pwc-archive/papers-with-abstracts huggingface dataset:
A Variational Feature Encoding Method of 3D Object for Probabilistic Semantic SLAM
Authors: H. W. Yu, B. H. Lee
Date: 2018-08-30 00:00:00
Tasks: Bayesian Inference, Object, Object Recognition, Retrieval, Semantic SLAM, Simultaneous Localization and Mapping

This paper presents a feature encoding method of complex 3D objects for
high-level semantic features. Recent approaches to object recognition methods
become important for semantic simultaneous localization and mapping (SLAM).
However, there is a lack of consideration of the probabilistic observation
model for 3D objects, as the shape of a 3D object basically follows a complex
probability distribution. Furthermore, since the mobile robot equipped with a
range sensor observes only a single view, much information of the object shape
is discarded. These limitations are the major obstacles to semantic SLAM and
view-independent loop closure using 3D object shapes as features. In order to
enable the numerical analysis for the Bayesian inference, we approximate the
true observation model of 3D objects to tractable distributions. Since the
observation likelihood can be obtained from the generative model, we formulate
the true generative model for 3D object with the Bayesian networks. To capture
these complex distributions, we apply a variational auto-encoder. To analyze
the approximated distributions and encoded features, we perform classification
with maximum likelihood estimation and shape retrieval.
VirtualIdentity: Privacy-Preserving User Profiling
Authors: Sisi Wang, Wing-Sea Poon, Golnoosh Farnadi, Caleb Horst, Kebra Thompson, Michael Nickels, Rafael Dowsley, Anderson C. A. Nascimento, Martine De Cock
Date: 2018-08-30 00:00:00
Tasks: Privacy Preserving

User profiling from user generated content (UGC) is a common practice that
supports the business models of many social media companies. Existing systems
require that the UGC is fully exposed to the module that constructs the user
profiles. In this paper we show that it is possible to build user profiles
without ever accessing the user's original data, and without exposing the
trained machine learning models for user profiling -- which are the
intellectual property of the company -- to the users of the social media site.
We present VirtualIdentity, an application that uses secure multi-party
cryptographic protocols to detect the age, gender and personality traits of
users by classifying their user-generated text and personal pictures with
trained support vector machine models in a privacy-preserving manner.
Dense Scene Flow from Stereo Disparity and Optical Flow
Authors: René Schuster, Oliver Wasenmüller, Didier Stricker
Date: 2018-08-30 00:00:00
Tasks: Optical Flow Estimation, Scene Flow Estimation

Scene flow describes 3D motion in a 3D scene. It can either be modeled as a
single task, or it can be reconstructed from the auxiliary tasks of stereo
depth and optical flow estimation. While the second method can achieve
real-time performance by using real-time auxiliary methods, it will typically
produce non-dense results. In this representation of a basic combination
approach for scene flow estimation, we will tackle the problem of non-density
by interpolation.
Direct Output Connection for a High-Rank Language Model
Authors: Sho Takase, Jun Suzuki, Masaaki Nagata
Date: 2018-08-30 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Constituency Parsing, Headline Generation, Language Modeling, Language Modelling, Machine Translation, Translation, Vocal Bursts Intensity Prediction

This paper proposes a state-of-the-art recurrent neural network (RNN)
language model that combines probability distributions computed not only from a
final RNN layer but also from middle layers. Our proposed method raises the
expressive power of a language model based on the matrix factorization
interpretation of language modeling introduced by Yang et al. (2018). The
proposed method improves the current state-of-the-art language model and
achieves the best score on the Penn Treebank and WikiText-2, which are the
standard benchmark datasets. Moreover, we indicate our proposed method
contributes to two application tasks: machine translation and headline
generation. Our code is publicly available at:
https://github.com/nttcslab-nlp/doc_lm.
Baidu Apollo Auto-Calibration System - An Industry-Level Data-Driven and Learning based Vehicle Longitude Dynamic Calibrating Algorithm
Authors: Fan Zhu, Lin Ma, Xin Xu, Dingfeng Guo, Xiao Cui, Qi Kong
Date: 2018-08-30 00:00:00
Tasks: Autonomous Driving, Autonomous Vehicles, BIG-bench Machine Learning

For any autonomous driving vehicle, control module determines its road
performance and safety, i.e. its precision and stability should stay within a
carefully-designed range. Nonetheless, control algorithms require vehicle
dynamics (such as longitudinal dynamics) as inputs, which, unfortunately, are
obscure to calibrate in real time. As a result, to achieve reasonable
performance, most, if not all, research-oriented autonomous vehicles do manual
calibrations in a one-by-one fashion. Since manual calibration is not
sustainable once entering into mass production stage for industrial purposes,
we here introduce a machine-learning based auto-calibration system for
autonomous driving vehicles. In this paper, we will show how we build a
data-driven longitudinal calibration procedure using machine learning
techniques. We first generated offline calibration tables from human driving
data. The offline table serves as an initial guess for later uses and it only
needs twenty-minutes data collection and process. We then used an
online-learning algorithm to appropriately update the initial table (the
offline table) based on real-time performance analysis. This longitudinal
auto-calibration system has been deployed to more than one hundred Baidu Apollo
self-driving vehicles (including hybrid family vehicles and electronic
delivery-only vehicles) since April 2018. By August 27, 2018, it had been
tested for more than two thousands hours, ten thousands kilometers (6,213
miles) and yet proven to be effective.
Semi-Supervised Training for Improving Data Efficiency in End-to-End Speech Synthesis
Authors: Yu-An Chung, Yuxuan Wang, Wei-Ning Hsu, Yu Zhang, RJ Skerry-Ryan
Date: 2018-08-30 00:00:00
Tasks: Decoder, Speech Synthesis, text-to-speech, Text to Speech

Although end-to-end text-to-speech (TTS) models such as Tacotron have shown
excellent results, they typically require a sizable set of high-quality <text,
audio> pairs for training, which are expensive to collect. In this paper, we
propose a semi-supervised training framework to improve the data efficiency of
Tacotron. The idea is to allow Tacotron to utilize textual and acoustic
knowledge contained in large, publicly-available text and speech corpora.
Importantly, these external data are unpaired and potentially noisy.
Specifically, first we embed each word in the input text into word vectors and
condition the Tacotron encoder on them. We then use an unpaired speech corpus
to pre-train the Tacotron decoder in the acoustic domain. Finally, we fine-tune
the model using available paired data. We demonstrate that the proposed
framework enables Tacotron to generate intelligible speech using less than half
an hour of paired training data.
Learning Neural Templates for Text Generation
Authors: Sam Wiseman, Stuart M. Shieber, Alexander M. Rush
Date: 2018-08-30 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Decoder, Text Generation

While neural, encoder-decoder models have had significant empirical success in text generation, there remain several unaddressed problems with this style of generation. Encoder-decoder models are largely (a) uninterpretable, and (b) difficult to control in terms of their phrasing or content. This work proposes a neural generation system using a hidden semi-markov model (HSMM) decoder, which learns latent, discrete templates jointly with learning to generate. We show that this model learns useful templates, and that these templates make generation both more interpretable and controllable. Furthermore, we show that this approach scales to real data sets and achieves strong performance nearing that of encoder-decoder text generation models.
ExIt-OOS: Towards Learning from Planning in Imperfect Information Games
Authors: Andy Kitchen, Michela Benedetti
Date: 2018-08-30 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

The current state of the art in playing many important perfect information
games, including Chess and Go, combines planning and deep reinforcement
learning with self-play. We extend this approach to imperfect information games
and present ExIt-OOS, a novel approach to playing imperfect information games
within the Expert Iteration framework and inspired by AlphaZero. We use Online
Outcome Sampling, an online search algorithm for imperfect information games in
place of MCTS. While training online, our neural strategy is used to improve
the accuracy of playouts in OOS, allowing a learning and planning feedback loop
for imperfect information games.
Story Ending Generation with Incremental Encoding and Commonsense Knowledge
Authors: Jian Guan, Yansen Wang, Minlie Huang
Date: 2018-08-30 00:00:00
Tasks: Image-guided Story Ending Generation

Generating a reasonable ending for a given story context, i.e., story ending
generation, is a strong indication of story comprehension. This task requires
not only to understand the context clues which play an important role in
planning the plot but also to handle implicit knowledge to make a reasonable,
coherent story.
  In this paper, we devise a novel model for story ending generation. The model
adopts an incremental encoding scheme to represent context clues which are
spanning in the story context. In addition, commonsense knowledge is applied
through multi-source attention to facilitate story comprehension, and thus to
help generate coherent and reasonable endings. Through building context clues
and using implicit knowledge, the model is able to produce reasonable story
endings. context clues implied in the post and make the inference based on it.
  Automatic and manual evaluation shows that our model can generate more
reasonable story endings than state-of-the-art baselines.
Rule-based OWL Modeling with ROWLTab Protege Plugin
Authors: Md. Kamruzzaman Sarker, Adila Krisnadhi, David Carral, Pascal Hitzler
Date: 2018-08-30 00:00:00

It has been argued that it is much easier to convey logical statements using
rules rather than OWL (or description logic (DL)) axioms. Based on recent
theoretical developments on transformations between rules and DLs, we have
developed ROWLTab, a Protege plugin that allows users to enter OWL axioms by
way of rules; the plugin then automatically converts these rules into OWL 2 DL
axioms if possible, and prompts the user in case such a conversion is not
possible without weakening the semantics of the rule. In this paper, we present
ROWLTab, together with a user evaluation of its effectiveness compared to
entering axioms using the standard Protege interface. Our evaluation shows that
modeling with ROWLTab is much quicker than the standard interface, while at the
same time, also less prone to errors for hard modeling tasks.
OWLAx: A Protege Plugin to Support Ontology Axiomatization through Diagramming
Authors: Md. Kamruzzaman Sarker, Adila A. Krisnadhi, Pascal Hitzler
Date: 2018-08-30 00:00:00

Once the conceptual overview, in terms of a somewhat informal class diagram,
has been designed in the course of engineering an ontology, the process of
adding many of the appropriate logical axioms is mostly a routine task. We
provide a Protege plugin which supports this task, together with a visual user
interface, based on established methods for ontology design pattern modeling.
Modeling OWL with Rules: The ROWL Protege Plugin
Authors: Md. Kamruzzaman Sarker, David Carral, Adila A. Krisnadhi, Pascal Hitzler
Date: 2018-08-30 00:00:00

In our experience, some ontology users find it much easier to convey logical
statements using rules rather than OWL (or description logic) axioms. Based on
recent theoretical developments on transformations between rules and
description logics, we develop ROWL, a Protege plugin that allows users to
enter OWL axioms by way of rules; the plugin then automatically converts these
rules into OWL DL axioms if possible, and prompts the user in case such a
conversion is not possible without weakening the semantics of the rule.
DP-ADMM: ADMM-based Distributed Learning with Differential Privacy
Authors: Zonghao Huang, Rui Hu, Yuanxiong Guo, Eric Chan-Tin, Yanmin Gong
Date: 2018-08-30 00:00:00
Tasks: BIG-bench Machine Learning

Alternating Direction Method of Multipliers (ADMM) is a widely used tool for machine learning in distributed settings, where a machine learning model is trained over distributed data sources through an interactive process of local computation and message passing. Such an iterative process could cause privacy concerns of data owners. The goal of this paper is to provide differential privacy for ADMM-based distributed machine learning. Prior approaches on differentially private ADMM exhibit low utility under high privacy guarantee and often assume the objective functions of the learning problems to be smooth and strongly convex. To address these concerns, we propose a novel differentially private ADMM-based distributed learning algorithm called DP-ADMM, which combines an approximate augmented Lagrangian function with time-varying Gaussian noise addition in the iterative process to achieve higher utility for general objective functions under the same differential privacy guarantee. We also apply the moments accountant method to bound the end-to-end privacy loss. The theoretical analysis shows that DP-ADMM can be applied to a wider class of distributed learning problems, is provably convergent, and offers an explicit utility-privacy tradeoff. To our knowledge, this is the first paper to provide explicit convergence and utility properties for differentially private ADMM-based distributed learning algorithms. The evaluation results demonstrate that our approach can achieve good convergence and model accuracy under high end-to-end differential privacy guarantee.
CNN-PS: CNN-based Photometric Stereo for General Non-Convex Surfaces
Authors: Satoshi Ikehata
Date: 2018-08-30 00:00:00
Proceeding: ECCV 2018 9

Most conventional photometric stereo algorithms inversely solve a BRDF-based
image formation model. However, the actual imaging process is often far more
complex due to the global light transport on the non-convex surfaces. This
paper presents a photometric stereo network that directly learns relationships
between the photometric stereo input and surface normals of a scene. For
handling unordered, arbitrary number of input images, we merge all the input
data to the intermediate representation called {\it observation map} that has a
fixed shape, is able to be fed into a CNN. To improve both training and
prediction, we take into account the rotational pseudo-invariance of the
observation map that is derived from the isotropic constraint. For training the
network, we create a synthetic photometric stereo dataset that is generated by
a physics-based renderer, therefore the global light transport is considered.
Our experimental results on both synthetic and real datasets show that our
method outperforms conventional BRDF-based photometric stereo algorithms
especially when scenes are highly non-convex.
Artifacts Detection and Error Block Analysis from Broadcasted Videos
Authors: Md Mehedi Hasan, Tasneem Rahman, Kiok Ahn, Oksam Chae
Date: 2018-08-30 00:00:00
Tasks: Retrieval

With the advancement of IPTV and HDTV technology, previous subtle errors in
videos are now becoming more prominent because of the structure oriented and
compression based artifacts. In this paper, we focus towards the development of
a real-time video quality check system. Light weighted edge gradient magnitude
information is incorporated to acquire the statistical information and the
distorted frames are then estimated based on the characteristics of their
surrounding frames. Then we apply the prominent texture patterns to classify
them in different block errors and analyze them not only in video error
detection application but also in error concealment, restoration and retrieval.
Finally, evaluating the performance through experiments on prominent datasets
and broadcasted videos show that the proposed algorithm is very much efficient
to detect errors for video broadcast and surveillance applications in terms of
computation time and analysis of distorted frames.
Differential and integral invariants under Mobius transformation
Authors: He Zhang, Hanlin Mo, You Hao, Qi Li, Hua Li
Date: 2018-08-30 00:00:00

One of the most challenging problems in the domain of 2-D image or 3-D shape
is to handle the non-rigid deformation. From the perspective of transformation
groups, the conformal transformation is a key part of the diffeomorphism.
According to the Liouville Theorem, an important part of the conformal
transformation is the Mobius transformation, so we focus on Mobius
transformation and propose two differential expressions that are invariable
under 2-D and 3-D Mobius transformation respectively. Next, we analyze the
absoluteness and relativity of invariance on them and their components. After
that, we propose integral invariants under Mobius transformation based on the
two differential expressions. Finally, we propose a conjecture about the
structure of differential invariants under conformal transformation according
to our observation on the composition of the above two differential invariants.
Discriminative Learning of Similarity and Group Equivariant Representations
Authors: Shubhendu Trivedi
Date: 2018-08-30 00:00:00
Tasks: Metric Learning

One of the most fundamental problems in machine learning is to compare examples: Given a pair of objects we want to return a value which indicates degree of (dis)similarity. Similarity is often task specific, and pre-defined distances can perform poorly, leading to work in metric learning. However, being able to learn a similarity-sensitive distance function also presupposes access to a rich, discriminative representation for the objects at hand. In this dissertation we present contributions towards both ends. In the first part of the thesis, assuming good representations for the data, we present a formulation for metric learning that makes a more direct attempt to optimize for the k-NN accuracy as compared to prior work. We also present extensions of this formulation to metric learning for kNN regression, asymmetric similarity learning and discriminative learning of Hamming distance. In the second part, we consider a situation where we are on a limited computational budget i.e. optimizing over a space of possible metrics would be infeasible, but access to a label aware distance metric is still desirable. We present a simple, and computationally inexpensive approach for estimating a well motivated metric that relies only on gradient estimates, discussing theoretical and experimental results. In the final part, we address representational issues, considering group equivariant convolutional neural networks (GCNNs). Equivariance to symmetry transformations is explicitly encoded in GCNNs; a classical CNN being the simplest example. In particular, we present a SO(3)-equivariant neural network architecture for spherical data, that operates entirely in Fourier space, while also providing a formalism for the design of fully Fourier neural networks that are equivariant to the action of any continuous compact group.
Towards Effective Deep Embedding for Zero-Shot Learning
Authors: Lei Zhang, Peng Wang, Lingqiao Liu, Chunhua Shen, Wei Wei, Yannning Zhang, Anton Van Den Hengel
Date: 2018-08-30 00:00:00
Tasks: Zero-Shot Learning

Zero-shot learning (ZSL) can be formulated as a cross-domain matching
problem: after being projected into a joint embedding space, a visual sample
will match against all candidate class-level semantic descriptions and be
assigned to the nearest class. In this process, the embedding space underpins
the success of such matching and is crucial for ZSL. In this paper, we conduct
an in-depth study on the construction of embedding space for ZSL and posit that
an ideal embedding space should satisfy two criteria: intra-class compactness
and inter-class separability. While the former encourages the embeddings of
visual samples of one class to distribute tightly close to the semantic
description embedding of this class, the latter requires embeddings from
different classes to be well separated from each other. Towards this goal, we
present a simple but effective two-branch network to simultaneously map
semantic descriptions and visual samples into a joint space, on which visual
embeddings are forced to regress to their class-level semantic embeddings and
the embeddings crossing classes are required to be distinguishable by a
trainable classifier. Furthermore, we extend our method to a transductive
setting to better handle the model bias problem in ZSL (i.e., samples from
unseen classes tend to be categorized into seen classes) with minimal extra
supervision. Specifically, we propose a pseudo labeling strategy to
progressively incorporate the testing samples into the training process and
thus balance the model between seen and unseen classes. Experimental results on
five standard ZSL datasets show the superior performance of the proposed method
and its transductive extension.
Rational Neural Networks for Approximating Jump Discontinuities of Graph Convolution Operator
Authors: Zhiqian Chen, Feng Chen, Rongjie Lai, Xuchao Zhang, Chang-Tien Lu
Date: 2018-08-30 00:00:00
Tasks: regression

For node level graph encoding, a recent important state-of-art method is the
graph convolutional networks (GCN), which nicely integrate local vertex
features and graph topology in the spectral domain. However, current studies
suffer from several drawbacks: (1) graph CNNs relies on Chebyshev polynomial
approximation which results in oscillatory approximation at jump
discontinuities; (2) Increasing the order of Chebyshev polynomial can reduce
the oscillations issue, but also incurs unaffordable computational cost; (3)
Chebyshev polynomials require degree $\Omega$(poly(1/$\epsilon$)) to
approximate a jump signal such as $|x|$, while rational function only needs
$\mathcal{O}$(poly log(1/$\epsilon$))\cite{liang2016deep,telgarsky2017neural}.
However, it's non-trivial to apply rational approximation without increasing
computational complexity due to the denominator. In this paper, the superiority
of rational approximation is exploited for graph signal recovering. RatioanlNet
is proposed to integrate rational function and neural networks. We show that
rational function of eigenvalues can be rewritten as a function of graph
Laplacian, which can avoid multiplication by the eigenvector matrix. Focusing
on the analysis of approximation on graph convolution operation, a graph signal
regression task is formulated. Under graph signal regression task, its time
complexity can be significantly reduced by graph Fourier transform. To overcome
the local minimum problem of neural networks model, a relaxed Remez algorithm
is utilized to initialize the weight parameters. Convergence rate of
RatioanlNet and polynomial based methods on jump signal is analyzed for a
theoretical guarantee. The extensive experimental results demonstrated that our
approach could effectively characterize the jump discontinuities, outperforming
competing methods by a substantial margin on both synthetic and real-world
graphs.