pwc-archive/papers-with-abstracts huggingface dataset:
Flexible End-to-End Dialogue System for Knowledge Grounded Conversation
Authors: Wenya Zhu, Kaixiang Mo, Yu Zhang, Zhangbin Zhu, Xuezheng Peng, Qiang Yang
Date: 2017-09-13 00:00:00
Tasks: Generative Question Answering, Question Answering

In knowledge grounded conversation, domain knowledge plays an important role
in a special domain such as Music. The response of knowledge grounded
conversation might contain multiple answer entities or no entity at all.
Although existing generative question answering (QA) systems can be applied to
knowledge grounded conversation, they either have at most one entity in a
response or cannot deal with out-of-vocabulary entities. We propose a fully
data-driven generative dialogue system GenDS that is capable of generating
responses based on input message and related knowledge base (KB). To generate
arbitrary number of answer entities even when these entities never appear in
the training set, we design a dynamic knowledge enquirer which selects
different answer entities at different positions in a single response,
according to different local context. It does not rely on the representations
of entities, enabling our model deal with out-of-vocabulary entities. We
collect a human-human conversation data (ConversMusic) with knowledge
annotations. The proposed method is evaluated on CoversMusic and a public
question answering dataset. Our proposed GenDS system outperforms baseline
methods significantly in terms of the BLEU, entity accuracy, entity recall and
human evaluation. Moreover,the experiments also demonstrate that GenDS works
better even on small datasets.
A New Multifocus Image Fusion Method Using Contourlet Transform
Authors: Fatemeh Vakili Moghadam, Hamid Reza Shahdoosti
Date: 2017-09-13 00:00:00

A new multifocus image fusion approach is presented in this paper. First the
contourlet transform is used to decompose the source images into different
components. Then, some salient features are extracted from components. In order
to extract salient features, spatial frequency is used. Subsequently, the best
coefficients from the components are selected by the maximum selection rule.
Finally, the inverse contourlet transform is applied to the selected
coefficients. Experiments show the superiority of the proposed method.
What is the Essence of a Claim? Cross-Domain Claim Identification
Authors: Johannes Daxenberger, Steffen Eger, Ivan Habernal, Christian Stab, Iryna Gurevych
Date: 2017-04-24 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Argument Mining, domain classification, General Classification

Argument mining has become a popular research area in NLP. It typically
includes the identification of argumentative components, e.g. claims, as the
central component of an argument. We perform a qualitative analysis across six
different datasets and show that these appear to conceptualize claims quite
differently. To learn about the consequences of such different
conceptualizations of claim for practical applications, we carried out
extensive experiments using state-of-the-art feature-rich and deep learning
systems, to identify claims in a cross-domain fashion. While the divergent
perception of claims in different datasets is indeed harmful to cross-domain
classification, we show that there are shared properties on the lexical level
as well as system configurations that can help to overcome these gaps.
Assessing State-of-the-Art Sentiment Models on State-of-the-Art Sentiment Datasets
Authors: Jeremy Barnes, Roman Klinger, Sabine Schulte im Walde
Date: 2017-09-13 00:00:00
Proceeding: WS 2017 9
Tasks: Sentiment Analysis, Word Embeddings

There has been a good amount of progress in sentiment analysis over the past
10 years, including the proposal of new methods and the creation of benchmark
datasets. In some papers, however, there is a tendency to compare models only
on one or two datasets, either because of time restraints or because the model
is tailored to a specific task. Accordingly, it is hard to understand how well
a certain model generalizes across different tasks and datasets. In this paper,
we contribute to this situation by comparing several models on six different
benchmarks, which belong to different domains and additionally have different
levels of granularity (binary, 3-class, 4-class and 5-class). We show that
Bi-LSTMs perform well across datasets and that both LSTMs and Bi-LSTMs are
particularly good at fine-grained sentiment tasks (i. e., with more than two
classes). Incorporating sentiment information into word embeddings during
training gives good results for datasets that are lexically similar to the
training data. With our experiments, we contribute to a better understanding of
the performance of different model architectures on different data sets.
Consequently, we detect novel state-of-the-art results on the SenTube datasets.
Learning Gating ConvNet for Two-Stream based Methods in Action Recognition
Authors: Jiagang Zhu, Wei Zou, Zheng Zhu
Date: 2017-09-12 00:00:00
Tasks: Action Classification, Action Recognition, Mixture-of-Experts, Multi-Task Learning, Temporal Action Localization, Vocal Bursts Valence Prediction

For the two-stream style methods in action recognition, fusing the two
streams' predictions is always by the weighted averaging scheme. This fusion
method with fixed weights lacks of pertinence to different action videos and
always needs trial and error on the validation set. In order to enhance the
adaptability of two-stream ConvNets and improve its performance, an end-to-end
trainable gated fusion method, namely gating ConvNet, for the two-stream
ConvNets is proposed in this paper based on the MoE (Mixture of Experts)
theory. The gating ConvNet takes the combination of feature maps from the same
layer of the spatial and the temporal nets as input and adopts ReLU (Rectified
Linear Unit) as the gating output activation function. To reduce the
over-fitting of gating ConvNet caused by the redundancy of parameters, a new
multi-task learning method is designed, which jointly learns the gating fusion
weights for the two streams and learns the gating ConvNet for action
classification. With our gated fusion method and multi-task learning approach,
a high accuracy of 94.5% is achieved on the dataset UCF101.
On labeling Android malware signatures using minhashing and further classification with Structural Equation Models
Authors: Ignacio Martín, José Alberto Hernández, Sergio de los Santos
Date: 2017-09-13 00:00:00
Tasks: Community Detection, General Classification

Multi-scanner Antivirus systems provide insightful information on the nature
of a suspect application; however there is often a lack of consensus and
consistency between different Anti-Virus engines. In this article, we analyze
more than 250 thousand malware signatures generated by 61 different Anti-Virus
engines after analyzing 82 thousand different Android malware applications. We
identify 41 different malware classes grouped into three major categories,
namely Adware, Harmful Threats and Unknown or Generic signatures. We further
investigate the relationships between such 41 classes using community detection
algorithms from graph theory to identify similarities between them; and we
finally propose a Structure Equation Model to identify which Anti-Virus engines
are more powerful at detecting each macro-category. As an application, we show
how such models can help in identifying whether Unknown malware applications
are more likely to be of Harmful or Adware type.
Conflict management in information fusion with belief functions
Authors: Arnaud Martin
Date: 2017-09-13 00:00:00
Tasks: Management

In Information fusion, the conflict is an important concept. Indeed,
combining several imperfect experts or sources allows conflict. In the theory
of belief functions, this notion has been discussed a lot. The mass appearing
on the empty set during the conjunctive combination rule is generally
considered as conflict, but that is not really a conflict. Some measures of
conflict have been proposed and some approaches have been proposed in order to
manage this conflict or to decide with conflicting mass functions. We recall in
this chapter some of them and we propose a discussion to consider the conflict
in information fusion with the theory of belief functions.
Joint Calibration of Panoramic Camera and Lidar Based on Supervised Learning
Authors: Mingwei Cao, Ming Yang, Chunxiang Wang, Yeqiang Qian, Bing Wang
Date: 2017-09-09 00:00:00
Tasks: Translation

In view of contemporary panoramic camera-laser scanner system, the
traditional calibration method is not suitable for panoramic cameras whose
imaging model is extremely nonlinear. The method based on statistical
optimization has the disadvantage that the requirement of the number of laser
scanner's channels is relatively high. Calibration equipments with extreme
accuracy for panoramic camera-laser scanner system are costly. Facing all these
in the calibration of panoramic camera-laser scanner system, a method based on
supervised learning is proposed. Firstly, corresponding feature points of
panoramic images and point clouds are gained to generate the training dataset
by designing a round calibration object. Furthermore, the traditional
calibration problem is transformed into a multiple nonlinear regression
optimization problem by designing a supervised learning network with
preprocessing of the panoramic imaging model. Back propagation algorithm is
utilized to regress the rotation and translation matrix with high accuracy.
Experimental results show that this method can quickly regress the calibration
parameters and the accuracy is better than the traditional calibration method
and the method based on statistical optimization. The calibration accuracy of
this method is really high, and it is more highly-automated.
Computing the Shapley Value in Allocation Problems: Approximations and Bounds, with an Application to the Italian VQR Research Assessment Program
Authors: Francesco Lupia, Angelo Mendicelli, Andrea Ribichini, Francesco Scarcello, Marco Schaerf
Date: 2017-09-13 00:00:00

In allocation problems, a given set of goods are assigned to agents in such a
way that the social welfare is maximised, that is, the largest possible global
worth is achieved. When goods are indivisible, it is possible to use money
compensation to perform a fair allocation taking into account the actual
contribution of all agents to the social welfare. Coalitional games provide a
formal mathematical framework to model such problems, in particular the Shapley
value is a solution concept widely used for assigning worths to agents in a
fair way. Unfortunately, computing this value is a $\#{\rm P}$-hard problem, so
that applying this good theoretical notion is often quite difficult in
real-world problems.
  We describe useful properties that allow us to greatly simplify the instances
of allocation problems, without affecting the Shapley value of any player.
Moreover, we propose algorithms for computing lower bounds and upper bounds of
the Shapley value, which in some cases provide the exact result and that can be
combined with approximation algorithms.
  The proposed techniques have been implemented and tested on a real-world
application of allocation problems, namely, the Italian research assessment
program, known as VQR. For the large university considered in the experiments,
the problem involves thousands of agents and goods (here, researchers and their
research products). The algorithms described in the paper are able to compute
the Shapley value for most of those agents, and to get a good approximation of
the Shapley value for all of them.
A relevance-scalability-interpretability tradeoff with temporally evolving user personas
Authors: Snigdha Panigrahi, Nadia Fawaz
Date: 2017-04-25 00:00:00

The current work characterizes the users of a VoD streaming space through
user-personas based on a tenure timeline and temporal behavioral features in
the absence of explicit user profiles. A combination of tenure timeline and
temporal characteristics caters to business needs of understanding the
evolution and phases of user behavior as their accounts age. The personas
constructed in this work successfully represent both dominant and niche
characterizations while providing insightful maturation of user behavior in the
system. The two major highlights of our personas are demonstration of stability
along tenure timelines on a population level, while exhibiting interesting
migrations between labels on an individual granularity and clear
interpretability of user labels. Finally, we show a trade-off between an
indispensable trio of guarantees, relevance-scalability-interpretability by
using summary information from personas in a CTR (Click through rate)
predictive model. The proposed method of uncovering latent personas, consequent
insights from these and application of information from personas to predictive
models are broadly applicable to other streaming based products.
Unsupervised Clustering and Active Learning of Hyperspectral Images with Nonlinear Diffusion
Authors: James M. Murphy, Mauro Maggioni
Date: 2017-04-26 00:00:00
Tasks: Active Learning, Clustering

The problem of unsupervised learning and segmentation of hyperspectral images
is a significant challenge in remote sensing. The high dimensionality of
hyperspectral data, presence of substantial noise, and overlap of classes all
contribute to the difficulty of automatically clustering and segmenting
hyperspectral images. We propose an unsupervised learning technique called
spectral-spatial diffusion learning (DLSS) that combines a geometric estimation
of class modes with a diffusion-inspired labeling that incorporates both
spectral and spatial information. The mode estimation incorporates the geometry
of the hyperspectral data by using diffusion distance to promote learning a
unique mode from each class. These class modes are then used to label all
points by a joint spectral-spatial nonlinear diffusion process. A related
variation of DLSS is also discussed, which enables active learning by
requesting labels for a very small number of well-chosen pixels, dramatically
boosting overall clustering results. Extensive experimental analysis
demonstrates the efficacy of the proposed methods against benchmark and
state-of-the-art hyperspectral analysis techniques on a variety of real
datasets, their robustness to choices of parameters, and their low
computational complexity.
Recursive Exponential Weighting for Online Non-convex Optimization
Authors: Lin Yang, Cheng Tan, Wing Shing Wong
Date: 2017-09-13 00:00:00

In this paper, we investigate the online non-convex optimization problem
which generalizes the classic {online convex optimization problem by relaxing
the convexity assumption on the cost function.
  For this type of problem, the classic exponential weighting online algorithm
has recently been shown to attain a sub-linear regret of $O(\sqrt{T\log T})$.
  In this paper, we introduce a novel recursive structure to the online
algorithm to define a recursive exponential weighting algorithm that attains a
regret of $O(\sqrt{T})$, matching the well-known regret lower bound.
  To the best of our knowledge, this is the first online algorithm with
provable $O(\sqrt{T})$ regret for the online non-convex optimization problem.
Bioplausible multiscale filtering in retino-cortical processing as a mechanism in perceptual grouping
Authors: Nasim Nematzadeh, David M. W. Powers, Trent W. Lewis
Date: 2017-02-27 00:00:00

Why does our visual system fail to reconstruct reality, when we look at
certain patterns? Where do Geometrical illusions start to emerge in the visual
pathway? How far should we take computational models of vision with the same
visual ability to detect illusions as we do? This study addresses these
questions, by focusing on a specific underlying neural mechanism involved in
our visual experiences that affects our final perception. Among many types of
visual illusion, Geometrical and, in particular, Tilt Illusions are rather
important, being characterized by misperception of geometric patterns involving
lines and tiles in combination with contrasting orientation, size or position.
Over the last decade, many new neurophysiological experiments have led to new
insights as to how, when and where retinal processing takes place, and the
encoding nature of the retinal representation that is sent to the cortex for
further processing. Based on these neurobiological discoveries, we provide
computer simulation evidence from modelling retinal ganglion cells responses to
some complex Tilt Illusions, suggesting that the emergence of tilt in these
illusions is partially related to the interaction of multiscale visual
processing performed in the retina. The output of our low-level filtering model
is presented for several types of Tilt Illusion, predicting that the final tilt
percept arises from multiple-scale processing of the Differences of Gaussians
and the perceptual interaction of foreground and background elements. Our
results suggest that this model has a high potential in revealing the
underlying mechanism connecting low-level filtering approaches to mid- and
high-level explanations such as Anchoring theory and Perceptual grouping.
Sketch-pix2seq: a Model to Generate Sketches of Multiple Categories
Authors: Yajing Chen, Shikui Tu, Yuqi Yi, Lei Xu
Date: 2017-09-13 00:00:00

Sketch is an important media for human to communicate ideas, which reflects
the superiority of human intelligence. Studies on sketch can be roughly
summarized into recognition and generation. Existing models on image
recognition failed to obtain satisfying performance on sketch classification.
But for sketch generation, a recent study proposed a sequence-to-sequence
variational-auto-encoder (VAE) model called sketch-rnn which was able to
generate sketches based on human inputs. The model achieved amazing results
when asked to learn one category of object, such as an animal or a vehicle.
However, the performance dropped when multiple categories were fed into the
model. Here, we proposed a model called sketch-pix2seq which could learn and
draw multiple categories of sketches. Two modifications were made to improve
the sketch-rnn model: one is to replace the bidirectional recurrent neural
network (BRNN) encoder with a convolutional neural network(CNN); the other is
to remove the Kullback-Leibler divergence from the objective function of VAE.
Experimental results showed that models with CNN encoders outperformed those
with RNN encoders in generating human-style sketches. Visualization of the
latent space illustrated that the removal of KL-divergence made the encoder
learn a posterior of latent space that reflected the features of different
categories. Moreover, the combination of CNN encoder and removal of
KL-divergence, i.e., the sketch-pix2seq model, had better performance in
learning and generating sketches of multiple categories and showed promising
results in creativity tasks.
Extremely Low Bit Neural Network: Squeeze the Last Bit Out with ADMM
Authors: Cong Leng, Hao Li, Shenghuo Zhu, Rong Jin
Date: 2017-07-24 00:00:00
Tasks: object-detection, Object Detection, Quantization

Although deep learning models are highly effective for various learning
tasks, their high computational costs prohibit the deployment to scenarios
where either memory or computational resources are limited. In this paper, we
focus on compressing and accelerating deep models with network weights
represented by very small numbers of bits, referred to as extremely low bit
neural network. We model this problem as a discretely constrained optimization
problem. Borrowing the idea from Alternating Direction Method of Multipliers
(ADMM), we decouple the continuous parameters from the discrete constraints of
network, and cast the original hard problem into several subproblems. We
propose to solve these subproblems using extragradient and iterative
quantization algorithms that lead to considerably faster convergency compared
to conventional optimization methods. Extensive experiments on image
recognition and object detection verify that the proposed algorithm is more
effective than state-of-the-art approaches when coming to extremely low bit
neural network.
Meta Networks for Neural Style Transfer
Authors: Falong Shen, Shuicheng Yan, Gang Zeng
Date: 2017-09-13 00:00:00
Tasks: GPU, Style Transfer

In this paper we propose a new method to get the specified network parameters
through one time feed-forward propagation of the meta networks and explore the
application to neural style transfer. Recent works on style transfer typically
need to train image transformation networks for every new style, and the style
is encoded in the network parameters by enormous iterations of stochastic
gradient descent. To tackle these issues, we build a meta network which takes
in the style image and produces a corresponding image transformations network
directly. Compared with optimization-based methods for every style, our meta
networks can handle an arbitrary new style within $19ms$ seconds on one modern
GPU card. The fast image transformation network generated by our meta network
is only 449KB, which is capable of real-time executing on a mobile device. We
also investigate the manifold of the style transfer networks by operating the
hidden features from meta networks. Experiments have well validated the
effectiveness of our method. Code and trained models has been released
https://github.com/FalongShen/styletransfer.
Co-training for Demographic Classification Using Deep Learning from Label Proportions
Authors: Ehsan Mohammady Ardehaly, Aron Culotta
Date: 2017-09-13 00:00:00
Tasks: Attribute, Classification, General Classification, Marketing, text-classification, Text Classification

Deep learning algorithms have recently produced state-of-the-art accuracy in
many classification tasks, but this success is typically dependent on access to
many annotated training examples. For domains without such data, an attractive
alternative is to train models with light, or distant supervision. In this
paper, we introduce a deep neural network for the Learning from Label
Proportion (LLP) setting, in which the training data consist of bags of
unlabeled instances with associated label distributions for each bag. We
introduce a new regularization layer, Batch Averager, that can be appended to
the last layer of any deep neural network to convert it from supervised
learning to LLP. This layer can be implemented readily with existing deep
learning packages. To further support domains in which the data consist of two
conditionally independent feature views (e.g. image and text), we propose a
co-training algorithm that iteratively generates pseudo bags and refits the
deep LLP model to improve classification accuracy. We demonstrate our models on
demographic attribute classification (gender and race/ethnicity), which has
many applications in social media analysis, public health, and marketing. We
conduct experiments to predict demographics of Twitter users based on their
tweets and profile image, without requiring any user-level annotations for
training. We find that the deep LLP approach outperforms baselines for both
text and image features separately. Additionally, we find that co-training
algorithm improves image and text classification by 4% and 8% absolute F1,
respectively. Finally, an ensemble of text and image classifiers further
improves the absolute F1 measure by 4% on average.
Embedding Riemannian Manifolds by the Heat Kernel of the Connection Laplacian
Authors: Hau-Tieng Wu
Date: 2013-05-18 00:00:00

Given a class of closed Riemannian manifolds with prescribed geometric
conditions, we introduce an embedding of the manifolds into $\ell^2$ based on
the heat kernel of the Connection Laplacian associated with the Levi-Civita
connection on the tangent bundle. As a result, we can construct a distance in
this class which leads to a pre-compactness theorem on the class under
consideration.