pwc-archive/papers-with-abstracts huggingface dataset:
On the Graded Acceptability of Arguments in Abstract and Instantiated Argumentation
Authors: Davide Grossi, Sanjay Modgil
Date: 2018-11-08 00:00:00

The paper develops a formal theory of the degree of justification of
arguments, which relies solely on the structure of an argumentation framework,
and which can be successfully interfaced with approaches to instantiated
argumentation. The theory is developed in three steps. First, the paper
introduces a graded generalization of the two key notions underpinning Dung's
semantics: self-defense and conflict-freeness. This leads to a natural
generalization of Dung's semantics, whereby standard extensions are weakened or
strengthened depending on the level of self-defense and conflict-freeness they
meet. The paper investigates the fixpoint theory of these semantics,
establishing existence results for them. Second, the paper shows how graded
semantics readily provide an approach to argument rankings, offering a novel
contribution to the recently growing research programme on ranking-based
semantics. Third, this novel approach to argument ranking is applied and
studied in the context of instantiated argumentation frameworks, and in so
doing is shown to account for a simple form of accrual of arguments within the
Dung paradigm. Finally, the theory is compared in detail with existing
approaches.
Repetitive Motion Estimation Network: Recover cardiac and respiratory signal from thoracic imaging
Authors: Xiaoxiao Li, Vivek Singh, Yifan Wu, Klaus Kirchberg, James Duncan, Ankur Kapoor
Date: 2018-11-08 00:00:00
Tasks: Motion Estimation

Tracking organ motion is important in image-guided interventions, but motion
annotations are not always easily available. Thus, we propose Repetitive Motion
Estimation Network (RMEN) to recover cardiac and respiratory signals. It learns
the spatio-temporal repetition patterns, embedding high dimensional motion
manifolds to 1D vectors with partial motion phase boundary annotations.
Compared with the best alternative models, our proposed RMEN significantly
decreased the QRS peaks detection offsets by 59.3%. Results showed that RMEN
could handle the irregular cardiac and respiratory motion cases. Repetitive
motion patterns learned by RMEN were visualized and indicated in the feature
maps.
Improving Multi-Person Pose Estimation using Label Correction
Authors: Naoki Kato, Tianqi Li, Kohei Nishino, Yusuke Uchida
Date: 2018-11-08 00:00:00
Tasks: Missing Labels, Multi-Person Pose Estimation, Pose Estimation

Significant attention is being paid to multi-person pose estimation methods
recently, as there has been rapid progress in the field owing to convolutional
neural networks. Especially, recent method which exploits part confidence maps
and Part Affinity Fields (PAFs) has achieved accurate real-time prediction of
multi-person keypoints. However, human annotated labels are sometimes
inappropriate for learning models. For example, if there is a limb that extends
outside an image, a keypoint for the limb may not have annotations because it
exists outside of the image, and thus the labels for the limb can not be
generated. If a model is trained with data including such missing labels, the
output of the model for the location, even though it is correct, is penalized
as a false positive, which is likely to cause negative effects on the
performance of the model. In this paper, we point out the existence of some
patterns of inappropriate labels, and propose a novel method for correcting
such labels with a teacher model trained on such incomplete data. Experiments
on the COCO dataset show that training with the corrected labels improves the
performance of the model and also speeds up training.
Marshall-Olkin Power-Law Distributions in Length-Frequency of Entities
Authors: Xiaoshi Zhong, Xiang Yu, Erik Cambria, Jagath C. Rajapakse
Date: 2018-11-08 00:00:00

Entities involve important concepts with concrete meanings and play important roles in numerous linguistic tasks. Entities have different forms in different linguistic tasks and researchers treat those different forms as different concepts. In this paper, we are curious to know whether there are some common characteristics that connect those different forms of entities. Specifically, we investigate the underlying distributions of entities from different types and different languages, trying to figure out some common characteristics behind those diverse entities. After analyzing twelve datasets about different types of entities and eighteen datasets about entities in different languages, we find that while these entities are dramatically diverse from each other in many aspects, their length-frequencies can be well characterized by a family of Marshall-Olkin power-law (MOPL) distributions. We conduct experiments on those thirty datasets about entities in different types and different languages, and experimental results demonstrate that MOPL models characterize the length-frequencies of entities much better than two state-of-the-art power-law models and an alternative log-normal model. Experimental results also demonstrate that MOPL models are scalable to the length-frequency of entities in large-scale real-world datasets.
Using Known Information to Accelerate HyperParameters Optimization Based on SMBO
Authors: Cheng Daning, Zhang Hanping, Xia Fen, Li Shigang, Zhang Yunquan
Date: 2018-11-08 00:00:00
Tasks: AutoML, BIG-bench Machine Learning, Hyperparameter Optimization, SMAC, SMAC+

Automl is the key technology for machine learning problem. Current state of art hyperparameter optimization methods are based on traditional black-box optimization methods like SMBO (SMAC, TPE). The objective function of black-box optimization is non-smooth, or time-consuming to evaluate, or in some way noisy. Recent years, many researchers offered the work about the properties of hyperparameters. However, traditional hyperparameter optimization methods do not take those information into consideration. In this paper, we use gradient information and machine learning model analysis information to accelerate traditional hyperparameter optimization methods SMBO. In our L2 norm experiments, our method yielded state-of-the-art performance, and in many cases outperformed the previous best configuration approach.
Speaker-adaptive neural vocoders for parametric speech synthesis systems
Authors: Eunwoo Song, Jin-Seob Kim, Kyungguen Byun, Hong-Goo Kang
Date: 2018-11-08 00:00:00
Tasks: Speech Synthesis, text-to-speech, Text to Speech

This paper proposes speaker-adaptive neural vocoders for parametric text-to-speech (TTS) systems. Recently proposed WaveNet-based neural vocoding systems successfully generate a time sequence of speech signal with an autoregressive framework. However, it remains a challenge to synthesize high-quality speech when the amount of a target speaker's training data is insufficient. To generate more natural speech signals with the constraint of limited training data, we propose a speaker adaptation task with an effective variation of neural vocoding models. In the proposed method, a speaker-independent training method is applied to capture universal attributes embedded in multiple speakers, and the trained model is then optimized to represent the specific characteristics of the target speaker. Experimental results verify that the proposed TTS systems with speaker-adaptive neural vocoders outperform those with traditional source-filter model-based vocoders and those with WaveNet vocoders, trained either speaker-dependently or speaker-independently. In particular, our TTS system achieves 3.80 and 3.77 MOS for the Korean male and Korean female speakers, respectively, even though we use only ten minutes' speech corpus for training the model.
Memory-based Deep Reinforcement Learning for Obstacle Avoidance in UAV with Limited Environment Knowledge
Authors: Abhik Singla, Sindhu Padakandla, Shalabh Bhatnagar
Date: 2018-11-08 00:00:00
Tasks: Decision Making, Deep Reinforcement Learning, Reinforcement Learning, Reinforcement Learning (RL)

This paper presents our method for enabling a UAV quadrotor, equipped with a
monocular camera, to autonomously avoid collisions with obstacles in
unstructured and unknown indoor environments. When compared to obstacle
avoidance in ground vehicular robots, UAV navigation brings in additional
challenges because the UAV motion is no more constrained to a well-defined
indoor ground or street environment. Horizontal structures in indoor and
outdoor environments like decorative items, furnishings, ceiling fans,
sign-boards, tree branches etc., also become relevant obstacles unlike those
for ground vehicular robots. Thus, methods of obstacle avoidance developed for
ground robots are clearly inadequate for UAV navigation. Current control
methods using monocular images for UAV obstacle avoidance are heavily dependent
on environment information. These controllers do not fully retain and utilize
the extensively available information about the ambient environment for
decision making. We propose a deep reinforcement learning based method for UAV
obstacle avoidance (OA) and autonomous exploration which is capable of doing
exactly the same. The crucial idea in our method is the concept of partial
observability and how UAVs can retain relevant information about the
environment structure to make better future navigation decisions. Our OA
technique uses recurrent neural networks with temporal attention and provides
better results compared to prior works in terms of distance covered during
navigation without collisions. In addition, our technique has a high inference
rate (a key factor in robotic applications) and is energy-efficient as it
minimizes oscillatory motion of UAV and reduces power wastage.
BAR: Bayesian Activity Recognition using variational inference
Authors: Ranganath Krishnan, Mahesh Subedar, Omesh Tickoo
Date: 2018-11-08 00:00:00
Tasks: Activity Recognition, Bayesian Inference, Variational Inference

Uncertainty estimation in deep neural networks is essential for designing
reliable and robust AI systems. Applications such as video surveillance for
identifying suspicious activities are designed with deep neural networks
(DNNs), but DNNs do not provide uncertainty estimates. Capturing reliable
uncertainty estimates in safety and security critical applications will help to
establish trust in the AI system. Our contribution is to apply Bayesian deep
learning framework to visual activity recognition application and quantify
model uncertainty along with principled confidence. We utilize the stochastic
variational inference technique while training the Bayesian DNNs to infer the
approximate posterior distribution around model parameters and perform Monte
Carlo sampling on the posterior of model parameters to obtain the predictive
distribution. We show that the Bayesian inference applied to DNNs provide
reliable confidence measures for visual activity recognition task as compared
to conventional DNNs. We also show that our method improves the visual activity
recognition precision-recall AUC by 6.2% compared to non-Bayesian baseline. We
evaluate our models on Moments-In-Time (MiT) activity recognition dataset by
selecting a subset of in- and out-of-distribution video samples.
Doc2Im: document to image conversion through self-attentive embedding
Authors: Mithun Das Gupta
Date: 2018-11-08 00:00:00
Tasks: Document To Image Conversion, document understanding, General Classification, Image to text, text-classification, Text Classification, Transfer Learning

Text classification is a fundamental task in NLP applications. Latest
research in this field has largely been divided into two major sub-fields.
Learning representations is one sub-field and learning deeper models, both
sequential and convolutional, which again connects back to the representation
is the other side. We posit the idea that the stronger the representation is,
the simpler classifier models are needed to achieve higher performance. In this
paper we propose a completely novel direction to text classification research,
wherein we convert text to a representation very similar to images, such that
any deep network able to handle images is equally able to handle text. We take
a deeper look at the representation of documents as an image and subsequently
utilize very simple convolution based models taken as is from computer vision
domain. This image can be cropped, re-scaled, re-sampled and augmented just
like any other image to work with most of the state-of-the-art large
convolution based models which have been designed to handle large image
datasets. We show impressive results with some of the latest benchmarks in the
related fields. We perform transfer learning experiments, both from text to
text domain and also from image to text domain. We believe this is a paradigm
shift from the way document understanding and text classification has been
traditionally done, and will drive numerous novel research ideas in the
community.
Distilling Critical Paths in Convolutional Neural Networks
Authors: Fuxun Yu, Zhuwei Qin, Xiang Chen
Date: 2018-10-28 00:00:00
Proceeding: NIPS Workshop CDNNRIA 2018
Tasks: Neural Network Compression

Neural network compression and acceleration are widely demanded currently due
to the resource constraints on most deployment targets. In this paper, through
analyzing the filter activation, gradients, and visualizing the filters'
functionality in convolutional neural networks, we show that the filters in
higher layers learn extremely task-specific features, which are exclusive for
only a small subset of the overall tasks, or even a single class. Based on such
findings, we reveal the critical paths of information flow for different
classes. And by their intrinsic property of exclusiveness, we propose a
critical path distillation method, which can effectively customize the
convolutional neural networks to small ones with much smaller model size and
less computation.
A Retinex-based Image Enhancement Scheme with Noise Aware Shadow-up Function
Authors: Chien Cheng Chien, Yuma Kinoshita, Sayaka Shiota, Hitoshi Kiya
Date: 2018-11-08 00:00:00
Tasks: Image Enhancement

This paper proposes a novel image contrast enhancement method based on both a
noise aware shadow-up function and Retinex (retina and cortex) decomposition.
Under low light conditions, images taken by digital cameras have low contrast
in dark or bright regions. This is due to a limited dynamic range that imaging
sensors have. For this reason, various contrast enhancement methods have been
proposed. Our proposed method can enhance the contrast of images without not
only over-enhancement but also noise amplification. In the proposed method, an
image is decomposed into illumination layer and reflectance layer based on the
retinex theory, and lightness information of the illumination layer is
adjusted. A shadow-up function is used for preventing over-enhancement. The
proposed mapping function, designed by using a noise aware histogram, allows
not only to enhance contrast of dark region, but also to avoid amplifying
noise, even under strong noise environments.
Towards Compositional Distributional Discourse Analysis
Authors: Bob Coecke, Giovanni De Felice, Dan Marsden, Alexis Toumi
Date: 2018-11-08 00:00:00
Tasks: Natural Language Understanding, Question Answering, Sentence

Categorical compositional distributional semantics provide a method to derive
the meaning of a sentence from the meaning of its individual words: the
grammatical reduction of a sentence automatically induces a linear map for
composing the word vectors obtained from distributional semantics. In this
paper, we extend this passage from word-to-sentence to sentence-to-discourse
composition. To achieve this we introduce a notion of basic anaphoric
discourses as a mid-level representation between natural language discourse
formalised in terms of basic discourse representation structures (DRS); and
knowledge base queries over the Semantic Web as described by basic graph
patterns in the Resource Description Framework (RDF). This provides a
high-level specification for compositional algorithms for question answering
and anaphora resolution, and allows us to give a picture of natural language
understanding as a process involving both statistical and logical resources.
Classical Copying versus Quantum Entanglement in Natural Language: The Case of VP-ellipsis
Authors: Gijs Wijnholds, Mehrnoosh Sadrzadeh
Date: 2018-11-08 00:00:00

This paper compares classical copying and quantum entanglement in natural
language by considering the case of verb phrase (VP) ellipsis. VP ellipsis is a
non-linear linguistic phenomenon that requires the reuse of resources, making
it the ideal test case for a comparative study of different copying behaviours
in compositional models of natural language. Following the line of research in
compositional distributional semantics set out by (Coecke et al., 2010) we
develop an extension of the Lambek calculus which admits a controlled form of
contraction to deal with the copying of linguistic resources. We then develop
two different compositional models of distributional meaning for this calculus.
In the first model, we follow the categorical approach of (Coecke et al., 2013)
in which a functorial passage sends the proofs of the grammar to linear maps on
vector spaces and we use Frobenius algebras to allow for copying. In the second
case, we follow the more traditional approach that one finds in categorial
grammars, whereby an intermediate step interprets proofs as non-linear lambda
terms, using multiple variable occurrences that model classical copying. As a
case study, we apply the models to derive different readings of ambiguous
elliptical phrases and compare the analyses that each model provides.
Quantum Semantic Correlations in Hate and Non-Hate Speeches
Authors: Francesco Galofaro, Zeno Toffano, Bich-Liên Doan
Date: 2018-11-08 00:00:00

This paper aims to apply the notions of quantum geometry and correlation to
the typification of semantic relations between couples of keywords in different
documents. In particular we analysed texts classified as hate / non hate
speeches, containing the keywords "women", "white", and "black". The paper
compares this approach to cosine similarity, a classical methodology, to cast
light on the notion of "similar meaning".
Applying Distributional Compositional Categorical Models of Meaning to Language Translation
Authors: Brian Tyrrell
Date: 2018-11-08 00:00:00
Tasks: Sentence, Translation

The aim of this paper is twofold: first we will use vector space
distributional compositional categorical models of meaning to compare the
meaning of sentences in Irish and in English (and thus ascertain when a
sentence is the translation of another sentence) using the cosine similarity
score. Then we shall outline a procedure which translates nouns by
understanding their context, using a conceptual space model of cognition. We
shall use metrics on the category ConvexRel to determine the distance between
concepts (and determine when a noun is the translation of another noun). This
paper will focus on applications to Irish, a member of the Gaelic family of
languages.
Information Flow in Pregroup Models of Natural Language
Authors: Peter M. Hines
Date: 2018-11-08 00:00:00
Tasks: Sentence

This paper is about pregroup models of natural languages, and how they relate
to the explicitly categorical use of pregroups in Compositional Distributional
Semantics and Natural Language Processing. These categorical interpretations
make certain assumptions about the nature of natural languages that, when
stated formally, may be seen to impose strong restrictions on pregroup grammars
for natural languages.
  We formalize this as a hypothesis about the form that pregroup models of
natural languages must take, and demonstrate by an artificial language example
that these restrictions are not imposed by the pregroup axioms themselves. We
compare and contrast the artificial language examples with natural languages
(using Welsh, a language where the 'noun' type cannot be taken as primitive, as
an illustrative example).
  The hypothesis is simply that there must exist a causal connection, or
information flow, between the words of a sentence in a language whose purpose
is to communicate information. This is not necessarily the case with formal
languages that are simply generated by a series of 'meaning-free' rules. This
imposes restrictions on the types of pregroup grammars that we expect to find
in natural languages; we formalize this in algebraic, categorical, and
graphical terms.
  We take some preliminary steps in providing conditions that ensure pregroup
models satisfy these conjectured properties, and discuss the more general forms
this hypothesis may take.
An Optimal Transport View on Generalization
Authors: Jingwei Zhang, Tongliang Liu, DaCheng Tao
Date: 2018-11-08 00:00:00
Tasks: Learning Theory

We derive upper bounds on the generalization error of learning algorithms
based on their \emph{algorithmic transport cost}: the expected Wasserstein
distance between the output hypothesis and the output hypothesis conditioned on
an input example. The bounds provide a novel approach to study the
generalization of learning algorithms from an optimal transport view and impose
less constraints on the loss function, such as sub-gaussian or bounded. We
further provide several upper bounds on the algorithmic transport cost in terms
of total variation distance, relative entropy (or KL-divergence), and VC
dimension, thus further bridging optimal transport theory and information
theory with statistical learning theory. Moreover, we also study different
conditions for loss functions under which the generalization error of a
learning algorithm can be upper bounded by different probability metrics
between distributions relating to the output hypothesis and/or the input data.
Finally, under our established framework, we analyze the generalization in deep
learning and conclude that the generalization error in deep neural networks
(DNNs) decreases exponentially to zero as the number of layers increases. Our
analyses of generalization error in deep learning mainly exploit the
hierarchical structure in DNNs and the contraction property of $f$-divergence,
which may be of independent interest in analyzing other learning models with
hierarchical structure.
Ordinal Regression using Noisy Pairwise Comparisons for Body Mass Index Range Estimation
Authors: Luisa Polania, Dongning Wang, Glenn Fung
Date: 2018-11-08 00:00:00
Tasks: General Classification, regression

Ordinal regression aims to classify instances into ordinal categories. In
this paper, body mass index (BMI) category estimation from facial images is
cast as an ordinal regression problem. In particular, noisy binary search
algorithms based on pairwise comparisons are employed to exploit the ordinal
relationship among BMI categories. Comparisons are performed with Siamese
architectures, one of which uses the Bradley-Terry model probabilities as
target. The Bradley-Terry model is an approach to describe probabilities of the
possible outcomes when elements of a set are repeatedly compared with one
another in pairs. Experimental results show that our approach outperforms
classification and regression-based methods at estimating BMI categories.
Calibration Wizard: A Guidance System for Camera Calibration Based on Modelling Geometric and Corner Uncertainty
Authors: Songyou Peng, Peter Sturm
Date: 2018-11-08 00:00:00
Proceeding: ICCV 2019 10
Tasks: Camera Calibration, Position

It is well known that the accuracy of a calibration depends strongly on the choice of camera poses from which images of a calibration object are acquired. We present a system -- Calibration Wizard -- that interactively guides a user towards taking optimal calibration images. For each new image to be taken, the system computes, from all previously acquired images, the pose that leads to the globally maximum reduction of expected uncertainty on intrinsic parameters and then guides the user towards that pose. We also show how to incorporate uncertainty in corner point position in a novel principled manner, for both, calibration and computation of the next best pose. Synthetic and real-world experiments are performed to demonstrate the effectiveness of Calibration Wizard.