pwc-archive/papers-with-abstracts huggingface dataset:
A Spiking Neural Network with Local Learning Rules Derived From Nonnegative Similarity Matching
Authors: Cengiz Pehlevan
Date: 2019-02-04 00:00:00

The design and analysis of spiking neural network algorithms will be
accelerated by the advent of new theoretical approaches. In an attempt at such
approach, we provide a principled derivation of a spiking algorithm for
unsupervised learning, starting from the nonnegative similarity matching cost
function. The resulting network consists of integrate-and-fire units and
exhibits local learning rules, making it biologically plausible and also
suitable for neuromorphic hardware. We show in simulations that the algorithm
can perform sparse feature extraction and manifold learning, two tasks which
can be formulated as nonnegative similarity matching problems.
Dictionary learning approach to monitoring of wind turbine drivetrain bearings
Authors: Sergio Martin-del-Campo, Fredrik Sandin, Daniel Strömbergsson
Date: 2019-02-04 00:00:00
Tasks: Anomaly Detection, Dictionary Learning

Condition monitoring is central to the efficient operation of wind farms due to the challenging operating conditions, rapid technology development and large number of aging wind turbines. In particular, predictive maintenance planning requires the early detection of faults with few false positives. Achieving this type of detection is a challenging problem due to the complex and weak signatures of some faults, particularly the faults that occur in some of the drivetrain bearings. Here, we investigate recently proposed condition monitoring methods based on unsupervised dictionary learning using vibration data recorded over 46 months under typical industrial operations. Thus, we contribute novel test results and real world data that are made publicly available. The results of former studies addressing condition monitoring tasks using dictionary learning indicate that unsupervised feature learning is useful for diagnosis and anomaly detection purposes. However, these studies are based on small sets of labeled data from test rigs operating under controlled conditions that focus on classification tasks, which are useful for quantitative method comparisons but gives little insight into how useful these approaches are in practice. In this study, dictionaries are learned from gearbox vibrations in six different turbines, and the dictionaries are subsequently propagated over a few years of monitoring data when faults are known to occur. We perform the experiment using two different sparse coding algorithms to investigate if the algorithm selected affects the features of abnormal conditions. We calculate the dictionary distance between the initial and propagated dictionaries and find the time periods of abnormal dictionary adaptation starting six months before a drivetrain bearing replacement and one year before the resulting gearbox replacement.
Learning to Solve Large-Scale Security-Constrained Unit Commitment Problems
Authors: Alinson S. Xavier, Feng Qiu, Shabbir Ahmed
Date: 2019-02-04 00:00:00

Security-Constrained Unit Commitment (SCUC) is a fundamental problem in power systems and electricity markets. In practical settings, SCUC is repeatedly solved via Mixed-Integer Linear Programming, sometimes multiple times per day, with only minor changes in input data. In this work, we propose a number of machine learning (ML) techniques to effectively extract information from previously solved instances in order to significantly improve the computational performance of MIP solvers when solving similar instances in the future. Based on statistical data, we predict redundant constraints in the formulation, good initial feasible solutions and affine subspaces where the optimal solution is likely to lie, leading to significant reduction in problem size. Computational results on a diverse set of realistic and large-scale instances show that, using the proposed techniques, SCUC can be solved on average 4.3x faster with optimality guarantees, and 10.2x faster without optimality guarantees, but with no observed reduction in solution quality. Out-of-distribution experiments provides evidence that the method is somewhat robust against dataset shift.
Partial Fingerprint Detection Using Core Point Location
Authors: Wajih Ullah Baig, Adeel Ejaz, Umar Munir, Kashif Sardar
Date: 2019-02-04 00:00:00

In Biometric identification, fingerprints based identification has been the
widely accepted mechanism. Automated fingerprints identification/verification
techniques are widely adopted in many civilian and forensic applications. In
forensic applications fingerprints are usually incomplete, broken, unclear or
degraded which are known as partial fingerprints. Fingerprints
identification/verification largely suffer from the problem of handling partial
fingerprints. In this paper a novel and simple approach is presented for
detecting partial fingerprints using core point location. Our techniques is
particularly useful during the acquisition stage as to determine whether a user
needs to re-align the finger to ensure a complete capture of fingerprint
area.This technique is tested on FVC-2002 DB1A. The results are very accurate
which are presented in the Results sections.
Comparison of brain connectomes using geodesic distance on manifold:a twin study
Authors: A. Yamin, M. Dayan, L. Squarcina, P. Brambilla, V. Murino, V. Diwadkar, D. Sona
Date: 2019-02-04 00:00:00

fMRI is a unique non-invasive approach for understanding the functional
organization of the human brain, and task-based fMRI promotes identification of
functionally relevant brain regions associated with a given task. Here, we use
fMRI (using the Poffenberger Paradigm) data collected in mono- and dizygotic
twin pairs to propose a novel approach for assessing similarity in functional
networks. In particular, we compared network similarity between pairs of twins
in task-relevant and task-orthogonal networks. The proposed method measures the
similarity between functional networks using a geodesic distance between graph
Laplacians. With method we show that networks are more similar in monozygotic
twins compared to dizygotic twins. Furthermore, the similarity in monozygotic
twins is higher for task-relevant, than task-orthogonal networks.
A Multi-Resolution Word Embedding for Document Retrieval from Large Unstructured Knowledge Bases
Authors: Tolgahan Cakaloglu, Xiaowei Xu
Date: 2019-02-02 00:00:00
Tasks: Information Retrieval, Open-Domain Question Answering, Question Answering, Retrieval, Word Embeddings

Deep language models learning a hierarchical representation proved to be a powerful tool for natural language processing, text mining and information retrieval. However, representations that perform well for retrieval must capture semantic meaning at different levels of abstraction or context-scopes. In this paper, we propose a new method to generate multi-resolution word embeddings that represent documents at multiple resolutions in terms of context-scopes. In order to investigate its performance,we use the Stanford Question Answering Dataset (SQuAD) and the Question Answering by Search And Reading (QUASAR) in an open-domain question-answering setting, where the first task is to find documents useful for answering a given question. To this end, we first compare the quality of various text-embedding methods for retrieval performance and give an extensive empirical comparison with the performance of various non-augmented base embeddings with and without multi-resolution representation. We argue that multi-resolution word embeddings are consistently superior to the original counterparts and deep residual neural models specifically trained for retrieval purposes can yield further significant gains when they are used for augmenting those embeddings.
Exploring attention mechanism for acoustic-based classification of speech utterances into system-directed and non-system-directed
Authors: Atta Norouzian, Bogdan Mazoure, Dermot Connolly, Daniel Willett
Date: 2019-02-01 00:00:00
Tasks: General Classification

Voice controlled virtual assistants (VAs) are now available in smartphones,
cars, and standalone devices in homes. In most cases, the user needs to first
"wake-up" the VA by saying a particular word/phrase every time he or she wants
the VA to do something. Eliminating the need for saying the wake-up word for
every interaction could improve the user experience. This would require the VA
to have the capability to detect the speech that is being directed at it and
respond accordingly. In other words, the challenge is to distinguish between
system-directed and non-system-directed speech utterances. In this paper, we
present a number of neural network architectures for tackling this
classification problem based on using only acoustic features. These
architectures are based on using convolutional, recurrent and feed-forward
layers. In addition, we investigate the use of an attention mechanism applied
to the output of the convolutional and the recurrent layers. It is shown that
incorporating the proposed attention mechanism into the models always leads to
significant improvement in classification accuracy. The best model achieved
equal error rates of 16.25 and 15.62 percents on two distinct realistic
datasets.
Automatic Hyperparameter Tuning Method for Local Outlier Factor, with Applications to Anomaly Detection
Authors: Zekun Xu, Deovrat Kakde, Arin Chaudhuri
Date: 2019-02-01 00:00:00
Tasks: Anomaly Detection

In recent years, there have been many practical applications of anomaly
detection such as in predictive maintenance, detection of credit fraud, network
intrusion, and system failure. The goal of anomaly detection is to identify in
the test data anomalous behaviors that are either rare or unseen in the
training data. This is a common goal in predictive maintenance, which aims to
forecast the imminent faults of an appliance given abundant samples of normal
behaviors. Local outlier factor (LOF) is one of the state-of-the-art models
used for anomaly detection, but the predictive performance of LOF depends
greatly on the selection of hyperparameters. In this paper, we propose a novel,
heuristic methodology to tune the hyperparameters in LOF. A tuned LOF model
that uses the proposed method shows good predictive performance in both
simulations and real data sets.
Multilevel MIMO Detection with Deep Learning
Authors: Vincent Corlay, Joseph J. Boutros, Philippe Ciblat, Loïc Brunel
Date: 2018-12-04 00:00:00
Tasks: Deep Learning

A quasi-static flat multiple-antenna channel is considered. We show how real
multilevel modulation symbols can be detected via deep neural networks. A
multi-plateau sigmoid function is introduced. Then, after showing the DNN
architecture for detection, we propose a twin-network neural structure. Batch
size and training statistics for efficient learning are investigated.
Near-Maximum-Likelihood performance with a relatively reasonable number of
parameters is achieved.
HAHE: Hierarchical Attentive Heterogeneous Information Network Embedding
Authors: Sheng Zhou, Jiajun Bu, Xin Wang, Jia-Wei Chen, Can Wang
Date: 2019-01-31 00:00:00
Tasks: Network Embedding

Heterogeneous information network (HIN) embedding has recently attracted much attention due to its effectiveness in dealing with the complex heterogeneous data. Meta path, which connects different object types with various semantic meanings, is widely used by existing HIN embedding works. However, several challenges have not been addressed so far. First, different meta paths convey different semantic meanings, while existing works assume that all nodes share same weights for meta paths and ignore the personalized preferences of different nodes on different meta paths. Second, given a meta path, nodes in HIN are connected by path instances while existing works fail to fully explore the differences between path instances that reflect nodes' preferences in the semantic space. rTo tackle the above challenges, we propose aHierarchical Attentive Heterogeneous information network Embedding (HAHE) model to capture the personalized preferences on meta paths and path instances in each semantic space. As path instances are based on a particular meta path, a hierarchical attention mechanism is naturally utilized to model the personalized preference on meta paths and path instances. Extensive experiments on several real-world datasets show that our proposed \model model significantly outperforms the state-of-the-art methods in terms of various data mining tasks.
Reasoning about Quantities in Natural Language
Authors: Subhro Roy, Tim Vieira, Dan Roth
Date: 2015-01-01 00:00:00
Proceeding: TACL 2015 1
Tasks: Math, Natural Language Inference, Natural Language Understanding

Little work from the Natural Language Processing community has targeted the role of quantities in Natural Language Understanding. This paper takes some key steps towards facilitating reasoning about quantities expressed in natural language. We investigate two different tasks of numerical reasoning. First, we consider Quantity Entailment, a new task formulated to understand the role of quantities in general textual inference tasks. Second, we consider the problem of automatically understanding and solving elementary school math word problems. In order to address these quantitative reasoning problems we first develop a computational approach which we show to successfully recognize and normalize textual expressions of quantities. We then use these capabilities to further develop algorithms to assist reasoning in the context of the aforementioned tasks.
Cross-Document Co-Reference Resolution using Sample-Based Clustering with Knowledge Enrichment
Authors: Sourav Dutta, Gerhard Weikum
Date: 2015-01-01 00:00:00
Proceeding: TACL 2015 1
Tasks: Clustering, Coreference Resolution, graph partitioning, Question Answering

Identifying and linking named entities across information sources is the basis of knowledge acquisition and at the heart of Web search, recommendations, and analytics. An important problem in this context is cross-document co-reference resolution (CCR): computing equivalence classes of textual mentions denoting the same entity, within and across documents. Prior methods employ ranking, clustering, or probabilistic graphical models using syntactic features and distant features from knowledge bases. However, these methods exhibit limitations regarding run-time and robustness. This paper presents the CROCS framework for unsupervised CCR, improving the state of the art in two ways. First, we extend the way knowledge bases are harnessed, by constructing a notion of semantic summaries for intra-document co-reference chains using co-occurring entity mentions belonging to different chains. Second, we reduce the computational cost by a new algorithm that embeds sample-based bisection, using spectral clustering or graph partitioning, in a hierarchical clustering process. This allows scaling up CCR to large corpora. Experiments with three datasets show significant gains in output quality, compared to the best prior methods, and the run-time efficiency of CROCS.
Efficient Inference and Structured Learning for Semantic Role Labeling
Authors: Oscar T{\"a}ckstr{\"o}m, Kuzman Ganchev, Dipanjan Das
Date: 2015-01-01 00:00:00
Proceeding: TACL 2015 1
Tasks: Semantic Parsing, Semantic Role Labeling

We present a dynamic programming algorithm for efficient constrained inference in semantic role labeling. The algorithm tractably captures a majority of the structural constraints examined by prior work in this area, which has resorted to either approximate methods or off-the-shelf integer linear programming solvers. In addition, it allows training a globally-normalized log-linear model with respect to constrained conditional likelihood. We show that the dynamic program is several times faster than an off-the-shelf integer linear programming solver, while reaching the same solution. Furthermore, we show that our structured model results in significant improvements over its local counterpart, achieving state-of-the-art results on both PropBank- and FrameNet-annotated corpora.
Sprite: Generalizing Topic Models with Structured Priors
Authors: Michael J. Paul, Mark Dredze
Date: 2015-01-01 00:00:00
Proceeding: TACL 2015 1
Tasks: Topic Models

We introduce Sprite, a family of topic models that incorporates structure into model priors as a function of underlying components. The structured priors can be constrained to model topic hierarchies, factorizations, correlations, and supervision, allowing Sprite to be tailored to particular settings. We demonstrate this flexibility by constructing a Sprite-based model to jointly infer topic hierarchies and author perspective, which we apply to corpora of political debates and online reviews. We show that the model learns intuitive topics, outperforming several other topic models at predictive tasks.
A Sense-Topic Model for Word Sense Induction with Unsupervised Data Enrichment
Authors: Jing Wang, Mohit Bansal, Kevin Gimpel, Brian D. Ziebart, Clement T. Yu
Date: 2015-01-01 00:00:00
Proceeding: TACL 2015 1
Tasks: Topic Models, Word Embeddings, Word Sense Induction

Word sense induction (WSI) seeks to automatically discover the senses of a word in a corpus via unsupervised methods. We propose a sense-topic model for WSI, which treats sense and topic as two separate latent variables to be inferred jointly. Topics are informed by the entire document, while senses are informed by the local context surrounding the ambiguous word. We also discuss unsupervised ways of enriching the original corpus in order to improve model performance, including using neural word embeddings and external corpora to expand the context of each data instance. We demonstrate significant improvements over the previous state-of-the-art, achieving the best results reported to date on the SemEval-2013 WSI task.
Which Step Do I Take First? Troubleshooting with Bayesian Models
Authors: Annie Louis, Mirella Lapata
Date: 2015-01-01 00:00:00
Proceeding: TACL 2015 1
Tasks: Community Question Answering, Information Retrieval, Language Acquisition, Navigate, Question Answering

Online discussion forums and community question-answering websites provide one of the primary avenues for online users to share information. In this paper, we propose text mining techniques which aid users navigate troubleshooting-oriented data such as questions asked on forums and their suggested solutions. We introduce Bayesian generative models of the troubleshooting data and apply them to two interrelated tasks: (a) predicting the complexity of the solutions (e.g., plugging a keyboard in the computer is easier compared to installing a special driver) and (b) presenting them in a ranked order from least to most complex. Experimental results show that our models are on par with human performance on these tasks, while outperforming baselines based on solution length or readability.
Gappy Pattern Matching on GPUs for On-Demand Extraction of Hierarchical Translation Grammars
Authors: Hua He, Jimmy Lin, Adam Lopez
Date: 2015-01-01 00:00:00
Proceeding: TACL 2015 1
Tasks: CPU, GPU, Machine Translation, Translation

Grammars for machine translation can be materialized on demand by finding source phrases in an indexed parallel corpus and extracting their translations. This approach is limited in practical applications by the computational expense of online lookup and extraction. For phrase-based models, recent work has shown that on-demand grammar extraction can be greatly accelerated by parallelization on general purpose graphics processing units (GPUs), but these algorithms do not work for hierarchical models, which require matching patterns that contain gaps. We address this limitation by presenting a novel GPU algorithm for on-demand hierarchical grammar extraction that is at least an order of magnitude faster than a comparable CPU algorithm when processing large batches of sentences. In terms of end-to-end translation, with decoding on the CPU, we increase throughput by roughly two thirds on a standard MT evaluation dataset. The GPU necessary to achieve these improvements increases the cost of a server by about a third. We believe that GPU-based extraction of hierarchical grammars is an attractive proposition, particularly for MT applications that demand high throughput.
A Bayesian Model of Grounded Color Semantics
Authors: Brian McMahan, Matthew Stone
Date: 2015-01-01 00:00:00
Proceeding: TACL 2015 1
Tasks: model

Natural language meanings allow speakers to encode important real-world distinctions, but corpora of grounded language use also reveal that speakers categorize the world in different ways and describe situations with different terminology. To learn meanings from data, we therefore need to link underlying representations of meaning to models of speaker judgment and speaker choice. This paper describes a new approach to this problem: we model variability through uncertainty in categorization boundaries and distributions over preferred vocabulary. We apply the approach to a large data set of color descriptions, where statistical evaluation documents its accuracy. The results are available as a Lexicon of Uncertain Color Standards (LUX), which supports future efforts in grounded language understanding and generation by probabilistically mapping 829 English color descriptions to potentially context-sensitive regions in HSV color space.
Unsupervised Declarative Knowledge Induction for Constraint-Based Learning of Information Structure in Scientific Documents
Authors: Yufan Guo, Roi Reichart, Anna Korhonen
Date: 2015-01-01 00:00:00
Proceeding: TACL 2015 1
Tasks: Topic Models

Inferring the information structure of scientific documents is useful for many NLP applications. Existing approaches to this task require substantial human effort. We propose a framework for constraint learning that reduces human involvement considerably. Our model uses topic models to identify latent topics and their key linguistic features in input documents, induces constraints from this information and maps sentences to their dominant information structure categories through a constrained unsupervised model. When the induced constraints are combined with a fully unsupervised model, the resulting model challenges existing lightly supervised feature-based models as well as unsupervised models that use manually constructed declarative knowledge. Our results demonstrate that useful declarative knowledge can be learned from data with very limited human involvement.
Entity Disambiguation with Web Links
Authors: Andrew Chisholm, Ben Hachey
Date: 2015-01-01 00:00:00
Proceeding: TACL 2015 1
Tasks: Entity Disambiguation, Entity Linking, Knowledge Base Population

Entity disambiguation with Wikipedia relies on structured information from redirect pages, article text, inter-article links, and categories. We explore whether web links can replace a curated encyclopaedia, obtaining entity prior, name, context, and coherence models from a corpus of web pages with links to Wikipedia. Experiments compare web link models to Wikipedia models on well-known conll and tac data sets. Results show that using 34 million web links approaches Wikipedia performance. Combining web link and Wikipedia models produces the best-known disambiguation accuracy of 88.7 on standard newswire test data.