pwc-archive/papers-with-abstracts huggingface dataset:
Deep Reinforcement Learning for Event-Driven Multi-Agent Decision Processes
Authors: Kunal Menda, Yi-Chun Chen, Justin Grana, James W. Bono, Brendan D. Tracey, Mykel J. Kochenderfer, David Wolpert
Date: 2017-09-19 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

The incorporation of macro-actions (temporally extended actions) into multi-agent decision problems has the potential to address the curse of dimensionality associated with such decision problems. Since macro-actions last for stochastic durations, multiple agents executing decentralized policies in cooperative environments must act asynchronously. We present an algorithm that modifies generalized advantage estimation for temporally extended actions, allowing a state-of-the-art policy optimization algorithm to optimize policies in Dec-POMDPs in which agents act asynchronously. We show that our algorithm is capable of learning optimal policies in two cooperative domains, one involving real-time bus holding control and one involving wildfire fighting with unmanned aircraft. Our algorithm works by framing problems as "event-driven decision processes," which are scenarios in which the sequence and timing of actions and events are random and governed by an underlying stochastic process. In addition to optimizing policies with continuous state and action spaces, our algorithm also facilitates the use of event-driven simulators, which do not require time to be discretized into time-steps. We demonstrate the benefit of using event-driven simulation in the context of multiple agents taking asynchronous actions. We show that fixed time-step simulation risks obfuscating the sequence in which closely separated events occur, adversely affecting the policies learned. In addition, we show that arbitrarily shrinking the time-step scales poorly with the number of agents.
Predicting Positive and Negative Links with Noisy Queries: Theory & Practice
Authors: Charalampos E. Tsourakakis, Michael Mitzenmacher, Kasper Green Larsen, Jarosław Błasiok, Ben Lawson, Preetum Nakkiran, Vasileios Nakos
Date: 2017-09-19 00:00:00
Tasks: Clustering

Social networks involve both positive and negative relationships, which can be captured in signed graphs. The {\em edge sign prediction problem} aims to predict whether an interaction between a pair of nodes will be positive or negative. We provide theoretical results for this problem that motivate natural improvements to recent heuristics. The edge sign prediction problem is related to correlation clustering; a positive relationship means being in the same cluster. We consider the following model for two clusters: we are allowed to query any pair of nodes whether they belong to the same cluster or not, but the answer to the query is corrupted with some probability $0<q<\frac{1}{2}$. Let $\delta=1-2q$ be the bias. We provide an algorithm that recovers all signs correctly with high probability in the presence of noise with $O(\frac{n\log n}{\delta^2}+\frac{\log^2 n}{\delta^6})$ queries. This is the best known result for this problem for all but tiny $\delta$, improving on the recent work of Mazumdar and Saha \cite{mazumdar2017clustering}. We also provide an algorithm that performs $O(\frac{n\log n}{\delta^4})$ queries, and uses breadth first search as its main algorithmic primitive. While both the running time and the number of queries for this algorithm are sub-optimal, our result relies on novel theoretical techniques, and naturally suggests the use of edge-disjoint paths as a feature for predicting signs in online social networks. Correspondingly, we experiment with using edge disjoint $s-t$ paths of short length as a feature for predicting the sign of edge $(s,t)$ in real-world signed networks. Empirical findings suggest that the use of such paths improves the classification accuracy, especially for pairs of nodes with no common neighbors.
An Attention-based Collaboration Framework for Multi-View Network Representation Learning
Authors: Meng Qu, Jian Tang, Jingbo Shang, Xiang Ren, Ming Zhang, Jiawei Han
Date: 2017-09-19 00:00:00
Tasks: Representation Learning

Learning distributed node representations in networks has been attracting
increasing attention recently due to its effectiveness in a variety of
applications. Existing approaches usually study networks with a single type of
proximity between nodes, which defines a single view of a network. However, in
reality there usually exists multiple types of proximities between nodes,
yielding networks with multiple views. This paper studies learning node
representations for networks with multiple views, which aims to infer robust
node representations across different views. We propose a multi-view
representation learning approach, which promotes the collaboration of different
views and lets them vote for the robust representations. During the voting
process, an attention mechanism is introduced, which enables each node to focus
on the most informative views. Experimental results on real-world networks show
that the proposed approach outperforms existing state-of-the-art approaches for
network representation learning with a single view and other competitive
approaches with multiple views.
HitFraud: A Broad Learning Approach for Collective Fraud Detection in Heterogeneous Information Networks
Authors: Bokai Cao, Mia Mao, Siim Viidu, Philip S. Yu
Date: 2017-09-13 00:00:00
Tasks: Fraud Detection

On electronic game platforms, different payment transactions have different
levels of risk. Risk is generally higher for digital goods in e-commerce.
However, it differs based on product and its popularity, the offer type
(packaged game, virtual currency to a game or subscription service), storefront
and geography. Existing fraud policies and models make decisions independently
for each transaction based on transaction attributes, payment velocities, user
characteristics, and other relevant information. However, suspicious
transactions may still evade detection and hence we propose a broad learning
approach leveraging a graph based perspective to uncover relationships among
suspicious transactions, i.e., inter-transaction dependency. Our focus is to
detect suspicious transactions by capturing common fraudulent behaviors that
would not be considered suspicious when being considered in isolation. In this
paper, we present HitFraud that leverages heterogeneous information networks
for collective fraud detection by exploring correlated and fast evolving
fraudulent behaviors. First, a heterogeneous information network is designed to
link entities of interest in the transaction database via different semantics.
Then, graph based features are efficiently discovered from the network
exploiting the concept of meta-paths, and decisions on frauds are made
collectively on test instances. Experiments on real-world payment transaction
data from Electronic Arts demonstrate that the prediction performance is
effectively boosted by HitFraud with fast convergence where the computation of
meta-path based features is largely optimized. Notably, recall can be improved
up to 7.93% and F-score 4.62% compared to baselines.
How do people explore virtual environments?
Authors: Vincent Sitzmann, Ana Serrano, Amy Pavel, Maneesh Agrawala, Diego Gutierrez, Belen Masia, Gordon Wetzstein
Date: 2016-12-13 00:00:00
Tasks: Video Synopsis

Understanding how people explore immersive virtual environments is crucial
for many applications, such as designing virtual reality (VR) content,
developing new compression algorithms, or learning computational models of
saliency or visual attention. Whereas a body of recent work has focused on
modeling saliency in desktop viewing conditions, VR is very different from
these conditions in that viewing behavior is governed by stereoscopic vision
and by the complex interaction of head orientation, gaze, and other kinematic
constraints. To further our understanding of viewing behavior and saliency in
VR, we capture and analyze gaze and head orientation data of 169 users
exploring stereoscopic, static omni-directional panoramas, for a total of 1980
head and gaze trajectories for three different viewing conditions. We provide a
thorough analysis of our data, which leads to several important insights, such
as the existence of a particular fixation bias, which we then use to adapt
existing saliency predictors to immersive VR conditions. In addition, we
explore other applications of our data and analysis, including automatic
alignment of VR video cuts, panorama thumbnails, panorama video synopsis, and
saliency-based compression.
Learning of Coordination Policies for Robotic Swarms
Authors: Qiyang Li, Xintong Du, Yizhou Huang, Quinlan Sykora, Angela P. Schoellig
Date: 2017-09-19 00:00:00

Inspired by biological swarms, robotic swarms are envisioned to solve
real-world problems that are difficult for individual agents. Biological swarms
can achieve collective intelligence based on local interactions and simple
rules; however, designing effective distributed policies for large-scale
robotic swarms to achieve a global objective can be challenging. Although it is
often possible to design an optimal centralized strategy for smaller numbers of
agents, those methods can fail as the number of agents increases. Motivated by
the growing success of machine learning, we develop a deep learning approach
that learns distributed coordination policies from centralized policies. In
contrast to traditional distributed control approaches, which are usually based
on human-designed policies for relatively simple tasks, this learning-based
approach can be adapted to more difficult tasks. We demonstrate the efficacy of
our proposed approach on two different tasks, the well-known rendezvous problem
and a more difficult particle assignment problem. For the latter, no known
distributed policy exists. From extensive simulations, it is shown that the
performance of the learned coordination policies is comparable to the
centralized policies, surpassing state-of-the-art distributed policies.
Thereby, our proposed approach provides a promising alternative for real-world
coordination problems that would be otherwise computationally expensive to
solve or intangible to explore.
An Analog Neural Network Computing Engine using CMOS-Compatible Charge-Trap-Transistor (CTT)
Authors: Yuan Du, Li Du, Xuefeng Gu, Jieqiong Du, X. Shawn Wang, Boyu Hu, Mingzhe Jiang, Xiaoliang Chen, Junjie Su, Subramanian S. Iyer, Mau-Chung Frank Chang
Date: 2017-09-19 00:00:00

An analog neural network computing engine based on CMOS-compatible
charge-trap transistor (CTT) is proposed in this paper. CTT devices are used as
analog multipliers. Compared to digital multipliers, CTT-based analog
multiplier shows significant area and power reduction. The proposed computing
engine is composed of a scalable CTT multiplier array and energy efficient
analog-digital interfaces. Through implementing the sequential analog fabric
(SAF), the engine mixed-signal interfaces are simplified and hardware overhead
remains constant regardless of the size of the array. A proof-of-concept 784 by
784 CTT computing engine is implemented using TSMC 28nm CMOS technology and
occupied 0.68mm2. The simulated performance achieves 76.8 TOPS (8-bit) with 500
MHz clock frequency and consumes 14.8 mW. As an example, we utilize this
computing engine to address a classic pattern recognition problem --
classifying handwritten digits on MNIST database and obtained a performance
comparable to state-of-the-art fully connected neural networks using 8-bit
fixed-point resolution.
Deep Automated Multi-task Learning
Authors: Davis Liang, Yan Shu
Date: 2017-09-16 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Multi-Task Learning, Prediction, Sentiment Analysis

Multi-task learning (MTL) has recently contributed to learning better
representations in service of various NLP tasks. MTL aims at improving the
performance of a primary task, by jointly training on a secondary task. This
paper introduces automated tasks, which exploit the sequential nature of the
input data, as secondary tasks in an MTL model. We explore next word
prediction, next character prediction, and missing word completion as potential
automated tasks. Our results show that training on a primary task in parallel
with a secondary automated task improves both the convergence speed and
accuracy for the primary task. We suggest two methods for augmenting an
existing network with automated tasks and establish better performance in topic
prediction, sentiment analysis, and hashtag recommendation. Finally, we show
that the MTL models can perform well on datasets that are small and colloquial
by nature.
Pairwise Choice Markov Chains
Authors: Stephen Ragain, Johan Ugander
Date: 2016-03-08 00:00:00
Proceeding: NeurIPS 2016 12

As datasets capturing human choices grow in richness and scale -- particularly in online domains -- there is an increasing need for choice models that escape traditional choice-theoretic axioms such as regularity, stochastic transitivity, and Luce's choice axiom. In this work we introduce the Pairwise Choice Markov Chain (PCMC) model of discrete choice, an inferentially tractable model that does not assume any of the above axioms while still satisfying the foundational axiom of uniform expansion, a considerably weaker assumption than Luce's choice axiom. We show that the PCMC model significantly outperforms the Multinomial Logit (MNL) model in prediction tasks on both synthetic and empirical datasets known to exhibit violations of Luce's axiom. Our analysis also synthesizes several recent observations connecting the Multinomial Logit model and Markov chains; the PCMC model retains the Multinomial Logit model as a special case.
Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges
Authors: Muhammad Usama, Junaid Qadir, Aunn Raza, Hunain Arif, Kok-Lim Alvin Yau, Yehia Elkhatib, Amir Hussain, Ala Al-Fuqaha
Date: 2017-09-19 00:00:00
Tasks: Anomaly Detection, BIG-bench Machine Learning, Feature Engineering, Self-Driving Cars, speech-recognition, Speech Recognition, Survey, Traffic Classification

While machine learning and artificial intelligence have long been applied in
networking research, the bulk of such works has focused on supervised learning.
Recently there has been a rising trend of employing unsupervised machine
learning using unstructured raw network data to improve network performance and
provide services such as traffic engineering, anomaly detection, Internet
traffic classification, and quality of service optimization. The interest in
applying unsupervised learning techniques in networking emerges from their
great success in other fields such as computer vision, natural language
processing, speech recognition, and optimal control (e.g., for developing
autonomous self-driving cars). Unsupervised learning is interesting since it
can unconstrain us from the need of labeled data and manual handcrafted feature
engineering thereby facilitating flexible, general, and automated methods of
machine learning. The focus of this survey paper is to provide an overview of
the applications of unsupervised learning in the domain of networking. We
provide a comprehensive survey highlighting the recent advancements in
unsupervised learning techniques and describe their applications for various
learning tasks in the context of networking. We also provide a discussion on
future directions and open research issues, while also identifying potential
pitfalls. While a few survey papers focusing on the applications of machine
learning in networking have previously been published, a survey of similar
scope and breadth is missing in literature. Through this paper, we advance the
state of knowledge by carefully synthesizing the insights from these survey
papers while also providing contemporary coverage of recent advances.
Methodology and Results for the Competition on Semantic Similarity Evaluation and Entailment Recognition for PROPOR 2016
Authors: Luciano Barbosa, Paulo R. Cavalin, Victor Guimaraes, Matthias Kormaksson
Date: 2017-09-19 00:00:00
Tasks: Semantic Similarity, Semantic Textual Similarity

In this paper, we present the methodology and the results obtained by our
teams, dubbed Blue Man Group, in the ASSIN (from the Portuguese {\it
Avalia\c{c}\~ao de Similaridade Sem\^antica e Infer\^encia Textual})
competition, held at PROPOR 2016\footnote{International Conference on the
Computational Processing of the Portuguese Language -
http://propor2016.di.fc.ul.pt/}. Our team's strategy consisted of evaluating
methods based on semantic word vectors, following two distinct directions: 1)
to make use of low-dimensional, compact, feature sets, and 2) deep
learning-based strategies dealing with high-dimensional feature vectors.
Evaluation results demonstrated that the first strategy was more promising, so
that the results from the second strategy have been discarded. As a result, by
considering the best run of each of the six teams, we have been able to achieve
the best accuracy and F1 values in entailment recognition, in the Brazilian
Portuguese set, and the best F1 score overall. In the semantic similarity task,
our team was ranked second in the Brazilian Portuguese set, and third
considering both sets.
Summable Reparameterizations of Wasserstein Critics in the One-Dimensional Setting
Authors: Christopher Grimm, Yuhang Song, Michael L. Littman
Date: 2017-09-19 00:00:00
Tasks: Density Estimation

Generative adversarial networks (GANs) are an exciting alternative to
algorithms for solving density estimation problems---using data to assess how
likely samples are to be drawn from the same distribution. Instead of
explicitly computing these probabilities, GANs learn a generator that can match
the given probabilistic source. This paper looks particularly at this matching
capability in the context of problems with one-dimensional outputs. We identify
a class of function decompositions with properties that make them well suited
to the critic role in a leading approach to GANs known as Wasserstein GANs. We
show that Taylor and Fourier series decompositions belong to our class, provide
examples of these critics outperforming standard GAN approaches, and suggest
how they can be scaled to higher dimensional problems in the future.
When 3D-Aided 2D Face Recognition Meets Deep Learning: An extended UR2D for Pose-Invariant Face Recognition
Authors: Xiang Xu, Pengfei Dou, Ha A. Le, Ioannis A. Kakadiaris
Date: 2017-09-19 00:00:00
Tasks: Deep Learning, Face Identification, Face Recognition, Robust Face Recognition, Template Matching

Most of the face recognition works focus on specific modules or demonstrate a
research idea. This paper presents a pose-invariant 3D-aided 2D face
recognition system (UR2D) that is robust to pose variations as large as 90? by
leveraging deep learning technology. The architecture and the interface of UR2D
are described, and each module is introduced in detail. Extensive experiments
are conducted on the UHDB31 and IJB-A, demonstrating that UR2D outperforms
existing 2D face recognition systems such as VGG-Face, FaceNet, and a
commercial off-the-shelf software (COTS) by at least 9% on the UHDB31 dataset
and 3% on the IJB-A dataset on average in face identification tasks. UR2D also
achieves state-of-the-art performance of 85% on the IJB-A dataset by comparing
the Rank-1 accuracy score from template matching. It fills a gap by providing a
3D-aided 2D face recognition system that has compatible results with 2D face
recognition systems using deep learning techniques.
Learning to Detect Violent Videos using Convolutional Long Short-Term Memory
Authors: Swathikiran Sudhakaran, Oswald Lanz
Date: 2017-09-19 00:00:00

Developing a technique for the automatic analysis of surveillance videos in
order to identify the presence of violence is of broad interest. In this work,
we propose a deep neural network for the purpose of recognizing violent videos.
A convolutional neural network is used to extract frame level features from a
video. The frame level features are then aggregated using a variant of the long
short term memory that uses convolutional gates. The convolutional neural
network along with the convolutional long short term memory is capable of
capturing localized spatio-temporal features which enables the analysis of
local motion taking place in the video. We also propose to use adjacent frame
differences as the input to the model thereby forcing it to encode the changes
occurring in the video. The performance of the proposed feature extraction
pipeline is evaluated on three standard benchmark datasets in terms of
recognition accuracy. Comparison of the results obtained with the state of the
art techniques revealed the promising capability of the proposed method in
recognizing violent videos.
Inference in Graphical Models via Semidefinite Programming Hierarchies
Authors: Murat A. Erdogdu, Yash Deshpande, Andrea Montanari
Date: 2017-09-19 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Combinatorial Optimization, Computational Efficiency, Denoising, Image Denoising

Maximum A posteriori Probability (MAP) inference in graphical models amounts
to solving a graph-structured combinatorial optimization problem. Popular
inference algorithms such as belief propagation (BP) and generalized belief
propagation (GBP) are intimately related to linear programming (LP) relaxation
within the Sherali-Adams hierarchy. Despite the popularity of these algorithms,
it is well understood that the Sum-of-Squares (SOS) hierarchy based on
semidefinite programming (SDP) can provide superior guarantees. Unfortunately,
SOS relaxations for a graph with $n$ vertices require solving an SDP with
$n^{\Theta(d)}$ variables where $d$ is the degree in the hierarchy. In
practice, for $d\ge 4$, this approach does not scale beyond a few tens of
variables. In this paper, we propose binary SDP relaxations for MAP inference
using the SOS hierarchy with two innovations focused on computational
efficiency. Firstly, in analogy to BP and its variants, we only introduce
decision variables corresponding to contiguous regions in the graphical model.
Secondly, we solve the resulting SDP using a non-convex Burer-Monteiro style
method, and develop a sequential rounding procedure. We demonstrate that the
resulting algorithm can solve problems with tens of thousands of variables
within minutes, and outperforms BP and GBP on practical problems such as image
denoising and Ising spin glasses. Finally, for specific graph types, we
establish a sufficient condition for the tightness of the proposed partial SOS
relaxation.
Convolutional Long Short-Term Memory Networks for Recognizing First Person Interactions
Authors: Swathikiran Sudhakaran, Oswald Lanz
Date: 2017-09-19 00:00:00

In this paper, we present a novel deep learning based approach for addressing
the problem of interaction recognition from a first person perspective. The
proposed approach uses a pair of convolutional neural networks, whose
parameters are shared, for extracting frame level features from successive
frames of the video. The frame level features are then aggregated using a
convolutional long short-term memory. The hidden state of the convolutional
long short-term memory, after all the input video frames are processed, is used
for classification in to the respective categories. The two branches of the
convolutional neural network perform feature encoding on a short time interval
whereas the convolutional long short term memory encodes the changes on a
longer temporal duration. In our network the spatio-temporal structure of the
input is preserved till the very final processing stage. Experimental results
show that our method outperforms the state of the art on most recent first
person interactions datasets that involve complex ego-motion. In particular, on
UTKinect-FirstPerson it competes with methods that use depth image and skeletal
joints information along with RGB images, while it surpasses all previous
methods that use only RGB images by more than 20% in recognition accuracy.