pwc-archive/papers-with-abstracts huggingface dataset:
Speech Intention Understanding in a Head-final Language: A Disambiguation Utilizing Intonation-dependency
Authors: Won Ik Cho, Hyeon Seung Lee, Ji Won Yoon, Seok Min Kim, Nam Soo Kim
Date: 2018-11-10 00:00:00
Tasks: Sentence

For a large portion of real-life utterances, the intention cannot be solely decided by either their semantic or syntactic characteristics. Although not all the sociolinguistic and pragmatic information can be digitized, at least phonetic features are indispensable in understanding the spoken language. Especially in head-final languages such as Korean, sentence-final prosody has great importance in identifying the speaker's intention. This paper suggests a system which identifies the inherent intention of a spoken utterance given its transcript, in some cases using auxiliary acoustic features. The main point here is a separate distinction for cases where discrimination of intention requires an acoustic cue. Thus, the proposed classification system decides whether the given utterance is a fragment, statement, question, command, or a rhetorical question/command, utilizing the intonation-dependency coming from the head-finality. Based on an intuitive understanding of the Korean language that is engaged in the data annotation, we construct a network which identifies the intention of a speech, and validate its utility with the test sentences. The system, if combined with up-to-date speech recognizers, is expected to be flexibly inserted into various language understanding modules.
StationPlot: A New Non-stationarity Quantification Tool for Detection of Epileptic Seizures
Authors: Sawon Pratiher, Subhankar Chattoraj, Rajdeep Mukherjee
Date: 2018-11-10 00:00:00
Tasks: Diagnostic, EEG, Electroencephalogram (EEG), Seizure Detection, Time Series, Time Series Analysis

A novel non-stationarity visualization tool known as StationPlot is developed
for deciphering the chaotic behavior of a dynamical time series. A family of
analytic measures enumerating geometrical aspects of the non-stationarity &
degree of variability is formulated by convex hull geometry (CHG) on
StationPlot. In the Euclidean space, both trend-stationary (TS) &
difference-stationary (DS) perturbations are comprehended by the asymmetric
structure of StationPlot's region of interest (ROI). The proposed method is
experimentally validated using EEG signals, where it comprehend the relative
temporal evolution of neural dynamics & its non-stationary morphology, thereby
exemplifying its diagnostic competence for seizure activity (SA) detection.
Experimental results & analysis-of-Variance (ANOVA) on the extracted CHG
features demonstrates better classification performances as compared to the
existing shallow feature based state-of-the-art & validates its efficacy as
geometry-rich discriminative descriptors for signal processing applications.
Innovative 3D Depth Map Generation From A Holoscopic 3D Image Based on Graph Cut Technique
Authors: Bodor Almatrouk, Mohammad Rafiq Swash, Abdul Hamid Sadka
Date: 2018-11-10 00:00:00
Tasks: Stereo Matching, Stereo Matching Hand

Holoscopic 3D imaging is a promising technique for capturing full colour
spatial 3D images using a single aperture holoscopic 3D camera. It mimics fly's
eye technique with a microlens array, which views the scene at a slightly
different angle to its adjacent lens that records three dimensional information
onto a two dimensional surface. This paper proposes a method of depth map
generation from a holoscopic 3D image based on graph cut technique. The
principal objective of this study is to estimate the depth information
presented in a holoscopic 3D image with high precision. As such, depth map
extraction is measured from a single still holoscopic 3D image which consists
of multiple viewpoint images. The viewpoints are extracted and utilised for
disparity calculation via disparity space image technique and pixels
displacement is measured with sub pixel accuracy to overcome the issue of the
narrow baseline between the viewpoint images for stereo matching. In addition,
cost aggregation is used to correlate the matching costs within a particular
neighbouring region using sum of absolute difference SAD combined with
gradient-based metric and winner takes all algorithm is employed to select the
minimum elements in the array as optimal disparity value. Finally, the optimal
depth map is obtained using graph cut technique. The proposed method extends
the utilisation of holoscopic 3D imaging system and enables the expansion of
the technology for various applications of autonomous robotics, medical,
inspection, AR VR, security and entertainment where 3D depth sensing and
measurement are a concern.
Densely Connected Attention Propagation for Reading Comprehension
Authors: Yi Tay, Luu Anh Tuan, Siu Cheung Hui, Jian Su
Date: 2018-11-10 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: All, Open-Domain Question Answering, Question Answering, Reading Comprehension

We propose DecaProp (Densely Connected Attention Propagation), a new densely
connected neural architecture for reading comprehension (RC). There are two
distinct characteristics of our model. Firstly, our model densely connects all
pairwise layers of the network, modeling relationships between passage and
query across all hierarchical levels. Secondly, the dense connectors in our
network are learned via attention instead of standard residual skip-connectors.
To this end, we propose novel Bidirectional Attention Connectors (BAC) for
efficiently forging connections throughout the network. We conduct extensive
experiments on four challenging RC benchmarks. Our proposed approach achieves
state-of-the-art results on all four, outperforming existing baselines by up to
$2.6\%-14.2\%$ in absolute F1 score.
Image Cartoon-Texture Decomposition Using Isotropic Patch Recurrence
Authors: Ruotao Xu, Yuhui Quan, Yong Xu
Date: 2018-11-10 00:00:00

Aiming at separating the cartoon and texture layers from an image,
cartoon-texture decomposition approaches resort to image priors to model
cartoon and texture respectively. In recent years, patch recurrence has emerged
as a powerful prior for image recovery. However, the existing strategies of
using patch recurrence are ineffective to cartoon-texture decomposition, as
both cartoon contours and texture patterns exhibit strong patch recurrence in
images. To address this issue, we introduce the isotropy prior of patch
recurrence, that the spatial configuration of similar patches in texture
exhibits the isotropic structure which is different from that in cartoon, to
model the texture component. Based on the isotropic patch recurrence, we
construct a nonlocal sparsification system which can effectively distinguish
well-patterned features from contour edges. Incorporating the constructed
nonlocal system into morphology component analysis, we develop an effective
method to both noiseless and noisy cartoon-texture decomposition. The
experimental results have demonstrated the superior performance of the proposed
method to the existing ones, as well as the effectiveness of the isotropic
patch recurrence prior.
Adversarially-Trained Normalized Noisy-Feature Auto-Encoder for Text Generation
Authors: Xiang Zhang, Yann Lecun
Date: 2018-11-10 00:00:00
Tasks: Decoder, Text Generation

This article proposes Adversarially-Trained Normalized Noisy-Feature
Auto-Encoder (ATNNFAE) for byte-level text generation. An ATNNFAE consists of
an auto-encoder where the internal code is normalized on the unit sphere and
corrupted by additive noise. Simultaneously, a replica of the decoder (sharing
the same parameters as the AE decoder) is used as the generator and fed with
random latent vectors. An adversarial discriminator is trained to distinguish
training samples reconstructed from the AE from samples produced through the
random-input generator, making the entire generator-discriminator path
differentiable for discrete data like text. The combined effect of noise
injection in the code and shared weights between the decoder and the generator
can prevent the mode collapsing phenomenon commonly observed in GANs. Since
perplexity cannot be applied to non-sequential text generation, we propose a
new evaluation method using the total variance distance between frequencies of
hash-coded byte-level n-grams (NGTVD). NGTVD is a single benchmark that can
characterize both the quality and the diversity of the generated texts.
Experiments are offered in 6 large-scale datasets in Arabic, Chinese and
English, with comparisons against n-gram baselines and recurrent neural
networks (RNNs). Ablation study on both the noise level and the discriminator
is performed. We find that RNNs have trouble competing with the n-gram
baselines, and the ATNNFAE results are generally competitive.
Fast On-the-fly Retraining-free Sparsification of Convolutional Neural Networks
Authors: Amir H. Ashouri, Tarek S. Abdelrahman, Alwyn Dos Remedios
Date: 2018-11-10 00:00:00
Proceeding: NIPS Workshop CDNNRIA 2018

Modern Convolutional Neural Networks (CNNs) are complex, encompassing millions of parameters. Their deployment exerts computational, storage and energy demands, particularly on embedded platforms. Existing approaches to prune or sparsify CNNs require retraining to maintain inference accuracy. Such retraining is not feasible in some contexts. In this paper, we explore the sparsification of CNNs by proposing three model-independent methods. Our methods are applied on-the-fly and require no retraining. We show that the state-of-the-art models' weights can be reduced by up to 73% (compression factor of 3.7x) without incurring more than 5% loss in Top-5 accuracy. Additional fine-tuning gains only 8% in sparsity, which indicates that our fast on-the-fly methods are effective.
R-SPIDER: A Fast Riemannian Stochastic Optimization Algorithm with Curvature Independent Rate
Authors: Jingzhao Zhang, Hongyi Zhang, Suvrit Sra
Date: 2018-11-10 00:00:00
Tasks: Stochastic Optimization

We study smooth stochastic optimization problems on Riemannian manifolds. Via
adapting the recently proposed SPIDER algorithm \citep{fang2018spider} (a
variance reduced stochastic method) to Riemannian manifold, we can achieve
faster rate than known algorithms in both the finite sum and stochastic
settings. Unlike previous works, by \emph{not} resorting to bounding iterate
distances, our analysis yields curvature independent convergence rates for both
the nonconvex and strongly convex cases.
CAPTAIN: Comprehensive Composition Assistance for Photo Taking
Authors: Farshid Farhat, Mohammad Mahdi Kamani, James Z. Wang
Date: 2018-11-10 00:00:00
Tasks: Clustering, Image Retrieval, Retrieval, set matching

Many people are interested in taking astonishing photos and sharing with
others. Emerging hightech hardware and software facilitate ubiquitousness and
functionality of digital photography. Because composition matters in
photography, researchers have leveraged some common composition techniques to
assess the aesthetic quality of photos computationally. However, composition
techniques developed by professionals are far more diverse than well-documented
techniques can cover. We leverage the vast underexplored innovations in
photography for computational composition assistance. We propose a
comprehensive framework, named CAPTAIN (Composition Assistance for Photo
Taking), containing integrated deep-learned semantic detectors, sub-genre
categorization, artistic pose clustering, personalized aesthetics-based image
retrieval, and style set matching. The framework is backed by a large dataset
crawled from a photo-sharing Website with mostly photography enthusiasts and
professionals. The work proposes a sequence of steps that have not been
explored in the past by researchers. The work addresses personal preferences
for composition through presenting a ranked-list of photographs to the user
based on user-specified weights in the similarity measure. The matching
algorithm recognizes the best shot among a sequence of shots with respect to
the user's preferred style set. We have conducted a number of experiments on
the newly proposed components and reported findings. A user study demonstrates
that the work is useful to those taking photos.
G2C: A Generator-to-Classifier Framework Integrating Multi-Stained Visual Cues for Pathological Glomerulus Classification
Authors: Bingzhe Wu, Xiaolu Zhang, Shiwan Zhao, Lingxi Xie, Caihong Zeng, Zhihong Liu, Guangyu Sun
Date: 2018-06-30 00:00:00
Tasks: Cancer Classification, Classification, Decision Making, General Classification, Image-to-Image Translation

Pathological glomerulus classification plays a key role in the diagnosis of
nephropathy. As the difference between different subcategories is subtle,
doctors often refer to slides from different staining methods to make
decisions. However, creating correspondence across various stains is
labor-intensive, bringing major difficulties in collecting data and training a
vision-based algorithm to assist nephropathy diagnosis. This paper provides an
alternative solution for integrating multi-stained visual cues for glomerulus
classification. Our approach, named generator-to-classifier (G2C), is a
two-stage framework. Given an input image from a specified stain, several
generators are first applied to estimate its appearances in other staining
methods, and a classifier follows to combine visual cues from different stains
for prediction (whether it is pathological, or which type of pathology it has).
We optimize these two stages in a joint manner. To provide a reasonable
initialization, we pre-train the generators in an unlabeled reference set under
an unpaired image-to-image translation task, and then fine-tune them together
with the classifier. We conduct experiments on a glomerulus type classification
dataset collected by ourselves (there are no publicly available datasets for
this purpose). Although joint optimization slightly harms the authenticity of
the generated patches, it boosts classification performance, suggesting more
effective visual cues are extracted in an automatic way. We also transfer our
model to a public dataset for breast cancer classification, and outperform the
state-of-the-arts significantly.
Mapping Navigation Instructions to Continuous Control Actions with Position-Visitation Prediction
Authors: Valts Blukis, Dipendra Misra, Ross A. Knepper, Yoav Artzi
Date: 2018-11-10 00:00:00
Tasks: continuous-control, Continuous Control, Imitation Learning, Instruction Following, Position

We propose an approach for mapping natural language instructions and raw
observations to continuous control of a quadcopter drone. Our model predicts
interpretable position-visitation distributions indicating where the agent
should go during execution and where it should stop, and uses the predicted
distributions to select the actions to execute. This two-step model
decomposition allows for simple and efficient training using a combination of
supervised learning and imitation learning. We evaluate our approach with a
realistic drone simulator, and demonstrate absolute task-completion accuracy
improvements of 16.85% over two state-of-the-art instruction-following methods.
An efficient branch-and-bound algorithm for submodular function maximization
Authors: Naoya Uematsu, Shunji Umetani, Yoshinobu Kawahara
Date: 2018-11-10 00:00:00

The submodular function maximization is an attractive optimization model that
appears in many real applications. Although a variety of greedy algorithms
quickly find good feasible solutions for many instances while guaranteeing
(1-1/e)-approximation ratio, we still encounter many real applications that ask
optimal or better feasible solutions within reasonable computation time. In
this paper, we present an efficient branch-and-bound algorithm for the
non-decreasing submodular function maximization problem based on its binary
integer programming (BIP) formulation with a huge number of constraints.
Nemhauser and Wolsey developed an exact algorithm called the constraint
generation algorithm that starts from a reduced BIP problem with a small subset
of constraints taken from the constraints and repeats solving a reduced BIP
problem while adding a new constraint at each iteration. However, their
algorithm is still computationally expensive due to many reduced BIP problems
to be solved. To overcome this, we propose an improved constraint generation
algorithm to add a promising set of constraints at each iteration. We
incorporate it into a branch-and-bound algorithm to attain good upper bounds
while solving a smaller number of reduced BIP problems. According to
computational results for well-known benchmark instances, our algorithm
achieved better performance than the state-of-the-art exact algorithms.
New Movement and Transformation Principle of Fuzzy Reasoning and Its Application to Fuzzy Neural Network
Authors: Chung-Jin Kwak, Son-Il Kwak, Dae-Song Kang, Song-Il Choe, Jin-Ung Kim, Hyok-Gi Chea
Date: 2018-11-10 00:00:00

In this paper, we propose a new fuzzy reasoning principle, so called Movement
and Transformation Principle(MTP). This Principle is to obtain a new fuzzy
reasoning result by Movement and Transformation the consequent fuzzy set in
response to the Movement, Transformation, and Movement-Transformation
operations between the antecedent fuzzy set and fuzzificated observation
information. And then we presented fuzzy modus ponens and fuzzy modus tollens
based on MTP. We compare proposed method with Mamdani fuzzy system, Sugeno
fuzzy system, Wang distance type fuzzy reasoning method and Hellendoorn
functional type method. And then we applied to the learning experiments of the
fuzzy neural network based on MTP and compared it with the Sugeno method.
Through prediction experiments of fuzzy neural network on the precipitation
data and security situation data, learning accuracy and time performance are
clearly improved. Consequently we show that our method based on MTP is
computationally simple and does not involve nonlinear operations, so it is easy
to handle mathematically.
Use of Neural Signals to Evaluate the Quality of Generative Adversarial Network Performance in Facial Image Generation
Authors: Zhengwei Wang, Graham Healy, Alan F. Smeaton, Tomas E. Ward
Date: 2018-11-10 00:00:00
Tasks: Brain Computer Interface, Generative Adversarial Network, Image Generation

There is a growing interest in using generative adversarial networks (GANs) to produce image content that is indistinguishable from real images as judged by a typical person. A number of GAN variants for this purpose have been proposed, however, evaluating GANs performance is inherently difficult because current methods for measuring the quality of their output are not always consistent with what a human perceives. We propose a novel approach that combines a brain-computer interface (BCI) with GANs to generate a measure we call Neuroscore, which closely mirrors the behavioral ground truth measured from participants tasked with discerning real from synthetic images. This technique we call a neuro-AI interface, as it provides an interface between a human's neural systems and an AI process. In this paper, we first compare the three most widely used metrics in the literature for evaluating GANs in terms of visual quality and compare their outputs with human judgments. Secondly we propose and demonstrate a novel approach using neural signals and rapid serial visual presentation (RSVP) that directly measures a human perceptual response to facial production quality, independent of a behavioral response measurement. The correlation between our proposed Neuroscore and human perceptual judgments has Pearson correlation statistics: $\mathrm{r}(48) = -0.767, \mathrm{p} = 2.089e-10$. We also present the bootstrap result for the correlation i.e., $\mathrm{p}\leq 0.0001$. Results show that our Neuroscore is more consistent with human judgment compared to the conventional metrics we evaluated. We conclude that neural signals have potential applications for high quality, rapid evaluation of GANs in the context of visual image synthesis.
Power Normalizing Second-order Similarity Network for Few-shot Learning
Authors: Hongguang Zhang, Piotr Koniusz
Date: 2018-11-10 00:00:00
Tasks: Few-Shot Learning, Scene Recognition

Second- and higher-order statistics of data points have played an important
role in advancing the state of the art on several computer vision problems such
as the fine-grained image and scene recognition. However, these statistics need
to be passed via an appropriate pooling scheme to obtain the best performance.
Power Normalizations are non-linear activation units which enjoy
probability-inspired derivations and can be applied in CNNs. In this paper, we
propose a similarity learning network leveraging second-order information and
Power Normalizations. To this end, we propose several formulations capturing
second-order statistics and derive a sigmoid-like Power Normalizing function to
demonstrate its interpretability. Our model is trained end-to-end to learn the
similarity between the support set and query images for the problem of one- and
few-shot learning. The evaluations on Omniglot, miniImagenet and Open MIC
datasets demonstrate that this network obtains state-of-the-art results on
several few-shot learning protocols.
Dual Latent Variable Model for Low-Resource Natural Language Generation in Dialogue Systems
Authors: Van-Khanh Tran, Le-Minh Nguyen
Date: 2018-11-10 00:00:00
Proceeding: CONLL 2018 10
Tasks: Decoder, Text Generation, Variational Inference

Recent deep learning models have shown improving results to natural language
generation (NLG) irrespective of providing sufficient annotated data. However,
a modest training data may harm such models performance. Thus, how to build a
generator that can utilize as much of knowledge from a low-resource setting
data is a crucial issue in NLG. This paper presents a variational neural-based
generation model to tackle the NLG problem of having limited labeled dataset,
in which we integrate a variational inference into an encoder-decoder generator
and introduce a novel auxiliary autoencoding with an effective training
procedure. Experiments showed that the proposed methods not only outperform the
previous models when having sufficient training dataset but also show strong
ability to work acceptably well when the training data is scarce.
Adversarial Sampling and Training for Semi-Supervised Information Retrieval
Authors: Dae Hoon Park, Yi Chang
Date: 2018-11-09 00:00:00
Tasks: Information Retrieval, Question Answering, Retrieval

Ad-hoc retrieval models with implicit feedback often have problems, e.g., the imbalanced classes in the data set. Too few clicked documents may hurt generalization ability of the models, whereas too many non-clicked documents may harm effectiveness of the models and efficiency of training. In addition, recent neural network-based models are vulnerable to adversarial examples due to the linear nature in them. To solve the problems at the same time, we propose an adversarial sampling and training framework to learn ad-hoc retrieval models with implicit feedback. Our key idea is (i) to augment clicked examples by adversarial training for better generalization and (ii) to obtain very informational non-clicked examples by adversarial sampling and training. Experiments are performed on benchmark data sets for common ad-hoc retrieval tasks such as Web search, item recommendation, and question answering. Experimental results indicate that the proposed approaches significantly outperform strong baselines especially for high-ranked documents, and they outperform IRGAN in NDCG@5 using only 5% of labeled data for the Web search task.