pwc-archive/papers-with-abstracts huggingface dataset:
Triple Trustworthiness Measurement for Knowledge Graph
Authors: Shengbin Jia, Yang Xiang, Xiaojun Chen
Date: 2018-09-25 00:00:00

The Knowledge graph (KG) uses the triples to describe the facts in the real
world. It has been widely used in intelligent analysis and applications.
However, possible noises and conflicts are inevitably introduced in the process
of constructing. And the KG based tasks or applications assume that the
knowledge in the KG is completely correct and inevitably bring about potential
deviations. In this paper, we establish a knowledge graph triple
trustworthiness measurement model that quantify their semantic correctness and
the true degree of the facts expressed. The model is a crisscrossing neural
network structure. It synthesizes the internal semantic information in the
triples and the global inference information of the KG to achieve the
trustworthiness measurement and fusion in the three levels of entity level,
relationship level, and KG global level. We analyzed the validity of the model
output confidence values, and conducted experiments in the real-world dataset
FB15K (from Freebase) for the knowledge graph error detection task. The
experimental results showed that compared with other models, our model achieved
significant and consistent improvements.
Vehicle Re-Identification in Context
Authors: Aytaç Kanacı, Xiatian Zhu, Shaogang Gong
Date: 2018-09-25 00:00:00
Tasks: Vehicle Re-Identification

Existing vehicle re-identification (re-id) evaluation benchmarks consider
strongly artificial test scenarios by assuming the availability of high quality
images and fine-grained appearance at an almost constant image scale,
reminiscent to images required for Automatic Number Plate Recognition, e.g.
VeRi-776. Such assumptions are often invalid in realistic vehicle re-id
scenarios where arbitrarily changing image resolutions (scales) are the norm.
This makes the existing vehicle re-id benchmarks limited for testing the true
performance of a re-id method. In this work, we introduce a more realistic and
challenging vehicle re-id benchmark, called Vehicle Re-Identification in
Context (VRIC). In contrast to existing datasets, VRIC is uniquely
characterised by vehicle images subject to more realistic and unconstrained
variations in resolution (scale), motion blur, illumination, occlusion, and
viewpoint. It contains 60,430 images of 5,622 vehicle identities captured by 60
different cameras at heterogeneous road traffic scenes in both day-time and
night-time.
Chinese User Service Intention Classification Based on Hybrid Neural Network
Authors: Shengbin Jia, Yang Xiang
Date: 2018-09-25 00:00:00
Tasks: Classification, General Classification, Intent Detection, Relation Extraction

In order to satisfy the consumers' increasing personalized service demand, the Intelligent service has arisen. User service intention recognition is an important challenge for intelligent service system to provide precise service. It is difficult for the intelligent system to understand the semantics of user demand which leads to poor recognition effect, because of the noise in user requirement descriptions. Therefore, a hybrid neural network classification model based on BiLSTM and CNN is proposed to recognize users service intentions. The model can fuse the temporal semantics and spatial semantics of the user descriptions. The experimental results show that our model achieves a better effect compared with other models, reaching 0.94 on the F1 score.
Pre and Post-hoc Diagnosis and Interpretation of Malignancy from Breast DCE-MRI
Authors: Gabriel Maicas, Andrew P. Bradley, Jacinto C. Nascimento, Ian Reid, Gustavo Carneiro
Date: 2018-09-25 00:00:00

We propose a new method for breast cancer screening from DCE-MRI based on a
post-hoc approach that is trained using weakly annotated data (i.e., labels are
available only at the image level without any lesion delineation). Our proposed
post-hoc method automatically diagnosis the whole volume and, for positive
cases, it localizes the malignant lesions that led to such diagnosis.
Conversely, traditional approaches follow a pre-hoc approach that initially
localises suspicious areas that are subsequently classified to establish the
breast malignancy -- this approach is trained using strongly annotated data
(i.e., it needs a delineation and classification of all lesions in an image).
Another goal of this paper is to establish the advantages and disadvantages of
both approaches when applied to breast screening from DCE-MRI. Relying on
experiments on a breast DCE-MRI dataset that contains scans of 117 patients,
our results show that the post-hoc method is more accurate for diagnosing the
whole volume per patient, achieving an AUC of 0.91, while the pre-hoc method
achieves an AUC of 0.81. However, the performance for localising the malignant
lesions remains challenging for the post-hoc method due to the weakly labelled
dataset employed during training.
Hypergraph Neural Networks
Authors: Yifan Feng, Haoxuan You, Zizhao Zhang, Rongrong Ji, Yue Gao
Date: 2018-09-25 00:00:00
Tasks: Object Recognition, Representation Learning

In this paper, we present a hypergraph neural networks (HGNN) framework for
data representation learning, which can encode high-order data correlation in a
hypergraph structure. Confronting the challenges of learning representation for
complex data in real practice, we propose to incorporate such data structure in
a hypergraph, which is more flexible on data modeling, especially when dealing
with complex data. In this method, a hyperedge convolution operation is
designed to handle the data correlation during representation learning. In this
way, traditional hypergraph learning procedure can be conducted using hyperedge
convolution operations efficiently. HGNN is able to learn the hidden layer
representation considering the high-order data structure, which is a general
framework considering the complex data correlations. We have conducted
experiments on citation network classification and visual object recognition
tasks and compared HGNN with graph convolutional networks and other traditional
methods. Experimental results demonstrate that the proposed HGNN method
outperforms recent state-of-the-art methods. We can also reveal from the
results that the proposed HGNN is superior when dealing with multi-modal data
compared with existing methods.
Non-Iterative Knowledge Fusion in Deep Convolutional Neural Networks
Authors: Mikhail Iu. Leontev, Viktoriia Islenteva, Sergey V. Sukhov
Date: 2018-09-25 00:00:00
Tasks: General Classification

Incorporation of a new knowledge into neural networks with simultaneous
preservation of the previous one is known to be a nontrivial problem. This
problem becomes even more complex when new knowledge is contained not in new
training examples, but inside the parameters (connection weights) of another
neural network. Here we propose and test two methods allowing combining the
knowledge contained in separate networks. One method is based on a simple
operation of summation of weights of constituent neural networks. Another
method assumes incorporation of a new knowledge by modification of weights
nonessential for the preservation of already stored information. We show that
with these methods the knowledge from one network can be transferred into
another one non-iteratively without requiring training sessions. The fused
network operates efficiently, performing classification far better than a
chance level. The efficiency of the methods is quantified on several publicly
available data sets in classification tasks both for shallow and deep neural
networks.
S-RL Toolbox: Environments, Datasets and Evaluation Metrics for State Representation Learning
Authors: Antonin Raffin, Ashley Hill, René Traoré, Timothée Lesort, Natalia Díaz-Rodríguez, David Filliat
Date: 2018-09-25 00:00:00
Tasks: Diversity, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Representation Learning

State representation learning aims at learning compact representations from
raw observations in robotics and control applications. Approaches used for this
objective are auto-encoders, learning forward models, inverse dynamics or
learning using generic priors on the state characteristics. However, the
diversity in applications and methods makes the field lack standard evaluation
datasets, metrics and tasks. This paper provides a set of environments, data
generators, robotic control tasks, metrics and tools to facilitate iterative
state representation learning and evaluation in reinforcement learning
settings.
Geometric-based Line Segment Tracking for HDR Stereo Sequences
Authors: Ruben Gomez-Ojeda, Javier Gonzalez-Jimenez
Date: 2018-09-25 00:00:00
Tasks: Visual Odometry

In this work, we propose a purely geometrical approach for the robust
matching of line segments for challenging stereo streams with severe
illumination changes or High Dynamic Range (HDR) environments. To that purpose,
we exploit the univocal nature of the matching problem, i.e. every observation
must be corresponded with a single feature or not corresponded at all. We state
the problem as a sparse, convex, L1-minimization of the matching vector
regularized by the geometric constraints. This formulation allows for the
robust tracking of line segments along sequences where traditional
appearance-based matching techniques tend to fail due to dynamic changes in
illumination conditions. Moreover, the proposed matching algorithm also results
in a considerable speed-up of previous state of the art techniques making it
suitable for real-time applications such as Visual Odometry (VO). This, of
course, comes at expense of a slightly lower number of matches in comparison
with appearance based methods, and also limits its application to continuous
video sequences, as it is rather constrained to small pose increments between
consecutive frames. We validate the claimed advantages by first evaluating the
matching performance in challenging video sequences, and then testing the
method in a benchmarked point and line based VO algorithm.
Sparse-Group Bayesian Feature Selection Using Expectation Propagation for Signal Recovery and Network Reconstruction
Authors: Edgar Steiger, Martin Vingron
Date: 2018-09-25 00:00:00
Tasks: feature selection, parameter estimation

We present a Bayesian method for feature selection in the presence of
grouping information with sparsity on the between- and within group level.
Instead of using a stochastic algorithm for parameter inference, we employ
expectation propagation, which is a deterministic and fast algorithm. Available
methods for feature selection in the presence of grouping information have a
number of short-comings: on one hand, lasso methods, while being fast,
underestimate the regression coefficients and do not make good use of the
grouping information, and on the other hand, Bayesian approaches, while
accurate in parameter estimation, often rely on the stochastic and slow Gibbs
sampling procedure to recover the parameters, rendering them infeasible e.g.
for gene network reconstruction. Our approach of a Bayesian sparse-group
framework with expectation propagation enables us to not only recover accurate
parameter estimates in signal recovery problems, but also makes it possible to
apply this Bayesian framework to large-scale network reconstruction problems.
The presented method is generic but in terms of application we focus on gene
regulatory networks. We show on simulated and experimental data that the method
constitutes a good choice for network reconstruction regarding the number of
correctly selected features, prediction on new data and reasonable computing
time.
Fully Implicit Online Learning
Authors: Chaobing Song, Ji Liu, Han Liu, Yong Jiang, Tong Zhang
Date: 2018-09-25 00:00:00

Regularized online learning is widely used in machine learning applications.
In online learning, performing exact minimization ($i.e.,$ implicit update) is
known to be beneficial to the numerical stability and structure of solution. In
this paper we study a class of regularized online algorithms without
linearizing the loss function or the regularizer, which we call \emph{fully
implicit online learning} (FIOL). We show that for arbitrary Bregman
divergence, FIOL has the $O(\sqrt{T})$ regret for general convex setting and
$O(\log T)$ regret for strongly convex setting, and the regret has an one-step
improvement effect because it avoids the approximation error of linearization.
Then we propose efficient algorithms to solve the subproblem of FIOL. We show
that even if the solution of the subproblem has no closed form, it can be
solved with complexity comparable to the linearized online algoritms.
Experiments validate the proposed approaches.
The jamming transition as a paradigm to understand the loss landscape of deep neural networks
Authors: Mario Geiger, Stefano Spigler, Stéphane d'Ascoli, Levent Sagun, Marco Baity-Jesi, Giulio Biroli, Matthieu Wyart
Date: 2018-09-25 00:00:00

Deep learning has been immensely successful at a variety of tasks, ranging from classification to AI. Learning corresponds to fitting training data, which is implemented by descending a very high-dimensional loss function. Understanding under which conditions neural networks do not get stuck in poor minima of the loss, and how the landscape of that loss evolves as depth is increased remains a challenge. Here we predict, and test empirically, an analogy between this landscape and the energy landscape of repulsive ellipses. We argue that in FC networks a phase transition delimits the over- and under-parametrized regimes where fitting can or cannot be achieved. In the vicinity of this transition, properties of the curvature of the minima of the loss are critical. This transition shares direct similarities with the jamming transition by which particles form a disordered solid as the density is increased, which also occurs in certain classes of computational optimization and learning problems such as the perceptron. Our analysis gives a simple explanation as to why poor minima of the loss cannot be encountered in the overparametrized regime, and puts forward the surprising result that the ability of fully connected networks to fit random data is independent of their depth. Our observations suggests that this independence also holds for real data. We also study a quantity $\Delta$ which characterizes how well ($\Delta<0$) or badly ($\Delta>0$) a datum is learned. At the critical point it is power-law distributed, $P_+(\Delta)\sim\Delta^\theta$ for $\Delta>0$ and $P_-(\Delta)\sim(-\Delta)^{-\gamma}$ for $\Delta<0$, with $\theta\approx0.3$ and $\gamma\approx0.2$. This observation suggests that near the transition the loss landscape has a hierarchical structure and that the learning dynamics is prone to avalanche-like dynamics, with abrupt changes in the set of patterns that are learned.
A Survey of Learning Causality with Data: Problems and Methods
Authors: Ruocheng Guo, Lu Cheng, Jundong Li, P. Richard Hahn, Huan Liu
Date: 2018-09-25 00:00:00
Tasks: BIG-bench Machine Learning, Survey

This work considers the question of how convenient access to copious data impacts our ability to learn causal effects and relations. In what ways is learning causality in the era of big data different from -- or the same as -- the traditional one? To answer this question, this survey provides a comprehensive and structured review of both traditional and frontier methods in learning causality and relations along with the connections between causality and machine learning. This work points out on a case-by-case basis how big data facilitates, complicates, or motivates each approach.
Hierarchical Deep Multiagent Reinforcement Learning with Temporal Abstraction
Authors: Hongyao Tang, Jianye Hao, Tangjie Lv, Yingfeng Chen, Zongzhang Zhang, Hangtian Jia, Chunxu Ren, Yan Zheng, Zhaopeng Meng, Changjie Fan, Li Wang
Date: 2018-09-25 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Multiagent reinforcement learning (MARL) is commonly considered to suffer from non-stationary environments and exponentially increasing policy space. It would be even more challenging when rewards are sparse and delayed over long trajectories. In this paper, we study hierarchical deep MARL in cooperative multiagent problems with sparse and delayed reward. With temporal abstraction, we decompose the problem into a hierarchy of different time scales and investigate how agents can learn high-level coordination based on the independent skills learned at the low level. Three hierarchical deep MARL architectures are proposed to learn hierarchical policies under different MARL paradigms. Besides, we propose a new experience replay mechanism to alleviate the issue of the sparse transitions at the high level of abstraction and the non-stationarity of multiagent learning. We empirically demonstrate the effectiveness of our approaches in two domains with extremely sparse feedback: (1) a variety of Multiagent Trash Collection tasks, and (2) a challenging online mobile game, i.e., Fever Basketball Defense.
Early Identification of Pathogenic Social Media Accounts
Authors: Hamidreza Alvari, Elham Shaabani, Paulo Shakarian
Date: 2018-09-25 00:00:00
Tasks: Causal Inference, Community Detection

Pathogenic Social Media (PSM) accounts such as terrorist supporters exploit
large communities of supporters for conducting attacks on social media. Early
detection of these accounts is crucial as they are high likely to be key users
in making a harmful message "viral". In this paper, we make the first attempt
on utilizing causal inference to identify PSMs within a short time frame around
their activity. We propose a time-decay causality metric and incorporate it
into a causal community detection-based algorithm. The proposed algorithm is
applied to groups of accounts sharing similar causality features and is
followed by a classification algorithm to classify accounts as PSM or not.
Unlike existing techniques that take significant time to collect information
such as network, cascade path, or content, our scheme relies solely on action
log of users. Results on a real-world dataset from Twitter demonstrate
effectiveness and efficiency of our approach. We achieved precision of 0.84 for
detecting PSMs only based on their first 10 days of activity; the misclassified
accounts were then detected 10 days later.
Automated, predictive, and interpretable inference of C. elegans escape dynamics
Authors: Bryan C. Daniels, William S. Ryu, Ilya Nemenman
Date: 2018-09-25 00:00:00

The roundworm C. elegans exhibits robust escape behavior in response to
rapidly rising temperature. The behavior lasts for a few seconds, shows history
dependence, involves both sensory and motor systems, and is too complicated to
model mechanistically using currently available knowledge. Instead we model the
process phenomenologically, and we use the Sir Isaac dynamical inference
platform to infer the model in a fully automated fashion directly from
experimental data. The inferred model requires incorporation of an unobserved
dynamical variable, and is biologically interpretable. The model makes accurate
predictions about the dynamics of the worm behavior, and it can be used to
characterize the functional logic of the dynamical system underlying the escape
response. This work illustrates the power of modern artificial intelligence to
aid in discovery of accurate and interpretable models of complex natural
systems.
Floyd-Warshall Reinforcement Learning: Learning from Past Experiences to Reach New Goals
Authors: Vikas Dhiman, Shurjo Banerjee, Jeffrey M. Siskind, Jason J. Corso
Date: 2018-09-25 00:00:00
Tasks: Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Consider mutli-goal tasks that involve static environments and dynamic goals.
Examples of such tasks, such as goal-directed navigation and pick-and-place in
robotics, abound. Two types of Reinforcement Learning (RL) algorithms are used
for such tasks: model-free or model-based. Each of these approaches has
limitations. Model-free RL struggles to transfer learned information when the
goal location changes, but achieves high asymptotic accuracy in single goal
tasks. Model-based RL can transfer learned information to new goal locations by
retaining the explicitly learned state-dynamics, but is limited by the fact
that small errors in modelling these dynamics accumulate over long-term
planning. In this work, we improve upon the limitations of model-free RL in
multi-goal domains. We do this by adapting the Floyd-Warshall algorithm for RL
and call the adaptation Floyd-Warshall RL (FWRL). The proposed algorithm learns
a goal-conditioned action-value function by constraining the value of the
optimal path between any two states to be greater than or equal to the value of
paths via intermediary states. Experimentally, we show that FWRL is more
sample-efficient and learns higher reward strategies in multi-goal tasks as
compared to Q-learning, model-based RL and other relevant baselines in a
tabular domain.
Scenic: A Language for Scenario Specification and Scene Generation
Authors: Daniel J. Fremont, Tommaso Dreossi, Shromona Ghosh, Xiangyu Yue, Alberto L. Sangiovanni-Vincentelli, Sanjit A. Seshia
Date: 2018-09-25 00:00:00
Tasks: Probabilistic Programming, Scene Generation, Synthetic Data Generation

We propose a new probabilistic programming language for the design and analysis of perception systems, especially those based on machine learning. Specifically, we consider the problems of training a perception system to handle rare events, testing its performance under different conditions, and debugging failures. We show how a probabilistic programming language can help address these problems by specifying distributions encoding interesting types of inputs and sampling these to generate specialized training and test sets. More generally, such languages can be used for cyber-physical systems and robotics to write environment models, an essential prerequisite to any formal analysis. In this paper, we focus on systems like autonomous cars and robots, whose environment is a "scene", a configuration of physical objects and agents. We design a domain-specific language, Scenic, for describing "scenarios" that are distributions over scenes. As a probabilistic programming language, Scenic allows assigning distributions to features of the scene, as well as declaratively imposing hard and soft constraints over the scene. We develop specialized techniques for sampling from the resulting distribution, taking advantage of the structure provided by Scenic's domain-specific syntax. Finally, we apply Scenic in a case study on a convolutional neural network designed to detect cars in road images, improving its performance beyond that achieved by state-of-the-art synthetic data generation methods.