pwc-archive/papers-with-abstracts huggingface dataset:
On Plans With Loops and Noise
Authors: Vaishak Belle
Date: 2018-09-14 00:00:00

In an influential paper, Levesque proposed a formal specification for
analysing the correctness of program-like plans, such as conditional plans,
iterative plans, and knowledge-based plans. He motivated a logical
characterisation within the situation calculus that included binary sensing
actions. While the characterisation does not immediately yield a practical
algorithm, the specification serves as a general skeleton to explore the
synthesis of program-like plans for reasonable, tractable fragments.
  Increasingly, classical plan structures are being applied to stochastic
environments such as robotics applications. This raises the question as to what
the specification for correctness should look like, since Levesque's account
makes the assumption that sensing is exact and actions are deterministic.
Building on a situation calculus theory for reasoning about degrees of belief
and noise, we revisit the execution semantics of generalised plans. The
specification is then used to analyse the correctness of example plans.
A Domain Agnostic Normalization Layer for Unsupervised Adversarial Domain Adaptation
Authors: Rob Romijnders, Panagiotis Meletis, Gijs Dubbelman
Date: 2018-09-14 00:00:00
Tasks: Domain Adaptation, Scene Segmentation

We propose a normalization layer for unsupervised domain adaption in semantic
scene segmentation. Normalization layers are known to improve convergence and
generalization and are part of many state-of-the-art fully-convolutional neural
networks. We show that conventional normalization layers worsen the performance
of current Unsupervised Adversarial Domain Adaption (UADA), which is a method
to improve network performance on unlabeled datasets and the focus of our
research. Therefore, we propose a novel Domain Agnostic Normalization layer and
thereby unlock the benefits of normalization layers for unsupervised
adversarial domain adaptation. In our evaluation, we adapt from the synthetic
GTA5 data set to the real Cityscapes data set, a common benchmark experiment,
and surpass the state-of-the-art. As our normalization layer is domain agnostic
at test time, we furthermore demonstrate that UADA using Domain Agnostic
Normalization improves performance on unseen domains, specifically on
Apolloscape and Mapillary.
Skeleton-to-Response: Dialogue Generation Guided by Retrieval Memory
Authors: Deng Cai, Yan Wang, Victoria Bi, Zhaopeng Tu, Xiaojiang Liu, Wai Lam, Shuming Shi
Date: 2018-09-14 00:00:00
Proceeding: NAACL 2019 6
Tasks: Dialogue Generation, Diversity, Information Retrieval, Informativeness, Response Generation, Retrieval

For dialogue response generation, traditional generative models generate responses solely from input queries. Such models rely on insufficient information for generating a specific response since a certain query could be answered in multiple ways. Consequentially, those models tend to output generic and dull responses, impeding the generation of informative utterances. Recently, researchers have attempted to fill the information gap by exploiting information retrieval techniques. When generating a response for a current query, similar dialogues retrieved from the entire training data are considered as an additional knowledge source. While this may harvest massive information, the generative models could be overwhelmed, leading to undesirable performance. In this paper, we propose a new framework which exploits retrieval results via a skeleton-then-response paradigm. At first, a skeleton is generated by revising the retrieved responses. Then, a novel generative model uses both the generated skeleton and the original query for response generation. Experimental results show that our approaches significantly improve the diversity and informativeness of the generated responses.
Efficient Rank Minimization via Solving Non-convexPenalties by Iterative Shrinkage-Thresholding Algorithm
Authors: Zaiyi Chen
Date: 2018-09-14 00:00:00

Rank minimization (RM) is a wildly investigated task of finding solutions by
exploiting low-rank structure of parameter matrices. Recently, solving RM
problem by leveraging non-convex relaxations has received significant
attention. It has been demonstrated by some theoretical and experimental work
that non-convex relaxation, e.g. Truncated Nuclear Norm Regularization (TNNR)
and Reweighted Nuclear Norm Regularization (RNNR), can provide a better
approximation of original problems than convex relaxations. However, designing
an efficient algorithm with theoretical guarantee remains a challenging
problem. In this paper, we propose a simple but efficient proximal-type method,
namely Iterative Shrinkage-Thresholding Algorithm(ISTA), with concrete analysis
to solve rank minimization problems with both non-convex weighted and
reweighted nuclear norm as low-rank regularizers. Theoretically, the proposed
method could converge to the critical point under very mild assumptions with
the rate in the order of $O(1/T)$. Moreover, the experimental results on both
synthetic data and real world data sets show that proposed algorithm
outperforms state-of-arts in both efficiency and accuracy.
Characterizing Variation in Crowd-Sourced Data for Training Neural Language Generators to Produce Stylistically Varied Outputs
Authors: Juraj Juraska, Marilyn Walker
Date: 2018-09-14 00:00:00
Proceeding: WS 2018 11
Tasks: Text Generation

One of the biggest challenges of end-to-end language generation from meaning
representations in dialogue systems is making the outputs more natural and
varied. Here we take a large corpus of 50K crowd-sourced utterances in the
restaurant domain and develop text analysis methods that systematically
characterize types of sentences in the training data. We then automatically
label the training data to allow us to conduct two kinds of experiments with a
neural generator. First, we test the effect of training the system with
different stylistic partitions and quantify the effect of smaller, but more
stylistically controlled training data. Second, we propose a method of labeling
the style variants during training, and show that we can modify the style of
the generated utterances using our stylistic labels. We contrast and compare
these methods that can be used with any existing large corpus, showing how they
vary in terms of semantic quality and stylistic control.
Deep CNN Frame Interpolation with Lessons Learned from Natural Language Processing
Authors: Kian Ghodoussi, Nihar Sheth, Zane Durante, Markie Wagner
Date: 2018-09-14 00:00:00
Tasks: Deep Learning

A major area of growth within deep learning has been the study and
implementation of convolutional neural networks. The general explanation within
the deep learning community of the robustness of convolutional neural networks
(CNNs) within image recognition rests upon the idea that CNNs are able to
extract localized features. However, recent developments in fields such as
Natural Language Processing are demonstrating that this paradigm may be
incorrect. In this paper, we analyze the current state of the field concerning
CNN's and present a hypothesis that provides a novel explanation for the
robustness of CNN models. From there, we demonstrate the effectiveness of our
approach by presenting novel deep CNN frame interpolation architecture that is
comparable to the state of the art interpolation models with a fraction of the
complexity.
Keypoint Based Weakly Supervised Human Parsing
Authors: Zhonghua Wu, Guosheng Lin, Jianfei Cai
Date: 2018-09-14 00:00:00
Tasks: Human Parsing, Segmentation, Semantic Segmentation

Fully convolutional networks (FCN) have achieved great success in human
parsing in recent years. In conventional human parsing tasks, pixel-level
labeling is required for guiding the training, which usually involves enormous
human labeling efforts. To ease the labeling efforts, we propose a novel weakly
supervised human parsing method which only requires simple object keypoint
annotations for learning. We develop an iterative learning method to generate
pseudo part segmentation masks from keypoint labels. With these pseudo masks,
we train an FCN network to output pixel-level human parsing predictions.
Furthermore, we develop a correlation network to perform joint prediction of
part and object segmentation masks and improve the segmentation performance.
The experiment results show that our weakly supervised method is able to
achieve very competitive human parsing results. Despite our method only uses
simple keypoint annotations for learning, we are able to achieve comparable
performance with fully supervised methods which use the expensive pixel-level
annotations.
Variational Autoencoder with Implicit Optimal Priors
Authors: Hiroshi Takahashi, Tomoharu Iwata, Yuki Yamanaka, Masanori Yamada, Satoshi Yagi
Date: 2018-09-14 00:00:00
Tasks: Density Estimation

The variational autoencoder (VAE) is a powerful generative model that can estimate the probability of a data point by using latent variables. In the VAE, the posterior of the latent variable given the data point is regularized by the prior of the latent variable using Kullback Leibler (KL) divergence. Although the standard Gaussian distribution is usually used for the prior, this simple prior incurs over-regularization. As a sophisticated prior, the aggregated posterior has been introduced, which is the expectation of the posterior over the data distribution. This prior is optimal for the VAE in terms of maximizing the training objective function. However, KL divergence with the aggregated posterior cannot be calculated in a closed form, which prevents us from using this optimal prior. With the proposed method, we introduce the density ratio trick to estimate this KL divergence without modeling the aggregated posterior explicitly. Since the density ratio trick does not work well in high dimensions, we rewrite this KL divergence that contains the high-dimensional density ratio into the sum of the analytically calculable term and the low-dimensional density ratio term, to which the density ratio trick is applied. Experiments on various datasets show that the VAE with this implicit optimal prior achieves high density estimation performance.
Macquarie University at BioASQ 6b: Deep learning and deep reinforcement learning for query-based multi-document summarisation
Authors: Diego Mollá
Date: 2018-09-14 00:00:00
Tasks: Deep Learning, Deep Reinforcement Learning, Position, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Sentence, Word Embeddings

This paper describes Macquarie University's contribution to the BioASQ
Challenge (BioASQ 6b, Phase B). We focused on the extraction of the ideal
answers, and the task was approached as an instance of query-based
multi-document summarisation. In particular, this paper focuses on the
experiments related to the deep learning and reinforcement learning approaches
used in the submitted runs. The best run used a deep learning model under a
regression-based framework. The deep learning architecture used features
derived from the output of LSTM chains on word embeddings, plus features based
on similarity with the query, and sentence position. The reinforcement learning
approach was a proof-of-concept prototype that trained a global policy using
REINFORCE. The global policy was implemented as a neural network that used
$tf.idf$ features encoding the candidate sentence, question, and context.
Learning to Fingerprint the Latent Structure in Question Articulation
Authors: Kumar Mrityunjay, Guntur Ravindra
Date: 2018-09-14 00:00:00

Abstract Machine understanding of questions is tightly related to recognition
of articulation in the context of the computational capabilities of an
underlying processing algorithm. In this paper a mathematical model to capture
and distinguish the latent structure in the articulation of questions is
presented. We propose an objective-driven approach to represent this latent
structure and show that such an approach is beneficial when examples of
complementary objectives are not available. We show that the latent structure
can be represented as a system that maximizes a cost function related to the
underlying objective. Further, we show that the optimization formulation can be
approximated to building a memory of patterns represented as a trained neural
auto-encoder. Experimental evaluation using many clusters of questions, each
related to an objective, shows 80% recognition accuracy and negligible false
positive across these clusters of questions. We then extend the same memory to
a related task where the goal is to iteratively refine a dataset of questions
based on the latent articulation. We also demonstrate a refinement scheme
called K-fingerprints, that achieves nearly 100% recognition with negligible
false positive across the different clusters of questions.
Dueling Bandits with Qualitative Feedback
Authors: Liyuan Xu, Junya Honda, Masashi Sugiyama
Date: 2018-09-14 00:00:00

We formulate and study a novel multi-armed bandit problem called the
qualitative dueling bandit (QDB) problem, where an agent observes not numeric
but qualitative feedback by pulling each arm. We employ the same regret as the
dueling bandit (DB) problem where the duel is carried out by comparing the
qualitative feedback. Although we can naively use classic DB algorithms for
solving the QDB problem, this reduction significantly worsens the
performance---actually, in the QDB problem, the probability that one arm wins
the duel over another arm can be directly estimated without carrying out actual
duels. In this paper, we propose such direct algorithms for the QDB problem.
Our theoretical analysis shows that the proposed algorithms significantly
outperform DB algorithms by incorporating the qualitative feedback, and
experimental results also demonstrate vast improvement over the existing DB
algorithms.
Supervised Machine Learning for Extractive Query Based Summarisation of Biomedical Data
Authors: Mandeep Kaur, Diego Mollá
Date: 2018-09-14 00:00:00
Proceeding: WS 2018 10
Tasks: BIG-bench Machine Learning, Classification, General Classification, regression

The automation of text summarisation of biomedical publications is a pressing
need due to the plethora of information available on-line. This paper explores
the impact of several supervised machine learning approaches for extracting
multi-document summaries for given queries. In particular, we compare
classification and regression approaches for query-based extractive
summarisation using data provided by the BioASQ Challenge. We tackled the
problem of annotating sentences for training classification systems and show
that a simple annotation approach outperforms regression-based summarisation.
Detection-by-Localization: Maintenance-Free Change Object Detector
Authors: Tanaka Kanji
Date: 2018-09-14 00:00:00
Tasks: Change Detection, Information Retrieval, Object, Retrieval

Recent researches demonstrate that self-localization performance is a very
useful measure of likelihood-of-change (LoC) for change detection. In this
paper, this "detection-by-localization" scheme is studied in a novel
generalized task of object-level change detection. In our framework, a given
query image is segmented into object-level subimages (termed "scene parts"),
which are then converted to subimage-level pixel-wise LoC maps via the
detection-by-localization scheme. Our approach models a self-localization
system as a ranking function, outputting a ranked list of reference images,
without requiring relevance score. Thanks to this new setting, we can
generalize our approach to a broad class of self-localization systems. Our
ranking based self-localization model allows to fuse self-localization results
from different modalities via an unsupervised rank fusion derived from a field
of multi-modal information retrieval (MMR).
Network Recasting: A Universal Method for Network Architecture Transformation
Authors: Joonsang Yu, Sungbum Kang, Ki-Young Choi
Date: 2018-09-14 00:00:00
Tasks: GPU

This paper proposes network recasting as a general method for network architecture transformation. The primary goal of this method is to accelerate the inference process through the transformation, but there can be many other practical applications. The method is based on block-wise recasting; it recasts each source block in a pre-trained teacher network to a target block in a student network. For the recasting, a target block is trained such that its output activation approximates that of the source block. Such a block-by-block recasting in a sequential manner transforms the network architecture while preserving the accuracy. This method can be used to transform an arbitrary teacher network type to an arbitrary student network type. It can even generate a mixed-architecture network that consists of two or more types of block. The network recasting can generate a network with fewer parameters and/or activations, which reduce the inference time significantly. Naturally, it can be used for network compression by recasting a trained network into a smaller network of the same type. Our experiments show that it outperforms previous compression approaches in terms of actual speedup on a GPU.
Random Warping Series: A Random Features Method for Time-Series Embedding
Authors: Lingfei Wu, Ian En-Hsu Yen, Jin-Feng Yi, Fangli Xu, Qi Lei, Michael Witbrock
Date: 2018-09-14 00:00:00
Tasks: Clustering, Dynamic Time Warping, Time Series, Time Series Analysis

Time series data analytics has been a problem of substantial interests for
decades, and Dynamic Time Warping (DTW) has been the most widely adopted
technique to measure dissimilarity between time series. A number of
global-alignment kernels have since been proposed in the spirit of DTW to
extend its use to kernel-based estimation method such as support vector
machine. However, those kernels suffer from diagonal dominance of the Gram
matrix and a quadratic complexity w.r.t. the sample size. In this work, we
study a family of alignment-aware positive definite (p.d.) kernels, with its
feature embedding given by a distribution of \emph{Random Warping Series
(RWS)}. The proposed kernel does not suffer from the issue of diagonal
dominance while naturally enjoys a \emph{Random Features} (RF) approximation,
which reduces the computational complexity of existing DTW-based techniques
from quadratic to linear in terms of both the number and the length of
time-series. We also study the convergence of the RF approximation for the
domain of time series of unbounded length. Our extensive experiments on 16
benchmark datasets demonstrate that RWS outperforms or matches state-of-the-art
classification and clustering methods in both accuracy and computational time.
Our code and data is available at {
\url{https://github.com/IBM/RandomWarpingSeries}}.
Online Cyber-Attack Detection in Smart Grid: A Reinforcement Learning Approach
Authors: Mehmet Necip Kurt, Oyetunji Ogundijo, Chong Li, Xiaodong Wang
Date: 2018-09-14 00:00:00
Tasks: Anomaly Detection, Cyber Attack Detection, Outlier Detection, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Early detection of cyber-attacks is crucial for a safe and reliable operation
of the smart grid. In the literature, outlier detection schemes making
sample-by-sample decisions and online detection schemes requiring perfect
attack models have been proposed. In this paper, we formulate the online
attack/anomaly detection problem as a partially observable Markov decision
process (POMDP) problem and propose a universal robust online detection
algorithm using the framework of model-free reinforcement learning (RL) for
POMDPs. Numerical studies illustrate the effectiveness of the proposed RL-based
algorithm in timely and accurate detection of cyber-attacks targeting the smart
grid.
SQL-to-Text Generation with Graph-to-Sequence Model
Authors: Kun Xu, Lingfei Wu, Zhiguo Wang, Yansong Feng, Vadim Sheinin
Date: 2018-09-14 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Graph-to-Sequence, SQL-to-Text, Text Generation

Previous work approaches the SQL-to-text generation task using vanilla
Seq2Seq models, which may not fully capture the inherent graph-structured
information in SQL query. In this paper, we first introduce a strategy to
represent the SQL query as a directed graph and then employ a graph-to-sequence
model to encode the global structure information into node embeddings. This
model can effectively learn the correlation between the SQL query pattern and
its interpretation. Experimental results on the WikiSQL dataset and
Stackoverflow dataset show that our model significantly outperforms the Seq2Seq
and Tree2Seq baselines, achieving the state-of-the-art performance.
Real-Time Nonparametric Anomaly Detection in High-Dimensional Settings
Authors: Mehmet Necip Kurt, Yasin Yilmaz, Xiaodong Wang
Date: 2018-09-14 00:00:00
Tasks: Anomaly Detection, Vocal Bursts Intensity Prediction

Timely detection of abrupt anomalies is crucial for real-time monitoring and security of modern systems producing high-dimensional data. With this goal, we propose effective and scalable algorithms. Proposed algorithms are nonparametric as both the nominal and anomalous multivariate data distributions are assumed unknown. We extract useful univariate summary statistics and perform anomaly detection in a single-dimensional space. We model anomalies as persistent outliers and propose to detect them via a cumulative sum-like algorithm. In case the observed data have a low intrinsic dimensionality, we learn a submanifold in which the nominal data are embedded and evaluate whether the sequentially acquired data persistently deviate from the nominal submanifold. Further, in the general case, we learn an acceptance region for nominal data via Geometric Entropy Minimization and evaluate whether the sequentially observed data persistently fall outside the acceptance region. We provide an asymptotic lower bound and an asymptotic approximation for the average false alarm period of the proposed algorithm. Moreover, we provide a sufficient condition to asymptotically guarantee that the decision statistic of the proposed algorithm does not diverge in the absence of anomalies. Experiments illustrate the effectiveness of the proposed schemes in quick and accurate anomaly detection in high-dimensional settings.