pwc-archive/papers-with-abstracts huggingface dataset:
Unsupervised Induction of Contingent Event Pairs from Film Scenes
Authors: Zhichao Hu, Elahe Rahimtoroghi, Larissa Munishkina, Reid Swanson, Marilyn A. Walker
Date: 2017-08-30 00:00:00
Proceeding: EMNLP 2013 10
Tasks: Common Sense Reasoning

Human engagement in narrative is partially driven by reasoning about
discourse relations between narrative events, and the expectations about what
is likely to happen next that results from such reasoning. Researchers in NLP
have tackled modeling such expectations from a range of perspectives, including
treating it as the inference of the contingent discourse relation, or as a type
of common-sense causal reasoning. Our approach is to model likelihood between
events by drawing on several of these lines of previous work. We implement and
evaluate different unsupervised methods for learning event pairs that are
likely to be contingent on one another. We refine event pairs that we learn
from a corpus of film scene descriptions utilizing web search counts, and
evaluate our results by collecting human judgments of contingency. Our results
indicate that the use of web search counts increases the average accuracy of
our best method to 85.64% over a baseline of 50%, as compared to an average
accuracy of 75.15% without web search.
Inferring Narrative Causality between Event Pairs in Films
Authors: Zhichao Hu, Marilyn A. Walker
Date: 2017-08-30 00:00:00
Proceeding: WS 2017 8
Tasks: Relation

To understand narrative, humans draw inferences about the underlying
relations between narrative events. Cognitive theories of narrative
understanding define these inferences as four different types of causality,
that include pairs of events A, B where A physically causes B (X drop, X
break), to pairs of events where A causes emotional state B (Y saw X, Y felt
fear). Previous work on learning narrative relations from text has either
focused on "strict" physical causality, or has been vague about what relation
is being learned. This paper learns pairs of causal events from a corpus of
film scene descriptions which are action rich and tend to be told in
chronological order. We show that event pairs induced using our methods are of
high quality and are judged to have a stronger causal relation than event pairs
from Rel-grams.
Automatically Generating Commit Messages from Diffs using Neural Machine Translation
Authors: Siyuan Jiang, Ameer Armaly, Collin McMillan
Date: 2017-08-30 00:00:00
Tasks: Machine Translation, NMT, Translation

Commit messages are a valuable resource in comprehension of software
evolution, since they provide a record of changes such as feature additions and
bug repairs. Unfortunately, programmers often neglect to write good commit
messages. Different techniques have been proposed to help programmers by
automatically writing these messages. These techniques are effective at
describing what changed, but are often verbose and lack context for
understanding the rationale behind a change. In contrast, humans write messages
that are short and summarize the high level rationale. In this paper, we adapt
Neural Machine Translation (NMT) to automatically "translate" diffs into commit
messages. We trained an NMT algorithm using a corpus of diffs and human-written
commit messages from the top 1k Github projects. We designed a filter to help
ensure that we only trained the algorithm on higher-quality commit messages.
Our evaluation uncovered a pattern in which the messages we generate tend to be
either very high or very low quality. Therefore, we created a quality-assurance
filter to detect cases in which we are unable to produce good messages, and
return a warning instead.
Maximum Likelihood Latent Space Embedding of Logistic Random Dot Product Graphs
Authors: Luke O'Connor, Muriel Médard, Soheil Feizi
Date: 2015-10-03 00:00:00
Tasks: Clustering, regression

A latent space model for a family of random graphs assigns real-valued
vectors to nodes of the graph such that edge probabilities are determined by
latent positions. Latent space models provide a natural statistical framework
for graph visualizing and clustering. A latent space model of particular
interest is the Random Dot Product Graph (RDPG), which can be fit using an
efficient spectral method; however, this method is based on a heuristic that
can fail, even in simple cases. Here, we consider a closely related latent
space model, the Logistic RDPG, which uses a logistic link function to map from
latent positions to edge likelihoods. Over this model, we show that
asymptotically exact maximum likelihood inference of latent position vectors
can be achieved using an efficient spectral method. Our method involves
computing top eigenvectors of a normalized adjacency matrix and scaling
eigenvectors using a regression step. The novel regression scaling step is an
essential part of the proposed method. In simulations, we show that our
proposed method is more accurate and more robust than common practices. We also
show the effectiveness of our approach over standard real networks of the
karate club and political blogs.
Dependency Language Models for Transition-based Dependency Parsing
Authors: Juntao Yu, Bernd Bohnet
Date: 2016-07-18 00:00:00
Proceeding: WS 2017 9
Tasks: Dependency Parsing, Transition-Based Dependency Parsing

In this paper, we present an approach to improve the accuracy of a strong
transition-based dependency parser by exploiting dependency language models
that are extracted from a large parsed corpus. We integrated a small number of
features based on the dependency language models into the parser. To
demonstrate the effectiveness of the proposed approach, we evaluate our parser
on standard English and Chinese data where the base parser could achieve
competitive accuracy scores. Our enhanced parser achieved state-of-the-art
accuracy on Chinese data and competitive results on English data. We gained a
large absolute improvement of one point (UAS) on Chinese and 0.5 points for
English.
Graphical Lasso and Thresholding: Equivalence and Closed-form Solutions
Authors: Salar Fattahi, Somayeh Sojoudi
Date: 2017-08-30 00:00:00
Tasks: Form

Graphical Lasso (GL) is a popular method for learning the structure of an undirected graphical model, which is based on an $l_1$ regularization technique. The objective of this paper is to compare the computationally-heavy GL technique with a numerically-cheap heuristic method that is based on simply thresholding the sample covariance matrix. To this end, two notions of sign-consistent and inverse-consistent matrices are developed, and then it is shown that the thresholding and GL methods are equivalent if: (i) the thresholded sample covariance matrix is both sign-consistent and inverse-consistent, and (ii) the gap between the largest thresholded and the smallest un-thresholded entries of the sample covariance matrix is not too small. By building upon this result, it is proved that the GL method---as a conic optimization problem---has an explicit closed-form solution if the thresholded sample covariance matrix has an acyclic structure. This result is then generalized to arbitrary sparse support graphs, where a formula is found to obtain an approximate solution of GL. Furthermore, it is shown that the approximation error of the derived explicit formula decreases exponentially fast with respect to the length of the minimum-length cycle of the sparsity graph. The developed results are demonstrated on synthetic data, functional MRI data, traffic flows for transportation networks, and massive randomly generated data sets. We show that the proposed method can obtain an accurate approximation of the GL for instances with the sizes as large as $80,000\times 80,000$ (more than 3.2 billion variables) in less than 30 minutes on a standard laptop computer running MATLAB, while other state-of-the-art methods do not converge within 4 hours.
Online Convolutional Dictionary Learning
Authors: Jialin Liu, Cristina Garcia-Cardona, Brendt Wohlberg, Wotao Yin
Date: 2017-06-29 00:00:00
Tasks: Dictionary Learning

While a number of different algorithms have recently been proposed for
convolutional dictionary learning, this remains an expensive problem. The
single biggest impediment to learning from large training sets is the memory
requirements, which grow at least linearly with the size of the training set
since all existing methods are batch algorithms. The work reported here
addresses this limitation by extending online dictionary learning ideas to the
convolutional context.
Inference of Fine-Grained Event Causality from Blogs and Films
Authors: Zhichao Hu, Elahe Rahimtoroghi, Marilyn A. Walker
Date: 2017-08-30 00:00:00
Proceeding: WS 2017 8

Human understanding of narrative is mainly driven by reasoning about causal
relations between events and thus recognizing them is a key capability for
computational models of language understanding. Computational work in this area
has approached this via two different routes: by focusing on acquiring a
knowledge base of common causal relations between events, or by attempting to
understand a particular story or macro-event, along with its storyline. In this
position paper, we focus on knowledge acquisition approach and claim that
newswire is a relatively poor source for learning fine-grained causal relations
between everyday events. We describe experiments using an unsupervised method
to learn causal relations between events in the narrative genres of
first-person narratives and film scene descriptions. We show that our method
learns fine-grained causal relations, judged by humans as likely to be causal
over 80% of the time. We also demonstrate that the learned event pairs do not
exist in publicly available event-pair datasets extracted from newswire.
Asymptotic Bias of Stochastic Gradient Search
Authors: Vladislav B. Tadic, Arnaud Doucet
Date: 2017-08-30 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

The asymptotic behavior of the stochastic gradient algorithm with a biased
gradient estimator is analyzed. Relying on arguments based on the dynamic
system theory (chain-recurrence) and the differential geometry (Yomdin theorem
and Lojasiewicz inequality), tight bounds on the asymptotic bias of the
iterates generated by such an algorithm are derived. The obtained results hold
under mild conditions and cover a broad class of high-dimensional nonlinear
algorithms. Using these results, the asymptotic properties of the
policy-gradient (reinforcement) learning and adaptive population Monte Carlo
sampling are studied. Relying on the same results, the asymptotic behavior of
the recursive maximum split-likelihood estimation in hidden Markov models is
analyzed, too.
Learning Fine-Grained Knowledge about Contingent Relations between Everyday Events
Authors: Elahe Rahimtoroghi, Ernesto Hernandez, Marilyn A. Walker
Date: 2017-08-30 00:00:00
Proceeding: WS 2016 9
Tasks: Common Sense Reasoning, Question Answering, Text Summarization

Much of the user-generated content on social media is provided by ordinary
people telling stories about their daily lives. We develop and test a novel
method for learning fine-grained common-sense knowledge from these stories
about contingent (causal and conditional) relationships between everyday
events. This type of knowledge is useful for text and story understanding,
information extraction, question answering, and text summarization. We test and
compare different methods for learning contingency relation, and compare what
is learned from topic-sorted story collections vs. general-domain stories. Our
experiments show that using topic-specific datasets enables learning
finer-grained knowledge about events and results in significant improvement
over the baselines. An evaluation on Amazon Mechanical Turk shows 82% of the
relations between events that we learn from topic-sorted stories are judged as
contingent.
Incorporating Feedback into Tree-based Anomaly Detection
Authors: Shubhomoy Das, Weng-Keen Wong, Alan Fern, Thomas G. Dietterich, Md Amran Siddiqui
Date: 2017-08-30 00:00:00
Tasks: Anomaly Detection

Anomaly detectors are often used to produce a ranked list of statistical
anomalies, which are examined by human analysts in order to extract the actual
anomalies of interest. Unfortunately, in realworld applications, this process
can be exceedingly difficult for the analyst since a large fraction of
high-ranking anomalies are false positives and not interesting from the
application perspective. In this paper, we aim to make the analyst's job easier
by allowing for analyst feedback during the investigation process. Ideally, the
feedback influences the ranking of the anomaly detector in a way that reduces
the number of false positives that must be examined before discovering the
anomalies of interest. In particular, we introduce a novel technique for
incorporating simple binary feedback into tree-based anomaly detectors. We
focus on the Isolation Forest algorithm as a representative tree-based anomaly
detector, and show that we can significantly improve its performance by
incorporating feedback, when compared with the baseline algorithm that does not
incorporate feedback. Our technique is simple and scales well as the size of
the data increases, which makes it suitable for interactive discovery of
anomalies in large datasets.
LangPro: Natural Language Theorem Prover
Authors: Lasha Abzianidze
Date: 2017-08-30 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Automated Theorem Proving, Natural Language Inference

LangPro is an automated theorem prover for natural language
(https://github.com/kovvalsky/LangPro). Given a set of premises and a
hypothesis, it is able to prove semantic relations between them. The prover is
based on a version of analytic tableau method specially designed for natural
logic. The proof procedure operates on logical forms that preserve linguistic
expressions to a large extent. %This property makes the logical forms easily
obtainable from syntactic trees. %, in particular, Combinatory Categorial
Grammar derivation trees. The nature of proofs is deductive and transparent. On
the FraCaS and SICK textual entailment datasets, the prover achieves high
results comparable to state-of-the-art.
Sparsity Invariant CNNs
Authors: Jonas Uhrig, Nick Schneider, Lukas Schneider, Uwe Franke, Thomas Brox, Andreas Geiger
Date: 2017-08-22 00:00:00
Tasks: Depth Completion, Depth Estimation, Depth Prediction

In this paper, we consider convolutional neural networks operating on sparse
inputs with an application to depth upsampling from sparse laser scan data.
First, we show that traditional convolutional networks perform poorly when
applied to sparse data even when the location of missing data is provided to
the network. To overcome this problem, we propose a simple yet effective sparse
convolution layer which explicitly considers the location of missing data
during the convolution operation. We demonstrate the benefits of the proposed
network architecture in synthetic and real experiments with respect to various
baseline approaches. Compared to dense baselines, the proposed sparse
convolution network generalizes well to novel datasets and is invariant to the
level of sparsity in the data. For our evaluation, we derive a novel dataset
from the KITTI benchmark, comprising 93k depth annotated RGB images. Our
dataset allows for training and evaluating depth upsampling and depth
prediction techniques in challenging real-world settings and will be made
available upon publication.
Fast(er) Exact Decoding and Global Training for Transition-Based Dependency Parsing via a Minimal Feature Set
Authors: Tianze Shi, Liang Huang, Lillian Lee
Date: 2017-08-30 00:00:00
Proceeding: EMNLP 2017 9
Tasks: ARC, Dependency Parsing, Transition-Based Dependency Parsing

We first present a minimal feature set for transition-based dependency
parsing, continuing a recent trend started by Kiperwasser and Goldberg (2016a)
and Cross and Huang (2016a) of using bi-directional LSTM features. We plug our
minimal feature set into the dynamic-programming framework of Huang and Sagae
(2010) and Kuhlmann et al. (2011) to produce the first implementation of
worst-case O(n^3) exact decoders for arc-hybrid and arc-eager transition
systems. With our minimal features, we also present O(n^3) global training
methods. Finally, using ensembles including our new parsers, we achieve the
best unlabeled attachment score reported (to our knowledge) on the Chinese
Treebank and the "second-best-in-class" result on the English Penn Treebank.
Optimal and Learning Control for Autonomous Robots
Authors: Jonas Buchli, Farbod Farshidian, Alexander Winkler, Timothy Sandy, Markus Giftthaler
Date: 2017-08-30 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Translation

Optimal and Learning Control for Autonomous Robots has been taught in the
Robotics, Systems and Controls Masters at ETH Zurich with the aim to teach
optimal control and reinforcement learning for closed loop control problems
from a unified point of view. The starting point is the formulation of of an
optimal control problem and deriving the different types of solutions and
algorithms from there. These lecture notes aim at supporting this unified view
with a unified notation wherever possible, and a bit of a translation help to
compare the terminology and notation in the different fields. The course
assumes basic knowledge of Control Theory, Linear Algebra and Stochastic
Calculus.
Collaborative Nested Sampling: Big Data vs. complex physical models
Authors: Johannes Buchner
Date: 2017-07-14 00:00:00
Tasks: parameter estimation

The data torrent unleashed by current and upcoming astronomical surveys
demands scalable analysis methods. Many machine learning approaches scale well,
but separating the instrument measurement from the physical effects of
interest, dealing with variable errors, and deriving parameter uncertainties is
often an after-thought. Classic forward-folding analyses with Markov Chain
Monte Carlo or Nested Sampling enable parameter estimation and model
comparison, even for complex and slow-to-evaluate physical models. However,
these approaches require independent runs for each data set, implying an
unfeasible number of model evaluations in the Big Data regime. Here I present a
new algorithm, collaborative nested sampling, for deriving parameter
probability distributions for each observation. Importantly, the number of
physical model evaluations scales sub-linearly with the number of data sets,
and no assumptions about homogeneous errors, Gaussianity, the form of the model
or heterogeneity/completeness of the observations need to be made.
Collaborative nested sampling has immediate application in speeding up analyses
of large surveys, integral-field-unit observations, and Monte Carlo
simulations.
Efficient Defenses Against Adversarial Attacks
Authors: Valentina Zantedeschi, Maria-Irina Nicolae, Ambrish Rawat
Date: 2017-07-21 00:00:00

Following the recent adoption of deep neural networks (DNN) accross a wide
range of applications, adversarial attacks against these models have proven to
be an indisputable threat. Adversarial samples are crafted with a deliberate
intention of undermining a system. In the case of DNNs, the lack of better
understanding of their working has prevented the development of efficient
defenses. In this paper, we propose a new defense method based on practical
observations which is easy to integrate into models and performs better than
state-of-the-art defenses. Our proposed solution is meant to reinforce the
structure of a DNN, making its prediction more stable and less likely to be
fooled by adversarial samples. We conduct an extensive experimental study
proving the efficiency of our method against multiple attacks, comparing it to
numerous defenses, both in white-box and black-box setups. Additionally, the
implementation of our method brings almost no overhead to the training
procedure, while maintaining the prediction performance of the original model
on clean samples.
Designing and building the mlpack open-source machine learning library
Authors: Ryan R. Curtin, Marcus Edel
Date: 2017-08-17 00:00:00
Tasks: BIG-bench Machine Learning

mlpack is an open-source C++ machine learning library with an emphasis on
speed and flexibility. Since its original inception in 2007, it has grown to be
a large project implementing a wide variety of machine learning algorithms,
from standard techniques such as decision trees and logistic regression to
modern techniques such as deep neural networks as well as other
recently-published cutting-edge techniques not found in any other library.
mlpack is quite fast, with benchmarks showing mlpack outperforming other
libraries' implementations of the same methods. mlpack has an active community,
with contributors from around the world---including some from PUST. This short
paper describes the goals and design of mlpack, discusses how the open-source
community functions, and shows an example usage of mlpack for a simple data
science problem.
Adversarial nets with perceptual losses for text-to-image synthesis
Authors: Miriam Cha, Youngjune Gwon, H. T. Kung
Date: 2017-08-30 00:00:00
Tasks: Descriptive, Image Generation

Recent approaches in generative adversarial networks (GANs) can automatically
synthesize realistic images from descriptive text. Despite the overall fair
quality, the generated images often expose visible flaws that lack structural
definition for an object of interest. In this paper, we aim to extend state of
the art for GAN-based text-to-image synthesis by improving perceptual quality
of generated images. Differentiated from previous work, our synthetic image
generator optimizes on perceptual loss functions that measure pixel, feature
activation, and texture differences against a natural image. We present
visually more compelling synthetic images of birds and flowers generated from
text descriptions in comparison to some of the most prominent existing work.
Disguised Face Identification (DFI) with Facial KeyPoints using Spatial Fusion Convolutional Network
Authors: Amarjot Singh, Devendra Patil, G Meghana Reddy, SN Omkar
Date: 2017-08-30 00:00:00
Tasks: Classification, Deep Learning, Face Identification, General Classification, Keypoint Detection

Disguised face identification (DFI) is an extremely challenging problem due
to the numerous variations that can be introduced using different disguises.
This paper introduces a deep learning framework to first detect 14 facial
key-points which are then utilized to perform disguised face identification.
Since the training of deep learning architectures relies on large annotated
datasets, two annotated facial key-points datasets are introduced. The
effectiveness of the facial keypoint detection framework is presented for each
keypoint. The superiority of the key-point detection framework is also
demonstrated by a comparison with other deep networks. The effectiveness of
classification performance is also demonstrated by comparison with the
state-of-the-art face disguise classification methods.
TrajectoryNet: An Embedded GPS Trajectory Representation for Point-based Classification Using Recurrent Neural Networks
Authors: Xiang Jiang, Erico N de Souza, Ahmad Pesaranghader, Baifan Hu, Daniel L. Silver, Stan Matwin
Date: 2017-05-07 00:00:00
Tasks: General Classification

Understanding and discovering knowledge from GPS (Global Positioning System)
traces of human activities is an essential topic in mobility-based urban
computing. We propose TrajectoryNet-a neural network architecture for
point-based trajectory classification to infer real world human transportation
modes from GPS traces. To overcome the challenge of capturing the underlying
latent factors in the low-dimensional and heterogeneous feature space imposed
by GPS data, we develop a novel representation that embeds the original feature
space into another space that can be understood as a form of basis expansion.
We also enrich the feature space via segment-based information and use Maxout
activations to improve the predictive power of Recurrent Neural Networks
(RNNs). We achieve over 98% classification accuracy when detecting four types
of transportation modes, outperforming existing models without additional
sensory data or location-based prior knowledge.