pwc-archive/papers-with-abstracts huggingface dataset:
Generation of Virtual Dual Energy Images from Standard Single-Shot Radiographs using Multi-scale and Conditional Adversarial Network
Authors: Bo Zhou, Xunyu Lin, Brendan Eck, Jun Hou, David L. Wilson
Date: 2018-10-22 00:00:00
Tasks: Diagnostic

Dual-energy (DE) chest radiographs provide greater diagnostic information than standard radiographs by separating the image into bone and soft tissue, revealing suspicious lesions which may otherwise be obstructed from view. However, acquisition of DE images requires two physical scans, necessitating specialized hardware and processing, and images are prone to motion artifact. Generation of virtual DE images from standard, single-shot chest radiographs would expand the diagnostic value of standard radiographs without changing the acquisition procedure. We present a Multi-scale Conditional Adversarial Network (MCA-Net) which produces high-resolution virtual DE bone images from standard, single-shot chest radiographs. Our proposed MCA-Net is trained using the adversarial network so that it learns sharp details for the production of high-quality bone images. Then, the virtual DE soft tissue image is generated by processing the standard radiograph with the virtual bone image using a cross projection transformation. Experimental results from 210 patient DE chest radiographs demonstrated that the algorithm can produce high-quality virtual DE chest radiographs. Important structures were preserved, such as coronary calcium in bone images and lung lesions in soft tissue images. The average structure similarity index and the peak signal to noise ratio of the produced bone images in testing data were 96.4 and 41.5, which are significantly better than results from previous methods. Furthermore, our clinical evaluation results performed on the publicly available dataset indicates the clinical values of our algorithms. Thus, our algorithm can produce high-quality DE images that are potentially useful for radiologists, computer-aided diagnostics, and other diagnostic tasks.
On The Stability of Interpretable Models
Authors: Riccardo Guidotti, Salvatore Ruggieri
Date: 2018-10-22 00:00:00
Tasks: Classification, feature selection, General Classification, Model Selection

Interpretable classification models are built with the purpose of providing a
comprehensible description of the decision logic to an external oversight
agent. When considered in isolation, a decision tree, a set of classification
rules, or a linear model, are widely recognized as human-interpretable.
However, such models are generated as part of a larger analytical process. Bias
in data collection and preparation, or in model's construction may severely
affect the accountability of the design process. We conduct an experimental
study of the stability of interpretable models with respect to feature
selection, instance selection, and model selection. Our conclusions should
raise awareness and attention of the scientific community on the need of a
stability impact assessment of interpretable models.
Adversarial Online Learning with noise
Authors: Alon Resler, Yishay Mansour
Date: 2018-10-22 00:00:00

We present and study models of adversarial online learning where the feedback
observed by the learner is noisy, and the feedback is either full information
feedback or bandit feedback. Specifically, we consider binary losses xored with
the noise, which is a Bernoulli random variable. We consider both a constant
noise rate and a variable noise rate. Our main results are tight regret bounds
for learning with noise in the adversarial online learning model.
Baseline Detection in Historical Documents using Convolutional U-Nets
Authors: Michael Fink, Thomas Layer, Georg Mackenbrock, Michael Sprinzl
Date: 2018-10-22 00:00:00

Baseline detection is still a challenging task for heterogeneous collections
of historical documents. We present a novel approach to baseline extraction in
such settings, turning out the winning entry to the ICDAR 2017 Competition on
Baseline detection (cBAD). It utilizes deep convolutional nets (CNNs) for both,
the actual extraction of baselines, as well as for a simple form of layout
analysis in a pre-processing step. To the best of our knowledge it is the first
CNN-based system for baseline extraction applying a U-net architecture and
sliding window detection, profiting from a high local accuracy of the candidate
lines extracted. Final baseline post-processing complements our approach,
compensating for inaccuracies mainly due to missing context information during
sliding window detection. We experimentally evaluate the components of our
system individually on the cBAD dataset. Moreover, we investigate how it
generalizes to different data by means of the dataset used for the baseline
extraction task of the ICDAR 2017 Competition on Layout Analysis for
Challenging Medieval Manuscripts (HisDoc). A comparison with the results
reported for HisDoc shows that it also outperforms the contestants of the
latter.
On the k-Boundedness for Existential Rules
Authors: Stathis Delivorias, Michel Leclere, Marie-Laure Mugnier, Federico Ulliana
Date: 2018-10-22 00:00:00

The chase is a fundamental tool for existential rules. Several chase variants
are known, which differ on how they handle redundancies possibly caused by the
introduction of nulls. Given a chase variant, the halting problem takes as
input a set of existential rules and asks if this set of rules ensures the
termination of the chase for any factbase. It is well-known that this problem
is undecidable for all known chase variants. The related problem of boundedness
asks if a given set of existential rules is bounded, i.e., whether there is a
predefined upper bound on the number of (breadth-first) steps of the chase,
independently from any factbase. This problem is already undecidable in the
specific case of datalog rules. However, knowing that a set of rules is bounded
for some chase variant does not help much in practice if the bound is unknown.
Hence, in this paper, we investigate the decidability of the k-boundedness
problem, which asks whether a given set of rules is bounded by an integer k. We
prove that k-boundedness is decidable for three chase variants, namely the
oblivious, semi-oblivious and restricted chase.
BioSentVec: creating sentence embeddings for biomedical texts
Authors: Qingyu Chen, Yifan Peng, Zhiyong Lu
Date: 2018-10-22 00:00:00
Tasks: Articles, Benchmarking, Sentence, Sentence Embeddings, Sentence Embeddings For Biomedical Texts, Word Embeddings

Sentence embeddings have become an essential part of today's natural language processing (NLP) systems, especially together advanced deep learning methods. Although pre-trained sentence encoders are available in the general domain, none exists for biomedical texts to date. In this work, we introduce BioSentVec: the first open set of sentence embeddings trained with over 30 million documents from both scholarly articles in PubMed and clinical notes in the MIMIC-III Clinical Database. We evaluate BioSentVec embeddings in two sentence pair similarity tasks in different text genres. Our benchmarking results demonstrate that the BioSentVec embeddings can better capture sentence semantics compared to the other competitive alternatives and achieve state-of-the-art performance in both tasks. We expect BioSentVec to facilitate the research and development in biomedical text mining and to complement the existing resources in biomedical word embeddings. BioSentVec is publicly available at https://github.com/ncbi-nlp/BioSentVec
From Hard to Soft: Understanding Deep Network Nonlinearities via Vector Quantization and Statistical Inference
Authors: Randall Balestriero, Richard G. Baraniuk
Date: 2018-10-22 00:00:00
Proceeding: ICLR 2019 5
Tasks: Quantization

Nonlinearity is crucial to the performance of a deep (neural) network (DN).
To date there has been little progress understanding the menagerie of available
nonlinearities, but recently progress has been made on understanding the r\^ole
played by piecewise affine and convex nonlinearities like the ReLU and absolute
value activation functions and max-pooling. In particular, DN layers
constructed from these operations can be interpreted as {\em max-affine spline
operators} (MASOs) that have an elegant link to vector quantization (VQ) and
$K$-means. While this is good theoretical progress, the entire MASO approach is
predicated on the requirement that the nonlinearities be piecewise affine and
convex, which precludes important activation functions like the sigmoid,
hyperbolic tangent, and softmax. {\em This paper extends the MASO framework to
these and an infinitely large class of new nonlinearities by linking
deterministic MASOs with probabilistic Gaussian Mixture Models (GMMs).} We show
that, under a GMM, piecewise affine, convex nonlinearities like ReLU, absolute
value, and max-pooling can be interpreted as solutions to certain natural
"hard" VQ inference problems, while sigmoid, hyperbolic tangent, and softmax
can be interpreted as solutions to corresponding "soft" VQ inference problems.
We further extend the framework by hybridizing the hard and soft VQ
optimizations to create a $\beta$-VQ inference that interpolates between hard,
soft, and linear VQ inference. A prime example of a $\beta$-VQ DN nonlinearity
is the {\em swish} nonlinearity, which offers state-of-the-art performance in a
range of computer vision tasks but was developed ad hoc by experimentation.
Finally, we validate with experiments an important assertion of our theory,
namely that DN performance can be significantly improved by enforcing
orthogonality in its linear filters.
Optimal terminal dimensionality reduction in Euclidean space
Authors: Shyam Narayanan, Jelani Nelson
Date: 2018-10-22 00:00:00
Tasks: Dimensionality Reduction, LEMMA

Let $\varepsilon\in(0,1)$ and $X\subset\mathbb R^d$ be arbitrary with $|X|$
having size $n>1$. The Johnson-Lindenstrauss lemma states there exists
$f:X\rightarrow\mathbb R^m$ with $m = O(\varepsilon^{-2}\log n)$ such that $$
\forall x\in X\ \forall y\in X, \|x-y\|_2 \le \|f(x)-f(y)\|_2 \le
(1+\varepsilon)\|x-y\|_2 . $$ We show that a strictly stronger version of this
statement holds, answering one of the main open questions of [MMMR18]:
"$\forall y\in X$" in the above statement may be replaced with "$\forall
y\in\mathbb R^d$", so that $f$ not only preserves distances within $X$, but
also distances to $X$ from the rest of space. Previously this stronger version
was only known with the worse bound $m = O(\varepsilon^{-4}\log n)$. Our proof
is via a tighter analysis of (a specific instantiation of) the embedding recipe
of [MMMR18].
A Review on Learning Planning Action Models for Socio-Communicative HRI
Authors: Ankuj Arora, Humbert Fiorino, Damien Pellier, Sylvie Pesty
Date: 2018-10-22 00:00:00

For social robots to be brought more into widespread use in the fields of
companionship, care taking and domestic help, they must be capable of
demonstrating social intelligence. In order to be acceptable, they must exhibit
socio-communicative skills. Classic approaches to program HRI from observed
human-human interactions fails to capture the subtlety of multimodal
interactions as well as the key structural differences between robots and
humans. The former arises due to a difficulty in quantifying and coding
multimodal behaviours, while the latter due to a difference of the degrees of
liberty between a robot and a human. However, the notion of reverse engineering
from multimodal HRI traces to learn the underlying behavioral blueprint of the
robot given multimodal traces seems an option worth exploring. With this
spirit, the entire HRI can be seen as a sequence of exchanges of speech acts
between the robot and human, each act treated as an action, bearing in mind
that the entire sequence is goal-driven. Thus, this entire interaction can be
treated as a sequence of actions propelling the interaction from its initial to
goal state, also known as a plan in the domain of AI planning. In the same
domain, this action sequence that stems from plan execution can be represented
as a trace. AI techniques, such as machine learning, can be used to learn
behavioral models (also known as symbolic action models in AI), intended to be
reusable for AI planning, from the aforementioned multimodal traces. This
article reviews recent machine learning techniques for learning planning action
models which can be applied to the field of HRI with the intent of rendering
robots as socio-communicative.
Knowledge Graph Completion to Predict Polypharmacy Side Effects
Authors: Brandon Malone, Alberto García-Durán, Mathias Niepert
Date: 2018-10-22 00:00:00
Tasks: Knowledge Graph Completion, Prediction

The polypharmacy side effect prediction problem considers cases in which two
drugs taken individually do not result in a particular side effect; however,
when the two drugs are taken in combination, the side effect manifests. In this
work, we demonstrate that multi-relational knowledge graph completion achieves
state-of-the-art results on the polypharmacy side effect prediction problem.
Empirical results show that our approach is particularly effective when the
protein targets of the drugs are well-characterized. In contrast to prior work,
our approach provides more interpretable predictions and hypotheses for wet lab
validation.
Cost-Sensitive Robustness against Adversarial Examples
Authors: Xiao Zhang, David Evans
Date: 2018-10-22 00:00:00
Proceeding: ICLR 2019 5
Tasks: General Classification

Several recent works have developed methods for training classifiers that are
certifiably robust against norm-bounded adversarial perturbations. These
methods assume that all the adversarial transformations are equally important,
which is seldom the case in real-world applications. We advocate for
cost-sensitive robustness as the criteria for measuring the classifier's
performance for tasks where some adversarial transformation are more important
than others. We encode the potential harm of each adversarial transformation in
a cost matrix, and propose a general objective function to adapt the robust
training method of Wong & Kolter (2018) to optimize for cost-sensitive
robustness. Our experiments on simple MNIST and CIFAR10 models with a variety
of cost matrices show that the proposed approach can produce models with
substantially reduced cost-sensitive robust error, while maintaining
classification accuracy.
Multi-Agent Actor-Critic with Generative Cooperative Policy Network
Authors: Heechang Ryu, Hayong Shin, Jinkyoo Park
Date: 2018-10-22 00:00:00
Tasks: Multi-agent Reinforcement Learning, Reinforcement Learning, Reinforcement Learning (RL)

We propose an efficient multi-agent reinforcement learning approach to derive
equilibrium strategies for multi-agents who are participating in a Markov game.
Mainly, we are focused on obtaining decentralized policies for agents to
maximize the performance of a collaborative task by all the agents, which is
similar to solving a decentralized Markov decision process. We propose to use
two different policy networks: (1) decentralized greedy policy network used to
generate greedy action during training and execution period and (2) generative
cooperative policy network (GCPN) used to generate action samples to make other
agents improve their objectives during training period. We show that the
samples generated by GCPN enable other agents to explore the policy space more
effectively and favorably to reach a better policy in terms of achieving the
collaborative tasks.
Graph Convolutional Reinforcement Learning
Authors: Jiechuan Jiang, Chen Dun, Tiejun Huang, Zongqing Lu
Date: 2018-10-22 00:00:00
Proceeding: ICLR 2020 1
Tasks: Decision Making, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Relation

Learning to cooperate is crucially important in multi-agent environments. The key is to understand the mutual interplay between agents. However, multi-agent environments are highly dynamic, where agents keep moving and their neighbors change quickly. This makes it hard to learn abstract representations of mutual interplay between agents. To tackle these difficulties, we propose graph convolutional reinforcement learning, where graph convolution adapts to the dynamics of the underlying graph of the multi-agent environment, and relation kernels capture the interplay between agents by their relation representations. Latent features produced by convolutional layers from gradually increased receptive fields are exploited to learn cooperation, and cooperation is further improved by temporal relation regularization for consistency. Empirically, we show that our method substantially outperforms existing methods in a variety of cooperative scenarios.
Field Of Interest Proposal for Augmented Mitotic Cell Count: Comparison of two Convolutional Networks
Authors: Marc Aubreville, Christof A. Bertram, Robert Klopfleisch, Andreas Maier
Date: 2018-10-22 00:00:00
Tasks: Diagnostic, whole slide images

Most tumor grading systems for human as for veterinary histopathology are
based upon the absolute count of mitotic figures in a certain reference area of
a histology slide. Since time for prognostication is limited in a diagnostic
setting, the pathologist will often almost arbitrarily choose a certain field
of interest assumed to have the highest mitotic activity. However, as mitotic
figures are commonly very sparse on the slide and often have a patchy
distribution, this poses a sampling problem which is known to be able to
influence the tumor prognostication. On the other hand, automatic detection of
mitotic figures can't yet be considered reliable enough for clinical
application. In order to aid the work of the human expert and at the same time
reduce variance in tumor grading, it is beneficial to assess the whole slide
image (WSI) for the highest mitotic activity and use this as a reference region
for human counting. For this task, we compare two methods for region of
interest proposal, both based on convolutional neural networks (CNN). For both
approaches, the CNN performs a segmentation of the WSI to assess mitotic
activity. The first method performs a segmentation at the original image
resolution, while the second approach performs a segmentation operation at a
significantly reduced resolution, cutting down on processing complexity. We
evaluate the approach using a dataset of 32 completely annotated whole slide
images of canine mast cell tumors, where 22 were used for training of the
network and 10 for test. Our results indicate that, while the overall
correlation to the ground truth mitotic activity is considerably higher (0.94
vs. 0.83) for the approach based upon the fine resolution network, the field of
interest choices are only marginally better. Both approaches propose fields of
interest that contain a mitotic count in the upper quartile of respective
slides.
Learning sparse transformations through backpropagation
Authors: Peter Bloem
Date: 2018-10-22 00:00:00

Many transformations in deep learning architectures are sparsely connected.
When such transformations cannot be designed by hand, they can be learned, even
through plain backpropagation, for instance in attention mechanisms. However,
during learning, such sparse structures are often represented in a dense form,
as we do not know beforehand which elements will eventually become non-zero. We
introduce the adaptive, sparse hyperlayer, a method for learning a sparse
transformation, paramatrized sparsely: as index-tuples with associated values.
To overcome the lack of gradients from such a discrete structure, we introduce
a method of randomly sampling connections, and backpropagating over the
randomly wired computation graph. To show that this approach allows us to train
a model to competitive performance on real data, we use it to build two
architectures. First, an attention mechanism for visual classification. Second,
we implement a method for differentiable sorting: specifically, learning to
sort unlabeled MNIST digits, given only the correct order.
Compositional Coding Capsule Network with K-Means Routing for Text Classification
Authors: Hao Ren, Hong Lu
Date: 2018-10-22 00:00:00
Tasks: Classification, Clustering, General Classification, Sentiment Analysis, text-classification, Text Classification

Text classification is a challenging problem which aims to identify the category of texts. In the process of training, word embeddings occupy a large part of parameters. Under the limitation of limited computing resources, it indirectly limits the ability of subsequent network designs. In order to reduce the number of parameters, the compositional coding mechanism has been proposed recently. Based on this, this paper further explores compositional coding and proposes a compositional weighted coding method. And we apply capsule network to model the relationship between word embeddings, a new routing algorithm, which is based on k-means clustering theory, is proposed to fully mine the relationship between word embeddings. Combined with our compositional weighted coding method and the routing algorithm, we design a neural network for text classification. Experiments conducted on eight challenging text classification datasets show that the proposed method achieves competitive accuracy compared to the state-of-the-art approach with significantly fewer parameters.
Node Representation Learning for Directed Graphs
Authors: Megha Khosla, Jurek Leonhardt, Wolfgang Nejdl, Avishek Anand
Date: 2018-10-22 00:00:00
Tasks: General Classification, Graph Reconstruction, Link Prediction, Multi-Label Classification, Node Classification, Representation Learning

We propose a novel approach for learning node representations in directed graphs, which maintains separate views or embedding spaces for the two distinct node roles induced by the directionality of the edges. We argue that the previous approaches either fail to encode the edge directionality or their encodings cannot be generalized across tasks. With our simple \emph{alternating random walk} strategy, we generate role specific vertex neighborhoods and train node embeddings in their corresponding source/target roles while fully exploiting the semantics of directed graphs. We also unearth the limitations of evaluations on directed graphs in previous works and propose a clear strategy for evaluating link prediction and graph reconstruction in directed graphs. We conduct extensive experiments to showcase our effectiveness on several real-world datasets on link prediction, node classification and graph reconstruction tasks. We show that the embeddings from our approach are indeed robust, generalizable and well performing across multiple kinds of tasks and graphs. We show that we consistently outperform all baselines for node classification task. In addition to providing a theoretical interpretation of our method we also show that we are considerably more robust than the other directed graph approaches.
What is an Ontology?
Authors: Fabian Neuhaus
Date: 2018-10-22 00:00:00

In the knowledge engineering community "ontology" is usually defined in the
tradition of Gruber as an "explicit specification of a conceptualization".
Several variations of this definition exist. In the paper we argue that (with
one notable exception) these definitions are of no explanatory value, because
they violate one of the basic rules for good definitions: The defining
statement (the definiens) should be clearer than the term that is defined (the
definiendum). In the paper we propose a different definition of "ontology" and
discuss how it helps to explain various phenomena: the ability of ontologies to
change, the role of the choice of vocabulary, the significance of annotations,
the possibility of collaborative ontology development, and the relationship
between ontological conceptualism and ontological realism.