pwc-archive/papers-with-abstracts huggingface dataset:
Design and software implementation of subsystems for creating and using the ontological base of a research scientist
Authors: O. V. Palagin, K. S. Malakhov, V. Yu. Velichko, O. S. Shurov
Date: 2018-02-17 00:00:00

Creation of the information systems and tools for scientific research and
development support has always been one of the central directions of the
development of computer science. The main features of the modern evolution of
scientific research and development are the transdisciplinary approach and the
deep intellectualisation of all stages of the life cycle of formulation and
solution of scientific problems. The theoretical and practical aspects of the
development of perspective complex knowledge-oriented information systems and
their components are considered in the paper. The analysis of existing
scientific information systems (or current research information systems, CRIS)
and synthesis of general principles of design of the research and development
workstation environment of a researcher and its components are carried out in
the work. The functional components of knowledge-oriented information system
research and development workstation environment of a researcher are designed.
Designed and developed functional components of knowledge-oriented information
system developing research and development workstation environment,including
functional models and software implementation of the software subsystem for
creation and use of ontological knowledge base for research fellow
publications, as part of personalized knowledge base of scientific researcher.
Research in modern conditions of e-Science paradigm requires pooling scientific
community and intensive exchange of research results that may be achieved
through the use of scientific information systems. research and development
workstation environment allows to solve problems of contructivisation and
formalisation of knowledge representation, obtained during the research process
and collective accomplices interaction.
To the problem of "The Instrumental complex for ontological engineering purpose" software system design
Authors: A. V. Palagin, N. G. Petrenko, V. Yu. Velychko, K. S. Malakhov, Yu. L. Tikhonov
Date: 2018-02-10 00:00:00

The given work describes methodological principles of design instrumental
complex of ontological purpose. Instrumental complex intends for the
implementation of the integrated information technologies automated build of
domain ontologies. Results focus on enhancing the effectiveness of the
automatic analysis and understanding of natural-language texts, building of
knowledge description of subject areas (primarily in the area of science and
technology) and for interdisciplinary research in conjunction with the solution
of complex problems.
The Gaussian Process Autoregressive Regression Model (GPAR)
Authors: James Requeima, Will Tebbutt, Wessel Bruinsma, Richard E. Turner
Date: 2018-02-20 00:00:00
Tasks: Gaussian Processes, model, regression

Multi-output regression models must exploit dependencies between outputs to
maximise predictive performance. The application of Gaussian processes (GPs) to
this setting typically yields models that are computationally demanding and
have limited representational power. We present the Gaussian Process
Autoregressive Regression (GPAR) model, a scalable multi-output GP model that
is able to capture nonlinear, possibly input-varying, dependencies between
outputs in a simple and tractable way: the product rule is used to decompose
the joint distribution over the outputs into a set of conditionals, each of
which is modelled by a standard GP. GPAR's efficacy is demonstrated on a
variety of synthetic and real-world problems, outperforming existing GP models
and achieving state-of-the-art performance on established benchmarks.
Combinatorial Preconditioners for Proximal Algorithms on Graphs
Authors: Thomas Möllenhoff, Zhenzhang Ye, Tao Wu, Daniel Cremers
Date: 2018-01-16 00:00:00
Tasks: BIG-bench Machine Learning

We present a novel preconditioning technique for proximal optimization
methods that relies on graph algorithms to construct effective preconditioners.
Such combinatorial preconditioners arise from partitioning the graph into
forests. We prove that certain decompositions lead to a theoretically optimal
condition number. We also show how ideal decompositions can be realized using
matroid partitioning and propose efficient greedy variants thereof for
large-scale problems. Coupled with specialized solvers for the resulting scaled
proximal subproblems, the preconditioned algorithm achieves competitive
performance in machine learning and vision applications.
Adversarial classification: An adversarial risk analysis approach
Authors: Roi Naveiro, Alberto Redondo, David Ríos Insua, Fabrizio Ruggeri
Date: 2018-02-21 00:00:00
Tasks: Classification, General Classification

Classification problems in security settings are usually contemplated as confrontations in which one or more adversaries try to fool a classifier to obtain a benefit. Most approaches to such adversarial classification problems have focused on game theoretical ideas with strong underlying common knowledge assumptions, which are actually not realistic in security domains. We provide an alternative framework to such problem based on adversarial risk analysis, which we illustrate with several examples. Computational and implementation issues are discussed.
Density Weighted Connectivity of Grass Pixels in Image Frames for Biomass Estimation
Authors: Ligang Zhang, Brijesh Verma, David Stockwell, Sujan Chowdhury
Date: 2018-02-21 00:00:00

Accurate estimation of the biomass of roadside grasses plays a significant
role in applications such as fire-prone region identification. Current
solutions heavily depend on field surveys, remote sensing measurements and
image processing using reference markers, which often demand big investments of
time, effort and cost. This paper proposes Density Weighted Connectivity of
Grass Pixels (DWCGP) to automatically estimate grass biomass from roadside
image data. The DWCGP calculates the length of continuously connected grass
pixels along a vertical orientation in each image column, and then weights the
length by the grass density in a surrounding region of the column. Grass pixels
are classified using feedforward artificial neural networks and the dominant
texture orientation at every pixel is computed using multi-orientation Gabor
wavelet filter vote. Evaluations on a field survey dataset show that the DWCGP
reduces Root-Mean-Square Error from 5.84 to 5.52 by additionally considering
grass density on top of grass height. The DWCGP shows robustness to
non-vertical grass stems and to changes of both Gabor filter parameters and
surrounding region widths. It also has performance close to human observation
and higher than eight baseline approaches, as well as promising results for
classifying low vs. high fire risk and identifying fire-prone road regions.
Spectrally approximating large graphs with smaller graphs
Authors: Andreas Loukas, Pierre Vandergheynst
Date: 2018-02-21 00:00:00
Proceeding: ICML 2018 7
Tasks: Clustering

How does coarsening affect the spectrum of a general graph? We provide
conditions such that the principal eigenvalues and eigenspaces of a coarsened
and original graph Laplacian matrices are close. The achieved approximation is
shown to depend on standard graph-theoretic properties, such as the degree and
eigenvalue distributions, as well as on the ratio between the coarsened and
actual graph sizes. Our results carry implications for learning methods that
utilize coarsening. For the particular case of spectral clustering, they imply
that coarse eigenvectors can be used to derive good quality assignments even
without refinement---this phenomenon was previously observed, but lacked formal
justification.
BKTreebank: Building a Vietnamese Dependency Treebank
Authors: Kiem-Hieu Nguyen
Date: 2017-10-16 00:00:00
Proceeding: LREC 2018 5
Tasks: Dependency Parsing, POS, POS Tagging

Dependency treebank is an important resource in any language. In this paper,
we present our work on building BKTreebank, a dependency treebank for
Vietnamese. Important points on designing POS tagset, dependency relations, and
annotation guidelines are discussed. We describe experiments on POS tagging and
dependency parsing on the treebank. Experimental results show that the treebank
is a useful resource for Vietnamese language processing.
Epistemic Graphs for Representing and Reasoning with Positive and Negative Influences of Arguments
Authors: Anthony Hunter, Sylwia Polberg, Matthias Thimm
Date: 2018-02-21 00:00:00
Tasks: Specificity

This paper introduces epistemic graphs as a generalization of the epistemic approach to probabilistic argumentation. In these graphs, an argument can be believed or disbelieved up to a given degree, thus providing a more fine--grained alternative to the standard Dung's approaches when it comes to determining the status of a given argument. Furthermore, the flexibility of the epistemic approach allows us to both model the rationale behind the existing semantics as well as completely deviate from them when required. Epistemic graphs can model both attack and support as well as relations that are neither support nor attack. The way other arguments influence a given argument is expressed by the epistemic constraints that can restrict the belief we have in an argument with a varying degree of specificity. The fact that we can specify the rules under which arguments should be evaluated and we can include constraints between unrelated arguments permits the framework to be more context--sensitive. It also allows for better modelling of imperfect agents, which can be important in multi--agent applications.
3D-SSD: Learning Hierarchical Features from RGB-D Images for Amodal 3D Object Detection
Authors: Qianhui Luo, Huifang Ma, Yue Wang, Li Tang, Rong Xiong
Date: 2017-11-01 00:00:00
Tasks: 3D Object Detection, object-detection, Object Detection

This paper aims at developing a faster and a more accurate solution to the
amodal 3D object detection problem for indoor scenes. It is achieved through a
novel neural network that takes a pair of RGB-D images as the input and
delivers oriented 3D bounding boxes as the output. The network, named 3D-SSD,
composed of two parts: hierarchical feature fusion and multi-layer prediction.
The hierarchical feature fusion combines appearance and geometric features from
RGB-D images while the multi-layer prediction utilizes multi-scale features for
object detection. As a result, the network can exploit 2.5D representations in
a synergetic way to improve the accuracy and efficiency. The issue of object
sizes is addressed by attaching a set of 3D anchor boxes with varying sizes to
every location of the prediction layers. At the end stage, the category scores
for 3D anchor boxes are generated with adjusted positions, sizes and
orientations respectively, leading to the final detections using non-maximum
suppression. In the training phase, the positive samples are identified with
the aid of 2D ground truth to avoid the noisy estimation of depth from raw
data, which guide to a better converged model. Experiments performed on the
challenging SUN RGB-D dataset show that our algorithm outperforms the
state-of-the-art Deep Sliding Shape by 10.2% mAP and 88x faster. Further,
experiments also suggest our approach achieves comparable accuracy and is 386x
faster than the state-of-art method on the NYUv2 dataset even with a smaller
input image size.
Variational Sequential Monte Carlo
Authors: Christian A. Naesseth, Scott W. Linderman, Rajesh Ranganath, David M. Blei
Date: 2017-05-31 00:00:00
Tasks: Bayesian Inference, State Space Models, Variational Inference

Many recent advances in large scale probabilistic inference rely on
variational methods. The success of variational approaches depends on (i)
formulating a flexible parametric family of distributions, and (ii) optimizing
the parameters to find the member of this family that most closely approximates
the exact posterior. In this paper we present a new approximating family of
distributions, the variational sequential Monte Carlo (VSMC) family, and show
how to optimize it in variational inference. VSMC melds variational inference
(VI) and sequential Monte Carlo (SMC), providing practitioners with flexible,
accurate, and powerful Bayesian inference. The VSMC family is a variational
family that can approximate the posterior arbitrarily well, while still
allowing for efficient optimization of its parameters. We demonstrate its
utility on state space models, stochastic volatility models for financial data,
and deep Markov models of brain neural circuits.
High-dimensional Bayesian inference via the Unadjusted Langevin Algorithm
Authors: Alain Durmus, Eric Moulines
Date: 2016-05-05 00:00:00
Tasks: Bayesian Inference, Vocal Bursts Intensity Prediction

We consider in this paper the problem of sampling a high-dimensional
probability distribution $\pi$ having a density with respect to the Lebesgue
measure on $\mathbb{R}^d$, known up to a normalization constant $x \mapsto
\pi(x)= \mathrm{e}^{-U(x)}/\int_{\mathbb{R}^d} \mathrm{e}^{-U(y)} \mathrm{d}
y$. Such problem naturally occurs for example in Bayesian inference and machine
learning. Under the assumption that $U$ is continuously differentiable, $\nabla
U$ is globally Lipschitz and $U$ is strongly convex, we obtain non-asymptotic
bounds for the convergence to stationarity in Wasserstein distance of order $2$
and total variation distance of the sampling method based on the Euler
discretization of the Langevin stochastic differential equation, for both
constant and decreasing step sizes. The dependence on the dimension of the
state space of these bounds is explicit. The convergence of an appropriately
weighted empirical measure is also investigated and bounds for the mean square
error and exponential deviation inequality are reported for functions which are
measurable and bounded. An illustration to Bayesian inference for binary
regression is presented to support our claims.
Multiclass Weighted Loss for Instance Segmentation of Cluttered Cells
Authors: Fidel A. Guerrero-Pena, Pedro D. Marrero Fernandez, Tsang Ing Ren, Mary Yui, Ellen Rothenberg, Alexandre Cunha
Date: 2018-02-21 00:00:00
Tasks: Instance Segmentation, Semantic Segmentation

We propose a new multiclass weighted loss function for instance segmentation
of cluttered cells. We are primarily motivated by the need of developmental
biologists to quantify and model the behavior of blood T-cells which might help
us in understanding their regulation mechanisms and ultimately help researchers
in their quest for developing an effective immuno-therapy cancer treatment.
Segmenting individual touching cells in cluttered regions is challenging as the
feature distribution on shared borders and cell foreground are similar thus
difficulting discriminating pixels into proper classes. We present two novel
weight maps applied to the weighted cross entropy loss function which take into
account both class imbalance and cell geometry. Binary ground truth training
data is augmented so the learning model can handle not only foreground and
background but also a third touching class. This framework allows training
using U-Net. Experiments with our formulations have shown superior results when
compared to other similar schemes, outperforming binary class models with
significant improvement of boundary adequacy and instance detection. We
validate our results on manually annotated microscope images of T-cells.
Emergence of Structured Behaviors from Curiosity-Based Intrinsic Motivation
Authors: Nick Haber, Damian Mrowca, Li Fei-Fei, Daniel L. K. Yamins
Date: 2018-02-21 00:00:00
Tasks: motion prediction, Object

Infants are experts at playing, with an amazing ability to generate novel
structured behaviors in unstructured environments that lack clear extrinsic
reward signals. We seek to replicate some of these abilities with a neural
network that implements curiosity-driven intrinsic motivation. Using a simple
but ecologically naturalistic simulated environment in which the agent can move
and interact with objects it sees, the agent learns a world model predicting
the dynamic consequences of its actions. Simultaneously, the agent learns to
take actions that adversarially challenge the developing world model, pushing
the agent to explore novel and informative interactions with its environment.
We demonstrate that this policy leads to the self-supervised emergence of a
spectrum of complex behaviors, including ego motion prediction, object
attention, and object gathering. Moreover, the world model that the agent
learns supports improved performance on object dynamics prediction and
localization tasks. Our results are a proof-of-principle that computational
models of intrinsic motivation might account for key features of developmental
visuomotor learning in infants.
Learning Image Conditioned Label Space for Multilabel Classification
Authors: Yi-Nan Li, Mei-Chen Yeh
Date: 2018-02-21 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification

This work addresses the task of multilabel image classification. Inspired by
the great success from deep convolutional neural networks (CNNs) for
single-label visual-semantic embedding, we exploit extending these models for
multilabel images. Specifically, we propose an image-dependent ranking model,
which returns a ranked list of labels according to its relevance to the input
image. In contrast to conventional CNN models that learn an image
representation (i.e. the image embedding vector), the developed model learns a
mapping (i.e. a transformation matrix) from an image in an attempt to
differentiate between its relevant and irrelevant labels. Despite the
conceptual simplicity of our approach, experimental results on a public
benchmark dataset demonstrate that the proposed model achieves state-of-the-art
performance while using fewer training images than other multilabel
classification methods.
Matching Article Pairs with Graphical Decomposition and Convolutions
Authors: Bang Liu, Di Niu, Haojie Wei, Jinghong Lin, Yancheng He, Kunfeng Lai, Yu Xu
Date: 2018-02-21 00:00:00
Proceeding: ACL 2019 7
Tasks: Articles, document understanding, Question Answering, Sentence, Text Matching

Identifying the relationship between two articles, e.g., whether two articles published from different sources describe the same breaking news, is critical to many document understanding tasks. Existing approaches for modeling and matching sentence pairs do not perform well in matching longer documents, which embody more complex interactions between the enclosed entities than a sentence does. To model article pairs, we propose the Concept Interaction Graph to represent an article as a graph of concepts. We then match a pair of articles by comparing the sentences that enclose the same concept vertex through a series of encoding techniques, and aggregate the matching signals through a graph convolutional network. To facilitate the evaluation of long article matching, we have created two datasets, each consisting of about 30K pairs of breaking news articles covering diverse topics in the open domain. Extensive evaluations of the proposed methods on the two datasets demonstrate significant improvements over a wide range of state-of-the-art methods for natural language matching.
Learning to Play with Intrinsically-Motivated Self-Aware Agents
Authors: Nick Haber, Damian Mrowca, Li Fei-Fei, Daniel L. K. Yamins
Date: 2018-02-21 00:00:00
Tasks: motion prediction, Object

Infants are experts at playing, with an amazing ability to generate novel
structured behaviors in unstructured environments that lack clear extrinsic
reward signals. We seek to mathematically formalize these abilities using a
neural network that implements curiosity-driven intrinsic motivation. Using a
simple but ecologically naturalistic simulated environment in which an agent
can move and interact with objects it sees, we propose a "world-model" network
that learns to predict the dynamic consequences of the agent's actions.
Simultaneously, we train a separate explicit "self-model" that allows the agent
to track the error map of its own world-model, and then uses the self-model to
adversarially challenge the developing world-model. We demonstrate that this
policy causes the agent to explore novel and informative interactions with its
environment, leading to the generation of a spectrum of complex behaviors,
including ego-motion prediction, object attention, and object gathering.
Moreover, the world-model that the agent learns supports improved performance
on object dynamics prediction, detection, localization and recognition tasks.
Taken together, our results are initial steps toward creating flexible
autonomous agents that self-supervise in complex novel physical environments.
Nonparametric Bayesian Sparse Graph Linear Dynamical Systems
Authors: Rahi Kalantari, Joydeep Ghosh, Mingyuan Zhou
Date: 2018-02-21 00:00:00
Tasks: Time Series, Time Series Analysis

A nonparametric Bayesian sparse graph linear dynamical system (SGLDS) is
proposed to model sequentially observed multivariate data. SGLDS uses the
Bernoulli-Poisson link together with a gamma process to generate an infinite
dimensional sparse random graph to model state transitions. Depending on the
sparsity pattern of the corresponding row and column of the graph affinity
matrix, a latent state of SGLDS can be categorized as either a non-dynamic
state or a dynamic one. A normal-gamma construction is used to shrink the
energy captured by the non-dynamic states, while the dynamic states can be
further categorized into live, absorbing, or noise-injection states, which
capture different types of dynamical components of the underlying time series.
The state-of-the-art performance of SGLDS is demonstrated with experiments on
both synthetic and real data.
Generalization in Machine Learning via Analytical Learning Theory
Authors: Kenji Kawaguchi, Yoshua Bengio, Vikas Verma, Leslie Pack Kaelbling
Date: 2018-02-21 00:00:00
Tasks: BIG-bench Machine Learning, Deep Learning, Learning Theory, One-Shot Learning, Representation Learning

This paper introduces a novel measure-theoretic theory for machine learning
that does not require statistical assumptions. Based on this theory, a new
regularization method in deep learning is derived and shown to outperform
previous methods in CIFAR-10, CIFAR-100, and SVHN. Moreover, the proposed
theory provides a theoretical basis for a family of practically successful
regularization methods in deep learning. We discuss several consequences of our
results on one-shot learning, representation learning, deep learning, and
curriculum learning. Unlike statistical learning theory, the proposed learning
theory analyzes each problem instance individually via measure theory, rather
than a set of problem instances via statistics. As a result, it provides
different types of results and insights when compared to statistical learning
theory.
Unsupervised Deep Homography: A Fast and Robust Homography Estimation Model
Authors: Ty Nguyen, Steven W. Chen, Shreyas S. Shivakumar, Camillo J. Taylor, Vijay Kumar
Date: 2017-09-12 00:00:00
Tasks: Homography Estimation, Pose Estimation

Homography estimation between multiple aerial images can provide relative
pose estimation for collaborative autonomous exploration and monitoring. The
usage on a robotic system requires a fast and robust homography estimation
algorithm. In this study, we propose an unsupervised learning algorithm that
trains a Deep Convolutional Neural Network to estimate planar homographies. We
compare the proposed algorithm to traditional feature-based and direct methods,
as well as a corresponding supervised learning algorithm. Our empirical results
demonstrate that compared to traditional approaches, the unsupervised algorithm
achieves faster inference speed, while maintaining comparable or better
accuracy and robustness to illumination variation. In addition, on both a
synthetic dataset and representative real-world aerial dataset, our
unsupervised method has superior adaptability and performance compared to the
supervised deep learning method.