pwc-archive/papers-with-abstracts huggingface dataset:
Network Coding Based Evolutionary Network Formation for Dynamic Wireless Networks
Authors: Minhae Kwon, Hyunggon Park
Date: 2017-12-02 00:00:00

In this paper, we aim to find a robust network formation strategy that can
adaptively evolve the network topology against network dynamics in a
distributed manner. We consider a network coding deployed wireless ad hoc
network where source nodes are connected to terminal nodes with the help of
intermediate nodes. We show that mixing operations in network coding can induce
packet anonymity that allows the inter-connections in a network to be
decoupled. This enables each intermediate node to consider complex network
inter-connections as a node-environment interaction such that the Markov
decision process (MDP) can be employed at each intermediate node. The optimal
policy that can be obtained by solving the MDP provides each node with optimal
amount of changes in transmission range given network dynamics (e.g., the
number of nodes in the range and channel condition). Hence, the network can be
adaptively and optimally evolved by responding to the network dynamics. The
proposed strategy is used to maximize long-term utility, which is achieved by
considering both current network conditions and future network dynamics. We
define the utility of an action to include network throughput gain and the cost
of transmission power. We show that the resulting network of the proposed
strategy eventually converges to stationary networks, which maintain the states
of the nodes. Moreover, we propose to determine initial transmission ranges and
initial network topology that can expedite the convergence of the proposed
algorithm. Our simulation results confirm that the proposed strategy builds a
network which adaptively changes its topology in the presence of network
dynamics. Moreover, the proposed strategy outperforms existing strategies in
terms of system goodput and successful connectivity ratio.
PFAx: Predictable Feature Analysis to Perform Control
Authors: Stefan Richthofer, Laurenz Wiskott
Date: 2017-12-02 00:00:00
Tasks: Dimensionality Reduction, feature selection, global-optimization, Reinforcement Learning

Predictable Feature Analysis (PFA) (Richthofer, Wiskott, ICMLA 2015) is an
algorithm that performs dimensionality reduction on high dimensional input
signal. It extracts those subsignals that are most predictable according to a
certain prediction model. We refer to these extracted signals as predictable
features.
  In this work we extend the notion of PFA to take supplementary information
into account for improving its predictions. Such information can be a
multidimensional signal like the main input to PFA, but is regarded external.
That means it won't participate in the feature extraction - no features get
extracted or composed of it. Features will be exclusively extracted from the
main input such that they are most predictable based on themselves and the
supplementary information. We refer to this enhanced PFA as PFAx (PFA
extended).
  Even more important than improving prediction quality is to observe the
effect of supplementary information on feature selection. PFAx transparently
provides insight how the supplementary information adds to prediction quality
and whether it is valuable at all. Finally we show how to invert that relation
and can generate the supplementary information such that it would yield a
certain desired outcome of the main signal.
  We apply this to a setting inspired by reinforcement learning and let the
algorithm learn how to control an agent in an environment. With this method it
is feasible to locally optimize the agent's state, i.e. reach a certain goal
that is near enough. We are preparing a follow-up paper that extends this
method such that also global optimization is feasible.
DR-Net: Transmission Steered Single Image Dehazing Network with Weakly Supervised Refinement
Authors: Chongyi Li, Jichang Guo, Fatih Porikli, Chunle Guo, Huzhu Fu, Xi Li
Date: 2017-12-02 00:00:00
Tasks: Image Dehazing, Single Image Dehazing, Weakly-supervised Learning

Despite the recent progress in image dehazing, several problems remain
largely unsolved such as robustness for varying scenes, the visual quality of
reconstructed images, and effectiveness and flexibility for applications. To
tackle these problems, we propose a new deep network architecture for single
image dehazing called DR-Net. Our model consists of three main subnetworks: a
transmission prediction network that predicts transmission map for the input
image, a haze removal network that reconstructs latent image steered by the
transmission map, and a refinement network that enhances the details and color
properties of the dehazed result via weakly supervised learning. Compared to
previous methods, our method advances in three aspects: (i) pure data-driven
model; (ii) the end-to-end system; (iii) superior robustness, accuracy, and
applicability. Extensive experiments demonstrate that our DR-Net outperforms
the state-of-the-art methods on both synthetic and real images in qualitative
and quantitative metrics. Additionally, the utility of DR-Net has been
illustrated by its potential usage in several important computer vision tasks.
Artificial intelligence in peer review: How can evolutionary computation support journal editors?
Authors: Maciej J. Mrowinski, Piotr Fronczak, Agata Fronczak, Marcel Ausloos, Olgica Nedic
Date: 2017-12-02 00:00:00

With the volume of manuscripts submitted for publication growing every year,
the deficiencies of peer review (e.g. long review times) are becoming more
apparent. Editorial strategies, sets of guidelines designed to speed up the
process and reduce editors workloads, are treated as trade secrets by
publishing houses and are not shared publicly. To improve the effectiveness of
their strategies, editors in small publishing groups are faced with undertaking
an iterative trial-and-error approach. We show that Cartesian Genetic
Programming, a nature-inspired evolutionary algorithm, can dramatically improve
editorial strategies. The artificially evolved strategy reduced the duration of
the peer review process by 30%, without increasing the pool of reviewers (in
comparison to a typical human-developed strategy). Evolutionary computation has
typically been used in technological processes or biological ecosystems. Our
results demonstrate that genetic programs can improve real-world social systems
that are usually much harder to understand and control than physical systems.
Improving Visually Grounded Sentence Representations with Self-Attention
Authors: Kang Min Yoo, Youhyun Shin, Sang-goo Lee
Date: 2017-12-02 00:00:00
Tasks: Sentence, Visual Grounding

Sentence representation models trained only on language could potentially
suffer from the grounding problem. Recent work has shown promising results in
improving the qualities of sentence representations by jointly training them
with associated image features. However, the grounding capability is limited
due to distant connection between input sentences and image features by the
design of the architecture. In order to further close the gap, we propose
applying self-attention mechanism to the sentence encoder to deepen the
grounding effect. Our results on transfer tasks show that self-attentive
encoders are better for visual grounding, as they exploit specific words with
strong visual associations.
Distributional Equivalence and Structure Learning for Bow-free Acyclic Path Diagrams
Authors: Christopher Nowzohour, Marloes H. Maathuis, Robin J. Evans, Peter Bühlmann
Date: 2015-08-07 00:00:00

We consider the problem of structure learning for bow-free acyclic path
diagrams (BAPs). BAPs can be viewed as a generalization of linear Gaussian DAG
models that allow for certain hidden variables. We present a first method for
this problem using a greedy score-based search algorithm. We also prove some
necessary and some sufficient conditions for distributional equivalence of BAPs
which are used in an algorithmic ap- proach to compute (nearly) equivalent
model structures. This allows us to infer lower bounds of causal effects. We
also present applications to real and simulated datasets using our publicly
available R-package.
Learning with Options that Terminate Off-Policy
Authors: Anna Harutyunyan, Peter Vrancx, Pierre-Luc Bacon, Doina Precup, Ann Nowe
Date: 2017-11-10 00:00:00

A temporally abstract action, or an option, is specified by a policy and a
termination condition: the policy guides option behavior, and the termination
condition roughly determines its length. Generally, learning with longer
options (like learning with multi-step returns) is known to be more efficient.
However, if the option set for the task is not ideal, and cannot express the
primitive optimal policy exactly, shorter options offer more flexibility and
can yield a better solution. Thus, the termination condition puts learning
efficiency at odds with solution quality. We propose to resolve this dilemma by
decoupling the behavior and target terminations, just like it is done with
policies in off-policy learning. To this end, we give a new algorithm,
Q(\beta), that learns the solution with respect to any termination condition,
regardless of how the options actually terminate. We derive Q(\beta) by casting
learning with options into a common framework with well-studied multi-step
off-policy learning. We validate our algorithm empirically, and show that it
holds up to its motivating claims.
LOGAN: Membership Inference Attacks Against Generative Models
Authors: Jamie Hayes, Luca Melis, George Danezis, Emiliano De Cristofaro
Date: 2017-05-22 00:00:00

Generative models estimate the underlying distribution of a dataset to
generate realistic samples according to that distribution. In this paper, we
present the first membership inference attacks against generative models: given
a data point, the adversary determines whether or not it was used to train the
model. Our attacks leverage Generative Adversarial Networks (GANs), which
combine a discriminative and a generative model, to detect overfitting and
recognize inputs that were part of training datasets, using the discriminator's
capacity to learn statistical differences in distributions.
  We present attacks based on both white-box and black-box access to the target
model, against several state-of-the-art generative models, over datasets of
complex representations of faces (LFW), objects (CIFAR-10), and medical images
(Diabetic Retinopathy). We also discuss the sensitivity of the attacks to
different training parameters, and their robustness against mitigation
strategies, finding that defenses are either ineffective or lead to
significantly worse performances of the generative models in terms of training
stability and/or sample quality.
MAgent: A Many-Agent Reinforcement Learning Platform for Artificial Collective Intelligence
Authors: Lianmin Zheng, Jiacheng Yang, Han Cai, Wei-Nan Zhang, Jun Wang, Yong Yu
Date: 2017-12-02 00:00:00
Tasks: GPU, Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We introduce MAgent, a platform to support research and development of
many-agent reinforcement learning. Unlike previous research platforms on single
or multi-agent reinforcement learning, MAgent focuses on supporting the tasks
and the applications that require hundreds to millions of agents. Within the
interactions among a population of agents, it enables not only the study of
learning algorithms for agents' optimal polices, but more importantly, the
observation and understanding of individual agent's behaviors and social
phenomena emerging from the AI society, including communication languages,
leaderships, altruism. MAgent is highly scalable and can host up to one million
agents on a single GPU server. MAgent also provides flexible configurations for
AI researchers to design their customized environments and agents. In this
demo, we present three environments designed on MAgent and show emerged
collective intelligence by learning from scratch.
Online Reinforcement Learning in Stochastic Games
Authors: Chen-Yu Wei, Yi-Te Hong, Chi-Jen Lu
Date: 2017-12-02 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We study online reinforcement learning in average-reward stochastic games
(SGs). An SG models a two-player zero-sum game in a Markov environment, where
state transitions and one-step payoffs are determined simultaneously by a
learner and an adversary. We propose the UCSG algorithm that achieves a
sublinear regret compared to the game value when competing with an arbitrary
opponent. This result improves previous ones under the same setting. The regret
bound has a dependency on the diameter, which is an intrinsic value related to
the mixing property of SGs. If we let the opponent play an optimistic best
response to the learner, UCSG finds an $\varepsilon$-maximin stationary policy
with a sample complexity of
$\tilde{\mathcal{O}}\left(\text{poly}(1/\varepsilon)\right)$, where
$\varepsilon$ is the gap to the best policy.
Tracking the Best Expert in Non-stationary Stochastic Environments
Authors: Chen-Yu Wei, Yi-Te Hong, Chi-Jen Lu
Date: 2017-12-02 00:00:00
Proceeding: NeurIPS 2016 12

We study the dynamic regret of multi-armed bandit and experts problem in non-stationary stochastic environments. We introduce a new parameter $\Lambda$, which measures the total statistical variance of the loss distributions over $T$ rounds of the process, and study how this amount affects the regret. We investigate the interaction between $\Lambda$ and $\Gamma$, which counts the number of times the distributions change, as well as $\Lambda$ and $V$, which measures how far the distributions deviates over time. One striking result we find is that even when $\Gamma$, $V$, and $\Lambda$ are all restricted to constant, the regret lower bound in the bandit setting still grows with $T$. The other highlight is that in the full-information setting, a constant regret becomes achievable with constant $\Gamma$ and $\Lambda$, as it can be made independent of $T$, while with constant $V$ and $\Lambda$, the regret still has a $T^{1/3}$ dependency. We not only propose algorithms with upper bound guarantee, but prove their matching lower bounds as well.
Mitigating the Impact of Speech Recognition Errors on Chatbot using Sequence-to-Sequence Model
Authors: Pin-Jung Chen, I-Hung Hsu, Yi-Yao Huang, Hung-Yi Lee
Date: 2017-09-22 00:00:00
Tasks: Chatbot, Decoder, Domain Adaptation, speech-recognition, Speech Recognition

We apply sequence-to-sequence model to mitigate the impact of speech
recognition errors on open domain end-to-end dialog generation. We cast the
task as a domain adaptation problem where ASR transcriptions and original text
are in two different domains. In this paper, our proposed model includes two
individual encoders for each domain data and make their hidden states similar
to ensure the decoder predict the same dialog text. The method shows that the
sequence-to-sequence model can learn the ASR transcriptions and original text
pair having the same meaning and eliminate the speech recognition errors.
Experimental results on Cornell movie dialog dataset demonstrate that the
domain adaption system help the spoken dialog system generate more similar
responses with the original text answers.
Interactive Reinforcement Learning for Object Grounding via Self-Talking
Authors: Yan Zhu, Shaoting Zhang, Dimitris Metaxas
Date: 2017-12-02 00:00:00
Tasks: Object, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Visual Grounding

Humans are able to identify a referred visual object in a complex scene via a
few rounds of natural language communications. Success communication requires
both parties to engage and learn to adapt for each other. In this paper, we
introduce an interactive training method to improve the natural language
conversation system for a visual grounding task. During interactive training,
both agents are reinforced by the guidance from a common reward function. The
parametrized reward function also cooperatively updates itself via
interactions, and contribute to accomplishing the task. We evaluate the method
on GuessWhat?! visual grounding task, and significantly improve the task
success rate. However, we observe language drifting problem during training and
propose to use reward engineering to improve the interpretability for the
generated conversations. Our result also indicates evaluating goal-ended visual
conversation tasks require semantic relevant metrics beyond task success rate.
Lecture video indexing using boosted margin maximizing neural networks
Authors: Di Ma, Xi Zhang, Xu Ouyang, Gady Agam
Date: 2017-12-02 00:00:00

This paper presents a novel approach for lecture video indexing using a
boosted deep convolutional neural network system. The indexing is performed by
matching high quality slide images, for which text is either known or
extracted, to lower resolution video frames with possible noise, perspective
distortion, and occlusions. We propose a deep neural network integrated with a
boosting framework composed of two sub-networks targeting feature extraction
and similarity determination to perform the matching. The trained network is
given as input a pair of slide image and a candidate video frame image and
produces the similarity between them. A boosting framework is integrated into
our proposed network during the training process. Experimental results show
that the proposed approach is much more capable of handling occlusion, spatial
transformations, and other types of noises when compared with known approaches.
Supervised Hashing based on Energy Minimization
Authors: Zihao Hu, Xiyi Luo, Hongtao Lu, Yong Yu
Date: 2017-12-02 00:00:00
Tasks: Retrieval

Recently, supervised hashing methods have attracted much attention since they
can optimize retrieval speed and storage cost while preserving semantic
information. Because hashing codes learning is NP-hard, many methods resort to
some form of relaxation technique. But the performance of these methods can
easily deteriorate due to the relaxation. Luckily, many supervised hashing
formulations can be viewed as energy functions, hence solving hashing codes is
equivalent to learning marginals in the corresponding conditional random field
(CRF). By minimizing the KL divergence between a fully factorized distribution
and the Gibbs distribution of this CRF, a set of consistency equations can be
obtained, but updating them in parallel may not yield a local optimum since the
variational lower bound is not guaranteed to increase. In this paper, we use a
linear approximation of the sigmoid function to convert these consistency
equations to linear systems, which have a closed-form solution. By applying
this novel technique to two classical hashing formulations KSH and SPLH, we
obtain two new methods called EM (energy minimizing based)-KSH and EM-SPLH.
Experimental results on three datasets show the superiority of our methods.
Labeled Memory Networks for Online Model Adaptation
Authors: Shiv Shankar, Sunita Sarawagi
Date: 2017-07-05 00:00:00
Tasks: Few-Shot Learning, Meta-Learning, model

Augmenting a neural network with memory that can grow without growing the
number of trained parameters is a recent powerful concept with many exciting
applications. We propose a design of memory augmented neural networks (MANNs)
called Labeled Memory Networks (LMNs) suited for tasks requiring online
adaptation in classification models. LMNs organize the memory with classes as
the primary key.The memory acts as a second boosted stage following a regular
neural network thereby allowing the memory and the primary network to play
complementary roles. Unlike existing MANNs that write to memory for every
instance and use LRU based memory replacement, LMNs write only for instances
with non-zero loss and use label-based memory replacement. We demonstrate
significant accuracy gains on various tasks including word-modelling and
few-shot learning. In this paper, we establish their potential in online
adapting a batch trained neural network to domain-relevant labeled data at
deployment time. We show that LMNs are better than other MANNs designed for
meta-learning. We also found them to be more accurate and faster than
state-of-the-art methods of retuning model parameters for adapting to
domain-specific labeled data.
Anesthesiologist-level forecasting of hypoxemia with only SpO2 data using deep learning
Authors: Gabriel Erion, Hugh Chen, Scott M. Lundberg, Su-In Lee
Date: 2017-12-02 00:00:00

We use a deep learning model trained only on a patient's blood oxygenation
data (measurable with an inexpensive fingertip sensor) to predict impending
hypoxemia (low blood oxygen) more accurately than trained anesthesiologists
with access to all the data recorded in a modern operating room. We also
provide a simple way to visualize the reason why a patient's risk is low or
high by assigning weight to the patient's past blood oxygen values. This work
has the potential to provide cutting-edge clinical decision support in
low-resource settings, where rates of surgical complication and death are
substantially greater than in high-resource areas.
Where Classification Fails, Interpretation Rises
Authors: Chanh Nguyen, Georgi Georgiev, Yujie Ji, Ting Wang
Date: 2017-12-02 00:00:00
Tasks: Classification, General Classification

An intriguing property of deep neural networks is their inherent
vulnerability to adversarial inputs, which significantly hinders their
application in security-critical domains. Most existing detection methods
attempt to use carefully engineered patterns to distinguish adversarial inputs
from their genuine counterparts, which however can often be circumvented by
adaptive adversaries. In this work, we take a completely different route by
leveraging the definition of adversarial inputs: while deceiving for deep
neural networks, they are barely discernible for human visions. Building upon
recent advances in interpretable models, we construct a new detection framework
that contrasts an input's interpretation against its classification. We
validate the efficacy of this framework through extensive experiments using
benchmark datasets and attacks. We believe that this work opens a new direction
for designing adversarial input detection methods.
Recurrent Neural Network Language Models for Open Vocabulary Event-Level Cyber Anomaly Detection
Authors: Aaron Tuor, Ryan Baerwolf, Nicolas Knowles, Brian Hutchinson, Nicole Nichols, Rob Jasper
Date: 2017-12-02 00:00:00
Tasks: Anomaly Detection, Feature Engineering

Automated analysis methods are crucial aids for monitoring and defending a
network to protect the sensitive or confidential data it hosts. This work
introduces a flexible, powerful, and unsupervised approach to detecting
anomalous behavior in computer and network logs, one that largely eliminates
domain-dependent feature engineering employed by existing methods. By treating
system logs as threads of interleaved "sentences" (event log lines) to train
online unsupervised neural network language models, our approach provides an
adaptive model of normal network behavior. We compare the effectiveness of both
standard and bidirectional recurrent neural network language models at
detecting malicious activity within network log data. Extending these models,
we introduce a tiered recurrent architecture, which provides context by
modeling sequences of users' actions over time. Compared to Isolation Forest
and Principal Components Analysis, two popular anomaly detection algorithms, we
observe superior performance on the Los Alamos National Laboratory Cyber
Security dataset. For log-line-level red team detection, our best performing
character-based model provides test set area under the receiver operator
characteristic curve of 0.98, demonstrating the strong fine-grained anomaly
detection performance of this approach on open vocabulary logging sources.