pwc-archive/papers-with-abstracts huggingface dataset:
Providing Explanations for Recommendations in Reciprocal Environments
Authors: Akiva Kleinerman, Ariel Rosenfeld, Sarit Kraus
Date: 2018-07-03 00:00:00
Tasks: Recommendation Systems

Automated platforms which support users in finding a mutually beneficial
match, such as online dating and job recruitment sites, are becoming
increasingly popular. These platforms often include recommender systems that
assist users in finding a suitable match. While recommender systems which
provide explanations for their recommendations have shown many benefits,
explanation methods have yet to be adapted and tested in recommending suitable
matches. In this paper, we introduce and extensively evaluate the use of
"reciprocal explanations" -- explanations which provide reasoning as to why
both parties are expected to benefit from the match. Through an extensive
empirical evaluation, in both simulated and real-world dating platforms with
287 human participants, we find that when the acceptance of a recommendation
involves a significant cost (e.g., monetary or emotional), reciprocal
explanations outperform standard explanation methods which consider the
recommendation receiver alone. However, contrary to what one may expect, when
the cost of accepting a recommendation is negligible, reciprocal explanations
are shown to be less effective than the traditional explanation methods.
Local Gradients Smoothing: Defense against localized adversarial attacks
Authors: Muzammal Naseer, Salman H. Khan, Fatih Porikli
Date: 2018-07-03 00:00:00
Tasks: Adversarial Attack

Deep neural networks (DNNs) have shown vulnerability to adversarial attacks,
i.e., carefully perturbed inputs designed to mislead the network at inference
time. Recently introduced localized attacks, Localized and Visible Adversarial
Noise (LaVAN) and Adversarial patch, pose a new challenge to deep learning
security by adding adversarial noise only within a specific region without
affecting the salient objects in an image. Driven by the observation that such
attacks introduce concentrated high-frequency changes at a particular image
location, we have developed an effective method to estimate noise location in
gradient domain and transform those high activation regions caused by
adversarial noise in image domain while having minimal effect on the salient
object that is important for correct classification. Our proposed Local
Gradients Smoothing (LGS) scheme achieves this by regularizing gradients in the
estimated noisy region before feeding the image to DNN for inference. We have
shown the effectiveness of our method in comparison to other defense methods
including Digital Watermarking, JPEG compression, Total Variance Minimization
(TVM) and Feature squeezing on ImageNet dataset. In addition, we systematically
study the robustness of the proposed defense mechanism against Back Pass
Differentiable Approximation (BPDA), a state of the art attack recently
developed to break defenses that transform an input sample to minimize the
adversarial effect. Compared to other defense mechanisms, LGS is by far the
most resistant to BPDA in localized adversarial attack setting.
Generating Multi-Categorical Samples with Generative Adversarial Networks
Authors: Ramiro Camino, Christian Hammerschmidt, Radu State
Date: 2018-07-03 00:00:00

We propose a method to train generative adversarial networks on mutivariate
feature vectors representing multiple categorical values. In contrast to the
continuous domain, where GAN-based methods have delivered considerable results,
GANs struggle to perform equally well on discrete data. We propose and compare
several architectures based on multiple (Gumbel) softmax output layers taking
into account the structure of the data. We evaluate the performance of our
architecture on datasets with different sparsity, number of features, ranges of
categorical values, and dependencies among the features. Our proposed
architecture and method outperforms existing models.
ReCoNet: Real-time Coherent Video Style Transfer Network
Authors: Chang Gao, Derun Gu, Fangjun Zhang, Yizhou Yu
Date: 2018-07-03 00:00:00
Tasks: Semantic Segmentation, Style Transfer, Video Style Transfer

Image style transfer models based on convolutional neural networks usually
suffer from high temporal inconsistency when applied to videos. Some video
style transfer models have been proposed to improve temporal consistency, yet
they fail to guarantee fast processing speed, nice perceptual style quality and
high temporal consistency at the same time. In this paper, we propose a novel
real-time video style transfer model, ReCoNet, which can generate temporally
coherent style transfer videos while maintaining favorable perceptual styles. A
novel luminance warping constraint is added to the temporal loss at the output
level to capture luminance changes between consecutive frames and increase
stylization stability under illumination effects. We also propose a novel
feature-map-level temporal loss to further enhance temporal consistency on
traceable objects. Experimental results indicate that our model exhibits
outstanding performance both qualitatively and quantitatively.
On decision regions of narrow deep neural networks
Authors: Hans-Peter Beise, Steve Dias Da Cruz, Udo Schröder
Date: 2018-07-03 00:00:00

We show that for neural network functions that have width less or equal to the input dimension all connected components of decision regions are unbounded. The result holds for continuous and strictly monotonic activation functions as well as for the ReLU activation function. This complements recent results on approximation capabilities by [Hanin 2017 Approximating] and connectivity of decision regions by [Nguyen 2018 Neural] for such narrow neural networks. Our results are illustrated by means of numerical experiments.
Quantified Markov Logic Networks
Authors: Víctor Gutiérrez-Basulto, Jean Christoph Jung, Ondrej Kuzelka
Date: 2018-07-03 00:00:00

Markov Logic Networks (MLNs) are well-suited for expressing statistics such
as "with high probability a smoker knows another smoker" but not for expressing
statements such as "there is a smoker who knows most other smokers", which is
necessary for modeling, e.g. influencers in social networks. To overcome this
shortcoming, we study quantified MLNs which generalize MLNs by introducing
statistical universal quantifiers, allowing to express also the latter type of
statistics in a principled way. Our main technical contribution is to show that
the standard reasoning tasks in quantified MLNs, maximum a posteriori and
marginal inference, can be reduced to their respective MLN counterparts in
polynomial time.
Styling with Attention to Details
Authors: Ayushi Dalmia, Sachindra Joshi, Raghavendra Singh, Vikas Raykar
Date: 2018-07-03 00:00:00
Tasks: Decoder, Retrieval

Fashion as characterized by its nature, is driven by style. In this paper, we
propose a method that takes into account the style information to complete a
given set of selected fashion items with a complementary fashion item.
Complementary items are those items that can be worn along with the selected
items according to the style. Addressing this problem facilitates in
automatically generating stylish fashion ensembles leading to a richer shopping
experience for users.
  Recently, there has been a surge of online social websites where fashion
enthusiasts post the outfit of the day and other users can like and comment on
them. These posts contain a gold-mine of information about style. In this
paper, we exploit these posts to train a deep neural network which captures
style in an automated manner. We pose the problem of predicting complementary
fashion items as a sequence to sequence problem where the input is the selected
set of fashion items and the output is a complementary fashion item based on
the style information learned by the model. We use the encoder decoder
architecture to solve this problem of completing the set of fashion items. We
evaluate the goodness of the proposed model through a variety of experiments.
We empirically observe that our proposed model outperforms competitive baseline
like apriori algorithm by ~28 in terms of accuracy for top-1 recommendation to
complete the fashion ensemble. We also perform retrieval based experiments to
understand the ability of the model to learn style and rank the complementary
fashion items and find that using attention in our encoder decoder model helps
in improving the mean reciprocal rank by ~24. Qualitatively we find the
complementary fashion items generated by our proposed model are richer than the
apriori algorithm.
Anomaly Detection Using GANs for Visual Inspection in Noisy Training Data
Authors: Masanari Kimura, Takashi Yanagihara
Date: 2018-07-03 00:00:00
Tasks: Anomaly Detection, Unsupervised Anomaly Detection

The detection and the quantification of anomalies in image data are critical
tasks in industrial scenes such as detecting micro scratches on product. In
recent years, due to the difficulty of defining anomalies and the limit of
correcting their labels, research on unsupervised anomaly detection using
generative models has attracted attention. Generally, in those studies, only
normal images are used for training to model the distribution of normal images.
The model measures the anomalies in the target images by reproducing the most
similar images and scoring image patches indicating their fit to the learned
distribution. This approach is based on a strong presumption; the trained model
should not be able to generate abnormal images. However, in reality, the model
can generate abnormal images mainly due to noisy normal data which include
small abnormal pixels, and such noise severely affects the accuracy of the
model. Therefore, we propose a novel anomaly detection method to distort the
distribution of the model with existing abnormal images. The proposed method
detects pixel-level micro anomalies with a high accuracy from 1024x1024 high
resolution images which are actually used in an industrial scene. In this
paper, we share experimental results on open datasets, due to the
confidentiality of the data.
Welfare and Distributional Impacts of Fair Classification
Authors: Lily Hu, Yi-Ling Chen
Date: 2018-07-03 00:00:00
Tasks: Classification, Fairness, General Classification, Translation

Current methodologies in machine learning analyze the effects of various
statistical parity notions of fairness primarily in light of their impacts on
predictive accuracy and vendor utility loss. In this paper, we propose a new
framework for interpreting the effects of fairness criteria by converting the
constrained loss minimization problem into a social welfare maximization
problem. This translation moves a classifier and its output into utility space
where individuals, groups, and society at-large experience different welfare
changes due to classification assignments. Under this characterization,
predictions and fairness constraints are seen as shaping societal welfare and
distribution and revealing individuals' implied welfare weights in
society--weights that may then be interpreted through a fairness lens. The
social welfare formulation of the fairness problem brings to the fore concerns
of distributive justice that have always had a central albeit more implicit
role in standard algorithmic fairness approaches.
Weakly Supervised Deep Recurrent Neural Networks for Basic Dance Step Generation
Authors: Nelson Yalta, Shinji Watanabe, Kazuhiro Nakadai, Tetsuya OGATA
Date: 2018-07-03 00:00:00
Tasks: Motion Estimation

Synthesizing human's movements such as dancing is a flourishing research field which has several applications in computer graphics. Recent studies have demonstrated the advantages of deep neural networks (DNNs) for achieving remarkable performance in motion and music tasks with little effort for feature pre-processing. However, applying DNNs for generating dance to a piece of music is nevertheless challenging, because of 1) DNNs need to generate large sequences while mapping the music input, 2) the DNN needs to constraint the motion beat to the music, and 3) DNNs require a considerable amount of hand-crafted data. In this study, we propose a weakly supervised deep recurrent method for real-time basic dance generation with audio power spectrum as input. The proposed model employs convolutional layers and a multilayered Long Short-Term memory (LSTM) to process the audio input. Then, another deep LSTM layer decodes the target dance sequence. Notably, this end-to-end approach has 1) an auto-conditioned decode configuration that reduces accumulation of feedback error of large dance sequence, 2) uses a contrastive cost function to regulate the mapping between the music and motion beat, and 3) trains with weak labels generated from the motion beat, reducing the amount of hand-crafted data. We evaluate the proposed network based on i) the similarities between generated and the baseline dancer motion with a cross entropy measure for large dance sequences, and ii) accurate timing between the music and motion beat with an F-measure. Experimental results revealed that, after training using a small dataset, the model generates basic dance steps with low cross entropy and maintains an F-measure score similar to that of a baseline dancer.
Getting the subtext without the text: Scalable multimodal sentiment classification from visual and acoustic modalities
Authors: Nathaniel Blanchard, Daniel Moreira, Aparna Bharati, Walter J. Scheirer
Date: 2018-07-03 00:00:00
Proceeding: WS 2018 7
Tasks: General Classification, Sentiment Analysis, Sentiment Classification

In the last decade, video blogs (vlogs) have become an extremely popular
method through which people express sentiment. The ubiquitousness of these
videos has increased the importance of multimodal fusion models, which
incorporate video and audio features with traditional text features for
automatic sentiment detection. Multimodal fusion offers a unique opportunity to
build models that learn from the full depth of expression available to human
viewers. In the detection of sentiment in these videos, acoustic and video
features provide clarity to otherwise ambiguous transcripts. In this paper, we
present a multimodal fusion model that exclusively uses high-level video and
audio features to analyze spoken sentences for sentiment. We discard
traditional transcription features in order to minimize human intervention and
to maximize the deployability of our model on at-scale real-world data. We
select high-level features for our model that have been successful in nonaffect
domains in order to test their generalizability in the sentiment detection
domain. We train and test our model on the newly released CMU Multimodal
Opinion Sentiment and Emotion Intensity (CMUMOSEI) dataset, obtaining an F1
score of 0.8049 on the validation set and an F1 score of 0.6325 on the held-out
challenge test set.
Stochastic Channel Decorrelation Network and Its Application to Visual Tracking
Authors: Jie Guo, Tingfa Xu, Shenwang Jiang, Ziyi Shen
Date: 2018-07-03 00:00:00
Tasks: Diversity, Visual Tracking

Deep convolutional neural networks (CNNs) have dominated many computer vision
domains because of their great power to extract good features automatically.
However, many deep CNNs-based computer vison tasks suffer from lack of training
data while there are millions of parameters in the deep models. Obviously,
these two biphase violation facts will result in parameter redundancy of many
poorly designed deep CNNs. Therefore, we look deep into the existing CNNs and
find that the redundancy of network parameters comes from the correlation
between features in different channels within a convolutional layer. To solve
this problem, we propose the stochastic channel decorrelation (SCD) block
which, in every iteration, randomly selects multiple pairs of channels within a
convolutional layer and calculates their normalized cross correlation (NCC).
Then a squared max-margin loss is proposed as the objective of SCD to suppress
correlation and keep diversity between channels explicitly. The proposed SCD is
very flexible and can be applied to any current existing CNN models simply.
Based on the SCD and the Fully-Convolutional Siamese Networks, we proposed a
visual tracking algorithm to verify the effectiveness of SCD.
One-Class Kernel Spectral Regression
Authors: Shervin Rahimzadeh Arashloo, Josef Kittler
Date: 2018-07-03 00:00:00
Tasks: Graph Embedding, One-class classifier, Outlier Detection, regression

The paper introduces a new efficient nonlinear one-class classifier
formulated as the Rayleigh quotient criterion optimisation. The method,
operating in a reproducing kernel Hilbert space, minimises the scatter of
target distribution along an optimal projection direction while at the same
time keeping projections of positive observations distant from the mean of the
negative class. We provide a graph embedding view of the problem which can then
be solved efficiently using the spectral regression approach. In this sense,
unlike previous similar methods which often require costly eigen-computations
of dense matrices, the proposed approach casts the problem under consideration
into a regression framework which is computationally more efficient. In
particular, it is shown that the dominant complexity of the proposed method is
the complexity of computing the kernel matrix. Additional appealing
characteristics of the proposed one-class classifier are: 1-the ability to be
trained in an incremental fashion (allowing for application in streaming data
scenarios while also reducing the computational complexity in a non-streaming
operation mode); 2-being unsupervised, but providing the option for refining
the solution using negative training examples, when available; And last but not
the least, 3-the use of the kernel trick which facilitates a nonlinear mapping
of the data into a high-dimensional feature space to seek better solutions.
A Mean-Field Optimal Control Formulation of Deep Learning
Authors: Weinan E, Jiequn Han, Qianxiao Li
Date: 2018-07-03 00:00:00
Tasks: Deep Learning

Recent work linking deep neural networks and dynamical systems opened up new
avenues to analyze deep learning. In particular, it is observed that new
insights can be obtained by recasting deep learning as an optimal control
problem on difference or differential equations. However, the mathematical
aspects of such a formulation have not been systematically explored. This paper
introduces the mathematical formulation of the population risk minimization
problem in deep learning as a mean-field optimal control problem. Mirroring the
development of classical optimal control, we state and prove optimality
conditions of both the Hamilton-Jacobi-Bellman type and the Pontryagin type.
These mean-field results reflect the probabilistic nature of the learning
problem. In addition, by appealing to the mean-field Pontryagin's maximum
principle, we establish some quantitative relationships between population and
empirical learning problems. This serves to establish a mathematical foundation
for investigating the algorithmic and theoretical connections between optimal
control and deep learning.
Domain Aware Markov Logic Networks
Authors: Happy Mittal, Ayush Bhardwaj, Vibhav Gogate, Parag Singla
Date: 2018-07-03 00:00:00

Combining logic and probability has been a long stand- ing goal of AI
research. Markov Logic Networks (MLNs) achieve this by attaching weights to
formulas in first-order logic, and can be seen as templates for constructing
features for ground Markov networks. Most techniques for learning weights of
MLNs are domain-size agnostic, i.e., the size of the domain is not explicitly
taken into account while learn- ing the parameters of the model. This often
results in ex- treme probabilities when testing on domain sizes different from
those seen during training. In this paper, we propose Domain Aware Markov logic
Networks (DA-MLNs) which present a principled solution to this problem. While
defin- ing the ground network distribution, DA-MLNs divide the ground feature
weight by a scaling factor which is a function of the number of connections the
ground atoms appearing in the feature are involved in. We show that standard
MLNs fall out as a special case of our formalism when this func- tion evaluates
to a constant equal to 1. Experiments on the benchmark Friends & Smokers domain
show that our ap- proach results in significantly higher accuracies compared to
existing methods when testing on domains whose sizes different from those seen
during training.
Solving Atari Games Using Fractals And Entropy
Authors: Sergio Hernandez Cerezo, Guillem Duran Ballester, Spiros Baxevanakis
Date: 2018-07-03 00:00:00
Tasks: Atari Games

In this paper, we introduce a novel MCTS based approach that is derived from
the laws of the thermodynamics. The algorithm coined Fractal Monte Carlo (FMC),
allows us to create an agent that takes intelligent actions in both continuous
and discrete environments while providing control over every aspect of the
agent behavior. Results show that FMC is several orders of magnitude more
efficient than similar techniques, such as MCTS, in the Atari games tested.
Stochastic Constraint Optimization using Propagation on Ordered Binary Decision Diagrams
Authors: Anna L. D. Latour, Behrouz Babaki, Siegfried Nijssen
Date: 2018-07-03 00:00:00

A number of problems in relational Artificial Intelligence can be viewed as
Stochastic Constraint Optimization Problems (SCOPs). These are constraint
optimization problems that involve objectives or constraints with a stochastic
component. Building on the recently proposed language SC-ProbLog for modeling
SCOPs, we propose a new method for solving these problems. Earlier methods used
Probabilistic Logic Programming (PLP) techniques to create Ordered Binary
Decision Diagrams (OBDDs), which were decomposed into smaller constraints in
order to exploit existing constraint programming (CP) solvers. We argue that
this approach has as drawback that a decomposed representation of an OBDD does
not guarantee domain consistency during search, and hence limits the efficiency
of the solver. For the specific case of monotonic distributions, we suggest an
alternative method for using CP in SCOP, based on the development of a new
propagator; we show that this propagator is linear in the size of the OBDD, and
has the potential to be more efficient than the decomposition method, as it
maintains domain consistency.