pwc-archive/papers-with-abstracts huggingface dataset:
The division of labor in communication: Speakers help listeners account for asymmetries in visual perspective
Authors: Robert D. Hawkins, Hyowon Gweon, Noah D. Goodman
Date: 2018-07-24 00:00:00
Tasks: Known Unknowns, Navigate

Recent debates over adults' theory of mind use have been fueled by surprising failures of perspective-taking in communication, suggesting that perspective-taking can be relatively effortful. How, then, should speakers and listeners allocate their resources to achieve successful communication? We begin with the observation that this shared goal induces a natural division of labor: the resources one agent chooses to allocate toward perspective-taking should depend on their expectations about the other's allocation. We formalize this idea in a resource-rational model augmenting recent probabilistic weighting accounts with a mechanism for (costly) control over the degree of perspective-taking. In a series of simulations, we first derive an intermediate degree of perspective weighting as an optimal tradeoff between expected costs and benefits of perspective-taking. We then present two behavioral experiments testing novel predictions of our model. In Experiment 1, we manipulated the presence or absence of occlusions in a director-matcher task and found that speakers spontaneously produced more informative descriptions to account for "known unknowns" in their partner's private view. In Experiment 2, we compared the scripted utterances used by confederates in prior work with those produced in interactions with unscripted directors. We found that confederates were systematically less informative than listeners would initially expect given the presence of occlusions, but listeners used violations to adaptively make fewer errors over time. Taken together, our work suggests that people are not simply "mindblind"; they use contextually appropriate expectations to navigate the division of labor with their partner. We discuss how a resource rational framework may provide a more deeply explanatory foundation for understanding flexible perspective-taking under processing constraints.
Cross-lingual Argumentation Mining: Machine Translation (and a bit of Projection) is All You Need!
Authors: Steffen Eger, Johannes Daxenberger, Christian Stab, Iryna Gurevych
Date: 2018-07-24 00:00:00
Proceeding: COLING 2018 8
Tasks: All, Cross-Lingual Transfer, Machine Translation, Translation, Word Embeddings

Argumentation mining (AM) requires the identification of complex discourse
structures and has lately been applied with success monolingually. In this
work, we show that the existing resources are, however, not adequate for
assessing cross-lingual AM, due to their heterogeneity or lack of complexity.
We therefore create suitable parallel corpora by (human and machine)
translating a popular AM dataset consisting of persuasive student essays into
German, French, Spanish, and Chinese. We then compare (i) annotation projection
and (ii) bilingual word embeddings based direct transfer strategies for
cross-lingual AM, finding that the former performs considerably better and
almost eliminates the loss from cross-lingual transfer. Moreover, we find that
annotation projection works equally well when using either costly human or
cheap machine translations. Our code and data are available at
\url{http://github.com/UKPLab/coling2018-xling_argument_mining}.
Deep-CLASS at ISIC Machine Learning Challenge 2018
Authors: Sara Nasiri, Matthias Jung, Julien Helsper, Madjid Fathi
Date: 2018-07-24 00:00:00
Tasks: BIG-bench Machine Learning, Classification, Deep Learning, General Classification

This paper reports the method and evaluation results of MedAusbild team for
ISIC challenge task. Since early 2017, our team has worked on melanoma
classification [1][6], and has employed deep learning since beginning of 2018
[7]. Deep learning helps researchers absolutely to treat and detect diseases by
analyzing medical data (e.g., medical images). One of the representative models
among the various deep-learning models is a convolutional neural network (CNN).
Although our team has an experience with segmentation and classification of
benign and malignant skin-lesions, we have participated in the task 3 of ISIC
Challenge 2018 for classification of seven skin diseases, explained in this
paper.
Computational speedups using small quantum devices
Authors: Vedran Dunjko, Yimin Ge, J. Ignacio Cirac
Date: 2018-07-24 00:00:00

Suppose we have a small quantum computer with only M qubits. Can such a
device genuinely speed up certain algorithms, even when the problem size is
much larger than M? Here we answer this question to the affirmative. We present
a hybrid quantum-classical algorithm to solve 3SAT problems involving n>>M
variables that significantly speeds up its fully classical counterpart. This
question may be relevant in view of the current quest to build small quantum
computers.
Space-Time Extension of the MEM Approach for Electromagnetic Neuroimaging
Authors: Marie-Christine Roubaud, Jean-Marc Lina, Julie Carrier, B Torrésani
Date: 2018-07-24 00:00:00
Tasks: Dimensionality Reduction

The wavelet Maximum Entropy on the Mean (wMEM) approach to the MEG inverse
problem is revisited and extended to infer brain activity from full space-time
data. The resulting dimensionality increase is tackled using a collection of
techniques , that includes time and space dimension reduction (using
respectively wavelet and spatial filter based reductions), Kronecker product
modeling for covariance matrices, and numerical manipulation of the free energy
directly in matrix form. This leads to a smooth numerical optimization problem
of reasonable dimension, solved using standard approaches. The method is
applied to the MEG inverse problem. Results of a simulation study in the
context of slow wave localization from sleep MEG data are presented and
discussed.
  Index Terms: MEG inverse problem, maximum entropy on the mean, wavelet
decomposition, spatial filters, Kronecker covariance factorization, sleep slow
waves.
The Double Sphere Camera Model
Authors: Vladyslav Usenko, Nikolaus Demmel, Daniel Cremers
Date: 2018-07-24 00:00:00
Tasks: 3D Reconstruction, Autonomous Driving, model, Motion Estimation, valid, Visual Odometry

Vision-based motion estimation and 3D reconstruction, which have numerous
applications (e.g., autonomous driving, navigation systems for airborne devices
and augmented reality) are receiving significant research attention. To
increase the accuracy and robustness, several researchers have recently
demonstrated the benefit of using large field-of-view cameras for such
applications. In this paper, we provide an extensive review of existing models
for large field-of-view cameras. For each model we provide projection and
unprojection functions and the subspace of points that result in valid
projection. Then, we propose the Double Sphere camera model that well fits with
large field-of-view lenses, is computationally inexpensive and has a
closed-form inverse. We evaluate the model using a calibration dataset with
several different lenses and compare the models using the metrics that are
relevant for Visual Odometry, i.e., reprojection error, as well as computation
time for projection and unprojection functions and their Jacobians. We also
provide qualitative results and discuss the performance of all models.
Dermoscopic Image Analysis for ISIC Challenge 2018
Authors: Jinyi Zou, Xiao Ma, Cheng Zhong, Yao Zhang
Date: 2018-07-24 00:00:00
Tasks: Attribute, Classification, General Classification, Lesion Segmentation, Multi-class Classification, Scene Parsing

This short paper reports the algorithms we used and the evaluation
performances for ISIC Challenge 2018. Our team participates in all the tasks in
this challenge. In lesion segmentation task, the pyramid scene parsing network
(PSPNet) is modified to segment the lesions. In lesion attribute detection
task, the modified PSPNet is also adopted in a multi-label way. In disease
classification task, the DenseNet-169 is adopted for multi-class
classification.
Otem&Utem: Over- and Under-Translation Evaluation Metric for NMT
Authors: Jing Yang, Biao Zhang, Yue Qin, Xiangwen Zhang, Qian Lin, Jinsong Su
Date: 2018-07-24 00:00:00
Tasks: Machine Translation, NMT, Translation

Although neural machine translation(NMT) yields promising translation
performance, it unfortunately suffers from over- and under-translation is- sues
[Tu et al., 2016], of which studies have become research hotspots in NMT. At
present, these studies mainly apply the dominant automatic evaluation metrics,
such as BLEU, to evaluate the overall translation quality with respect to both
adequacy and uency. However, they are unable to accurately measure the ability
of NMT systems in dealing with the above-mentioned issues. In this paper, we
propose two quantitative metrics, the Otem and Utem, to automatically evaluate
the system perfor- mance in terms of over- and under-translation respectively.
Both metrics are based on the proportion of mismatched n-grams between gold
ref- erence and system translation. We evaluate both metrics by comparing their
scores with human evaluations, where the values of Pearson Cor- relation
Coefficient reveal their strong correlation. Moreover, in-depth analyses on
various translation systems indicate some inconsistency be- tween BLEU and our
proposed metrics, highlighting the necessity and significance of our metrics.
Hyperspectral Images Classification Using Energy Profiles of Spatial and Spectral Features
Authors: Hamid Reza Shahdoosti
Date: 2018-07-24 00:00:00
Tasks: Classification, General Classification

This paper proposes a spatial feature extraction method based on energy of
the features for classification of the hyperspectral data. A proposed
orthogonal filter set extracts spatial features with maximum energy from the
principal components and then, a profile is constructed based on these
features. The important characteristic of the proposed approach is that the
filter sets coefficients are extracted from statistical properties of data,
thus they are more consistent with the type and texture of the remotely sensed
images compared with those of other filters such as Gabor. To assess the
performance of the proposed feature extraction method, the extracted features
are fed into a support vector machine (SVM) classifier. Experiments on the
widely used hyperspectral images namely, Indian Pines, and Salinas data sets
reveal that the proposed approach improves the classification results in
comparison with some recent spectral spatial classification methods.
Example Mining for Incremental Learning in Medical Imaging
Authors: Pratyush Kumar, Muktabh Mayank Srivastava
Date: 2018-07-24 00:00:00
Tasks: Incremental Learning

Incremental Learning is well known machine learning approach wherein the
weights of the learned model are dynamically and gradually updated to
generalize on new unseen data without forgetting the existing knowledge.
Incremental learning proves to be time as well as resource-efficient solution
for deployment of deep learning algorithms in real world as the model can
automatically and dynamically adapt to new data as and when annotated data
becomes available. The development and deployment of Computer Aided Diagnosis
(CAD) tools in medical domain is another scenario, where incremental learning
becomes very crucial as collection and annotation of a comprehensive dataset
spanning over multiple pathologies and imaging machines might take years.
However, not much has so far been explored in this direction. In the current
work, we propose a robust and efficient method for incremental learning in
medical imaging domain. Our approach makes use of Hard Example Mining technique
(which is commonly used as a solution to heavy class imbalance) to
automatically select a subset of dataset to fine-tune the existing network
weights such that it adapts to new data while retaining existing knowledge. We
develop our approach for incremental learning of our already under test model
for detecting dental caries. Further, we apply our approach to one publicly
available dataset and demonstrate that our approach reaches the accuracy of
training on entire dataset at once, while availing the benefits of incremental
learning scenario.
A Temporal Difference Reinforcement Learning Theory of Emotion: unifying emotion, cognition and adaptive behavior
Authors: Joost Broekens
Date: 2018-07-24 00:00:00
Tasks: Learning Theory, Reinforcement Learning, Reinforcement Learning (RL)

Emotions are intimately tied to motivation and the adaptation of behavior,
and many animal species show evidence of emotions in their behavior. Therefore,
emotions must be related to powerful mechanisms that aid survival, and,
emotions must be evolutionary continuous phenomena. How and why did emotions
evolve in nature, how do events get emotionally appraised, how do emotions
relate to cognitive complexity, and, how do they impact behavior and learning?
In this article I propose that all emotions are manifestations of reward
processing, in particular Temporal Difference (TD) error assessment.
Reinforcement Learning (RL) is a powerful computational model for the learning
of goal oriented tasks by exploration and feedback. Evidence indicates that
RL-like processes exist in many animal species. Key in the processing of
feedback in RL is the notion of TD error, the assessment of how much better or
worse a situation just became, compared to what was previously expected (or,
the estimated gain or loss of utility - or well-being - resulting from new
evidence). I propose a TDRL Theory of Emotion and discuss its ramifications for
our understanding of emotions in humans, animals and machines, and present
psychological, neurobiological and computational evidence in its support.
Combining Heterogeneously Labeled Datasets For Training Segmentation Networks
Authors: Jana Kemnitz, Christian F. Baumgartner, Wolfgang Wirth, Felix Eckstein, Sebastian K. Eder, Ender Konukoglu
Date: 2018-07-24 00:00:00
Tasks: Anatomy, Missing Labels

Accurate segmentation of medical images is an important step towards
analyzing and tracking disease related morphological alterations in the
anatomy. Convolutional neural networks (CNNs) have recently emerged as a
powerful tool for many segmentation tasks in medical imaging. The performance
of CNNs strongly depends on the size of the training data and combining data
from different sources is an effective strategy for obtaining larger training
datasets. However, this is often challenged by heterogeneous labeling of the
datasets. For instance, one of the dataset may be missing labels or a number of
labels may have been combined into a super label. In this work we propose a
cost function which allows integration of multiple datasets with heterogeneous
label subsets into a joint training. We evaluated the performance of this
strategy on thigh MR and a cardiac MR datasets in which we artificially merged
labels for half of the data. We found the proposed cost function substantially
outperforms a naive masking approach, obtaining results very close to using the
full annotations.
SAAGs: Biased Stochastic Variance Reduction Methods for Large-scale Learning
Authors: Vinod Kumar Chauhan, Anuj Sharma, Kalpana Dahiya
Date: 2018-07-24 00:00:00

Stochastic approximation is one of the effective approach to deal with the
large-scale machine learning problems and the recent research has focused on
reduction of variance, caused by the noisy approximations of the gradients. In
this paper, we have proposed novel variants of SAAG-I and II (Stochastic
Average Adjusted Gradient) (Chauhan et al. 2017), called SAAG-III and IV,
respectively. Unlike SAAG-I, starting point is set to average of previous epoch
in SAAG-III, and unlike SAAG-II, the snap point and starting point are set to
average and last iterate of previous epoch in SAAG-IV, respectively. To
determine the step size, we have used Stochastic Backtracking-Armijo line
Search (SBAS) which performs line search only on selected mini-batch of data
points. Since backtracking line search is not suitable for large-scale problems
and the constants used to find the step size, like Lipschitz constant, are not
always available so SBAS could be very effective in such cases. We have
extended SAAGs (I, II, III and IV) to solve non-smooth problems and designed
two update rules for smooth and non-smooth problems. Moreover, our theoretical
results have proved linear convergence of SAAG-IV for all the four combinations
of smoothness and strong-convexity, in expectation. Finally, our experimental
studies have proved the efficacy of proposed methods against the state-of-art
techniques.
CReaM: Condensed Real-time Models for Depth Prediction using Convolutional Neural Networks
Authors: Andrew Spek, Thanuja Dharmasiri, Tom Drummond
Date: 2018-07-24 00:00:00
Tasks: Depth Estimation, Depth Prediction, Semantic Segmentation, Transfer Learning

Since the resurgence of CNNs the robotic vision community has developed a
range of algorithms that perform classification, semantic segmentation and
structure prediction (depths, normals, surface curvature) using neural
networks. While some of these models achieve state-of-the art results and super
human level performance, deploying these models in a time critical robotic
environment remains an ongoing challenge. Real-time frameworks are of paramount
importance to build a robotic society where humans and robots integrate
seamlessly. To this end, we present a novel real-time structure prediction
framework that predicts depth at 30fps on an NVIDIA-TX2. At the time of
writing, this is the first piece of work to showcase such a capability on a
mobile platform. We also demonstrate with extensive experiments that neural
networks with very large model capacities can be leveraged in order to train
accurate condensed model architectures in a "from teacher to student" style
knowledge transfer.
Anomaly detection in static networks using egonets
Authors: Srijan Sengupta
Date: 2018-07-24 00:00:00
Tasks: Anomaly Detection

Network data has rapidly emerged as an important and active area of
statistical methodology. In this paper we consider the problem of anomaly
detection in networks. Given a large background network, we seek to detect
whether there is a small anomalous subgraph present in the network, and if such
a subgraph is present, which nodes constitute the subgraph. We propose an
inferential tool based on egonets to answer this question. The proposed method
is computationally efficient and naturally amenable to parallel computing, and
easily extends to a wide variety of network models. We demonstrate through
simulation studies that the egonet method works well under a wide variety of
network models. We obtain some fascinating empirical results by applying the
egonet method on several well-studied benchmark datasets.
Competitive Inner-Imaging Squeeze and Excitation for Residual Network
Authors: Yang Hu, Guihua Wen, Mingnan Luo, Dan Dai, Jiajiong Ma, Zhiwen Yu
Date: 2018-07-24 00:00:00

Residual networks, which use a residual unit to supplement the identity
mappings, enable very deep convolutional architecture to operate well, however,
the residual architecture has been proved to be diverse and redundant, which
may leads to low-efficient modeling. In this work, we propose a competitive
squeeze-excitation (SE) mechanism for the residual network. Re-scaling the
value for each channel in this structure will be determined by the residual and
identity mappings jointly, and this design enables us to expand the meaning of
channel relationship modeling in residual blocks. Modeling of the competition
between residual and identity mappings cause the identity flow to control the
complement of the residual feature maps for itself. Furthermore, we design a
novel inner-imaging competitive SE block to shrink the consumption and re-image
the global features of intermediate network structure, by using the
inner-imaging mechanism, we can model the channel-wise relations with
convolution in spatial. We carry out experiments on the CIFAR, SVHN, and
ImageNet datasets, and the proposed method can challenge state-of-the-art
results.
The Variational Homoencoder: Learning to learn high capacity generative models from few examples
Authors: Luke B. Hewitt, Maxwell I. Nye, Andreea Gane, Tommi Jaakkola, Joshua B. Tenenbaum
Date: 2018-07-24 00:00:00
Tasks: General Classification

Hierarchical Bayesian methods can unify many related tasks (e.g. k-shot
classification, conditional and unconditional generation) as inference within a
single generative model. However, when this generative model is expressed as a
powerful neural network such as a PixelCNN, we show that existing learning
techniques typically fail to effectively use latent variables. To address this,
we develop a modification of the Variational Autoencoder in which encoded
observations are decoded to new elements from the same class. This technique,
which we call a Variational Homoencoder (VHE), produces a hierarchical latent
variable model which better utilises latent variables. We use the VHE framework
to learn a hierarchical PixelCNN on the Omniglot dataset, which outperforms all
existing models on test set likelihood and achieves strong performance on
one-shot generation and classification tasks. We additionally validate the VHE
on natural images from the YouTube Faces database. Finally, we develop
extensions of the model that apply to richer dataset structures such as
factorial and hierarchical categories.
Panchromatic Sharpening of Remote Sensing Images Using a Multi-scale Approach
Authors: Hamid Reza Shahdoosti
Date: 2018-07-24 00:00:00

An ideal fusion method preserves the Spectral information in fused image and
adds spatial information to it with no spectral distortion. Recently wavelet
kalman filter method is proposed which uses ARSIS concept to fuses MS and PAN
images. This method is applied in a multiscale version, i.e. the variable index
is scale instead of time. With the aim of fusion we present a more detailed
study on this model and discuss about rationality of its assumptions such as
first order markov model and Gaussian distribution of the posterior density.
Finally, we propose a method using wavelet Kalman Particle filter to improve
the spectral and spatial quality of the fused image. We show that our model is
more consistent with natural MS and PAN images. Visual and statistical analyzes
show that the proposed algorithm clearly improves the fusion quality in terms
of: correlation coefficient, ERGAS, UIQI, and Q4; compared to other methods
including IHS, HMP, PCA, A`trous, udWI, udWPC, Adaptive IHS, Improved Adaptive
PCA and wavelet kalman filter.