pwc-archive/papers-with-abstracts huggingface dataset:
Toward Performance Optimization in IoT-based Next-Gen Wireless Sensor Networks
Authors: Muzammil Behzad, Manal Abdullah, Muhammad Talal Hassan, Yao Ge, Mahmood Ashraf Khan
Date: 2018-06-23 00:00:00

In this paper, we propose a novel framework for performance optimization in
Internet of Things (IoT)-based next-generation wireless sensor networks. In
particular, a computationally-convenient system is presented to combat two
major research problems in sensor networks. First is the conventionally-tackled
resource optimization problem which triggers the drainage of battery at a
faster rate within a network. Such drainage promotes inefficient resource usage
thereby causing sudden death of the network. The second main bottleneck for
such networks is that of data degradation. This is because the nodes in such
networks communicate via a wireless channel, where the inevitable presence of
noise corrupts the data making it unsuitable for practical applications.
Therefore, we present a layer-adaptive method via 3-tier communication
mechanism to ensure the efficient use of resources. This is supported with a
mathematical coverage model that deals with the formation of coverage holes. We
also present a transform-domain based robust algorithm to effectively remove
the unwanted components from the data. Our proposed framework offers a handy
algorithm that enjoys desirable complexity for real-time applications as shown
by the extensive simulation results.
Many-Goals Reinforcement Learning
Authors: Vivek Veeriah, Junhyuk Oh, Satinder Singh
Date: 2018-06-22 00:00:00
Tasks: All, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

All-goals updating exploits the off-policy nature of Q-learning to update all
possible goals an agent could have from each transition in the world, and was
introduced into Reinforcement Learning (RL) by Kaelbling (1993). In prior work
this was mostly explored in small-state RL problems that allowed tabular
representations and where all possible goals could be explicitly enumerated and
learned separately. In this paper we empirically explore 3 different extensions
of the idea of updating many (instead of all) goals in the context of RL with
deep neural networks (or DeepRL for short). First, in a direct adaptation of
Kaelbling's approach we explore if many-goals updating can be used to achieve
mastery in non-tabular visual-observation domains. Second, we explore whether
many-goals updating can be used to pre-train a network to subsequently learn
faster and better on a single main task of interest. Third, we explore whether
many-goals updating can be used to provide auxiliary task updates in training a
network to learn faster and better on a single main task of interest. We
provide comparisons to baselines for each of the 3 extensions.
Approximability of Discriminators Implies Diversity in GANs
Authors: Yu Bai, Tengyu Ma, Andrej Risteski
Date: 2018-06-27 00:00:00
Proceeding: ICLR 2019 5
Tasks: Diversity

While Generative Adversarial Networks (GANs) have empirically produced impressive results on learning complex real-world distributions, recent works have shown that they suffer from lack of diversity or mode collapse. The theoretical work of Arora et al. suggests a dilemma about GANs' statistical properties: powerful discriminators cause overfitting, whereas weak discriminators cannot detect mode collapse. By contrast, we show in this paper that GANs can in principle learn distributions in Wasserstein distance (or KL-divergence in many cases) with polynomial sample complexity, if the discriminator class has strong distinguishing power against the particular generator class (instead of against all possible generators). For various generator classes such as mixture of Gaussians, exponential families, and invertible and injective neural networks generators, we design corresponding discriminators (which are often neural nets of specific architectures) such that the Integral Probability Metric (IPM) induced by the discriminators can provably approximate the Wasserstein distance and/or KL-divergence. This implies that if the training is successful, then the learned distribution is close to the true distribution in Wasserstein distance or KL divergence, and thus cannot drop modes. Our preliminary experiments show that on synthetic datasets the test IPM is well correlated with KL divergence or the Wasserstein distance, indicating that the lack of diversity in GANs may be caused by the sub-optimality in optimization instead of statistical inefficiency.
This Looks Like That: Deep Learning for Interpretable Image Recognition
Authors: Chaofan Chen, Oscar Li, Chaofan Tao, Alina Jade Barnett, Jonathan Su, Cynthia Rudin
Date: 2018-06-27 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Deep Learning, General Classification, image-classification, Image Classification

When we are faced with challenging image classification tasks, we often explain our reasoning by dissecting the image, and pointing out prototypical aspects of one class or another. The mounting evidence for each of the classes helps us make our final decision. In this work, we introduce a deep network architecture -- prototypical part network (ProtoPNet), that reasons in a similar way: the network dissects the image by finding prototypical parts, and combines evidence from the prototypes to make a final classification. The model thus reasons in a way that is qualitatively similar to the way ornithologists, physicians, and others would explain to people on how to solve challenging image classification tasks. The network uses only image-level labels for training without any annotations for parts of images. We demonstrate our method on the CUB-200-2011 dataset and the Stanford Cars dataset. Our experiments show that ProtoPNet can achieve comparable accuracy with its analogous non-interpretable counterpart, and when several ProtoPNets are combined into a larger network, it can achieve an accuracy that is on par with some of the best-performing deep models. Moreover, ProtoPNet provides a level of interpretability that is absent in other interpretable deep models.
Knowledge Compilation in Multi-Agent Epistemic Logics
Authors: Liangda Fang, Kewen Wang, Zhe Wang, Ximing Wen
Date: 2018-06-27 00:00:00

Epistemic logics are a primary formalism for multi-agent systems but major
reasoning tasks in such epistemic logics are intractable, which impedes
applications of multi-agent epistemic logics in automatic planning. Knowledge
compilation provides a promising way of resolving the intractability by
identifying expressive fragments of epistemic logics that are tractable for
important reasoning tasks such as satisfiability and forgetting. The property
of logical separability allows to decompose a formula into some of its
subformulas and thus modular algorithms for various reasoning tasks can be
developed. In this paper, by employing logical separability, we propose an
approach to knowledge compilation for the logic Kn by defining a normal form
SDNF. Among several novel results, we show that every epistemic formula can be
equivalently compiled into a formula in SDNF, major reasoning tasks in SDNF are
tractable, and formulas in SDNF enjoy the logical separability. Our results
shed some lights on modular approaches to knowledge compilation. Furthermore,
we apply our results in the multi-agent epistemic planning. Finally, we extend
the above result to the logic K45n that is Kn extended by introspection axioms
4 and 5.
Every Pixel Counts: Unsupervised Geometry Learning with Holistic 3D Motion Understanding
Authors: Zhenheng Yang, Peng Wang, Yang Wang, Wei Xu, Ram Nevatia
Date: 2018-06-27 00:00:00
Tasks: 3D geometry, Depth And Camera Motion, Depth Estimation, Optical Flow Estimation, Scene Flow Estimation

Learning to estimate 3D geometry in a single image by watching unlabeled
videos via deep convolutional network has made significant process recently.
Current state-of-the-art (SOTA) methods, are based on the learning framework of
rigid structure-from-motion, where only 3D camera ego motion is modeled for
geometry estimation.However, moving objects also exist in many videos, e.g.
moving cars in a street scene. In this paper, we tackle such motion by
additionally incorporating per-pixel 3D object motion into the learning
framework, which provides holistic 3D scene flow understanding and helps single
image geometry estimation. Specifically, given two consecutive frames from a
video, we adopt a motion network to predict their relative 3D camera pose and a
segmentation mask distinguishing moving objects and rigid background. An
optical flow network is used to estimate dense 2D per-pixel correspondence. A
single image depth network predicts depth maps for both images. The four types
of information, i.e. 2D flow, camera pose, segment mask and depth maps, are
integrated into a differentiable holistic 3D motion parser (HMP), where
per-pixel 3D motion for rigid background and moving objects are recovered. We
design various losses w.r.t. the two types of 3D motions for training the depth
and motion networks, yielding further error reduction for estimated geometry.
Finally, in order to solve the 3D motion confusion from monocular videos, we
combine stereo images into joint training. Experiments on KITTI 2015 dataset
show that our estimated geometry, 3D motion and moving object masks, not only
are constrained to be consistent, but also significantly outperforms other SOTA
algorithms, demonstrating the benefits of our approach.
Online optimal task offloading with one-bit feedback
Authors: Shangshu Zhao, Zhaowei Zhu, Fuqian Yang, Xiliang Luo
Date: 2018-06-27 00:00:00

Task offloading is an emerging technology in fog-enabled networks. It allows
users to transmit tasks to neighbor fog nodes so as to utilize the computing
resources of the networks. In this paper, we investigate a stochastic task
offloading model and propose a multi-armed bandit framework to formulate this
model. We consider the fact that different helper nodes prefer different kinds
of tasks. Further, we assume each helper node just feeds back one-bit
information to the task node to indicate the level of happiness. The key
challenge of this problem lies in the exploration-exploitation tradeoff. We
thus implement a UCB-type algorithm to maximize the long-term happiness metric.
Numerical simulations are given in the end of the paper to corroborate our
strategy.
Autonomous Wireless Systems with Artificial Intelligence
Authors: Haris Gacanin
Date: 2018-06-27 00:00:00
Tasks: Active Learning, Management

This paper discusses technology and opportunities to embrace artificial intelligence (AI) in the design of autonomous wireless systems. We aim to provide readers with motivation and general AI methodology of autonomous agents in the context of self-organization in real time by unifying knowledge management with sensing, reasoning and active learning. We highlight differences between training-based methods for matching problems and training-free methods for environment-specific problems. Finally, we conceptually introduce the functions of an autonomous agent with knowledge management.
Customizing an Adversarial Example Generator with Class-Conditional GANs
Authors: Shih-Hong Tsai
Date: 2018-06-27 00:00:00
Tasks: Adversarial Defense, Diversity

Adversarial examples are intentionally crafted data with the purpose of
deceiving neural networks into misclassification. When we talk about strategies
to create such examples, we usually refer to perturbation-based methods that
fabricate adversarial examples by applying invisible perturbations onto normal
data. The resulting data reserve their visual appearance to human observers,
yet can be totally unrecognizable to DNN models, which in turn leads to
completely misleading predictions. In this paper, however, we consider crafting
adversarial examples from existing data as a limitation to example diversity.
We propose a non-perturbation-based framework that generates native adversarial
examples from class-conditional generative adversarial networks.As such, the
generated data will not resemble any existing data and thus expand example
diversity, raising the difficulty in adversarial defense. We then extend this
framework to pre-trained conditional GANs, in which we turn an existing
generator into an "adversarial-example generator". We conduct experiments on
our approach for MNIST and CIFAR10 datasets and have satisfactory results,
showing that this approach can be a potential alternative to previous attack
strategies.
Neural Machine Translation for Query Construction and Composition
Authors: Tommaso Soru, Edgard Marx, André Valdestilhas, Diego Esteves, Diego Moussallem, Gustavo Publio
Date: 2018-06-27 00:00:00
Tasks: Code Generation, Knowledge Base Question Answering, Semantic Parsing, Translation

Research on question answering with knowledge base has recently seen an
increasing use of deep architectures. In this extended abstract, we study the
application of the neural machine translation paradigm for question parsing. We
employ a sequence-to-sequence model to learn graph patterns in the SPARQL graph
query language and their compositions. Instead of inducing the programs through
question-answer pairs, we expect a semi-supervised approach, where alignments
between questions and queries are built through templates. We argue that the
coverage of language utterances can be expanded using late notable works in
natural language generation.
Homogeneity of a region in the logarithmic image processing framework: application to region growing algorithms
Authors: Michel Jourlin, Guillaume Noyel
Date: 2018-06-27 00:00:00

The current paper deals with the role played by Logarithmic Image Processing
(LIP) operators for evaluating the homogeneity of a region. Two new criteria of
heterogeneity are introduced, one based on the LIP addition and the other based
on the LIP scalar multiplication. Such tools are able to manage Region Growing
algorithms following the Revol's technique: starting from an initial seed, they
consist of applying specific dilations to the growing region while its
inhomogeneity level does not exceed a certain level. The new approaches we
introduce are significantly improving Revol's existing technique by making it
robust to contrast variations in images. Such a property strongly reduces the
chaining effect arising in region growing processes.
LPRNet: License Plate Recognition via Deep Neural Networks
Authors: Sergey Zherzdev, Alexey Gruzdev
Date: 2018-06-27 00:00:00
Tasks: CPU, License Plate Recognition

This paper proposes LPRNet - end-to-end method for Automatic License Plate
Recognition without preliminary character segmentation. Our approach is
inspired by recent breakthroughs in Deep Neural Networks, and works in
real-time with recognition accuracy up to 95% for Chinese license plates: 3
ms/plate on nVIDIA GeForce GTX 1080 and 1.3 ms/plate on Intel Core i7-6700K
CPU. LPRNet consists of the lightweight Convolutional Neural Network, so it can
be trained in end-to-end way. To the best of our knowledge, LPRNet is the first
real-time License Plate Recognition system that does not use RNNs. As a result,
the LPRNet algorithm may be used to create embedded solutions for LPR that
feature high level accuracy even on challenging Chinese license plates.
Deep Steganalysis: End-to-End Learning with Supervisory Information beyond Class Labels
Authors: Wei Wang, Jing Dong, Yinlong Qian, Tieniu Tan
Date: 2018-06-27 00:00:00
Tasks: Steganalysis

Recently, deep learning has shown its power in steganalysis. However, the
proposed deep models have been often learned from pre-calculated noise
residuals with fixed high-pass filters rather than from raw images. In this
paper, we propose a new end-to-end learning framework that can learn
steganalytic features directly from pixels. In the meantime, the high-pass
filters are also automatically learned. Besides class labels, we make use of
additional pixel level supervision of cover-stego image pair to jointly and
iteratively train the proposed network which consists of a residual calculation
network and a steganalysis network. The experimental results prove the
effectiveness of the proposed architecture.
MTBI Identification From Diffusion MR Images Using Bag of Adversarial Visual Features
Authors: Shervin Minaee, Yao Wang, Alp Aygar, Sohae Chung, Xiuyuan Wang, Yvonne W. Lui, Els Fieremans, Steven Flanagan, Joseph Rath
Date: 2018-06-27 00:00:00

In this work, we propose bag of adversarial features (BAF) for identifying
mild traumatic brain injury (MTBI) patients from their diffusion magnetic
resonance images (MRI) (obtained within one month of injury) by incorporating
unsupervised feature learning techniques. MTBI is a growing public health
problem with an estimated incidence of over 1.7 million people annually in US.
Diagnosis is based on clinical history and symptoms, and accurate, concrete
measures of injury are lacking. Unlike most of previous works, which use
hand-crafted features extracted from different parts of brain for MTBI
classification, we employ feature learning algorithms to learn more
discriminative representation for this task. A major challenge in this field
thus far is the relatively small number of subjects available for training.
This makes it difficult to use an end-to-end convolutional neural network to
directly classify a subject from MR images. To overcome this challenge, we
first apply an adversarial auto-encoder (with convolutional structure) to learn
patch-level features, from overlapping image patches extracted from different
brain regions. We then aggregate these features through a bag-of-word approach.
We perform an extensive experimental study on a dataset of 227 subjects
(including 109 MTBI patients, and 118 age and sex matched healthy controls),
and compare the bag-of-deep-features with several previous approaches. Our
experimental results show that the BAF significantly outperforms earlier works
relying on the mean values of MR metrics in selected brain regions.
Divergence-Free Shape Interpolation and Correspondence
Authors: Marvin Eisenberger, Zorah Lähner, Daniel Cremers
Date: 2018-06-27 00:00:00

We present a novel method to model and calculate deformation fields between
shapes embedded in $\mathbb{R}^D$. Our framework combines naturally
interpolating the two input shapes and calculating correspondences at the same
time. The key idea is to compute a divergence-free deformation field
represented in a coarse-to-fine basis using the Karhunen-Lo\`eve expansion. The
advantages are that there is no need to discretize the embedding space and the
deformation is volume-preserving. Furthermore, the optimization is done on
downsampled versions of the shapes but the morphing can be applied to any
resolution without a heavy increase in complexity. We show results for shape
correspondence, registration, inter- and extrapolation on the TOSCA and FAUST
data sets.
Dynamic Assortment Selection under the Nested Logit Models
Authors: Xi Chen, Chao Shi, Yining Wang, Yuan Zhou
Date: 2018-06-27 00:00:00
Tasks: Management

We study a stylized dynamic assortment planning problem during a selling season of finite length $T$. At each time period, the seller offers an arriving customer an assortment of substitutable products and the customer makes the purchase among offered products according to a discrete choice model. The goal of the seller is to maximize the expected revenue, or equivalently, to minimize the worst-case expected regret. One key challenge is that utilities of products are unknown to the seller and need to be learned. Although the dynamic assortment planning problem has received increasing attention in revenue management, most existing work is based on the multinomial logit choice models (MNL). In this paper, we study the problem of dynamic assortment planning under a more general choice model -- the nested logit model, which models hierarchical choice behavior and is ``the most widely used member of the GEV (generalized extreme value) family''. By leveraging the revenue-ordered structure of the optimal assortment within each nest, we develop a novel upper confidence bound (UCB) policy with an aggregated estimation scheme. Our policy simultaneously learns customers' choice behavior and makes dynamic decisions on assortments based on the current knowledge. It achieves the accumulated regret at the order of $\tilde{O}(\sqrt{MNT})$, where $M$ is the number of nests and $N$ is the number of products in each nest. We further provide a lower bound result of $\Omega(\sqrt{MT})$, which shows the near optimality of the upper bound when $T$ is much larger than $M$ and $N$. When the number of items per nest $N$ is large, we further provide a discretization heuristic for better performance of our algorithm. Numerical results are presented to demonstrate the empirical performance of our proposed algorithms.
Context Proposals for Saliency Detection
Authors: Aymen Azaza, Joost Van de Weijer, Ali Douik, Marc Masana
Date: 2018-06-27 00:00:00
Tasks: Object, object-detection, Object Detection, RGB Salient Object Detection, Saliency Detection, Saliency Prediction, Salient Object Detection

One of the fundamental properties of a salient object region is its contrast
with the immediate context. The problem is that numerous object regions exist
which potentially can all be salient. One way to prevent an exhaustive search
over all object regions is by using object proposal algorithms. These return a
limited set of regions which are most likely to contain an object. Several
saliency estimation methods have used object proposals. However, they focus on
the saliency of the proposal only, and the importance of its immediate context
has not been evaluated.
  In this paper, we aim to improve salient object detection. Therefore, we
extend object proposal methods with context proposals, which allow to
incorporate the immediate context in the saliency computation. We propose
several saliency features which are computed from the context proposals. In the
experiments, we evaluate five object proposal methods for the task of saliency
segmentation, and find that Multiscale Combinatorial Grouping outperforms the
others. Furthermore, experiments show that the proposed context features
improve performance, and that our method matches results on the FT datasets and
obtains competitive results on three other datasets (PASCAL-S, MSRA-B and
ECSSD).
Learn-to-Score: Efficient 3D Scene Exploration by Predicting View Utility
Authors: Benjamin Hepp, Debadeepta Dey, Sudipta N. Sinha, Ashish Kapoor, Neel Joshi, Otmar Hilliges
Date: 2018-06-27 00:00:00
Proceeding: ECCV 2018 9

Camera equipped drones are nowadays being used to explore large scenes and
reconstruct detailed 3D maps. When free space in the scene is approximately
known, an offline planner can generate optimal plans to efficiently explore the
scene. However, for exploring unknown scenes, the planner must predict and
maximize usefulness of where to go on the fly. Traditionally, this has been
achieved using handcrafted utility functions. We propose to learn a better
utility function that predicts the usefulness of future viewpoints. Our learned
utility function is based on a 3D convolutional neural network. This network
takes as input a novel volumetric scene representation that implicitly captures
previously visited viewpoints and generalizes to new scenes. We evaluate our
method on several large 3D models of urban scenes using simulated depth
cameras. We show that our method outperforms existing utility measures in terms
of reconstruction performance and is robust to sensor noise.