pwc-archive/papers-with-abstracts huggingface dataset:
Computational Complexity Analysis of Genetic Programming
Authors: Andrei Lissovoi, Pietro S. Oliveto
Date: 2018-11-11 00:00:00
Tasks: Evolutionary Algorithms

Genetic programming (GP) is an evolutionary computation technique to solve problems in an automated, domain-independent way. Rather than identifying the optimum of a function as in more traditional evolutionary optimization, the aim of GP is to evolve computer programs with a given functionality. While many GP applications have produced human competitive results, the theoretical understanding of what problem characteristics and algorithm properties allow GP to be effective is comparatively limited. Compared with traditional evolutionary algorithms for function optimization, GP applications are further complicated by two additional factors: the variable-length representation of candidate programs, and the difficulty of evaluating their quality efficiently. Such difficulties considerably impact the runtime analysis of GP, where space complexity also comes into play. As a result, initial complexity analyses of GP have focused on restricted settings such as the evolution of trees with given structures or the estimation of solution quality using only a small polynomial number of input/output examples. However, the first computational complexity analyses of GP for evolving proper functions with defined input/output behavior have recently appeared. In this chapter, we present an overview of the state of the art.
Machine Learning with Abstention for Automated Liver Disease Diagnosis
Authors: Kanza Hamid, Amina Asif, Wajid Abbasi, Durre Sabih, Fayyaz Minhas
Date: 2018-11-11 00:00:00
Tasks: BIG-bench Machine Learning, Diagnostic, General Classification, Prediction

This paper presents a novel approach for detection of liver abnormalities in
an automated manner using ultrasound images. For this purpose, we have
implemented a machine learning model that can not only generate labels (normal
and abnormal) for a given ultrasound image but it can also detect when its
prediction is likely to be incorrect. The proposed model abstains from
generating the label of a test example if it is not confident about its
prediction. Such behavior is commonly practiced by medical doctors who, when
given insufficient information or a difficult case, can chose to carry out
further clinical or diagnostic tests before generating a diagnosis. However,
existing machine learning models are designed in a way to always generate a
label for a given example even when the confidence of their prediction is low.
We have proposed a novel stochastic gradient based solver for the learning with
abstention paradigm and use it to make a practical, state of the art method for
liver disease classification. The proposed method has been benchmarked on a
data set of approximately 100 patients from MINAR, Multan, Pakistan and our
results show that the proposed scheme offers state of the art classification
performance.
Time-interval balancing in multi-processor scheduling of composite modular jobs (preliminary description)
Authors: Mark Sh. Levin
Date: 2018-11-11 00:00:00
Tasks: Clustering, Management, Scheduling

The article describes a special time-interval balancing in multi-processor
scheduling of composite modular jobs. This scheduling problem is close to
just-in-time planning approach. First, brief literature surveys are presented
on just-in-time scheduling and due-data/due-window scheduling problems.
Further, the problem and its formulation are proposed for the time-interval
balanced scheduling of composite modular jobs. The illustrative real world
planning example for modular home-building is described. Here, the main
objective function consists in a balance between production of the typical
building modules (details) and the assembly processes of the building(s) (by
several teams). The assembly plan has to be modified to satisfy the balance
requirements. The solving framework is based on the following: (i) clustering
of initial set of modular detail types to obtain about ten basic detail types
that correspond to main manufacturing conveyors; (ii) designing a preliminary
plan of assembly for buildings; (iii) detection of unbalanced time periods,
(iv) modification of the planning solution to improve the schedule balance. The
framework implements a metaheuristic based on local optimization approach. Two
other applications (supply chain management, information transmission systems)
are briefly described.
A 3-D Projection Model for X-ray Dark-field Imaging
Authors: Shiyang Hu, Lina Felsner, Andreas Maier, Veronika Ludwig, Gisela Anton, Christian Riess
Date: 2018-11-11 00:00:00

Talbot-Lau X-ray phase-contrast imaging is a novel imaging modality, which
provides not only an X-ray absorption image, but also additionally a
differential phase image and a dark-field image. The dark-field image is
related to small angle scattering and has an interesting property when canning
oriented structures: the recorded signal depends on the relative orientation of
the structure in the imaging system. Exactly this property allows to draw
conclusions about the orientation and to reconstruct the structure. However,
the reconstruction is a complex, non-trivial challenge. A lot of research was
conducted towards this goal in the last years and several reconstruction
algorithms were proposed. A key step of the reconstruction algorithm is the
inversion of a forward projection model. Up until now, only 2-D projection
models are available, with effectively limit the scanning trajectory to a 2-D
plane. To obtain true 3-D information, this limitation requires to combine
several 2-D scans, which leads to quite complex, impractical acquisitions
schemes. Furthermore, it is not possible with these models to use 3-D
trajectories that might allow simpler protocols, like for example a helical
trajectory. To address these limitations, we propose in this work a very
general 3-D projection model. Our projection model defines the dark-field
signal dependent on an arbitrarily chosen ray and sensitivity direction. We
derive the projection model under the assumption that the observed scatter
distribution has a Gaussian shape. We theoretically show the consistency of our
model with more constrained existing 2-D models. Furthermore, we experimentally
show the compatibility of our model with dark-field measurements of two
matchsticks. We believe that this 3-D projection model is an important step
towards more flexible trajectories and imaging protocols that are much better
applicable in practice.
Learning with tree-based tensor formats
Authors: Erwan Grelier, Anthony Nouy, Mathilde Chevreuil
Date: 2018-11-11 00:00:00
Tasks: Small Data Image Classification

This paper is concerned with the approximation of high-dimensional functions
in a statistical learning setting, by empirical risk minimization over model
classes of functions in tree-based tensor format. These are particular classes
of rank-structured functions that can be seen as deep neural networks with a
sparse architecture related to the tree and multilinear activation functions.
For learning in a given model class, we exploit the fact that tree-based tensor
formats are multilinear models and recast the problem of risk minimization over
a nonlinear set into a succession of learning problems with linear models.
Suitable changes of representation yield numerically stable learning problems
and allow to exploit sparsity. For high-dimensional problems or when only a
small data set is available, the selection of a good model class is a critical
issue. For a given tree, the selection of the tuple of tree-based ranks that
minimize the risk is a combinatorial problem. Here, we propose a rank
adaptation strategy which provides in practice a good convergence of the risk
as a function of the model class complexity. Finding a good tree is also a
combinatorial problem, which can be related to the choice of a particular
sparse architecture for deep neural networks. Here, we propose a stochastic
algorithm for minimizing the complexity of the representation of a given
function over a class of trees with a given arity, allowing changes in the
topology of the tree. This tree optimization algorithm is then included in a
learning scheme that successively adapts the tree and the corresponding
tree-based ranks. Contrary to classical learning algorithms for nonlinear model
classes, the proposed algorithms are numerically stable, reliable, and require
only a low level expertise of the user.
ReDecode Framework for Iterative Improvement in Paraphrase Generation
Authors: Milan Aggarwal, Nupur Kumari, Ayush Bansal, Balaji Krishnamurthy
Date: 2018-11-11 00:00:00
Tasks: Decoder, Information Retrieval, Paraphrase Generation, Question Answering, Retrieval, Sentence

Generating paraphrases, that is, different variations of a sentence conveying
the same meaning, is an important yet challenging task in NLP. Automatically
generating paraphrases has its utility in many NLP tasks like question
answering, information retrieval, conversational systems to name a few. In this
paper, we introduce iterative refinement of generated paraphrases within VAE
based generation framework. Current sequence generation models lack the
capability to (1) make improvements once the sentence is generated; (2) rectify
errors made while decoding. We propose a technique to iteratively refine the
output using multiple decoders, each one attending on the output sentence
generated by the previous decoder. We improve current state of the art results
significantly - with over 9% and 28% absolute increase in METEOR scores on
Quora question pairs and MSCOCO datasets respectively. We also show
qualitatively through examples that our re-decoding approach generates better
paraphrases compared to a single decoder by rectifying errors and making
improvements in paraphrase structure, inducing variations and introducing new
but semantically coherent information.
Pedestrian Collision Avoidance System (PeCAS): a Deep Learning Framework
Authors: Peetak Mitra
Date: 2018-11-11 00:00:00
Tasks: Collision Avoidance, Deep Learning, Raspberry Pi 3

We propose a new deep learning based framework to identify pedestrians, and
caution distracted drivers, in an effort to prevent the loss of life and
property. This framework uses two Convolutional Neural Networks (CNN), one
which detects pedestrians and the second which predicts the onset of drowsiness
in a driver, is implemented on a Raspberry Pi 3 Model B+, shows great promise.
The algorithm for implementing such a low-cost, low-compute model is presented
and the results discussed.
Multi-Source Neural Variational Inference
Authors: Richard Kurle, Stephan Günnemann, Patrick van der Smagt
Date: 2018-11-11 00:00:00
Tasks: Variational Inference

Learning from multiple sources of information is an important problem in
machine-learning research. The key challenges are learning representations and
formulating inference methods that take into account the complementarity and
redundancy of various information sources. In this paper we formulate a
variational autoencoder based multi-source learning framework in which each
encoder is conditioned on a different information source. This allows us to
relate the sources via the shared latent variables by computing divergence
measures between individual source's posterior approximations. We explore a
variety of options to learn these encoders and to integrate the beliefs they
compute into a consistent posterior approximation. We visualise learned beliefs
on a toy dataset and evaluate our methods for learning shared representations
and structured output prediction, showing trade-offs of learning separate
encoders for each information source. Furthermore, we demonstrate how conflict
detection and redundancy can increase robustness of inference in a multi-source
setting.
End-to-end Structure-Aware Convolutional Networks for Knowledge Base Completion
Authors: Chao Shang, Yun Tang, Jing Huang, Jinbo Bi, Xiaodong He, Bo-Wen Zhou
Date: 2018-11-11 00:00:00
Tasks: Decoder, Graph Embedding, Knowledge Base Completion, Knowledge Graph Embedding, Knowledge Graphs, Link Prediction

Knowledge graph embedding has been an active research topic for knowledge
base completion, with progressive improvement from the initial TransE, TransH,
DistMult et al to the current state-of-the-art ConvE. ConvE uses 2D convolution
over embeddings and multiple layers of nonlinear features to model knowledge
graphs. The model can be efficiently trained and scalable to large knowledge
graphs. However, there is no structure enforcement in the embedding space of
ConvE. The recent graph convolutional network (GCN) provides another way of
learning graph node embedding by successfully utilizing graph connectivity
structure. In this work, we propose a novel end-to-end Structure-Aware
Convolutional Network (SACN) that takes the benefit of GCN and ConvE together.
SACN consists of an encoder of a weighted graph convolutional network (WGCN),
and a decoder of a convolutional network called Conv-TransE. WGCN utilizes
knowledge graph node structure, node attributes and edge relation types. It has
learnable weights that adapt the amount of information from neighbors used in
local aggregation, leading to more accurate embeddings of graph nodes. Node
attributes in the graph are represented as additional nodes in the WGCN. The
decoder Conv-TransE enables the state-of-the-art ConvE to be translational
between entities and relations while keeps the same link prediction performance
as ConvE. We demonstrate the effectiveness of the proposed SACN on standard
FB15k-237 and WN18RR datasets, and it gives about 10% relative improvement over
the state-of-the-art ConvE in terms of HITS@1, HITS@3 and HITS@10.
A Progressively-trained Scale-invariant and Boundary-aware Deep Neural Network for the Automatic 3D Segmentation of Lung Lesions
Authors: Bo Zhou, Randolph Crawford, Belma Dogdas, Gregory Goldmacher, Antong Chen
Date: 2018-11-11 00:00:00
Tasks: Lesion Segmentation, Segmentation

Volumetric segmentation of lesions on CT scans is important for many types of
analysis, including lesion growth kinetic modeling in clinical trials and
machine learning of radiomic features. Manual segmentation is laborious, and
impractical for large-scale use. For routine clinical use, and in clinical
trials that apply the Response Evaluation Criteria In Solid Tumors (RECIST),
clinicians typically outline the boundaries of a lesion on a single slice to
extract diameter measurements. In this work, we have collected a large-scale
database, named LesionVis, with pixel-wise manual 2D lesion delineations on the
RECIST-slices. To extend the 2D segmentations to 3D, we propose a volumetric
progressive lesion segmentation (PLS) algorithm to automatically segment the 3D
lesion volume from 2D delineations using a scale-invariant and boundary-aware
deep convolutional network (SIBA-Net). The SIBA-Net copes with the size
transition of a lesion when the PLS progresses from the RECIST-slice to the
edge-slices, as well as when performing longitudinal assessment of lesions
whose size change over multiple time points. The proposed PLS-SiBA-Net (P-SiBA)
approach is assessed on the lung lesion cases from LesionVis. Our experimental
results demonstrate that the P-SiBA approach achieves mean Dice similarity
coefficients (DSC) of 0.81, which significantly improves 3D segmentation
accuracy compared with the approaches proposed previously (highest mean DSC at
0.78 on LesionVis). In summary, by leveraging the limited 2D delineations on
the RECIST-slices, P-SiBA is an effective semi-supervised approach to produce
accurate lesion segmentations in 3D.
An Optimal Control View of Adversarial Machine Learning
Authors: Xiaojin Zhu
Date: 2018-11-11 00:00:00
Tasks: BIG-bench Machine Learning, Data Poisoning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

I describe an optimal control view of adversarial machine learning, where the
dynamical system is the machine learner, the input are adversarial actions, and
the control costs are defined by the adversary's goals to do harm and be hard
to detect. This view encompasses many types of adversarial machine learning,
including test-item attacks, training-data poisoning, and adversarial reward
shaping. The view encourages adversarial machine learning researcher to utilize
advances in control theory and reinforcement learning.
An initial attempt of combining visual selective attention with deep reinforcement learning
Authors: Liu Yuezhang, Ruohan Zhang, Dana H. Ballard
Date: 2018-11-11 00:00:00
Tasks: Atari Games, Deep Reinforcement Learning, feature selection, Optical Flow Estimation, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Visual attention serves as a means of feature selection mechanism in the perceptual system. Motivated by Broadbent's leaky filter model of selective attention, we evaluate how such mechanism could be implemented and affect the learning process of deep reinforcement learning. We visualize and analyze the feature maps of DQN on a toy problem Catch, and propose an approach to combine visual selective attention with deep reinforcement learning. We experiment with optical flow-based attention and A2C on Atari games. Experiment results show that visual selective attention could lead to improvements in terms of sample efficiency on tested games. An intriguing relation between attention and batch normalization is also discovered.
HSD-CNN: Hierarchically self decomposing CNN architecture using class specific filter sensitivity analysis
Authors: K. Sai Ram, Jayanta Mukherjee, Amit Patra, Partha Pratim Das
Date: 2018-11-11 00:00:00
Tasks: Sensitivity

Conventional Convolutional neural networks (CNN) are trained on large domain
datasets and are hence typically over-represented and inefficient in limited
class applications. An efficient way to convert such large many-class
pre-trained networks into small few-class networks is through a hierarchical
decomposition of its feature maps. To alleviate this issue, we propose an
automated framework for such decomposition in Hierarchically Self Decomposing
CNN (HSD-CNN), in four steps. HSD-CNN is derived automatically using a
class-specific filter sensitivity analysis that quantifies the impact of
specific features on a class prediction. The decomposed hierarchical network
can be utilized and deployed directly to obtain sub-networks for a subset of
classes, and it is shown to perform better without the requirement of
retraining these sub-networks. Experimental results show that HSD-CNN generally
does not degrade accuracy if the full set of classes are used. Interestingly,
when operating on known subsets of classes, HSD-CNN has an improvement in
accuracy with a much smaller model size, requiring much fewer operations.
HSD-CNN flow is verified on the CIFAR10, CIFAR100 and CALTECH101 data sets. We
report accuracies up to $85.6\%$ ( $94.75\%$ ) on scenarios with 13 ( 4 )
classes of CIFAR100, using a pre-trained VGG-16 network on the full data set.
In this case, the proposed HSD-CNN requires $3.97 \times$ fewer parameters and
has $71.22\%$ savings in operations, in comparison to baseline VGG-16
containing features for all 100 classes.
Gaussian-Induced Convolution for Graphs
Authors: Jiatao Jiang, Zhen Cui, Chunyan Xu, Jian Yang
Date: 2018-11-11 00:00:00
Tasks: Graph Classification, Learning Representation On Graph

Learning representation on graph plays a crucial role in numerous tasks of
pattern recognition. Different from grid-shaped images/videos, on which local
convolution kernels can be lattices, however, graphs are fully coordinate-free
on vertices and edges. In this work, we propose a Gaussian-induced convolution
(GIC) framework to conduct local convolution filtering on irregular graphs.
Specifically, an edge-induced Gaussian mixture model is designed to encode
variations of subgraph region by integrating edge information into weighted
Gaussian models, each of which implicitly characterizes one component of
subgraph variations. In order to coarsen a graph, we derive a vertex-induced
Gaussian mixture model to cluster vertices dynamically according to the
connection of edges, which is approximately equivalent to the weighted graph
cut. We conduct our multi-layer graph convolution network on several public
datasets of graph classification. The extensive experiments demonstrate that
our GIC is effective and can achieve the state-of-the-art results.
Integrating Multiple Receptive Fields through Grouped Active Convolution
Authors: Yunho Jeon, Junmo Kim
Date: 2018-11-11 00:00:00

Convolutional networks have achieved great success in various vision tasks.
This is mainly due to a considerable amount of research on network structure.
In this study, instead of focusing on architectures, we focused on the
convolution unit itself. The existing convolution unit has a fixed shape and is
limited to observing restricted receptive fields. In earlier work, we proposed
the active convolution unit (ACU), which can freely define its shape and learn
by itself. In this paper, we provide a detailed analysis of the previously
proposed unit and show that it is an efficient representation of a sparse
weight convolution. Furthermore, we extend an ACU to a grouped ACU, which can
observe multiple receptive fields in one layer. We found that the performance
of a naive grouped convolution is degraded by increasing the number of groups;
however, the proposed unit retains the accuracy even though the number of
parameters decreases. Based on this result, we suggest a depthwise ACU, and
various experiments have shown that our unit is efficient and can replace the
existing convolutions.
Adapting multi-armed bandits policies to contextual bandits scenarios
Authors: David Cortes
Date: 2018-11-11 00:00:00
Tasks: Binary Classification, Classification, General Classification, Multi-Armed Bandits, regression, Thompson Sampling

This work explores adaptations of successful multi-armed bandits policies to the online contextual bandits scenario with binary rewards using binary classification algorithms such as logistic regression as black-box oracles. Some of these adaptations are achieved through bootstrapping or approximate bootstrapping, while others rely on other forms of randomness, resulting in more scalable approaches than previous works, and the ability to work with any type of classification algorithm. In particular, the Adaptive-Greedy algorithm shows a lot of promise, in many cases achieving better performance than upper confidence bound and Thompson sampling strategies, at the expense of more hyperparameters to tune.
ReSet: Learning Recurrent Dynamic Routing in ResNet-like Neural Networks
Authors: Iurii Kemaev, Daniil Polykovskiy, Dmitry Vetrov
Date: 2018-11-11 00:00:00
Tasks: image-classification, Image Classification

Neural Network is a powerful Machine Learning tool that shows outstanding
performance in Computer Vision, Natural Language Processing, and Artificial
Intelligence. In particular, recently proposed ResNet architecture and its
modifications produce state-of-the-art results in image classification
problems. ResNet and most of the previously proposed architectures have a fixed
structure and apply the same transformation to all input images. In this work,
we develop a ResNet-based model that dynamically selects Computational Units
(CU) for each input object from a learned set of transformations. Dynamic
selection allows the network to learn a sequence of useful transformations and
apply only required units to predict the image label. We compare our model to
ResNet-38 architecture and achieve better results than the original ResNet on
CIFAR-10.1 test set. While examining the produced paths, we discovered that the
network learned different routes for images from different classes and similar
routes for similar images.