pwc-archive/papers-with-abstracts huggingface dataset:
Exploring the Combination Rules of D Numbers From a Perspective of Conflict Redistribution
Authors: Xinyang Deng, Wen Jiang
Date: 2017-03-15 00:00:00

Dempster-Shafer theory of evidence is widely applied to uncertainty modelling
and knowledge reasoning because of its advantages in dealing with uncertain
information. But some conditions or requirements, such as exclusiveness
hypothesis and completeness constraint, limit the development and application
of that theory to a large extend. To overcome the shortcomings and enhance its
capability of representing the uncertainty, a novel model, called D numbers,
has been proposed recently. However, many key issues, for example how to
implement the combination of D numbers, remain unsolved. In the paper, we have
explored the combination of D Numbers from a perspective of conflict
redistribution, and proposed two combination rules being suitable for different
situations for the fusion of two D numbers. The proposed combination rules can
reduce to the classical Dempster's rule in Dempster-Shafer theory under a
certain conditions. Numerical examples and discussion about the proposed rules
are also given in the paper.
Robust Non-Rigid Registration with Reweighted Position and Transformation Sparsity
Authors: Kun Li, Jingyu Yang, Yu-Kun Lai, Daoliang Guo
Date: 2017-03-15 00:00:00
Tasks: Position

Non-rigid registration is challenging because it is ill-posed with high degrees of freedom and is thus sensitive to noise and outliers. We propose a robust non-rigid registration method using reweighted sparsities on position and transformation to estimate the deformations between 3-D shapes. We formulate the energy function with position and transformation sparsity on both the data term and the smoothness term, and define the smoothness constraint using local rigidity. The double sparsity based non-rigid registration model is enhanced with a reweighting scheme, and solved by transferring the model into four alternately-optimized subproblems which have exact solutions and guaranteed convergence. Experimental results on both public datasets and real scanned datasets show that our method outperforms the state-of-the-art methods and is more robust to noise and outliers than conventional non-rigid registration methods.
Source Camera Identification Based On Content-Adaptive Fusion Network
Authors: Pengpeng Yang, Wei Zhao, Rongrong Ni, Yao Zhao
Date: 2017-03-15 00:00:00

Source camera identification is still a hard task in forensics community,
especially for the case of the small query image size. In this paper, we
propose a solution to identify the source camera of the small-size images:
content-adaptive fusion network. In order to learn better feature
representation from the input data, content-adaptive convolutional neural
networks(CA-CNN) are constructed. We add a convolutional layer in preprocessing
stage. Moreover, with the purpose of capturing more comprehensive information,
we parallel three CA-CNNs: CA3-CNN, CA5-CNN, CA7-CNN to get the
content-adaptive fusion network. The difference of three CA-CNNs lies in the
convolutional kernel size of pre-processing layer. The experimental results
show that the proposed method is practicable and satisfactory.
Distributed-Representation Based Hybrid Recommender System with Short Item Descriptions
Authors: Junhua He, Hankz Hankui Zhuo, Jarvan Law
Date: 2017-03-15 00:00:00
Tasks: Collaborative Filtering, Recommendation Systems

Collaborative filtering (CF) aims to build a model from users' past behaviors
and/or similar decisions made by other users, and use the model to recommend
items for users. Despite of the success of previous collaborative filtering
approaches, they are all based on the assumption that there are sufficient
rating scores available for building high-quality recommendation models. In
real world applications, however, it is often difficult to collect sufficient
rating scores, especially when new items are introduced into the system, which
makes the recommendation task challenging. We find that there are often "short"
texts describing features of items, based on which we can approximate the
similarity of items and make recommendation together with rating scores. In
this paper we "borrow" the idea of vector representation of words to capture
the information of short texts and embed it into a matrix factorization
framework. We empirically show that our approach is effective by comparing it
with state-of-the-art approaches.
Face Recognition using Multi-Modal Low-Rank Dictionary Learning
Authors: Homa Foroughi, Moein Shakeri, Nilanjan Ray, Hong Zhang
Date: 2017-03-15 00:00:00
Tasks: Dictionary Learning, Face Recognition, Robust Face Recognition

Face recognition has been widely studied due to its importance in different
applications; however, most of the proposed methods fail when face images are
occluded or captured under illumination and pose variations. Recently several
low-rank dictionary learning methods have been proposed and achieved promising
results for noisy observations. While these methods are mostly developed for
single-modality scenarios, recent studies demonstrated the advantages of
feature fusion from multiple inputs. We propose a multi-modal structured
low-rank dictionary learning method for robust face recognition, using raw
pixels of face images and their illumination invariant representation. The
proposed method learns robust and discriminative representations from
contaminated face images, even if there are few training samples with large
intra-class variations. Extensive experiments on different datasets validate
the superior performance and robustness of our method to severe illumination
variations and occlusion.
Skin lesion segmentation based on preprocessing, thresholding and neural networks
Authors: Juana M. Gutiérrez-Arriola, Marta Gómez-Álvarez, Victor Osma-Ruiz, Nicolás Sáenz-Lechón, Rubén Fraile
Date: 2017-03-15 00:00:00
Tasks: Lesion Segmentation, Segmentation, Skin Lesion Segmentation

This abstract describes the segmentation system used to participate in the
challenge ISIC 2017: Skin Lesion Analysis Towards Melanoma Detection. Several
preprocessing techniques have been tested for three color representations (RGB,
YCbCr and HSV) of 392 images. Results have been used to choose the better
preprocessing for each channel. In each case a neural network is trained to
predict the Jaccard Index based on object characteristics. The system includes
black frames and reference circle detection algorithms but no special treatment
is done for hair removal. Segmentation is performed in two steps first the best
channel to be segmented is chosen by selecting the best neural network output.
If this output does not predict a Jaccard Index over 0.5 a more aggressive
preprocessing is performed using open and close morphological operations and
the segmentation of the channel that obtains the best output from the neural
networks is selected as the lesion.
A Proximity-Aware Hierarchical Clustering of Faces
Authors: Wei-An Lin, Jun-Cheng Chen, Rama Chellappa
Date: 2017-03-14 00:00:00
Tasks: Clustering, Face Clustering, Face Verification

In this paper, we propose an unsupervised face clustering algorithm called
"Proximity-Aware Hierarchical Clustering" (PAHC) that exploits the local
structure of deep representations. In the proposed method, a similarity measure
between deep features is computed by evaluating linear SVM margins. SVMs are
trained using nearest neighbors of sample data, and thus do not require any
external training data. Clusters are then formed by thresholding the similarity
scores. We evaluate the clustering performance using three challenging
unconstrained face datasets, including Celebrity in Frontal-Profile (CFP),
IARPA JANUS Benchmark A (IJB-A), and JANUS Challenge Set 3 (JANUS CS3)
datasets. Experimental results demonstrate that the proposed approach can
achieve significant improvements over state-of-the-art methods. Moreover, we
also show that the proposed clustering algorithm can be applied to curate a set
of large-scale and noisy training dataset while maintaining sufficient amount
of images and their variations due to nuisance factors. The face verification
performance on JANUS CS3 improves significantly by finetuning a DCNN model with
the curated MS-Celeb-1M dataset which contains over three million face images.
A Random Finite Set Model for Data Clustering
Authors: Dinh Phung, Ba-Ngu Bo
Date: 2017-03-14 00:00:00
Tasks: Clustering

The goal of data clustering is to partition data points into groups to
minimize a given objective function. While most existing clustering algorithms
treat each data point as vector, in many applications each datum is not a
vector but a point pattern or a set of points. Moreover, many existing
clustering methods require the user to specify the number of clusters, which is
not available in advance. This paper proposes a new class of models for data
clustering that addresses set-valued data as well as unknown number of
clusters, using a Dirichlet Process mixture of Poisson random finite sets. We
also develop an efficient Markov Chain Monte Carlo posterior inference
technique that can learn the number of clusters and mixture parameters
automatically from the data. Numerical studies are presented to demonstrate the
salient features of this new model, in particular its capacity to discover
extremely unbalanced clusters in data.
In Search of a Dataset for Handwritten Optical Music Recognition: Introducing MUSCIMA++
Authors: Jan Hajič jr., Pavel Pecina
Date: 2017-03-14 00:00:00

Optical Music Recognition (OMR) has long been without an adequate dataset and
ground truth for evaluating OMR systems, which has been a major problem for
establishing a state of the art in the field. Furthermore, machine learning
methods require training data. We analyze how the OMR processing pipeline can
be expressed in terms of gradually more complex ground truth, and based on this
analysis, we design the MUSCIMA++ dataset of handwritten music notation that
addresses musical symbol recognition and notation reconstruction. The MUSCIMA++
dataset version 0.9 consists of 140 pages of handwritten music, with 91255
manually annotated notation symbols and 82261 explicitly marked relationships
between symbol pairs. The dataset allows training and evaluating models for
symbol classification, symbol localization, and notation graph assembly, both
in isolation and jointly. Open-source tools are provided for manipulating the
dataset, visualizing the data and further annotation, and the dataset itself is
made available under an open license.
Classification in biological networks with hypergraphlet kernels
Authors: Jose Lugo-Martinez, Predrag Radivojac
Date: 2017-03-14 00:00:00
Tasks: Classification, Edge Classification, General Classification, Link Prediction

Biological and cellular systems are often modeled as graphs in which vertices
represent objects of interest (genes, proteins, drugs) and edges represent
relational ties among these objects (binds-to, interacts-with, regulates). This
approach has been highly successful owing to the theory, methodology and
software that support analysis and learning on graphs. Graphs, however, often
suffer from information loss when modeling physical systems due to their
inability to accurately represent multiobject relationships. Hypergraphs, a
generalization of graphs, provide a framework to mitigate information loss and
unify disparate graph-based methodologies. In this paper, we present a
hypergraph-based approach for modeling physical systems and formulate vertex
classification, edge classification and link prediction problems on
(hyper)graphs as instances of vertex classification on (extended, dual)
hypergraphs in a semi-supervised setting. We introduce a novel kernel method on
vertex- and edge-labeled (colored) hypergraphs for analysis and learning. The
method is based on exact and inexact (via hypergraph edit distances)
enumeration of small simple hypergraphs, referred to as hypergraphlets, rooted
at a vertex of interest. We extensively evaluate this method and show its
potential use in a positive-unlabeled setting to estimate the number of missing
and false positive links in protein-protein interaction networks.
A Logic of Knowing Why
Authors: Chao Xu, Yanjing Wang, Thomas Studer
Date: 2016-09-21 00:00:00

When we say "I know why he was late", we know not only the fact that he was
late, but also an explanation of this fact. We propose a logical framework of
"knowing why" inspired by the existing formal studies on why-questions,
scientific explanation, and justification logic. We introduce the Ky_i operator
into the language of epistemic logic to express "agent i knows why phi" and
propose a Kripke-style semantics of such expressions in terms of knowing an
explanation of phi. We obtain two sound and complete axiomatizations w.r.t. two
different model classes depending on different assumptions about introspection.
RECOD Titans at ISIC Challenge 2017
Authors: Afonso Menegola, Julia Tavares, Michel Fornaciali, Lin Tzy Li, Sandra Avila, Eduardo Valle
Date: 2017-03-14 00:00:00
Tasks: General Classification, Lesion Segmentation, Meta-Learning, Skin Lesion Segmentation

This extended abstract describes the participation of RECOD Titans in parts 1
and 3 of the ISIC Challenge 2017 "Skin Lesion Analysis Towards Melanoma
Detection" (ISBI 2017). Although our team has a long experience with melanoma
classification, the ISIC Challenge 2017 was the very first time we worked on
skin-lesion segmentation. For part 1 (segmentation), our final submission used
four of our models: two trained with all 2000 samples, without a validation
split, for 250 and for 500 epochs respectively; and other two trained and
validated with two different 1600/400 splits, for 220 epochs. Those four
models, individually, achieved between 0.780 and 0.783 official validation
scores. Our final submission averaged the output of those four models achieved
a score of 0.793. For part 3 (classification), the submitted test run as well
as our last official validation run were the result from a meta-model that
assembled seven base deep-learning models: three based on Inception-V4 trained
on our largest dataset; three based on Inception trained on our smallest
dataset; and one based on ResNet-101 trained on our smaller dataset. The
results of those component models were stacked in a meta-learning layer based
on an SVM trained on the validation set of our largest dataset.
Neural Graph Machines: Learning Neural Networks Using Graphs
Authors: Thang D. Bui, Sujith Ravi, Vivek Ramavajjala
Date: 2017-03-14 00:00:00
Tasks: Document Classification, General Classification, intent-classification, Intent Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

Label propagation is a powerful and flexible semi-supervised learning
technique on graphs. Neural networks, on the other hand, have proven track
records in many supervised learning tasks. In this work, we propose a training
framework with a graph-regularised objective, namely "Neural Graph Machines",
that can combine the power of neural networks and label propagation. This work
generalises previous literature on graph-augmented training of neural networks,
enabling it to be applied to multiple neural architectures (Feed-forward NNs,
CNNs and LSTM RNNs) and a wide range of graphs. The new objective allows the
neural networks to harness both labeled and unlabeled data by: (a) allowing the
network to train using labeled data as in the supervised setting, (b) biasing
the network to learn similar hidden representations for neighboring nodes on a
graph, in the same vein as label propagation. Such architectures with the
proposed objective can be trained efficiently using stochastic gradient descent
and scaled to large graphs, with a runtime that is linear in the number of
edges. The proposed joint training approach convincingly outperforms many
existing methods on a wide range of tasks (multi-label classification on social
graphs, news categorization, document classification and semantic intent
classification), with multiple forms of graph inputs (including graphs with and
without node-level features) and using different types of neural networks.
Distributed Dual Coordinate Ascent in General Tree Networks and Communication Network Effect on Synchronous Machine Learning
Authors: Myung Cho, Lifeng Lai, Weiyu Xu
Date: 2017-03-14 00:00:00
Tasks: BIG-bench Machine Learning

Due to the big size of data and limited data storage volume of a single computer or a single server, data are often stored in a distributed manner. Thus, performing large-scale machine learning operations with the distributed datasets through communication networks is often required. In this paper, we study the convergence rate of the distributed dual coordinate ascent for distributed machine learning problems in a general tree-structured network. Since a tree network model can be understood as the generalization of a star network model, our algorithm can be thought of as the generalization of the distributed dual coordinate ascent in a star network model. We provide the convergence rate of the distributed dual coordinate ascent over a general tree network in a recursive manner and analyze the network effect on the convergence rate. Secondly, by considering network communication delays, we optimize the distributed dual coordinate ascent algorithm to maximize its convergence speed. From our analytical result, we can choose the optimal number of local iterations depending on the communication delay severity to achieve the fastest convergence speed. In numerical experiments, we consider machine learning scenarios over communication networks, where local workers cannot directly reach to a central node due to constraints in communication, and demonstrate that the usability of our distributed dual coordinate ascent algorithm in tree networks. Additionally, we show that adapting number of local and global iterations to network communication delays in the distributed dual coordinated ascent algorithm can improve its convergence speed.
Multichannel End-to-end Speech Recognition
Authors: Tsubasa Ochiai, Shinji Watanabe, Takaaki Hori, John R. Hershey
Date: 2017-03-14 00:00:00
Proceeding: ICML 2017 8
Tasks: Decoder, Language Modeling, Language Modelling, Speech Enhancement, speech-recognition, Speech Recognition

The field of speech recognition is in the midst of a paradigm shift:
end-to-end neural networks are challenging the dominance of hidden Markov
models as a core technology. Using an attention mechanism in a recurrent
encoder-decoder architecture solves the dynamic time alignment problem,
allowing joint end-to-end training of the acoustic and language modeling
components. In this paper we extend the end-to-end framework to encompass
microphone array signal processing for noise suppression and speech enhancement
within the acoustic encoding network. This allows the beamforming components to
be optimized jointly within the recognition architecture to improve the
end-to-end speech recognition objective. Experiments on the noisy speech
benchmarks (CHiME-4 and AMI) show that our multichannel end-to-end system
outperformed the attention-based baseline with input from a conventional
adaptive beamformer.
A statistical model for aggregating judgments by incorporating peer predictions
Authors: John McCoy, Drazen Prelec
Date: 2017-03-14 00:00:00
Tasks: counterfactual, Multiple-choice

We propose a probabilistic model to aggregate the answers of respondents
answering multiple-choice questions. The model does not assume that everyone
has access to the same information, and so does not assume that the consensus
answer is correct. Instead, it infers the most probable world state, even if
only a minority vote for it. Each respondent is modeled as receiving a signal
contingent on the actual world state, and as using this signal to both
determine their own answer and predict the answers given by others. By
incorporating respondent's predictions of others' answers, the model infers
latent parameters corresponding to the prior over world states and the
probability of different signals being received in all possible world states,
including counterfactual ones. Unlike other probabilistic models for
aggregation, our model applies to both single and multiple questions, in which
case it estimates each respondent's expertise. The model shows good
performance, compared to a number of other probabilistic models, on data from
seven studies covering different types of expertise.
Discriminate-and-Rectify Encoders: Learning from Image Transformation Sets
Authors: Andrea Tacchetti, Stephen Voinea, Georgios Evangelopoulos
Date: 2017-03-14 00:00:00
Tasks: Face Verification, Object Recognition, Retrieval, Weakly-supervised Learning

The complexity of a learning task is increased by transformations in the
input space that preserve class identity. Visual object recognition for example
is affected by changes in viewpoint, scale, illumination or planar
transformations. While drastically altering the visual appearance, these
changes are orthogonal to recognition and should not be reflected in the
representation or feature encoding used for learning. We introduce a framework
for weakly supervised learning of image embeddings that are robust to
transformations and selective to the class distribution, using sets of
transforming examples (orbit sets), deep parametrizations and a novel
orbit-based loss. The proposed loss combines a discriminative, contrastive part
for orbits with a reconstruction error that learns to rectify orbit
transformations. The learned embeddings are evaluated in distance metric-based
tasks, such as one-shot classification under geometric transformations, as well
as face verification and retrieval under more realistic visual variability. Our
results suggest that orbit sets, suitably computed or observed, can be used for
efficient, weakly-supervised learning of semantically relevant image
embeddings.
Weighted Voting Via No-Regret Learning
Authors: Nika Haghtalab, Ritesh Noothigattu, Ariel D. Procaccia
Date: 2017-03-14 00:00:00

Voting systems typically treat all voters equally. We argue that perhaps they
should not: Voters who have supported good choices in the past should be given
higher weight than voters who have supported bad ones. To develop a formal
framework for desirable weighting schemes, we draw on no-regret learning.
Specifically, given a voting rule, we wish to design a weighting scheme such
that applying the voting rule, with voters weighted by the scheme, leads to
choices that are almost as good as those endorsed by the best voter in
hindsight. We derive possibility and impossibility results for the existence of
such weighting schemes, depending on whether the voting rule and the weighting
scheme are deterministic or randomized, as well as on the social choice axioms
satisfied by the voting rule.
Highway and Residual Networks learn Unrolled Iterative Estimation
Authors: Klaus Greff, Rupesh K. Srivastava, Jürgen Schmidhuber
Date: 2016-12-22 00:00:00

The past year saw the introduction of new architectures such as Highway
networks and Residual networks which, for the first time, enabled the training
of feedforward networks with dozens to hundreds of layers using simple gradient
descent. While depth of representation has been posited as a primary reason for
their success, there are indications that these architectures defy a popular
view of deep learning as a hierarchical computation of increasingly abstract
features at each layer.
  In this report, we argue that this view is incomplete and does not adequately
explain several recent findings. We propose an alternative viewpoint based on
unrolled iterative estimation -- a group of successive layers iteratively
refine their estimates of the same features instead of computing an entirely
new representation. We demonstrate that this viewpoint directly leads to the
construction of Highway and Residual networks. Finally we provide preliminary
experiments to discuss the similarities and differences between the two
architectures.