pwc-archive/papers-with-abstracts huggingface dataset:
Proceedings of eNTERFACE 2015 Workshop on Intelligent Interfaces
Authors: Matei Mancas, Christian Frisson, Joëlle Tilmanne, Nicolas D'Alessandro, Petr Barborka, Furkan Bayansar, Francisco Bernard, Rebecca Fiebrink, Alexis Heloir, Edgar Hemery, Sohaib Laraba, Alexis Moinet, Fabrizio Nunnari, Thierry Ravet, Loïc Reboursière, Alvaro Sarasua, Mickaël Tits, Noé Tits, François Zajéga, Paolo Alborno, Ksenia Kolykhalova, Emma Frid, Damiano Malafronte, Lisanne Huis in't Veld, Hüseyin Cakmak, Kevin El Haddad, Nicolas Riche, Julien Leroy, Pierre Marighetto, Bekir Berker Türker, Hossein Khaki, Roberto Pulisci, Emer Gilmartin, Fasih Haider, Kübra Cengiz, Martin Sulir, Ilaria Torre, Shabbir Marzban, Ramazan Yazıcı, Furkan Burak Bâgcı, Vedat Gazi Kılı, Hilal Sezer, Sena Büsra Yenge, Charles-Alexandre Delestage, Sylvie Leleu-Merviel, Muriel Meyer-Chemenska, Daniel Schmitt, Willy Yvart, Stéphane Dupont, Ozan Can Altiok, Aysegül Bumin, Ceren Dikmen, Ivan Giangreco, Silvan Heller, Emre Külah, Gueorgui Pironkov, Luca Rossetto, Yusuf Sahillioglu, Heiko Schuldt, Omar Seddati, Yusuf Setinkaya, Metin Sezgin, Claudiu Tanase, Emre Toyan, Sean Wood, Doguhan Yeke, Françcois Rocca, Pierre-Henri De Deken, Alessandra Bandrabur, Fabien Grisard, Axel Jean-Caurant, Vincent Courboulay, Radhwan Ben Madhkour, Ambroise Moreau
Date: 2018-01-19 00:00:00

The 11th Summer Workshop on Multimodal Interfaces eNTERFACE 2015 was hosted
by the Numediart Institute of Creative Technologies of the University of Mons
from August 10th to September 2015. During the four weeks, students and
researchers from all over the world came together in the Numediart Institute of
the University of Mons to work on eight selected projects structured around
intelligent interfaces. Eight projects were selected and their reports are
shown here.
SCUT-FBP5500: A Diverse Benchmark Dataset for Multi-Paradigm Facial Beauty Prediction
Authors: Lingyu Liang, Luojun Lin, Lianwen Jin, Duorui Xie, Mengru Li
Date: 2018-01-19 00:00:00
Tasks: Facial Beauty Prediction, General Classification, regression

Facial beauty prediction (FBP) is a significant visual recognition problem to
make assessment of facial attractiveness that is consistent to human
perception. To tackle this problem, various data-driven models, especially
state-of-the-art deep learning techniques, were introduced, and benchmark
dataset become one of the essential elements to achieve FBP. Previous works
have formulated the recognition of facial beauty as a specific supervised
learning problem of classification, regression or ranking, which indicates that
FBP is intrinsically a computation problem with multiple paradigms. However,
most of FBP benchmark datasets were built under specific computation
constrains, which limits the performance and flexibility of the computational
model trained on the dataset. In this paper, we argue that FBP is a
multi-paradigm computation problem, and propose a new diverse benchmark
dataset, called SCUT-FBP5500, to achieve multi-paradigm facial beauty
prediction. The SCUT-FBP5500 dataset has totally 5500 frontal faces with
diverse properties (male/female, Asian/Caucasian, ages) and diverse labels
(face landmarks, beauty scores within [1,~5], beauty score distribution), which
allows different computational models with different FBP paradigms, such as
appearance-based/shape-based facial beauty classification/regression model for
male/female of Asian/Caucasian. We evaluated the SCUT-FBP5500 dataset for FBP
using different combinations of feature and predictor, and various deep
learning methods. The results indicates the improvement of FBP and the
potential applications based on the SCUT-FBP5500.
EmoTxt: A Toolkit for Emotion Recognition from Text
Authors: Fabio Calefato, Filippo Lanubile, Nicole Novielli
Date: 2017-08-13 00:00:00
Tasks: Emotion Classification, Emotion Recognition, General Classification

We present EmoTxt, a toolkit for emotion recognition from text, trained and
tested on a gold standard of about 9K question, answers, and comments from
online interactions. We provide empirical evidence of the performance of
EmoTxt. To the best of our knowledge, EmoTxt is the first open-source toolkit
supporting both emotion recognition from text and training of custom emotion
classification models.
Demonstration of Topological Data Analysis on a Quantum Processor
Authors: He-Liang Huang, Xi-Lin Wang, Peter P. Rohde, Yi-Han Luo, You-Wei Zhao, Chang Liu, Li Li, Nai-Le Liu, Chao-Yang Lu, Jian-Wei Pan
Date: 2018-01-19 00:00:00
Tasks: Topological Data Analysis

Topological data analysis offers a robust way to extract useful information from noisy, unstructured data by identifying its underlying structure. Recently, an efficient quantum algorithm was proposed [Lloyd, Garnerone, Zanardi, Nat. Commun. 7, 10138 (2016)] for calculating Betti numbers of data points -- topological features that count the number of topological holes of various dimensions in a scatterplot. Here, we implement a proof-of-principle demonstration of this quantum algorithm by employing a six-photon quantum processor to successfully analyze the topological features of Betti numbers of a network including three data points, providing new insights into data analysis in the era of quantum computing.
BinaryRelax: A Relaxation Approach For Training Deep Neural Networks With Quantized Weights
Authors: Penghang Yin, Shuai Zhang, Jiancheng Lyu, Stanley Osher, Yingyong Qi, Jack Xin
Date: 2018-01-19 00:00:00
Tasks: Quantization

We propose BinaryRelax, a simple two-phase algorithm, for training deep
neural networks with quantized weights. The set constraint that characterizes
the quantization of weights is not imposed until the late stage of training,
and a sequence of \emph{pseudo} quantized weights is maintained. Specifically,
we relax the hard constraint into a continuous regularizer via Moreau envelope,
which turns out to be the squared Euclidean distance to the set of quantized
weights. The pseudo quantized weights are obtained by linearly interpolating
between the float weights and their quantizations. A continuation strategy is
adopted to push the weights towards the quantized state by gradually increasing
the regularization parameter. In the second phase, exact quantization scheme
with a small learning rate is invoked to guarantee fully quantized weights. We
test BinaryRelax on the benchmark CIFAR and ImageNet color image datasets to
demonstrate the superiority of the relaxed quantization approach and the
improved accuracy over the state-of-the-art training methods. Finally, we prove
the convergence of BinaryRelax under an approximate orthogonality condition.
A predictor-corrector method for the training of deep neural networks
Authors: Yatin Saraiya
Date: 2018-01-19 00:00:00

The training of deep neural nets is expensive. We present a predictor-
corrector method for the training of deep neural nets. It alternates a
predictor pass with a corrector pass using stochastic gradient descent with
backpropagation such that there is no loss in validation accuracy. No special
modifications to SGD with backpropagation is required by this methodology. Our
experiments showed a time improvement of 9% on the CIFAR-10 dataset.
Fully Point-wise Convolutional Neural Network for Modeling Statistical Regularities in Natural Images
Authors: Jing Zhang, Yang Cao, Yang Wang, Chenglin Wen, Chang Wen Chen
Date: 2018-01-19 00:00:00
Tasks: Color Constancy, Diversity, Image Dehazing

Modeling statistical regularity plays an essential role in ill-posed image
processing problems. Recently, deep learning based methods have been presented
to implicitly learn statistical representation of pixel distributions in
natural images and leverage it as a constraint to facilitate subsequent tasks,
such as color constancy and image dehazing. However, the existing CNN
architecture is prone to variability and diversity of pixel intensity within
and between local regions, which may result in inaccurate statistical
representation. To address this problem, this paper presents a novel fully
point-wise CNN architecture for modeling statistical regularities in natural
images. Specifically, we propose to randomly shuffle the pixels in the origin
images and leverage the shuffled image as input to make CNN more concerned with
the statistical properties. Moreover, since the pixels in the shuffled image
are independent identically distributed, we can replace all the large
convolution kernels in CNN with point-wise ($1*1$) convolution kernels while
maintaining the representation ability. Experimental results on two
applications: color constancy and image dehazing, demonstrate the superiority
of our proposed network over the existing architectures, i.e., using
1/10$\sim$1/100 network parameters and computational cost while achieving
comparable performance.
Describing Semantic Representations of Brain Activity Evoked by Visual Stimuli
Authors: Eri Matsuo, Ichiro Kobayashi, Shinji Nishimoto, Satoshi Nishida, Hideki Asoh
Date: 2018-01-19 00:00:00
Tasks: Image Captioning, Sentence

Quantitative modeling of human brain activity based on language
representations has been actively studied in systems neuroscience. However,
previous studies examined word-level representation, and little is known about
whether we could recover structured sentences from brain activity. This study
attempts to generate natural language descriptions of semantic contents from
human brain activity evoked by visual stimuli. To effectively use a small
amount of available brain activity data, our proposed method employs a
pre-trained image-captioning network model using a deep learning framework. To
apply brain activity to the image-captioning network, we train regression
models that learn the relationship between brain activity and deep-layer image
features. The results demonstrate that the proposed model can decode brain
activity and generate descriptions using natural language sentences. We also
conducted several experiments with data from different subsets of brain regions
known to process visual stimuli. The results suggest that semantic information
for sentence generations is widespread across the entire cortex.
A Dirichlet Process Mixture Model of Discrete Choice
Authors: Rico Krueger, Akshay Vij, Taha H. Rashidi
Date: 2018-01-19 00:00:00
Tasks: model

We present a mixed multinomial logit (MNL) model, which leverages the
truncated stick-breaking process representation of the Dirichlet process as a
flexible nonparametric mixing distribution. The proposed model is a Dirichlet
process mixture model and accommodates discrete representations of
heterogeneity, like a latent class MNL model. Yet, unlike a latent class MNL
model, the proposed discrete choice model does not require the analyst to fix
the number of mixture components prior to estimation, as the complexity of the
discrete mixing distribution is inferred from the evidence. For posterior
inference in the proposed Dirichlet process mixture model of discrete choice,
we derive an expectation maximisation algorithm. In a simulation study, we
demonstrate that the proposed model framework can flexibly capture
differently-shaped taste parameter distributions. Furthermore, we empirically
validate the model framework in a case study on motorists' route choice
preferences and find that the proposed Dirichlet process mixture model of
discrete choice outperforms a latent class MNL model and mixed MNL models with
common parametric mixing distributions in terms of both in-sample fit and
out-of-sample predictive ability. Compared to extant modelling approaches, the
proposed discrete choice model substantially abbreviates specification
searches, as it relies on less restrictive parametric assumptions and does not
require the analyst to specify the complexity of the discrete mixing
distribution prior to estimation.
Nonparametric Bayesian inference of the microcanonical stochastic block model
Authors: Tiago P. Peixoto
Date: 2016-10-09 00:00:00
Tasks: Bayesian Inference, Model Selection, Stochastic Block Model

A principled approach to characterize the hidden structure of networks is to
formulate generative models, and then infer their parameters from data. When
the desired structure is composed of modules or "communities", a suitable
choice for this task is the stochastic block model (SBM), where nodes are
divided into groups, and the placement of edges is conditioned on the group
memberships. Here, we present a nonparametric Bayesian method to infer the
modular structure of empirical networks, including the number of modules and
their hierarchical organization. We focus on a microcanonical variant of the
SBM, where the structure is imposed via hard constraints, i.e. the generated
networks are not allowed to violate the patterns imposed by the model. We show
how this simple model variation allows simultaneously for two important
improvements over more traditional inference approaches: 1. Deeper Bayesian
hierarchies, with noninformative priors replaced by sequences of priors and
hyperpriors, that not only remove limitations that seriously degrade the
inference on large networks, but also reveal structures at multiple scales; 2.
A very efficient inference algorithm that scales well not only for networks
with a large number of nodes and edges, but also with an unlimited number of
modules. We show also how this approach can be used to sample modular
hierarchies from the posterior distribution, as well as to perform model
selection. We discuss and analyze the differences between sampling from the
posterior and simply finding the single parameter estimate that maximizes it.
Furthermore, we expose a direct equivalence between our microcanonical approach
and alternative derivations based on the canonical SBM.
An End-to-End Deep Learning Histochemical Scoring System for Breast Cancer Tissue Microarray
Authors: Jingxin Liu, Bolei Xu, Chi Zheng, Yuanhao Gong, Jon Garibaldi, Daniele Soria, Andew Green, Ian O. Ellis, Wenbin Zou, Guoping Qiu
Date: 2018-01-19 00:00:00
Tasks: Decision Making

One of the methods for stratifying different molecular classes of breast
cancer is the Nottingham Prognostic Index Plus (NPI+) which uses breast cancer
relevant biomarkers to stain tumour tissues prepared on tissue microarray
(TMA). To determine the molecular class of the tumour, pathologists will have
to manually mark the nuclei activity biomarkers through a microscope and use a
semi-quantitative assessment method to assign a histochemical score (H-Score)
to each TMA core. Manually marking positively stained nuclei is a time
consuming, imprecise and subjective process which will lead to inter-observer
and intra-observer discrepancies. In this paper, we present an end-to-end deep
learning system which directly predicts the H-Score automatically. Our system
imitates the pathologists' decision process and uses one fully convolutional
network (FCN) to extract all nuclei region (tumour and non-tumour), a second
FCN to extract tumour nuclei region, and a multi-column convolutional neural
network which takes the outputs of the first two FCNs and the stain intensity
description image as input and acts as the high-level decision making mechanism
to directly output the H-Score of the input TMA image. To the best of our
knowledge, this is the first end-to-end system that takes a TMA image as input
and directly outputs a clinical score. We will present experimental results
which demonstrate that the H-Scores predicted by our model have very high and
statistically significant correlation with experienced pathologists' scores and
that the H-Score discrepancy between our algorithm and the pathologists is on
par with the inter-subject discrepancy between the pathologists.
What Does a TextCNN Learn?
Authors: Linyuan Gong, Ruyi Ji
Date: 2018-01-19 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification, Sentence, Sentence Classification, Sentiment Analysis

TextCNN, the convolutional neural network for text, is a useful deep learning
algorithm for sentence classification tasks such as sentiment analysis and
question classification. However, neural networks have long been known as black
boxes because interpreting them is a challenging task. Researchers have
developed several tools to understand a CNN for image classification by deep
visualization, but research about deep TextCNNs is still insufficient. In this
paper, we are trying to understand what a TextCNN learns on two classical NLP
datasets. Our work focuses on functions of different convolutional kernels and
correlations between convolutional kernels.
Nonparametric weighted stochastic block models
Authors: Tiago P. Peixoto
Date: 2017-08-04 00:00:00
Tasks: Model Selection

We present a Bayesian formulation of weighted stochastic block models that
can be used to infer the large-scale modular structure of weighted networks,
including their hierarchical organization. Our method is nonparametric, and
thus does not require the prior knowledge of the number of groups or other
dimensions of the model, which are instead inferred from data. We give a
comprehensive treatment of different kinds of edge weights (i.e. continuous or
discrete, signed or unsigned, bounded or unbounded), as well as arbitrary
weight transformations, and describe an unsupervised model selection approach
to choose the best network description. We illustrate the application of our
method to a variety of empirical weighted networks, such as global migrations,
voting patterns in congress, and neural connections in the human brain.
Calculus of the exponent of Kurdyka-Łojasiewicz inequality and its applications to linear convergence of first-order methods
Authors: Guoyin Li, Ting Kei Pong
Date: 2016-02-09 00:00:00

In this paper, we study the Kurdyka-{\L}ojasiewicz (KL) exponent, an important quantity for analyzing the convergence rate of first-order methods. Specifically, we develop various calculus rules to deduce the KL exponent of new (possibly nonconvex and nonsmooth) functions formed from functions with known KL exponents. In addition, we show that the well-studied Luo-Tseng error bound together with a mild assumption on the separation of stationary values implies that the KL exponent is $\frac12$. The Luo-Tseng error bound is known to hold for a large class of concrete structured optimization problems, and thus we deduce the KL exponent of a large class of functions whose exponents were previously unknown. Building upon this and the calculus rules, we are then able to show that for many convex or nonconvex optimization models for applications such as sparse recovery, their objective function's KL exponent is $\frac12$. This includes the least squares problem with smoothly clipped absolute deviation (SCAD) regularization or minimax concave penalty (MCP) regularization and the logistic regression problem with $\ell_1$ regularization. Since many existing local convergence rate analysis for first-order methods in the nonconvex scenario relies on the KL exponent, our results enable us to obtain explicit convergence rate for various first-order methods when they are applied to a large variety of practical optimization models. Finally, we further illustrate how our results can be applied to establishing local linear convergence of the proximal gradient algorithm and the inertial proximal algorithm with constant step-sizes for some specific models that arise in sparse recovery.
Deep Chain HDRI: Reconstructing a High Dynamic Range Image from a Single Low Dynamic Range Image
Authors: Siyeong Lee, Gwon Hwan An, Suk-Ju Kang
Date: 2018-01-19 00:00:00
Tasks: inverse tone mapping

In this paper, we propose a novel deep neural network model that reconstructs
a high dynamic range (HDR) image from a single low dynamic range (LDR) image.
The proposed model is based on a convolutional neural network composed of
dilated convolutional layers, and infers LDR images with various exposures and
illumination from a single LDR image of the same scene. Then, the final HDR
image can be formed by merging these inference results. It is relatively easy
for the proposed method to find the mapping between the LDR and an HDR with a
different bit depth because of the chaining structure inferring the
relationship between the LDR images with brighter (or darker) exposures from a
given LDR image. The method not only extends the range, but also has the
advantage of restoring the light information of the actual physical world. For
the HDR images obtained by the proposed method, the HDR-VDP2 Q score, which is
the most popular evaluation metric for HDR images, was 56.36 for a display with
a 1920$\times$1200 resolution, which is an improvement of 6 compared with the
scores of conventional algorithms. In addition, when comparing the peak
signal-to-noise ratio values for tone mapped HDR images generated by the
proposed and conventional algorithms, the average value obtained by the
proposed algorithm is 30.86 dB, which is 10 dB higher than those obtained by
the conventional algorithms.
Towards dense volumetric pancreas segmentation in CT using 3D fully convolutional networks
Authors: Holger Roth, Masahiro Oda, Natsuki Shimizu, Hirohisa ODA, Yuichiro Hayashi, Takayuki Kitasaka, Michitaka Fujiwara, Kazunari Misawa, Kensaku MORI
Date: 2017-11-17 00:00:00
Tasks: Decoder, Pancreas Segmentation, Segmentation

Pancreas segmentation in computed tomography imaging has been historically
difficult for automated methods because of the large shape and size variations
between patients. In this work, we describe a custom-build 3D fully
convolutional network (FCN) that can process a 3D image including the whole
pancreas and produce an automatic segmentation. We investigate two variations
of the 3D FCN architecture; one with concatenation and one with summation skip
connections to the decoder part of the network. We evaluate our methods on a
dataset from a clinical trial with gastric cancer patients, including 147
contrast enhanced abdominal CT scans acquired in the portal venous phase. Using
the summation architecture, we achieve an average Dice score of 89.7 $\pm$ 3.8
(range [79.8, 94.8]) % in testing, achieving the new state-of-the-art
performance in pancreas segmentation on this dataset.
DPC-Net: Deep Pose Correction for Visual Localization
Authors: Valentin Peretroukhin, Jonathan Kelly
Date: 2017-09-10 00:00:00
Tasks: Computational Efficiency, Translation, Visual Localization, Visual Odometry

We present a novel method to fuse the power of deep networks with the computational efficiency of geometric and probabilistic localization algorithms. In contrast to other methods that completely replace a classical visual estimator with a deep network, we propose an approach that uses a convolutional neural network to learn difficult-to-model corrections to the estimator from ground-truth training data. To this end, we derive a novel loss function for learning SE(3) corrections based on a matrix Lie groups approach, with a natural formulation for balancing translation and rotation errors. We use this loss to train a Deep Pose Correction network (DPC-Net) that predicts corrections for a particular estimator, sensor and environment. Using the KITTI odometry dataset, we demonstrate significant improvements to the accuracy of a computationally-efficient sparse stereo visual odometry pipeline, that render it as accurate as a modern computationally-intensive dense estimator. Further, we show how DPC-Net can be used to mitigate the effect of poorly calibrated lens distortion parameters.