pwc-archive/papers-with-abstracts huggingface dataset:
Learning Deep Gradient Descent Optimization for Image Deconvolution
Authors: Dong Gong, Zhen Zhang, Qinfeng Shi, Anton Van Den Hengel, Chunhua Shen, Yanning Zhang
Date: 2018-04-10 00:00:00
Tasks: Blind Image Deblurring, Deblurring, Image Deblurring, Image Deconvolution

As an integral component of blind image deblurring, non-blind deconvolution removes image blur with a given blur kernel, which is essential but difficult due to the ill-posed nature of the inverse problem. The predominant approach is based on optimization subject to regularization functions that are either manually designed, or learned from examples. Existing learning based methods have shown superior restoration quality but are not practical enough due to their restricted and static model design. They solely focus on learning a prior and require to know the noise level for deconvolution. We address the gap between the optimization-based and learning-based approaches by learning a universal gradient descent optimizer. We propose a Recurrent Gradient Descent Network (RGDN) by systematically incorporating deep neural networks into a fully parameterized gradient descent scheme. A hyper-parameter-free update unit shared across steps is used to generate updates from the current estimates, based on a convolutional neural network. By training on diverse examples, the Recurrent Gradient Descent Network learns an implicit image prior and a universal update rule through recursive supervision. The learned optimizer can be repeatedly used to improve the quality of diverse degenerated observations. The proposed method possesses strong interpretability and high generalization. Extensive experiments on synthetic benchmarks and challenging real-world images demonstrate that the proposed deep optimization method is effective and robust to produce favorable results as well as practical for real-world image deblurring applications.
A review of possible effects of cognitive biases on the interpretation of rule-based machine learning models
Authors: Tomáš Kliegr, Štěpán Bahník, Johannes Fürnkranz
Date: 2018-04-09 00:00:00
Tasks: BIG-bench Machine Learning, Interpretable Machine Learning

While the interpretability of machine learning models is often equated with their mere syntactic comprehensibility, we think that interpretability goes beyond that, and that human interpretability should also be investigated from the point of view of cognitive science. The goal of this paper is to discuss to what extent cognitive biases may affect human understanding of interpretable machine learning models, in particular of logical rules discovered from data. Twenty cognitive biases are covered, as are possible debiasing techniques that can be adopted by designers of machine learning algorithms and software. Our review transfers results obtained in cognitive psychology to the domain of machine learning, aiming to bridge the current gap between these two areas. It needs to be followed by empirical studies specifically focused on the machine learning domain.
Reference-Conditioned Super-Resolution by Neural Texture Transfer
Authors: Zhifei Zhang, Zhaowen Wang, Zhe Lin, Hairong Qi
Date: 2018-04-10 00:00:00
Tasks: Image Stylization, Image Super-Resolution, Reference-based Super-Resolution, Super-Resolution

With the recent advancement in deep learning, we have witnessed a great
progress in single image super-resolution. However, due to the significant
information loss of the image downscaling process, it has become extremely
challenging to further advance the state-of-the-art, especially for large
upscaling factors. This paper explores a new research direction in super
resolution, called reference-conditioned super-resolution, in which a reference
image containing desired high-resolution texture details is provided besides
the low-resolution image. We focus on transferring the high-resolution texture
from reference images to the super-resolution process without the constraint of
content similarity between reference and target images, which is a key
difference from previous example-based methods. Inspired by recent work on
image stylization, we address the problem via neural texture transfer. We
design an end-to-end trainable deep model which generates detail enriched
results by adaptively fusing the content from the low-resolution image with the
texture patterns from the reference image. We create a benchmark dataset for
the general research of reference-based super-resolution, which contains
reference images paired with low-resolution inputs with varying degrees of
similarity. Both objective and subjective evaluations demonstrate the great
potential of using reference images as well as the superiority of our results
over other state-of-the-art methods.
Mean Field Network based Graph Refinement with application to Airway Tree Extraction
Authors: Raghavendra Selvan, Max Welling, Jesper H. Pedersen, Jens Petersen, Marleen de Bruijne
Date: 2018-04-10 00:00:00
Tasks: Bayesian Inference

We present tree extraction in 3D images as a graph refinement task, of
obtaining a subgraph from an over-complete input graph. To this end, we
formulate an approximate Bayesian inference framework on undirected graphs
using mean field approximation (MFA). Mean field networks are used for
inference based on the interpretation that iterations of MFA can be seen as
feed-forward operations in a neural network. This allows us to learn the model
parameters from training data using back-propagation algorithm. We demonstrate
usefulness of the model to extract airway trees from 3D chest CT data. We first
obtain probability images using a voxel classifier that distinguishes airways
from background and use Bayesian smoothing to model individual airway branches.
This yields us joint Gaussian density estimates of position, orientation and
scale as node features of the input graph. Performance of the method is
compared with two methods: the first uses probability images from a trained
voxel classifier with region growing, which is similar to one of the best
performing methods at EXACT'09 airway challenge, and the second method is based
on Bayesian smoothing on these probability images. Using centerline distance as
error measure the presented method shows significant improvement compared to
these two methods.
Learning Latent Events from Network Message Logs
Authors: Siddhartha Satpathi, Supratim Deb, R. Srikant, He Yan
Date: 2018-04-10 00:00:00
Tasks: Change Point Detection

We consider the problem of separating error messages generated in large distributed data center networks into error events. In such networks, each error event leads to a stream of messages generated by hardware and software components affected by the event. These messages are stored in a giant message log. We consider the unsupervised learning problem of identifying the signatures of events that generated these messages; here, the signature of an error event refers to the mixture of messages generated by the event. One of the main contributions of the paper is a novel mapping of our problem which transforms it into a problem of topic discovery in documents. Events in our problem correspond to topics and messages in our problem correspond to words in the topic discovery problem. However, there is no direct analog of documents. Therefore, we use a non-parametric change-point detection algorithm, which has linear computational complexity in the number of messages, to divide the message log into smaller subsets called episodes, which serve as the equivalents of documents. After this mapping has been done, we use a well-known algorithm for topic discovery, called LDA, to solve our problem. We theoretically analyze the change-point detection algorithm, and show that it is consistent and has low sample complexity. We also demonstrate the scalability of our algorithm on a real data set consisting of $97$ million messages collected over a period of $15$ days, from a distributed data center network which supports the operations of a large wireless service provider.
Modular Generative Adversarial Networks
Authors: Bo Zhao, Bo Chang, Zequn Jie, Leonid Sigal
Date: 2018-04-10 00:00:00
Proceeding: ECCV 2018 9
Tasks: Attribute, Image Generation, Image-to-Image Translation, Translation

Existing methods for multi-domain image-to-image translation (or generation)
attempt to directly map an input image (or a random vector) to an image in one
of the output domains. However, most existing methods have limited scalability
and robustness, since they require building independent models for each pair of
domains in question. This leads to two significant shortcomings: (1) the need
to train exponential number of pairwise models, and (2) the inability to
leverage data from other domains when training a particular pairwise mapping.
Inspired by recent work on module networks, this paper proposes ModularGAN for
multi-domain image generation and image-to-image translation. ModularGAN
consists of several reusable and composable modules that carry on different
functions (e.g., encoding, decoding, transformations). These modules can be
trained simultaneously, leveraging data from all domains, and then combined to
construct specific GAN networks at test time, according to the specific image
translation task. This leads to ModularGAN's superior flexibility of generating
(or translating to) an image in any desired domain. Experimental results
demonstrate that our model not only presents compelling perceptual results but
also outperforms state-of-the-art methods on multi-domain facial attribute
transfer.
Toward Formalizing Teleportation of Pedagogical Artificial Agents
Authors: John Angel, Naveen Sundar Govindarajulu, Selmer Bringsjord
Date: 2018-04-10 00:00:00

Our paradigm for the use of artificial agents to teach requires among other
things that they persist through time in their interaction with human students,
in such a way that they "teleport" or "migrate" from an embodiment at one time
t to a different embodiment at later time t'. In this short paper, we report on
initial steps toward the formalization of such teleportation, in order to
enable an overseeing AI system to establish, mechanically, and verifiably, that
the human students in question will likely believe that the very same
artificial agent has persisted across such times despite the different
embodiments.
On the Robustness of the CVPR 2018 White-Box Adversarial Example Defenses
Authors: Anish Athalye, Nicholas Carlini
Date: 2018-04-10 00:00:00

Neural networks are known to be vulnerable to adversarial examples. In this
note, we evaluate the two white-box defenses that appeared at CVPR 2018 and
find they are ineffective: when applying existing techniques, we can reduce the
accuracy of the defended models to 0%.
Segmentation of Multiple Sclerosis lesion in brain MR images using Fuzzy C-Means
Authors: Saba Heidari Gheshlaghi, Abolfazl Madani, AmirAbolfazl Suratgar, Fardin Faraji
Date: 2018-04-10 00:00:00
Tasks: Clustering, Edge Detection, Lesion Segmentation

Magnetic resonance images (MRI) play an important role in supporting and
substituting clinical information in the diagnosis of multiple sclerosis (MS)
disease by presenting lesion in brain MR images. In this paper, an algorithm
for MS lesion segmentation from Brain MR Images has been presented. We revisit
the modification of properties of fuzzy -c means algorithms and the canny edge
detection. By changing and reformed fuzzy c-means clustering algorithms, and
applying canny contraction principle, a relationship between MS lesions and
edge detection is established. For the special case of FCM, we derive a
sufficient condition and clustering parameters, allowing identification of them
as (local) minima of the objective function.
TIDBD: Adapting Temporal-difference Step-sizes Through Stochastic Meta-descent
Authors: Alex Kearney, Vivek Veeriah, Jaden B. Travnik, Richard S. Sutton, Patrick M. Pilarski
Date: 2018-04-10 00:00:00
Tasks: Representation Learning

In this paper, we introduce a method for adapting the step-sizes of temporal
difference (TD) learning. The performance of TD methods often depends on well
chosen step-sizes, yet few algorithms have been developed for setting the
step-size automatically for TD learning. An important limitation of current
methods is that they adapt a single step-size shared by all the weights of the
learning system. A vector step-size enables greater optimization by specifying
parameters on a per-feature basis. Furthermore, adapting parameters at
different rates has the added benefit of being a simple form of representation
learning. We generalize Incremental Delta Bar Delta (IDBD)---a vectorized
adaptive step-size method for supervised learning---to TD learning, which we
name TIDBD. We demonstrate that TIDBD is able to find appropriate step-sizes in
both stationary and non-stationary prediction tasks, outperforming ordinary TD
methods and TD methods with scalar step-size adaptation; we demonstrate that it
can differentiate between features which are relevant and irrelevant for a
given task, performing representation learning; and we show on a real-world
robot prediction task that TIDBD is able to outperform ordinary TD methods and
TD methods augmented with AlphaBound and RMSprop.
Context-aware Data Aggregation with Localized Information Privacy
Authors: Bo Jiang, Ming Li, Ravi Tandon
Date: 2018-04-06 00:00:00
Tasks: Privacy Preserving

In this paper, localized information privacy (LIP) is proposed, as a new
privacy definition, which allows statistical aggregation while protecting
users' privacy without relying on a trusted third party. The notion of
context-awareness is incorporated in LIP by the introduction of priors, which
enables the design of privacy-preserving data aggregation with knowledge of
priors. We show that LIP relaxes the Localized Differential Privacy (LDP)
notion by explicitly modeling the adversary's knowledge. However, it is
stricter than $2\epsilon$-LDP and $\epsilon$-mutual information privacy. The
incorporation of local priors allows LIP to achieve higher utility compared to
other approaches. We then present an optimization framework for
privacy-preserving data aggregation, with the goal of minimizing the expected
squared error while satisfying the LIP privacy constraints. Utility-privacy
tradeoffs are obtained under several models in closed-form. We then validate
our analysis by {numerical analysis} using both synthetic and real-world data.
Results show that our LIP mechanism provides better utility-privacy tradeoffs
than LDP and when the prior is not uniformly distributed, the advantage of LIP
is even more significant.
An Estimation of Favorite Value in Emotion Generating Calculation by Fuzzy Petri Net
Authors: Takumi Ichimura, Kousuke Tanabe
Date: 2018-04-10 00:00:00

Emotion Generating Calculations (EGC) method based on the Emotion Eliciting
Condition Theory can decide whether an event arouses pleasure or not and
quantify the degree under the event. An event in the form of Case Frame
representation is classified into 12 types of calculations. However, the weak
point in EGC is Favorite Value (FV) as the personal taste information. In order
to improve the problem, this paper challenges to establish a learning method to
learn speaker's taste information from dialog. Especially, the learning method
employs Fuzzy Petri Net to find an appropriate FV to a word which has the
unknown FV. This paper discusses the effective learning method to improve a
weak point of EGC when a missing value of FV exists.
Cortex Neural Network: learning with Neural Network groups
Authors: Liyao Gao
Date: 2018-04-10 00:00:00
Tasks: Machine Translation

Neural Network has been successfully applied to many real-world problems,
such as image recognition and machine translation. However, for the current
architecture of neural networks, it is hard to perform complex cognitive tasks,
for example, to process the image and audio inputs together. Cortex, as an
important architecture in the brain, is important for animals to perform the
complex cognitive task. We view the architecture of Cortex in the brain as a
missing part in the design of the current artificial neural network. In this
paper, we purpose Cortex Neural Network (CrtxNN). The Cortex Neural Network is
an upper architecture of neural networks which motivated from cerebral cortex
in the brain to handle different tasks in the same learning system. It is able
to identify different tasks and solve them with different methods. In our
implementation, the Cortex Neural Network is able to process different
cognitive tasks and perform reflection to get a higher accuracy. We provide a
series of experiments to examine the capability of the cortex architecture on
traditional neural networks. Our experiments proved its ability on the Cortex
Neural Network can reach accuracy by 98.32% on MNIST and 62% on CIFAR10 at the
same time, which can promisingly reduce the loss by 40%.
Crafting a Toolchain for Image Restoration by Deep Reinforcement Learning
Authors: Ke Yu, Chao Dong, Liang Lin, Chen Change Loy
Date: 2018-04-10 00:00:00
Proceeding: CVPR 2018 6
Tasks: Deep Reinforcement Learning, Image Restoration, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We investigate a novel approach for image restoration by reinforcement
learning. Unlike existing studies that mostly train a single large network for
a specialized task, we prepare a toolbox consisting of small-scale
convolutional networks of different complexities and specialized in different
tasks. Our method, RL-Restore, then learns a policy to select appropriate tools
from the toolbox to progressively restore the quality of a corrupted image. We
formulate a step-wise reward function proportional to how well the image is
restored at each step to learn the action policy. We also devise a joint
learning scheme to train the agent and tools for better performance in handling
uncertainty. In comparison to conventional human-designed networks, RL-Restore
is capable of restoring images corrupted with complex and unknown distortions
in a more parameter-efficient manner using the dynamically formed toolchain.
Modeling Semantic Plausibility by Injecting World Knowledge
Authors: Su Wang, Greg Durrett, Katrin Erk
Date: 2018-04-02 00:00:00
Proceeding: NAACL 2018 6
Tasks: World Knowledge

Distributional data tells us that a man can swallow candy, but not that a man
can swallow a paintball, since this is never attested. However both are
physically plausible events. This paper introduces the task of semantic
plausibility: recognizing plausible but possibly novel events. We present a new
crowdsourced dataset of semantic plausibility judgments of single events such
as "man swallow paintball". Simple models based on distributional
representations perform poorly on this task, despite doing well on selection
preference, but injecting manually elicited knowledge about entity properties
provides a substantial performance boost. Our error analysis shows that our new
dataset is a great testbed for semantic plausibility models: more sophisticated
knowledge representation and propagation could address many of the remaining
errors.
A Mathematical Framework for Superintelligent Machines
Authors: Daniel J. Buehrer
Date: 2018-04-10 00:00:00

We describe a class calculus that is expressive enough to describe and
improve its own learning process. It can design and debug programs that satisfy
given input/output constraints, based on its ontology of previously learned
programs. It can improve its own model of the world by checking the actual
results of the actions of its robotic activators. For instance, it could check
the black box of a car crash to determine if it was probably caused by electric
failure, a stuck electronic gate, dark ice, or some other condition that it
must add to its ontology in order to meet its sub-goal of preventing such
crashes in the future. Class algebra basically defines the eval/eval-1 Galois
connection between the residuated Boolean algebras of 1. equivalence classes
and super/sub classes of class algebra type expressions, and 2. a residual
Boolean algebra of biclique relationships. It distinguishes which formulas are
equivalent, entailed, or unrelated, based on a simplification algorithm that
may be thought of as producing a unique pair of Karnaugh maps that describe the
rough sets of maximal bicliques of relations. Such maps divide the
n-dimensional space of up to 2n-1 conjunctions of up to n propositions into
clopen (i.e. a closed set of regions and their boundaries) causal sets. This
class algebra is generalized to type-2 fuzzy class algebra by using relative
frequencies as probabilities. It is also generalized to a class calculus
involving assignments that change the states of programs.
  INDEX TERMS 4-valued Boolean Logic, Artificial Intelligence, causal sets,
class algebra, consciousness, intelligent design, IS-A hierarchy, mathematical
logic, meta-theory, pointless topological space, residuated lattices, rough
sets, type-2 fuzzy sets
Pilot Comparative Study of Different Deep Features for Palmprint Identification in Low-Quality Images
Authors: A. S. Tarawneh, D. Chetverikov, A. B. Hassanat
Date: 2018-04-10 00:00:00
Tasks: Translation

Deep Convolutional Neural Networks (CNNs) are widespread, efficient tools of
visual recognition. In this paper, we present a comparative study of three
popular pre-trained CNN models: AlexNet, VGG-16 and VGG-19. We address the
problem of palmprint identification in low-quality imagery and apply Support
Vector Machines (SVMs) with all of the compared models. For the comparison, we
use the MOHI palmprint image database whose images are characterized by low
contrast, shadows, and varying illumination, scale, translation and rotation.
Another, high-quality database called COEP is also considered to study the
recognition gap between high-quality and low-quality imagery. Our experiments
show that the deeper pre-trained CNN models, e.g., VGG-16 and VGG-19, tend to
extract highly distinguishable features that recognize low-quality palmprints
more efficiently than the less deep networks such as AlexNet. Furthermore, our
experiments on the two databases using various models demonstrate that the
features extracted from lower-level fully connected layers provide higher
recognition rates than higher-layer features. Our results indicate that
different pre-trained models can be efficiently used in touchless
identification systems with low-quality palmprint images.
Photometric Stereo in Participating Media Considering Shape-Dependent Forward Scatter
Authors: Yuki Fujimura, Masaaki Iiyama, Atsushi Hashimoto, Michihiko Minoh
Date: 2018-04-09 00:00:00
Proceeding: CVPR 2018 6
Tasks: 3D Reconstruction

Images captured in participating media such as murky water, fog, or smoke are
degraded by scattered light. Thus, the use of traditional three-dimensional
(3D) reconstruction techniques in such environments is difficult. In this
paper, we propose a photometric stereo method for participating media. The
proposed method differs from previous studies with respect to modeling
shape-dependent forward scatter. In the proposed model, forward scatter is
described as an analytical form using lookup tables and is represented by
spatially-variant kernels. We also propose an approximation of a large-scale
dense matrix as a sparse matrix, which enables the removal of forward scatter.
Experiments with real and synthesized data demonstrate that the proposed method
improves 3D reconstruction in participating media.