pwc-archive/papers-with-abstracts huggingface dataset:
Distributed Robust Power System State Estimation
Authors: Vassilis Kekatos, Georgios B. Giannakis
Date: 2012-04-04 00:00:00
Tasks: State Estimation

Deregulation of energy markets, penetration of renewables, advanced metering
capabilities, and the urge for situational awareness, all call for system-wide
power system state estimation (PSSE). Implementing a centralized estimator
though is practically infeasible due to the complexity scale of an
interconnection, the communication bottleneck in real-time monitoring, regional
disclosure policies, and reliability issues. In this context, distributed PSSE
methods are treated here under a unified and systematic framework. A novel
algorithm is developed based on the alternating direction method of
multipliers. It leverages existing PSSE solvers, respects privacy policies,
exhibits low communication load, and its convergence to the centralized
estimates is guaranteed even in the absence of local observability. Beyond the
conventional least-squares based PSSE, the decentralized framework accommodates
a robust state estimator. By exploiting interesting links to the compressive
sampling advances, the latter jointly estimates the state and identifies
corrupted measurements. The novel algorithms are numerically evaluated using
the IEEE 14-, 118-bus, and a 4,200-bus benchmarks. Simulations demonstrate that
the attainable accuracy can be reached within a few inter-area exchanges, while
largest residual tests are outperformed.
Metric Embedding Autoencoders for Unsupervised Cross-Dataset Transfer Learning
Authors: Alexey Potapov, Sergey Rodionov, Hugo Latapie, Enzo Fenoglio
Date: 2018-07-18 00:00:00
Tasks: Person Re-Identification, Transfer Learning

Cross-dataset transfer learning is an important problem in person
re-identification (Re-ID). Unfortunately, not too many deep transfer Re-ID
models exist for realistic settings of practical Re-ID systems. We propose a
purely deep transfer Re-ID model consisting of a deep convolutional neural
network and an autoencoder. The latent code is divided into metric embedding
and nuisance variables. We then utilize an unsupervised training method that
does not rely on co-training with non-deep models. Our experiments show
improvements over both the baseline and competitors' transfer learning models.
Tiny-DSOD: Lightweight Object Detection for Resource-Restricted Usages
Authors: Yuxi Li, Jiuwei Li, Weiyao Lin, Jianguo Li
Date: 2018-07-29 00:00:00
Tasks: Object, object-detection, Object Detection

Object detection has made great progress in the past few years along with the
development of deep learning. However, most current object detection methods
are resource hungry, which hinders their wide deployment to many resource
restricted usages such as usages on always-on devices, battery-powered low-end
devices, etc. This paper considers the resource and accuracy trade-off for
resource-restricted usages during designing the whole object detection
framework. Based on the deeply supervised object detection (DSOD) framework, we
propose Tiny-DSOD dedicating to resource-restricted usages. Tiny-DSOD
introduces two innovative and ultra-efficient architecture blocks: depthwise
dense block (DDB) based backbone and depthwise feature-pyramid-network (D-FPN)
based front-end. We conduct extensive experiments on three famous benchmarks
(PASCAL VOC 2007, KITTI, and COCO), and compare Tiny-DSOD to the
state-of-the-art ultra-efficient object detection solutions such as Tiny-YOLO,
MobileNet-SSD (v1 & v2), SqueezeDet, Pelee, etc. Results show that Tiny-DSOD
outperforms these solutions in all the three metrics (parameter-size, FLOPs,
accuracy) in each comparison. For instance, Tiny-DSOD achieves 72.1% mAP with
only 0.95M parameters and 1.06B FLOPs, which is by far the state-of-the-arts
result with such a low resource requirement.
Learning to Interrupt: A Hierarchical Deep Reinforcement Learning Framework for Efficient Exploration
Authors: Tingguang Li, Jin Pan, Delong Zhu, Max Q. -H. Meng
Date: 2018-07-30 00:00:00
Tasks: Deep Reinforcement Learning, Efficient Exploration, Hierarchical Reinforcement Learning, Policy Gradient Methods, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

To achieve scenario intelligence, humans must transfer knowledge to robots by
developing goal-oriented algorithms, which are sometimes insensitive to
dynamically changing environments. While deep reinforcement learning achieves
significant success recently, it is still extremely difficult to be deployed in
real robots directly. In this paper, we propose a hybrid structure named
Option-Interruption in which human knowledge is embedded into a hierarchical
reinforcement learning framework. Our architecture has two key components:
options, represented by existing human-designed methods, can significantly
speed up the training process and interruption mechanism, based on learnable
termination functions, enables our system to quickly respond to the external
environment. To implement this architecture, we derive a set of update rules
based on policy gradient methods and present a complete training process. In
the experiment part, our method is evaluated in Four-room navigation and
exploration task, which shows the efficiency and flexibility of our framework.
U-Finger: Multi-Scale Dilated Convolutional Network for Fingerprint Image Denoising and Inpainting
Authors: Ramakrishna Prabhu, Xiaojing Yu, Zhangyang Wang, Ding Liu, Anxiao, Jiang
Date: 2018-07-29 00:00:00
Tasks: Denoising, Image Denoising, SSIM

This paper studies the challenging problem of fingerprint image denoising and
inpainting. To tackle the challenge of suppressing complicated artifacts (blur,
brightness, contrast, elastic transformation, occlusion, scratch, resolution,
rotation, and so on) while preserving fine textures, we develop a multi-scale
convolutional network, termed U- Finger. Based on the domain expertise, we show
that the usage of dilated convolutions as well as the removal of padding have
important positive impacts on the final restoration performance, in addition to
multi-scale cascaded feature modules. Our model achieves the overall ranking of
No.2 in the ECCV 2018 Chalearn LAP Inpainting Competition Track 3 (Fingerprint
Denoising and Inpainting). Among all participating teams, we obtain the MSE of
0.0231 (rank 2), PSNR 16.9688 dB (rank 2), and SSIM 0.8093 (rank 3) on the
hold-out testing set.
Deep Encoder-Decoder Models for Unsupervised Learning of Controllable Speech Synthesis
Authors: Gustav Eje Henter, Jaime Lorenzo-Trueba, Xin Wang, Junichi Yamagishi
Date: 2018-07-30 00:00:00
Tasks: Acoustic Modelling, Decoder, Emotional Speech Synthesis, Speech Synthesis, Variational Inference

Generating versatile and appropriate synthetic speech requires control over
the output expression separate from the spoken text. Important non-textual
speech variation is seldom annotated, in which case output control must be
learned in an unsupervised fashion. In this paper, we perform an in-depth study
of methods for unsupervised learning of control in statistical speech
synthesis. For example, we show that popular unsupervised training heuristics
can be interpreted as variational inference in certain autoencoder models. We
additionally connect these models to VQ-VAEs, another, recently-proposed class
of deep variational autoencoders, which we show can be derived from a very
similar mathematical argument. The implications of these new probabilistic
interpretations are discussed. We illustrate the utility of the various
approaches with an application to acoustic modelling for emotional speech
synthesis, where the unsupervised methods for learning expression control
(without access to emotional labels) are found to give results that in many
aspects match or surpass the previous best supervised approach.
Improving Transferability of Deep Neural Networks
Authors: Parijat Dube, Bishwaranjan Bhattacharjee, Elisabeth Petit-Bois, Matthew Hill
Date: 2018-07-30 00:00:00
Tasks: image-classification, Image Classification, Small Data Image Classification, Transfer Learning

Learning from small amounts of labeled data is a challenge in the area of
deep learning. This is currently addressed by Transfer Learning where one
learns the small data set as a transfer task from a larger source dataset.
Transfer Learning can deliver higher accuracy if the hyperparameters and source
dataset are chosen well. One of the important parameters is the learning rate
for the layers of the neural network. We show through experiments on the
ImageNet22k and Oxford Flowers datasets that improvements in accuracy in range
of 127% can be obtained by proper choice of learning rates. We also show that
the images/label parameter for a dataset can potentially be used to determine
optimal learning rates for the layers to get the best overall accuracy. We
additionally validate this method on a sample of real-world image
classification tasks from a public visual recognition API.
To learn image super-resolution, use a GAN to learn how to do image degradation first
Authors: Adrian Bulat, Jing Yang, Georgios Tzimiropoulos
Date: 2018-07-30 00:00:00
Proceeding: ECCV 2018 9
Tasks: Generative Adversarial Network, Image Super-Resolution, Super-Resolution

This paper is on image and face super-resolution. The vast majority of prior
work for this problem focus on how to increase the resolution of low-resolution
images which are artificially generated by simple bilinear down-sampling (or in
a few cases by blurring followed by down-sampling).We show that such methods
fail to produce good results when applied to real-world low-resolution, low
quality images. To circumvent this problem, we propose a two-stage process
which firstly trains a High-to-Low Generative Adversarial Network (GAN) to
learn how to degrade and downsample high-resolution images requiring, during
training, only unpaired high and low-resolution images. Once this is achieved,
the output of this network is used to train a Low-to-High GAN for image
super-resolution using this time paired low- and high-resolution images. Our
main result is that this network can be now used to efectively increase the
quality of real-world low-resolution images. We have applied the proposed
pipeline for the problem of face super-resolution where we report large
improvement over baselines and prior work although the proposed method is
potentially applicable to other object categories.
Norms, Institutions, and Robots
Authors: Stevan Tomic, Federico Pecora, Alessandro Saffiotti
Date: 2018-07-30 00:00:00

Interactions within human societies are usually regulated by social norms. If robots are to be accepted into human society, it is essential that they are aware of and capable of reasoning about social norms. In this paper, we focus on how to represent social norms in societies with humans and robots, and how artificial agents such as robots can reason about social norms in order to plan appropriate behavior. We use the notion of institution as a way to formally define and encapsulate norms, and we provide a formal framework for institutions. Our framework borrows ideas from the field of multi-agent systems to define abstract normative models, and ideas from the field of robotics to define physical executions as state-space trajectories. By bridging the two in a common model, our framework allows us to use the same abstract institution across physical domains and agent types. We then make our framework computational via a reduction to CSP and show experiments where this reduction is used for norm verification, planning, and plan execution in a domain including a mixture of humans and robots.
Factor analysis of dynamic PET images: beyond Gaussian noise
Authors: Yanna Cruz Cavalcanti, Thomas Oberlin, Nicolas Dobigeon, Cédric Févotte, Simon Stute, Maria-Joao Ribeiro, Clovis Tauber
Date: 2018-07-30 00:00:00

Factor analysis has proven to be a relevant tool for extracting tissue
time-activity curves (TACs) in dynamic PET images, since it allows for an
unsupervised analysis of the data. Reliable and interpretable results are
possible only if considered with respect to suitable noise statistics. However,
the noise in reconstructed dynamic PET images is very difficult to
characterize, despite the Poissonian nature of the count-rates. Rather than
explicitly modeling the noise distribution, this work proposes to study the
relevance of several divergence measures to be used within a factor analysis
framework. To this end, the $\beta$-divergence, widely used in other
applicative domains, is considered to design the data-fitting term involved in
three different factor models. The performances of the resulting algorithms are
evaluated for different values of $\beta$, in a range covering Gaussian,
Poissonian and Gamma-distributed noises. The results obtained on two different
types of synthetic images and one real image show the interest of applying
non-standard values of $\beta$ to improve factor analysis.
Comparator Networks
Authors: Weidi Xie, Li Shen, Andrew Zisserman
Date: 2018-07-30 00:00:00
Proceeding: ECCV 2018 9
Tasks: Face Recognition, image-classification, Image Classification, Image Retrieval, Retrieval

The objective of this work is set-based verification, e.g. to decide if two
sets of images of a face are of the same person or not. The traditional
approach to this problem is to learn to generate a feature vector per image,
aggregate them into one vector to represent the set, and then compute the
cosine similarity between sets. Instead, we design a neural network
architecture that can directly learn set-wise verification. Our contributions
are: (i) We propose a Deep Comparator Network (DCN) that can ingest a pair of
sets (each may contain a variable number of images) as inputs, and compute a
similarity between the pair--this involves attending to multiple discriminative
local regions (landmarks), and comparing local descriptors between pairs of
faces; (ii) To encourage high-quality representations for each set, internal
competition is introduced for recalibration based on the landmark score; (iii)
Inspired by image retrieval, a novel hard sample mining regime is proposed to
control the sampling process, such that the DCN is complementary to the
standard image classification models. Evaluations on the IARPA Janus face
recognition benchmarks show that the comparator networks outperform the
previous state-of-the-art results by a large margin.
Leveraging Motion Priors in Videos for Improving Human Segmentation
Authors: Yu-Ting Chen, Wen-Yen Chang, Hai-Lun Lu, Ting-Fan Wu, Min Sun
Date: 2018-07-30 00:00:00
Proceeding: ECCV 2018 9
Tasks: Active Learning, Domain Adaptation, Optical Flow Estimation, Reinforcement Learning, Segmentation, Semantic Segmentation

Despite many advances in deep-learning based semantic segmentation,
performance drop due to distribution mismatch is often encountered in the real
world. Recently, a few domain adaptation and active learning approaches have
been proposed to mitigate the performance drop. However, very little attention
has been made toward leveraging information in videos which are naturally
captured in most camera systems. In this work, we propose to leverage "motion
prior" in videos for improving human segmentation in a weakly-supervised active
learning setting. By extracting motion information using optical flow in
videos, we can extract candidate foreground motion segments (referred to as
motion prior) potentially corresponding to human segments. We propose to learn
a memory-network-based policy model to select strong candidate segments
(referred to as strong motion prior) through reinforcement learning. The
selected segments have high precision and are directly used to finetune the
model. In a newly collected surveillance camera dataset and a publicly
available UrbanStreet dataset, our proposed method improves the performance of
human segmentation across multiple scenes and modalities (i.e., RGB to Infrared
(IR)). Last but not least, our method is empirically complementary to existing
domain adaptation approaches such that additional performance gain is achieved
by combining our weakly-supervised active learning approach with domain
adaptation approaches.
REFUGE CHALLENGE 2018-Task 2:Deep Optic Disc and Cup Segmentation in Fundus Images Using U-Net and Multi-scale Feature Matching Networks
Authors: Vivek Kumar Singh, Hatem A. Rashwan, Adel Saleh, Farhan Akram, Md. Mostafa Kamal Sarker, Nidhi Pandey, Saddam Abdulwahab
Date: 2018-07-30 00:00:00
Tasks: Decoder, Segmentation, Task 2

In this paper, an optic disc and cup segmentation method is proposed using
U-Net followed by a multi-scale feature matching network. The proposed method
targets task 2 of the REFUGE challenge 2018. In order to solve the segmentation
problem of task 2, we firstly crop the input image using single shot multibox
detector (SSD). The cropped image is then passed to an encoder-decoder network
with skip connections also known as generator. Afterwards, both the ground
truth and generated images are fed to a convolution neural network (CNN) to
extract their multi-level features. A dice loss function is then used to match
the features of the two images by minimizing the error at each layer. The
aggregation of error from each layer is back-propagated through the generator
network to enforce it to generate a segmented image closer to the ground truth.
The CNN network improves the performance of the generator network without
increasing the complexity of the model.
Kalman Filter-based Heuristic Ensemble (KFHE): A new perspective on multi-class ensemble classification using Kalman filters
Authors: Arjun Pakrashi, Brian Mac Namee
Date: 2018-07-30 00:00:00
Tasks: Classification, General Classification, State Estimation

This paper introduces a new perspective on multi-class ensemble
classification that considers training an ensemble as a state estimation
problem. The new perspective considers the final ensemble classifier model as a
static state, which can be estimated using a Kalman filter that combines noisy
estimates made by individual classifier models. A new algorithm based on this
perspective, the Kalman Filter-based Heuristic Ensemble (KFHE), is also
presented in this paper which shows the practical applicability of the new
perspective. Experiments performed on 30 datasets compare KFHE with
state-of-the-art multi-class ensemble classification algorithms and show the
potential and effectiveness of the new perspective and algorithm. Existing
ensemble approaches trade off classification accuracy against robustness to
class label noise, but KFHE is shown to be significantly better or at least as
good as the state-of-the-art algorithms for datasets both with and without
class label noise.
High-dimensional estimation via sum-of-squares proofs
Authors: Prasad Raghavendra, Tselil Schramm, David Steurer
Date: 2018-07-30 00:00:00
Tasks: Vocal Bursts Intensity Prediction

Estimation is the computational task of recovering a hidden parameter $x$ associated with a distribution $D_x$, given a measurement $y$ sampled from the distribution. High dimensional estimation problems arise naturally in statistics, machine learning, and complexity theory. Many high dimensional estimation problems can be formulated as systems of polynomial equations and inequalities, and thus give rise to natural probability distributions over polynomial systems. Sum-of-squares proofs provide a powerful framework to reason about polynomial systems, and further there exist efficient algorithms to search for low-degree sum-of-squares proofs. Understanding and characterizing the power of sum-of-squares proofs for estimation problems has been a subject of intense study in recent years. On one hand, there is a growing body of work utilizing sum-of-squares proofs for recovering solutions to polynomial systems when the system is feasible. On the other hand, a general technique referred to as pseudocalibration has been developed towards showing lower bounds on the degree of sum-of-squares proofs. Finally, the existence of sum-of-squares refutations of a polynomial system has been shown to be intimately connected to the existence of spectral algorithms. In this article we survey these developments.
Faster Convergence & Generalization in DNNs
Authors: Gaurav Singh, John Shawe-Taylor
Date: 2018-07-30 00:00:00
Tasks: General Classification

Deep neural networks have gained tremendous popularity in last few years.
They have been applied for the task of classification in almost every domain.
Despite the success, deep networks can be incredibly slow to train for even
moderate sized models on sufficiently large datasets. Additionally, these
networks require large amounts of data to be able to generalize. The importance
of speeding up convergence, and generalization in deep networks can not be
overstated. In this work, we develop an optimization algorithm based on
generalized-optimal updates derived from minibatches that lead to faster
convergence. Towards the end, we demonstrate on two benchmark datasets that the
proposed method achieves two orders of magnitude speed up over traditional
back-propagation, and is more robust to noise/over-fitting.
A Non-structural Representation Scheme for Articulated Shapes
Authors: Asli Genctav, Sibel Tari
Date: 2018-07-30 00:00:00
Tasks: Clustering

For representing articulated shapes, as an alternative to the structured
models based on graphs representing part hierarchy, we propose a pixel-based
distinctness measure. Its spatial distribution yields a partitioning of the
shape into a set of regions each of which is represented via size normalized
probability distribution of the distinctness. Without imposing any structural
relation among parts, pairwise shape similarity is formulated as the cost of an
optimal assignment between respective regions. The matching is performed via
Hungarian algorithm permitting some unmatched regions. The proposed similarity
measure is employed in the context of clustering a set of shapes. The
clustering results obtained on three articulated shape datasets show that our
method performs comparable to state of the art methods utilizing component
graphs or trees even though we are not explicitly modeling component relations.
Local Linear Forests
Authors: Rina Friedberg, Julie Tibshirani, Susan Athey, Stefan Wager
Date: 2018-07-30 00:00:00
Tasks: Causal Inference, Heterogeneous Treatment Effect Estimation, regression, valid

Random forests are a powerful method for non-parametric regression, but are limited in their ability to fit smooth signals, and can show poor predictive performance in the presence of strong, smooth effects. Taking the perspective of random forests as an adaptive kernel method, we pair the forest kernel with a local linear regression adjustment to better capture smoothness. The resulting procedure, local linear forests, enables us to improve on asymptotic rates of convergence for random forests with smooth signals, and provides substantial gains in accuracy on both real and simulated data. We prove a central limit theorem valid under regularity conditions on the forest and smoothness constraints, and propose a computationally efficient construction for confidence intervals. Moving to a causal inference application, we discuss the merits of local regression adjustments for heterogeneous treatment effect estimation, and give an example on a dataset exploring the effect word choice has on attitudes to the social safety net. Last, we include simulation results on real and generated data.