pwc-archive/papers-with-abstracts huggingface dataset:
Are You Sure You Want To Do That? Classification with Verification
Authors: Harris Chan, Atef Chaudhury, Kevin Shen
Date: 2018-09-07 00:00:00
Tasks: General Classification, Memorization

Classification systems typically act in isolation, meaning they are required
to implicitly memorize the characteristics of all candidate classes in order to
classify. The cost of this is increased memory usage and poor sample
efficiency. We propose a model which instead verifies using reference images
during the classification process, reducing the burden of memorization. The
model uses iterative nondifferentiable queries in order to classify an image.
We demonstrate that such a model is feasible to train and can match baseline
accuracy while being more parameter efficient. However, we show that finding
the correct balance between image recognition and verification is essential to
pushing the model towards desired behavior, suggesting that a pipeline of
recognition followed by verification is a more promising approach.
Reservoir Computing based Neural Image Filters
Authors: Samiran Ganguly, Yunfei Gu, Yunkun Xie, Mircea R. Stan, Avik W. Ghosh, Nibir K. Dhar
Date: 2018-09-07 00:00:00

Clean images are an important requirement for machine vision systems to
recognize visual features correctly. However, the environment, optics,
electronics of the physical imaging systems can introduce extreme distortions
and noise in the acquired images. In this work, we explore the use of reservoir
computing, a dynamical neural network model inspired from biological systems,
in creating dynamic image filtering systems that extracts signal from noise
using inverse modeling. We discuss the possibility of implementing these
networks in hardware close to the sensors.
A Transfer-Learnable Natural Language Interface for Databases
Authors: Wenlu Wang, Yingtao Tian, Hongyu Xiong, Haixun Wang, Wei-Shinn Ku
Date: 2018-09-07 00:00:00
Tasks: Management, Natural Language Queries

Relational database management systems (RDBMSs) are powerful because they are
able to optimize and answer queries against any relational database. A natural
language interface (NLI) for a database, on the other hand, is tailored to
support that specific database. In this work, we introduce a general purpose
transfer-learnable NLI with the goal of learning one model that can be used as
NLI for any relational database. We adopt the data management principle of
separating data and its schema, but with the additional support for the
idiosyncrasy and complexity of natural languages. Specifically, we introduce an
automatic annotation mechanism that separates the schema and the data, where
the schema also covers knowledge about natural language. Furthermore, we
propose a customized sequence model that translates annotated natural language
queries to SQL statements. We show in experiments that our approach outperforms
previous NLI methods on the WikiSQL dataset and the model we learned can be
applied to another benchmark dataset OVERNIGHT without retraining.
Neural Generation of Diverse Questions using Answer Focus, Contextual and Linguistic Features
Authors: Vrindavan Harrison, Marilyn Walker
Date: 2018-09-07 00:00:00
Proceeding: WS 2018 11
Tasks: coreference-resolution, Coreference Resolution, Decoder, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Question Generation, Question-Generation, Sentence, Sentence Embedding, Sentence-Embedding

Question Generation is the task of automatically creating questions from
textual input. In this work we present a new Attentional Encoder--Decoder
Recurrent Neural Network model for automatic question generation. Our model
incorporates linguistic features and an additional sentence embedding to
capture meaning at both sentence and word levels. The linguistic features are
designed to capture information related to named entity recognition, word case,
and entity coreference resolution. In addition our model uses a copying
mechanism and a special answer signal that enables generation of numerous
diverse questions on a given sentence. Our model achieves state of the art
results of 19.98 Bleu_4 on a benchmark Question Generation dataset,
outperforming all previously published results by a significant margin. A human
evaluation also shows that these added features improve the quality of the
generated questions.
Constrained Generation of Semantically Valid Graphs via Regularizing Variational Autoencoders
Authors: Tengfei Ma, Jie Chen, Cao Xiao
Date: 2018-09-07 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Decoder, Time Series, Time Series Analysis, valid

Deep generative models have achieved remarkable success in various data
domains, including images, time series, and natural languages. There remain,
however, substantial challenges for combinatorial structures, including graphs.
One of the key challenges lies in the difficulty of ensuring semantic validity
in context. For examples, in molecular graphs, the number of bonding-electron
pairs must not exceed the valence of an atom; whereas in protein interaction
networks, two proteins may be connected only when they belong to the same or
correlated gene ontology terms. These constraints are not easy to be
incorporated into a generative model. In this work, we propose a regularization
framework for variational autoencoders as a step toward semantic validity. We
focus on the matrix representation of graphs and formulate penalty terms that
regularize the output distribution of the decoder to encourage the satisfaction
of validity constraints. Experimental results confirm a much higher likelihood
of sampling valid graphs in our approach, compared with others reported in the
literature.
Revisiting Inaccuracies of Time Series Averaging under Dynamic Time Warping
Authors: Brijnesh Jain
Date: 2018-09-07 00:00:00
Tasks: Dynamic Time Warping, Time Series, Time Series Analysis, Time Series Averaging

This article revisits an analysis on inaccuracies of time series averaging
under dynamic time warping conducted by \cite{Niennattrakul2007}. The authors
presented a correctness-criterion and introduced drift-outs of averages from
clusters. They claimed that averages are inaccurate if they are incorrect or
drift-outs. Furthermore, they conjectured that such inaccuracies are caused by
the lack of triangle inequality. We show that a rectified version of the
correctness-criterion is unsatisfiable and that the concept of drift-out is
geometrically and operationally inconclusive. Satisfying the triangle
inequality is insufficient to achieve correctness and unnecessary to overcome
the drift-out phenomenon. We place the concept of drift-out on a principled
basis and show that sample means as global minimizers of a Fr\'echet function
never drift out. The adjusted drift-out is a way to test to which extent an
approximation is coherent. Empirical results show that solutions obtained by
the state-of-the-art methods SSG and DBA are incoherent approximations of a
sample mean in over a third of all trials.
Action-conditional Sequence Modeling for Recommendation
Authors: Elena Smirnova
Date: 2018-09-07 00:00:00
Tasks: Collaborative Filtering, Recommendation Systems

In many online applications interactions between a user and a web-service are
organized in a sequential way, e.g., user browsing an e-commerce website. In
this setting, recommendation system acts throughout user navigation by showing
items. Previous works have addressed this recommendation setup through the task
of predicting the next item user will interact with. In particular, Recurrent
Neural Networks (RNNs) has been shown to achieve substantial improvements over
collaborative filtering baselines. In this paper, we consider interactions
triggered by the recommendations of deployed recommender system in addition to
browsing behavior. Indeed, it is reported that in online services interactions
with recommendations represent up to 30\% of total interactions. Moreover, in
practice, recommender system can greatly influence user behavior by promoting
specific items. In this paper, we extend the RNN modeling framework by taking
into account user interaction with recommended items. We propose and evaluate
RNN architectures that consist of the recommendation action module and the
state-action fusion module. Using real-world large-scale datasets we
demonstrate improved performance on the next item prediction task compared to
the baselines.
Neural Allocentric Intuitive Physics Prediction from Real Videos
Authors: Zhihua Wang, Stefano Rosa, Yishu Miao, Zihang Lai, Linhai Xie, Andrew Markham, Niki Trigoni
Date: 2018-09-07 00:00:00
Tasks: Prediction

Humans are able to make rich predictions about the future dynamics of
physical objects from a glance. On the other hand, most existing computer
vision approaches require strong assumptions about the underlying system,
ad-hoc modeling, or annotated datasets, to carry out even simple predictions.
To tackle this gap, we propose a new perspective on the problem of learning
intuitive physics that is inspired by the spatial memory representation of
objects and spaces in human brains, in particular the co-existence of
egocentric and allocentric spatial representations. We present a generic
framework that learns a layered representation of the physical world, using a
cascade of invertible modules. In this framework, real images are first
converted to a synthetic domain representation that reduces complexity arising
from lighting and texture. Then, an allocentric viewpoint transformer removes
viewpoint complexity by projecting images to a canonical view. Finally, a novel
Recurrent Latent Variation Network (RLVN) architecture learns the dynamics of
the objects interacting with the environment and predicts future motion,
leveraging the availability of unlimited synthetic simulations. Predicted
frames are then projected back to the original camera view and translated back
to the real world domain. Experimental results show the ability of the
framework to consistently and accurately predict several frames in the future
and the ability to adapt to real images.
Feature Learning for Meta-Paths in Knowledge Graphs
Authors: Sebastian Bischoff
Date: 2018-09-07 00:00:00
Tasks: Clustering, Graph Embedding, Knowledge Graphs, Link Prediction

In this thesis, we study the problem of feature learning on heterogeneous
knowledge graphs. These features can be used to perform tasks such as link
prediction, classification and clustering on graphs. Knowledge graphs provide
rich semantics encoded in the edge and node types. Meta-paths consist of these
types and abstract paths in the graph. Until now, meta-paths can only be used
as categorical features with high redundancy and are therefore unsuitable for
machine learning models. We propose meta-path embeddings to solve this problem
by learning semantical and compact vector representations of them. Current
graph embedding methods only embed nodes and edge types and therefore miss
semantics encoded in the combination of them. Our method embeds meta-paths
using the skipgram model with an extension to deal with the redundancy and high
amount of meta-paths in big knowledge graphs. We critically evaluate our
embedding approach by predicting links on Wikidata. The experiments indicate
that we learn a sensible embedding of the meta-paths but can improve it
further.
Distributed dynamic modeling and monitoring for large-scale industrial processes under closed-loop control
Authors: Wenqing Li, Chunhui Zhao, Biao Huang
Date: 2018-09-07 00:00:00

For large-scale industrial processes under closed-loop control, process
dynamics directly resulting from control action are typical characteristics and
may show different behaviors between real faults and normal changes of
operating conditions. However, conventional distributed monitoring approaches
do not consider the closed-loop control mechanism and only explore static
characteristics, which thus are incapable of distinguishing between real
process faults and nominal changes of operating conditions, leading to
unnecessary alarms. In this regard, this paper proposes a distributed
monitoring method for closed-loop industrial processes by concurrently
exploring static and dynamic characteristics. First, the large-scale
closed-loop process is decomposed into several subsystems by developing a
sparse slow feature analysis (SSFA) algorithm which capture changes of both
static and dynamic information. Second, distributed models are developed to
separately capture static and dynamic characteristics from the local and global
aspects. Based on the distributed monitoring system, a two-level monitoring
strategy is proposed to check different influences on process characteristics
resulting from changes of the operating conditions and control action, and thus
the two changes can be well distinguished from each other. Case studies are
conducted based on both benchmark data and real industrial process data to
illustrate the effectiveness of the proposed method.
Neural-Augmented Static Analysis of Android Communication
Authors: Jinman Zhao, Aws Albarghouthi, Vaibhav Rastogi, Somesh Jha, Damien Octeau
Date: 2018-09-11 00:00:00

We address the problem of discovering communication links between
applications in the popular Android mobile operating system, an important
problem for security and privacy in Android. Any scalable static analysis in
this complex setting is bound to produce an excessive amount of
false-positives, rendering it impractical. To improve precision, we propose to
augment static analysis with a trained neural-network model that estimates the
probability that a communication link truly exists. We describe a
neural-network architecture that encodes abstractions of communicating objects
in two applications and estimates the probability with which a link indeed
exists. At the heart of our architecture are type-directed encoders (TDE), a
general framework for elegantly constructing encoders of a compound data type
by recursively composing encoders for its constituent types. We evaluate our
approach on a large corpus of Android applications, and demonstrate that it
achieves very high accuracy. Further, we conduct thorough interpretability
studies to understand the internals of the learned neural networks.
AWE: Asymmetric Word Embedding for Textual Entailment
Authors: Tengfei Ma, Chiamin Wu, Cao Xiao, Jimeng Sun
Date: 2018-09-11 00:00:00
Tasks: Natural Language Inference, Paraphrase Identification, Relation, Sentence, Sentence Similarity, Word Embeddings

Textual entailment is a fundamental task in natural language processing. It
refers to the directional relation between text fragments such that the
"premise" can infer "hypothesis". In recent years deep learning methods have
achieved great success in this task. Many of them have considered the
inter-sentence word-word interactions between the premise-hypothesis pairs,
however, few of them considered the "asymmetry" of these interactions.
Different from paraphrase identification or sentence similarity evaluation,
textual entailment is essentially determining a directional (asymmetric)
relation between the premise and the hypothesis. In this paper, we propose a
simple but effective way to enhance existing textual entailment algorithms by
using asymmetric word embeddings. Experimental results on SciTail and SNLI
datasets show that the learned asymmetric word embeddings could significantly
improve the word-word interaction based textual entailment models. It is
noteworthy that the proposed AWE-DeIsTe model can get 2.1% accuracy improvement
over prior state-of-the-art on SciTail.
Solving Imperfect-Information Games via Discounted Regret Minimization
Authors: Noam Brown, Tuomas Sandholm
Date: 2018-09-11 00:00:00
Tasks: counterfactual

Counterfactual regret minimization (CFR) is a family of iterative algorithms
that are the most popular and, in practice, fastest approach to approximately
solving large imperfect-information games. In this paper we introduce novel CFR
variants that 1) discount regrets from earlier iterations in various ways (in
some cases differently for positive and negative regrets), 2) reweight
iterations in various ways to obtain the output strategies, 3) use a
non-standard regret minimizer and/or 4) leverage "optimistic regret matching".
They lead to dramatically improved performance in many settings. For one, we
introduce a variant that outperforms CFR+, the prior state-of-the-art
algorithm, in every game tested, including large-scale realistic settings. CFR+
is a formidable benchmark: no other algorithm has been able to outperform it.
Finally, we show that, unlike CFR+, many of the important new variants are
compatible with modern imperfect-information-game pruning techniques and one is
also compatible with sampling in the game tree.
Can LSTM Learn to Capture Agreement? The Case of Basque
Authors: Shauli Ravfogel, Francis M. Tyers, Yoav Goldberg
Date: 2018-09-11 00:00:00
Proceeding: WS 2018 11
Tasks: Diagnostic, Prediction, Sentence

Sequential neural networks models are powerful tools in a variety of Natural
Language Processing (NLP) tasks. The sequential nature of these models raises
the questions: to what extent can these models implicitly learn hierarchical
structures typical to human language, and what kind of grammatical phenomena
can they acquire?
  We focus on the task of agreement prediction in Basque, as a case study for a
task that requires implicit understanding of sentence structure and the
acquisition of a complex but consistent morphological system. Analyzing
experimental results from two syntactic prediction tasks -- verb number
prediction and suffix recovery -- we find that sequential models perform worse
on agreement prediction in Basque than one might expect on the basis of a
previous agreement prediction work in English. Tentative findings based on
diagnostic classifiers suggest the network makes use of local heuristics as a
proxy for the hierarchical structure of the sentence. We propose the Basque
agreement prediction task as challenging benchmark for models that attempt to
learn regularities in human language.
Training and Prediction Data Discrepancies: Challenges of Text Classification with Noisy, Historical Data
Authors: Emilia Apostolova, R. Andrew Kreek
Date: 2018-09-11 00:00:00
Proceeding: WS 2018 11
Tasks: BIG-bench Machine Learning, General Classification, Prediction, text-classification, Text Classification

Industry datasets used for text classification are rarely created for that
purpose. In most cases, the data and target predictions are a by-product of
accumulated historical data, typically fraught with noise, present in both the
text-based document, as well as in the targeted labels. In this work, we
address the question of how well performance metrics computed on noisy,
historical data reflect the performance on the intended future machine learning
model input. The results demonstrate the utility of dirty training datasets
used to build prediction models for cleaner (and different) prediction inputs.
Evaluating Semantic Rationality of a Sentence: A Sememe-Word-Matching Neural Network based on HowNet
Authors: Shu Liu, Jingjing Xu, Xuancheng Ren, Xu sun
Date: 2018-09-11 00:00:00
Tasks: Language Modeling, Language Modelling, Sentence

Automatic evaluation of semantic rationality is an important yet challenging
task, and current automatic techniques cannot well identify whether a sentence
is semantically rational. The methods based on the language model do not
measure the sentence by rationality but by commonness. The methods based on the
similarity with human written sentences will fail if human-written references
are not available. In this paper, we propose a novel model called
Sememe-Word-Matching Neural Network (SWM-NN) to tackle semantic rationality
evaluation by taking advantage of sememe knowledge base HowNet. The advantage
is that our model can utilize a proper combination of sememes to represent the
fine-grained semantic meanings of a word within the specific contexts. We use
the fine-grained semantic representation to help the model learn the semantic
dependency among words. To evaluate the effectiveness of the proposed model, we
build a large-scale rationality evaluation dataset. Experimental results on
this dataset show that the proposed model outperforms the competitive baselines
with a 5.4\% improvement in accuracy.
Efficient Road Lane Marking Detection with Deep Learning
Authors: Ping-Rong Chen, Shao-Yuan Lo, Hsueh-Ming Hang, Sheng-Wei Chan, Jing-Jhih Lin
Date: 2018-09-11 00:00:00
Tasks: Deep Learning, Lane Detection, Real-Time Semantic Segmentation, Semantic Segmentation

Lane mark detection is an important element in the road scene analysis for
Advanced Driver Assistant System (ADAS). Limited by the onboard computing
power, it is still a challenge to reduce system complexity and maintain high
accuracy at the same time. In this paper, we propose a Lane Marking Detector
(LMD) using a deep convolutional neural network to extract robust lane marking
features. To improve its performance with a target of lower complexity, the
dilated convolution is adopted. A shallower and thinner structure is designed
to decrease the computational cost. Moreover, we also design post-processing
algorithms to construct 3rd-order polynomial models to fit into the curved
lanes. Our system shows promising results on the captured road scenes.
Assessing Composition in Sentence Vector Representations
Authors: Allyson Ettinger, Ahmed Elgohary, Colin Phillips, Philip Resnik
Date: 2018-09-11 00:00:00
Proceeding: COLING 2018 8
Tasks: Sentence

An important component of achieving language understanding is mastering the
composition of sentence meaning, but an immediate challenge to solving this
problem is the opacity of sentence vector representations produced by current
neural sentence composition models. We present a method to address this
challenge, developing tasks that directly target compositional meaning
information in sentence vector representations with a high degree of precision
and control. To enable the creation of these controlled tasks, we introduce a
specialized sentence generation system that produces large, annotated sentence
sets meeting specified syntactic, semantic and lexical constraints. We describe
the details of the method and generation system, and then present results of
experiments applying our method to probe for compositional information in
embeddings from a number of existing sentence composition models. We find that
the method is able to extract useful information about the differing capacities
of these models, and we discuss the implications of our results with respect to
these systems' capturing of sentence information. We make available for public
use the datasets used for these experiments, as well as the generation system.
Efficient Statistics, in High Dimensions, from Truncated Samples
Authors: Constantinos Daskalakis, Themis Gouleakis, Christos Tzamos, Manolis Zampetakis
Date: 2018-09-11 00:00:00
Tasks: Vocal Bursts Intensity Prediction

We provide an efficient algorithm for the classical problem, going back to Galton, Pearson, and Fisher, of estimating, with arbitrary accuracy the parameters of a multivariate normal distribution from truncated samples. Truncated samples from a $d$-variate normal ${\cal N}(\mathbf{\mu},\mathbf{\Sigma})$ means a samples is only revealed if it falls in some subset $S \subseteq \mathbb{R}^d$; otherwise the samples are hidden and their count in proportion to the revealed samples is also hidden. We show that the mean $\mathbf{\mu}$ and covariance matrix $\mathbf{\Sigma}$ can be estimated with arbitrary accuracy in polynomial-time, as long as we have oracle access to $S$, and $S$ has non-trivial measure under the unknown $d$-variate normal distribution. Additionally we show that without oracle access to $S$, any non-trivial estimation is impossible.