pwc-archive/papers-with-abstracts huggingface dataset:
Deep Joint Rain Detection and Removal from a Single Image
Authors: Wenhan Yang, Robby T. Tan, Jiashi Feng, Jiaying Liu, Zongming Guo, Shuicheng Yan
Date: 2016-09-25 00:00:00
Proceeding: CVPR 2017 7
Tasks: Rain Removal

In this paper, we address a rain removal problem from a single image, even in
the presence of heavy rain and rain streak accumulation. Our core ideas lie in
the new rain image models and a novel deep learning architecture. We first
modify an existing model comprising a rain streak layer and a background layer,
by adding a binary map that locates rain streak regions. Second, we create a
new model consisting of a component representing rain streak accumulation
(where individual streaks cannot be seen, and thus visually similar to mist or
fog), and another component representing various shapes and directions of
overlapping rain streaks, which usually happen in heavy rain. Based on the
first model, we develop a multi-task deep learning architecture that learns the
binary rain streak map, the appearance of rain streaks, and the clean
background, which is our ultimate output. The additional binary map is
critically beneficial, since its loss function can provide additional strong
information to the network. To handle rain streak accumulation (again, a
phenomenon visually similar to mist or fog) and various shapes and directions
of overlapping rain streaks, we propose a recurrent rain detection and removal
network that removes rain streaks and clears up the rain accumulation
iteratively and progressively. In each recurrence of our method, a new
contextualized dilated network is developed to exploit regional contextual
information and outputs better representation for rain detection. The
evaluation on real images, particularly on heavy rain, shows the effectiveness
of our novel models and architecture, outperforming the state-of-the-art
methods significantly. Our codes and data sets will be publicly available.
SPARTan: Scalable PARAFAC2 for Large & Sparse Data
Authors: Ioakeim Perros, Evangelos E. Papalexakis, Fei Wang, Richard Vuduc, Elizabeth Searles, Michael Thompson, Jimeng Sun
Date: 2017-03-13 00:00:00

In exploratory tensor mining, a common problem is how to analyze a set of
variables across a set of subjects whose observations do not align naturally.
For example, when modeling medical features across a set of patients, the
number and duration of treatments may vary widely in time, meaning there is no
meaningful way to align their clinical records across time points for analysis
purposes. To handle such data, the state-of-the-art tensor model is the
so-called PARAFAC2, which yields interpretable and robust output and can
naturally handle sparse data. However, its main limitation up to now has been
the lack of efficient algorithms that can handle large-scale datasets.
  In this work, we fill this gap by developing a scalable method to compute the
PARAFAC2 decomposition of large and sparse datasets, called SPARTan. Our method
exploits special structure within PARAFAC2, leading to a novel algorithmic
reformulation that is both fast (in absolute time) and more memory-efficient
than prior work. We evaluate SPARTan on both synthetic and real datasets,
showing 22X performance gains over the best previous implementation and also
handling larger problem instances for which the baseline fails. Furthermore, we
are able to apply SPARTan to the mining of temporally-evolving phenotypes on
data taken from real and medically complex pediatric patients. The clinical
meaningfulness of the phenotypes identified in this process, as well as their
temporal evolution over time for several patients, have been endorsed by
clinical experts.
Deep Visual Foresight for Planning Robot Motion
Authors: Chelsea Finn, Sergey Levine
Date: 2016-10-03 00:00:00
Tasks: Model-based Reinforcement Learning, Model Predictive Control, Reinforcement Learning, Reinforcement Learning (RL), Video Prediction

A key challenge in scaling up robot learning to many skills and environments
is removing the need for human supervision, so that robots can collect their
own data and improve their own performance without being limited by the cost of
requesting human feedback. Model-based reinforcement learning holds the promise
of enabling an agent to learn to predict the effects of its actions, which
could provide flexible predictive models for a wide range of tasks and
environments, without detailed human supervision. We develop a method for
combining deep action-conditioned video prediction models with model-predictive
control that uses entirely unlabeled training data. Our approach does not
require a calibrated camera, an instrumented training set-up, nor precise
sensing and actuation. Our results show that our method enables a real robot to
perform nonprehensile manipulation -- pushing objects -- and can handle novel
objects not seen during training.
Unsupervised learning of phase transitions: from principal component analysis to variational autoencoders
Authors: Sebastian Johann Wetzel
Date: 2017-03-07 00:00:00

We employ unsupervised machine learning techniques to learn latent parameters
which best describe states of the two-dimensional Ising model and the
three-dimensional XY model. These methods range from principal component
analysis to artificial neural network based variational autoencoders. The
states are sampled using a Monte-Carlo simulation above and below the critical
temperature. We find that the predicted latent parameters correspond to the
known order parameters. The latent representation of the states of the models
in question are clustered, which makes it possible to identify phases without
prior knowledge of their existence or the underlying Hamiltonian. Furthermore,
we find that the reconstruction loss function can be used as a universal
identifier for phase transitions.
Multi-Label Image Classification with Regional Latent Semantic Dependencies
Authors: Jun-Jie Zhang, Qi Wu, Chunhua Shen, Jian Zhang, Jianfeng Lu
Date: 2016-12-04 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification, Multi-Label Image Classification

Deep convolution neural networks (CNN) have demonstrated advanced performance
on single-label image classification, and various progress also have been made
to apply CNN methods on multi-label image classification, which requires to
annotate objects, attributes, scene categories etc. in a single shot. Recent
state-of-the-art approaches to multi-label image classification exploit the
label dependencies in an image, at global level, largely improving the labeling
capacity. However, predicting small objects and visual concepts is still
challenging due to the limited discrimination of the global visual features. In
this paper, we propose a Regional Latent Semantic Dependencies model (RLSD) to
address this problem. The utilized model includes a fully convolutional
localization architecture to localize the regions that may contain multiple
highly-dependent labels. The localized regions are further sent to the
recurrent neural networks (RNN) to characterize the latent semantic
dependencies at the regional level. Experimental results on several benchmark
datasets show that our proposed model achieves the best performance compared to
the state-of-the-art models, especially for predicting small objects occurred
in the images. In addition, we set up an upper bound model (RLSD+ft-RPN) using
bounding box coordinates during training, the experimental results also show
that our RLSD can approach the upper bound without using the bounding-box
annotations, which is more realistic in the real world.
Recommendation under Capacity Constraints
Authors: Konstantina Christakopoulou, Jaya Kawale, Arindam Banerjee
Date: 2017-01-18 00:00:00

In this paper, we investigate the common scenario where every candidate item
for recommendation is characterized by a maximum capacity, i.e., number of
seats in a Point-of-Interest (POI) or size of an item's inventory. Despite the
prevalence of the task of recommending items under capacity constraints in a
variety of settings, to the best of our knowledge, none of the known
recommender methods is designed to respect capacity constraints. To close this
gap, we extend three state-of-the art latent factor recommendation approaches:
probabilistic matrix factorization (PMF), geographical matrix factorization
(GeoMF), and bayesian personalized ranking (BPR), to optimize for both
recommendation accuracy and expected item usage that respects the capacity
constraints. We introduce the useful concepts of user propensity to listen and
item capacity. Our experimental results in real-world datasets, both for the
domain of item recommendation and POI recommendation, highlight the benefit of
our method for the setting of recommendation under capacity constraints.
Pufferfish Privacy Mechanisms for Correlated Data
Authors: Shuang Song, Yizhen Wang, Kamalika Chaudhuri
Date: 2016-03-13 00:00:00

Many modern databases include personal and sensitive correlated data, such as
private information on users connected together in a social network, and
measurements of physical activity of single subjects across time. However,
differential privacy, the current gold standard in data privacy, does not
adequately address privacy issues in this kind of data.
  This work looks at a recent generalization of differential privacy, called
Pufferfish, that can be used to address privacy in correlated data. The main
challenge in applying Pufferfish is a lack of suitable mechanisms. We provide
the first mechanism -- the Wasserstein Mechanism -- which applies to any
general Pufferfish framework. Since this mechanism may be computationally
inefficient, we provide an additional mechanism that applies to some practical
cases such as physical activity measurements across time, and is
computationally efficient. Our experimental evaluations indicate that this
mechanism provides privacy and utility for synthetic as well as real data in
two separate domains.
Leak Event Identification in Water Systems Using High Order CRF
Authors: Qing Han, Wentao Zhu, Yang Shi
Date: 2017-03-12 00:00:00
Tasks: Vocal Bursts Intensity Prediction

Today, detection of anomalous events in civil infrastructures (e.g. water
pipe breaks and leaks) is time consuming and often takes hours or days. Pipe
breakage as one of the most frequent types of failure of water networks often
causes community disruptions ranging from temporary interruptions in services
to extended loss of business and relocation of residents. In this project, we
design and implement a two-phase approach for leak event identification, which
leverages dynamic data from multiple information sources including IoT sensing
data (pressure values and/or flow rates), geophysical data (water systems), and
human inputs (tweets posted on Twitter). In the approach, a high order
Conditional Random Field (CRF) is constructed that enforces predictions based
on IoT observations consistent with human inputs to improve the performance of
event identifications.
  Considering the physical water network as a graph, a CRF model is built and
learned by the Structured Support Vector Machine (SSVM) using node features
such as water pressure and flow rate. After that, we built the high order CRF
system by enforcing twitter leakage detection information. An optimal inference
algorithm is proposed for the adapted high order CRF model. Experimental
results show the effectiveness of our system.
Abductive, Causal, and Counterfactual Conditionals Under Incomplete Probabilistic Knowledge
Authors: Niki Pfeifer, Leena Tulkki
Date: 2017-03-09 00:00:00
Tasks: counterfactual

We study abductive, causal, and non-causal conditionals in indicative and
counterfactual formulations using probabilistic truth table tasks under
incomplete probabilistic knowledge (N = 80). We frame the task as a
probability-logical inference problem. The most frequently observed response
type across all conditions was a class of conditional event interpretations of
conditionals; it was followed by conjunction interpretations. An interesting
minority of participants neglected some of the relevant imprecision involved in
the premises when inferring lower or upper probability bounds on the target
conditional/counterfactual ("halfway responses"). We discuss the results in the
light of coherence-based probability logic and the new paradigm psychology of
reasoning.
Robustness from structure: Inference with hierarchical spiking networks on analog neuromorphic hardware
Authors: Mihai A. Petrovici, Anna Schroeder, Oliver Breitwieser, Andreas Grübl, Johannes Schemmel, Karlheinz Meier
Date: 2017-03-12 00:00:00

How spiking networks are able to perform probabilistic inference is an
intriguing question, not only for understanding information processing in the
brain, but also for transferring these computational principles to neuromorphic
silicon circuits. A number of computationally powerful spiking network models
have been proposed, but most of them have only been tested, under ideal
conditions, in software simulations. Any implementation in an analog, physical
system, be it in vivo or in silico, will generally lead to distorted dynamics
due to the physical properties of the underlying substrate. In this paper, we
discuss several such distortive effects that are difficult or impossible to
remove by classical calibration routines or parameter training. We then argue
that hierarchical networks of leaky integrate-and-fire neurons can offer the
required robustness for physical implementation and demonstrate this with both
software simulations and emulation on an accelerated analog neuromorphic
device.
Multiscale Hierarchical Convolutional Networks
Authors: Jörn-Henrik Jacobsen, Edouard Oyallon, Stéphane Mallat, Arnold W. M. Smeulders
Date: 2017-03-12 00:00:00
Tasks: Attribute

Deep neural network algorithms are difficult to analyze because they lack
structure allowing to understand the properties of underlying transforms and
invariants. Multiscale hierarchical convolutional networks are structured deep
convolutional networks where layers are indexed by progressively higher
dimensional attributes, which are learned from training data. Each new layer is
computed with multidimensional convolutions along spatial and attribute
variables. We introduce an efficient implementation of such networks where the
dimensionality is progressively reduced by averaging intermediate layers along
attribute indices. Hierarchical networks are tested on CIFAR image data bases
where they obtain comparable precisions to state of the art networks, with much
fewer parameters. We study some properties of the attributes learned from these
databases.
Hardware-Driven Nonlinear Activation for Stochastic Computing Based Deep Convolutional Neural Networks
Authors: Ji Li, Zihao Yuan, Zhe Li, Caiwen Ding, Ao Ren, Qinru Qiu, Jeffrey Draper, Yanzhi Wang
Date: 2017-03-12 00:00:00
Tasks: CPU, GPU

Recently, Deep Convolutional Neural Networks (DCNNs) have made unprecedented
progress, achieving the accuracy close to, or even better than human-level
perception in various tasks. There is a timely need to map the latest software
DCNNs to application-specific hardware, in order to achieve orders of magnitude
improvement in performance, energy efficiency and compactness. Stochastic
Computing (SC), as a low-cost alternative to the conventional binary computing
paradigm, has the potential to enable massively parallel and highly scalable
hardware implementation of DCNNs. One major challenge in SC based DCNNs is
designing accurate nonlinear activation functions, which have a significant
impact on the network-level accuracy but cannot be implemented accurately by
existing SC computing blocks. In this paper, we design and optimize SC based
neurons, and we propose highly accurate activation designs for the three most
frequently used activation functions in software DCNNs, i.e, hyperbolic
tangent, logistic, and rectified linear units. Experimental results on LeNet-5
using MNIST dataset demonstrate that compared with a binary ASIC hardware DCNN,
the DCNN with the proposed SC neurons can achieve up to 61X, 151X, and 2X
improvement in terms of area, power, and energy, respectively, at the cost of
small precision degradation.In addition, the SC approach achieves up to 21X and
41X of the area, 41X and 72X of the power, and 198200X and 96443X of the
energy, compared with CPU and GPU approaches, respectively, while the error is
increased by less than 3.07%. ReLU activation is suggested for future SC based
DCNNs considering its superior performance under a small bit stream length.
A Hypercat-enabled Semantic Internet of Things Data Hub: Technical Report
Authors: Ilias Tachmazidis, Sotiris Batsakis, John Davies, Alistair Duke, Mauro Vallati, Grigoris Antoniou, Sandra Stincic Clarke
Date: 2017-03-01 00:00:00

An increasing amount of information is generated from the rapidly increasing
number of sensor networks and smart devices. A wide variety of sources generate
and publish information in different formats, thus highlighting
interoperability as one of the key prerequisites for the success of Internet of
Things (IoT). The BT Hypercat Data Hub provides a focal point for the sharing
and consumption of available datasets from a wide range of sources. In this
work, we propose a semantic enrichment of the BT Hypercat Data Hub, using
well-accepted Semantic Web standards and tools. We propose an ontology that
captures the semantics of the imported data and present the BT SPARQL Endpoint
by means of a mapping between SPARQL and SQL queries. Furthermore, federated
SPARQL queries allow queries over multiple hub-based and external data sources.
Finally, we provide two use cases in order to illustrate the advantages
afforded by our semantic approach.
Local Patch Encoding-Based Method for Single Image Super-Resolution
Authors: Yang Zhao, Ronggang Wang, Wei Jia, Jianchao Yang, Wenmin Wang, Wen Gao
Date: 2017-03-12 00:00:00
Tasks: Dictionary Learning, Image Super-Resolution, Super-Resolution

Recent learning-based super-resolution (SR) methods often focus on dictionary
learning or network training. In this paper, we discuss in detail a new SR
method based on local patch encoding (LPE) instead of traditional dictionary
learning. The proposed method consists of a learning stage and a reconstructing
stage. In the learning stage, image patches are classified into different
classes by means of the proposed LPE, and then a projection matrix is computed
for each class by utilizing a simple constraint. In the reconstructing stage,
an input LR patch can be simply reconstructed by computing its LPE code and
then multiplying the corresponding projection matrix. Furthermore, we discuss
the relationship between the proposed method and the anchored neighborhood
regression methods; we also analyze the extendibility of the proposed method.
The experimental results on several image sets demonstrate the effectiveness of
the LPE-based methods.
Group Component Analysis for Multiblock Data: Common and Individual Feature Extraction
Authors: Guoxu Zhou, Andrzej Cichocki, Yu Zhang, Danilo Mandic
Date: 2012-12-17 00:00:00
Tasks: blind source separation, Clustering, Dimensionality Reduction

Very often data we encounter in practice is a collection of matrices rather
than a single matrix. These multi-block data are naturally linked and hence
often share some common features and at the same time they have their own
individual features, due to the background in which they are measured and
collected. In this study we proposed a new scheme of common and individual
feature analysis (CIFA) that processes multi-block data in a linked way aiming
at discovering and separating their common and individual features. According
to whether the number of common features is given or not, two efficient
algorithms were proposed to extract the common basis which is shared by all
data. Then feature extraction is performed on the common and the individual
spaces separately by incorporating the techniques such as dimensionality
reduction and blind source separation. We also discussed how the proposed CIFA
can significantly improve the performance of classification and clustering
tasks by exploiting common and individual features of samples respectively. Our
experimental results show some encouraging features of the proposed methods in
comparison to the state-of-the-art methods on synthetic and real data.
SurfNet: Generating 3D shape surfaces using deep residual networks
Authors: Ayan Sinha, Asim Unmesh, Qi-Xing Huang, Karthik Ramani
Date: 2017-03-12 00:00:00
Proceeding: CVPR 2017 7
Tasks: 3D Shape Generation, Image Generation

3D shape models are naturally parameterized using vertices and faces, \ie,
composed of polygons forming a surface. However, current 3D learning paradigms
for predictive and generative tasks using convolutional neural networks focus
on a voxelized representation of the object. Lifting convolution operators from
the traditional 2D to 3D results in high computational overhead with little
additional benefit as most of the geometry information is contained on the
surface boundary. Here we study the problem of directly generating the 3D shape
surface of rigid and non-rigid shapes using deep convolutional neural networks.
We develop a procedure to create consistent `geometry images' representing the
shape surface of a category of 3D objects. We then use this consistent
representation for category-specific shape surface generation from a parametric
representation or an image by developing novel extensions of deep residual
networks for the task of geometry image generation. Our experiments indicate
that our network learns a meaningful representation of shape surfaces allowing
it to interpolate between shape orientations and poses, invent new shape
surfaces and reconstruct 3D shape surfaces from previously unseen images.
Prostate Cancer Diagnosis using Deep Learning with 3D Multiparametric MRI
Authors: Saifeng Liu, Huaixiu Zheng, Yesu Feng, Wei Li
Date: 2017-03-12 00:00:00
Tasks: BIG-bench Machine Learning, Data Augmentation, Deep Learning, General Classification

A novel deep learning architecture (XmasNet) based on convolutional neural
networks was developed for the classification of prostate cancer lesions, using
the 3D multiparametric MRI data provided by the PROSTATEx challenge. End-to-end
training was performed for XmasNet, with data augmentation done through 3D
rotation and slicing, in order to incorporate the 3D information of the lesion.
XmasNet outperformed traditional machine learning models based on engineered
features, for both train and test data. For the test data, XmasNet outperformed
69 methods from 33 participating groups and achieved the second highest AUC
(0.84) in the PROSTATEx challenge. This study shows the great potential of deep
learning for cancer imaging.
Competitive and Penalized Clustering Auto-encoder
Authors: Zihao Wang, Yiu-ming Cheung
Date: 2015-08-28 00:00:00
Tasks: Clustering

The paper has been withdrawn since more effective experiments should be
completed.
  Auto-encoders (AE) has been widely applied in different fields of machine
learning. However, as a deep model, there are a large amount of learnable
parameters in the AE, which would cause over-fitting and slow learning speed in
practice. Many researchers have been study the intrinsic structure of AE and
showed different useful methods to regularize those parameters. In this paper,
we present a novel regularization method based on a clustering algorithm which
is able to classify the parameters into different groups. With this
regularization, parameters in a given group have approximate equivalent values
and over-fitting problem could be alleviated. Moreover, due to the competitive
behavior of clustering algorithm, this model also overcomes some intrinsic
problems of clustering algorithms like the determination of number of clusters.
Experiments on handwritten digits recognition verify the effectiveness of our
novel model.