä
Date: 2018-09-13 00:00:00
Tasks: Classification, General Classification

This paper proposes a new algorithm for Gaussian process classification based
on posterior linearisation (PL). In PL, a Gaussian approximation to the
posterior density is obtained iteratively using the best possible linearisation
of the conditional mean of the labels and accounting for the linearisation
error. PL has some theoretical advantages over expectation propagation (EP):
all calculated covariance matrices are positive definite and there is a local
convergence theorem. In experimental data, PL has better performance than EP
with the noisy threshold likelihood and the parallel implementation of the
algorithms.
Unsupervised Machine Commenting with Neural Variational Topic Model
Authors: Shuming Ma, Lei Cui, Furu Wei, Xu sun
Date: 2018-09-13 00:00:00
Tasks: Articles, model, Retrieval

Article comments can provide supplementary opinions and facts for readers,
thereby increase the attraction and engagement of articles. Therefore,
automatically commenting is helpful in improving the activeness of the
community, such as online forums and news websites. Previous work shows that
training an automatic commenting system requires large parallel corpora.
Although part of articles are naturally paired with the comments on some
websites, most articles and comments are unpaired on the Internet. To fully
exploit the unpaired data, we completely remove the need for parallel data and
propose a novel unsupervised approach to train an automatic article commenting
model, relying on nothing but unpaired articles and comments. Our model is
based on a retrieval-based commenting framework, which uses news to retrieve
comments based on the similarity of their topics. The topic representation is
obtained from a neural variational topic model, which is trained in an
unsupervised manner. We evaluate our model on a news comment dataset.
Experiments show that our proposed topic-based approach significantly
outperforms previous lexicon-based models. The model also profits from paired
corpora and achieves state-of-the-art performance under semi-supervised
scenarios.
Valid Simultaneous Inference in High-Dimensional Settings (with the hdm package for R)
Authors: Philipp Bach, Victor Chernozhukov, Martin Spindler
Date: 2018-09-13 00:00:00
Tasks: valid

Due to the increasing availability of high-dimensional empirical applications
in many research disciplines, valid simultaneous inference becomes more and
more important. For instance, high-dimensional settings might arise in economic
studies due to very rich data sets with many potential covariates or in the
analysis of treatment heterogeneities. Also the evaluation of potentially more
complicated (non-linear) functional forms of the regression relationship leads
to many potential variables for which simultaneous inferential statements might
be of interest. Here we provide a review of classical and modern methods for
simultaneous inference in (high-dimensional) settings and illustrate their use
by a case study using the R package hdm. The R package hdm implements valid
joint powerful and efficient hypothesis tests for a potentially large number of
coeffcients as well as the construction of simultaneous confidence intervals
and, therefore, provides useful methods to perform valid post-selection
inference based on the LASSO.
LiveBot: Generating Live Video Comments Based on Visual and Textual Contexts
Authors: Shuming Ma, Lei Cui, Damai Dai, Furu Wei, Xu sun
Date: 2018-09-13 00:00:00
Tasks: Retrieval

We introduce the task of automatic live commenting. Live commenting, which is
also called `video barrage', is an emerging feature on online video sites that
allows real-time comments from viewers to fly across the screen like bullets or
roll at the right side of the screen. The live comments are a mixture of
opinions for the video and the chit chats with other comments. Automatic live
commenting requires AI agents to comprehend the videos and interact with human
viewers who also make the comments, so it is a good testbed of an AI agent's
ability of dealing with both dynamic vision and language. In this work, we
construct a large-scale live comment dataset with 2,361 videos and 895,929 live
comments. Then, we introduce two neural models to generate live comments based
on the visual and textual contexts, which achieve better performance than
previous neural baselines such as the sequence-to-sequence model. Finally, we
provide a retrieval-based evaluation protocol for automatic live commenting
where the model is asked to sort a set of candidate comments based on the
log-likelihood score, and evaluated on metrics such as mean-reciprocal-rank.
Putting it all together, we demonstrate the first `LiveBot'.
Identifying Real Estate Opportunities using Machine Learning
Authors: Alejandro Baldominos, Iván Blanco, Antonio José Moreno, Rubén Iturrarte, Óscar Bernárdez, Carlos Afonso
Date: 2018-09-13 00:00:00
Tasks: BIG-bench Machine Learning, Feature Engineering, regression

The real estate market is exposed to many fluctuations in prices because of
existing correlations with many variables, some of which cannot be controlled
or might even be unknown. Housing prices can increase rapidly (or in some
cases, also drop very fast), yet the numerous listings available online where
houses are sold or rented are not likely to be updated that often. In some
cases, individuals interested in selling a house (or apartment) might include
it in some online listing, and forget about updating the price. In other cases,
some individuals might be interested in deliberately setting a price below the
market price in order to sell the home faster, for various reasons. In this
paper, we aim at developing a machine learning application that identifies
opportunities in the real estate market in real time, i.e., houses that are
listed with a price substantially below the market price. This program can be
useful for investors interested in the housing market. We have focused in a use
case considering real estate assets located in the Salamanca district in Madrid
(Spain) and listed in the most relevant Spanish online site for home sales and
rentals. The application is formally implemented as a regression problem that
tries to estimate the market price of a house given features retrieved from
public online listings. For building this application, we have performed a
feature engineering stage in order to discover relevant features that allows
for attaining a high predictive performance. Several machine learning
algorithms have been tested, including regression trees, k-nearest neighbors,
support vector machines and neural networks, identifying advantages and
handicaps of each of them.
Coordination-driven learning in multi-agent problem spaces
Authors: Sean L. Barton, Nicholas R. Waytowich, Derrik E. Asher
Date: 2018-09-13 00:00:00
Tasks: Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We discuss the role of coordination as a direct learning objective in
multi-agent reinforcement learning (MARL) domains. To this end, we present a
novel means of quantifying coordination in multi-agent systems, and discuss the
implications of using such a measure to optimize coordinated agent policies.
This concept has important implications for adversary-aware RL, which we take
to be a sub-domain of multi-agent learning.
Query-Efficient Black-Box Attack by Active Learning
Authors: Pengcheng Li, Jin-Feng Yi, Lijun Zhang
Date: 2018-09-13 00:00:00
Tasks: Active Learning, Adversarial Attack

Deep neural network (DNN) as a popular machine learning model is found to be
vulnerable to adversarial attack. This attack constructs adversarial examples
by adding small perturbations to the raw input, while appearing unmodified to
human eyes but will be misclassified by a well-trained classifier. In this
paper, we focus on the black-box attack setting where attackers have almost no
access to the underlying models. To conduct black-box attack, a popular
approach aims to train a substitute model based on the information queried from
the target DNN. The substitute model can then be attacked using existing
white-box attack approaches, and the generated adversarial examples will be
used to attack the target DNN. Despite its encouraging results, this approach
suffers from poor query efficiency, i.e., attackers usually needs to query a
huge amount of times to collect enough information for training an accurate
substitute model. To this end, we first utilize state-of-the-art white-box
attack methods to generate samples for querying, and then introduce an active
learning strategy to significantly reduce the number of queries needed.
Besides, we also propose a diversity criterion to avoid the sampling bias. Our
extensive experimental results on MNIST and CIFAR-10 show that the proposed
method can reduce more than $90\%$ of queries while preserve attacking success
rates and obtain an accurate substitute model which is more than $85\%$ similar
with the target oracle.
Deep Network Uncertainty Maps for Indoor Navigation
Authors: Francesco Verdoja, Jens Lundell, Ville Kyrki
Date: 2018-09-13 00:00:00
Tasks: Autonomous Navigation

Most mobile robots for indoor use rely on 2D laser scanners for localization, mapping and navigation. These sensors, however, cannot detect transparent surfaces or measure the full occupancy of complex objects such as tables. Deep Neural Networks have recently been proposed to overcome this limitation by learning to estimate object occupancy. These estimates are nevertheless subject to uncertainty, making the evaluation of their confidence an important issue for these measures to be useful for autonomous navigation and mapping. In this work we approach the problem from two sides. First we discuss uncertainty estimation in deep models, proposing a solution based on a fully convolutional neural network. The proposed architecture is not restricted by the assumption that the uncertainty follows a Gaussian model, as in the case of many popular solutions for deep model uncertainty estimation, such as Monte-Carlo Dropout. We present results showing that uncertainty over obstacle distances is actually better modeled with a Laplace distribution. Then, we propose a novel approach to build maps based on Deep Neural Network uncertainty models. In particular, we present an algorithm to build a map that includes information over obstacle distance estimates while taking into account the level of uncertainty in each estimate. We show how the constructed map can be used to increase global navigation safety by planning trajectories which avoid areas of high uncertainty, enabling higher autonomy for mobile robots in indoor settings.