pwc-archive/papers-with-abstracts huggingface dataset:
Triaging Content Severity in Online Mental Health Forums
Authors: Arman Cohan, Sydney Young, Andrew Yates, Nazli Goharian
Date: 2017-02-22 00:00:00

Mental health forums are online communities where people express their issues
and seek help from moderators and other users. In such forums, there are often
posts with severe content indicating that the user is in acute distress and
there is a risk of attempted self-harm. Moderators need to respond to these
severe posts in a timely manner to prevent potential self-harm. However, the
large volume of daily posted content makes it difficult for the moderators to
locate and respond to these critical posts. We present a framework for triaging
user content into four severity categories which are defined based on
indications of self-harm ideation. Our models are based on a feature-rich
classification framework which includes lexical, psycholinguistic, contextual
and topic modeling features. Our approaches improve the state of the art in
triaging the content severity in mental health forums by large margins (up to
17% improvement over the F-1 scores). Using the proposed model, we analyze the
mental state of users and we show that overall, long-term users of the forum
demonstrate a decreased severity of risk over time. Our analysis on the
interaction of the moderators with the users further indicates that without an
automatic way to identify critical content, it is indeed challenging for the
moderators to provide timely response to the users in need.
Sketchy Decisions: Convex Low-Rank Matrix Optimization with Optimal Storage
Authors: Alp Yurtsever, Madeleine Udell, Joel A. Tropp, Volkan Cevher
Date: 2017-02-22 00:00:00

This paper concerns a fundamental class of convex matrix optimization
problems. It presents the first algorithm that uses optimal storage and
provably computes a low-rank approximation of a solution. In particular, when
all solutions have low rank, the algorithm converges to a solution. This
algorithm, SketchyCGM, modifies a standard convex optimization scheme, the
conditional gradient method, to store only a small randomized sketch of the
matrix variable. After the optimization terminates, the algorithm extracts a
low-rank approximation of the solution from the sketch. In contrast to
nonconvex heuristics, the guarantees for SketchyCGM do not rely on statistical
models for the problem data. Numerical work demonstrates the benefits of
SketchyCGM over heuristics.
Adversarial examples for generative models
Authors: Jernej Kos, Ian Fischer, Dawn Song
Date: 2017-02-22 00:00:00
Tasks: Classification, General Classification

We explore methods of producing adversarial examples on deep generative
models such as the variational autoencoder (VAE) and the VAE-GAN. Deep learning
architectures are known to be vulnerable to adversarial examples, but previous
work has focused on the application of adversarial examples to classification
tasks. Deep generative models have recently become popular due to their ability
to model input data distributions and generate realistic examples from those
distributions. We present three classes of attacks on the VAE and VAE-GAN
architectures and demonstrate them against networks trained on MNIST, SVHN and
CelebA. Our first attack leverages classification-based adversaries by
attaching a classifier to the trained encoder of the target generative model,
which can then be used to indirectly manipulate the latent representation. Our
second attack directly uses the VAE loss function to generate a target
reconstruction image from the adversarial example. Our third attack moves
beyond relying on classification or the standard loss for the gradient and
directly optimizes against differences in source and target latent
representations. We also motivate why an attacker might be interested in
deploying such techniques against a target generative network.
Tackling Error Propagation through Reinforcement Learning: A Case of Greedy Dependency Parsing
Authors: Minh Le, Antske Fokkens
Date: 2017-02-22 00:00:00
Proceeding: EACL 2017 4
Tasks: Dependency Parsing, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Error propagation is a common problem in NLP. Reinforcement learning explores
erroneous states during training and can therefore be more robust when mistakes
are made early in a process. In this paper, we apply reinforcement learning to
greedy dependency parsing which is known to suffer from error propagation.
Reinforcement learning improves accuracy of both labeled and unlabeled
dependencies of the Stanford Neural Dependency Parser, a high performance
greedy parser, while maintaining its efficiency. We investigate the portion of
errors which are the result of error propagation and confirm that reinforcement
learning reduces the occurrence of error propagation.
Options Discovery with Budgeted Reinforcement Learning
Authors: Aurélia Léon, Ludovic Denoyer
Date: 2016-11-21 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We consider the problem of learning hierarchical policies for Reinforcement
Learning able to discover options, an option corresponding to a sub-policy over
a set of primitive actions. Different models have been proposed during the last
decade that usually rely on a predefined set of options. We specifically
address the problem of automatically discovering options in decision processes.
We describe a new learning model called Budgeted Option Neural Network (BONN)
able to discover options based on a budgeted learning objective. The BONN model
is evaluated on different classical RL problems, demonstrating both
quantitative and qualitative interesting results.
Dialectometric analysis of language variation in Twitter
Authors: Gonzalo Donoso, David Sanchez
Date: 2017-02-22 00:00:00
Proceeding: WS 2017 4

In the last few years, microblogging platforms such as Twitter have given
rise to a deluge of textual data that can be used for the analysis of informal
communication between millions of individuals. In this work, we propose an
information-theoretic approach to geographic language variation using a corpus
based on Twitter. We test our models with tens of concepts and their associated
keywords detected in Spanish tweets geolocated in Spain. We employ
dialectometric measures (cosine similarity and Jensen-Shannon divergence) to
quantify the linguistic distance on the lexical level between cells created in
a uniform grid over the map. This can be done for a single concept or in the
general case taking into account an average of the considered variants. The
latter permits an analysis of the dialects that naturally emerge from the data.
Interestingly, our results reveal the existence of two dialect macrovarieties.
The first group includes a region-specific speech spoken in small towns and
rural areas whereas the second cluster encompasses cities that tend to use a
more uniform variety. Since the results obtained with the two different metrics
qualitatively agree, our work suggests that social media corpora can be
efficiently used for dialectometric analyses.
Causal Inference by Stochastic Complexity
Authors: Kailash Budhathoki, Jilles Vreeken
Date: 2017-02-22 00:00:00
Tasks: Causal Inference

The algorithmic Markov condition states that the most likely causal direction
between two random variables X and Y can be identified as that direction with
the lowest Kolmogorov complexity. Due to the halting problem, however, this
notion is not computable.
  We hence propose to do causal inference by stochastic complexity. That is, we
propose to approximate Kolmogorov complexity via the Minimum Description Length
(MDL) principle, using a score that is mini-max optimal with regard to the
model class under consideration. This means that even in an adversarial
setting, such as when the true distribution is not in this class, we still
obtain the optimal encoding for the data relative to the class.
  We instantiate this framework, which we call CISC, for pairs of univariate
discrete variables, using the class of multinomial distributions. Experiments
show that CISC is highly accurate on synthetic, benchmark, as well as
real-world data, outperforming the state of the art by a margin, and scales
extremely well with regard to sample and domain sizes.
MomentsNet: a simple learning-free method for binary image recognition
Authors: Jiasong Wu, Shijie Qiu, Youyong Kong, Yang Chen, Lotfi Senhadji, Huazhong Shu
Date: 2017-02-22 00:00:00
Tasks: Deep Learning

In this paper, we propose a new simple and learning-free deep learning
network named MomentsNet, whose convolution layer, nonlinear processing layer
and pooling layer are constructed by Moments kernels, binary hashing and
block-wise histogram, respectively. Twelve typical moments (including
geometrical moment, Zernike moment, Tchebichef moment, etc.) are used to
construct the MomentsNet whose recognition performance for binary image is
studied. The results reveal that MomentsNet has better recognition performance
than its corresponding moments in almost all cases and ZernikeNet achieves the
best recognition performance among MomentsNet constructed by twelve moments.
ZernikeNet also shows better recognition performance on binary image database
than that of PCANet, which is a learning-based deep learning network.
Memory Matching Networks for Genomic Sequence Classification
Authors: Jack Lanchantin, Ritambhara Singh, Yanjun Qi
Date: 2017-02-22 00:00:00
Tasks: Classification, General Classification

When analyzing the genome, researchers have discovered that proteins bind to
DNA based on certain patterns of the DNA sequence known as "motifs". However,
it is difficult to manually construct motifs due to their complexity. Recently,
externally learned memory models have proven to be effective methods for
reasoning over inputs and supporting sets. In this work, we present memory
matching networks (MMN) for classifying DNA sequences as protein binding sites.
Our model learns a memory bank of encoded motifs, which are dynamic memory
modules, and then matches a new test sequence to each of the motifs to classify
the sequence as a binding or nonbinding site.
Improving a Strong Neural Parser with Conjunction-Specific Features
Authors: Jessica Ficler, Yoav Goldberg
Date: 2017-02-22 00:00:00
Proceeding: EACL 2017 4
Tasks: Dependency Parsing

While dependency parsers reach very high overall accuracy, some dependency
relations are much harder than others. In particular, dependency parsers
perform poorly in coordination construction (i.e., correctly attaching the
"conj" relation). We extend a state-of-the-art dependency parser with
conjunction-specific features, focusing on the similarity between the conjuncts
head words. Training the extended parser yields an improvement in "conj"
attachment as well as in overall dependency parsing accuracy on the Stanford
dependency conversion of the Penn TreeBank.
Semi-Supervised Classification with Graph Convolutional Networks
Authors: Thomas N. Kipf, Max Welling
Date: 2016-09-09 00:00:00
Tasks: Document Classification, Drug Discovery, General Classification, Graph Classification, Graph Regression, Heterogeneous Node Classification, Molecular Property Prediction, Node Classification, Node Classification on Non-Homophilic (Heterophilic) Graphs, Node Property Prediction, Recommendation Systems, Skeleton Based Action Recognition

We present a scalable approach for semi-supervised learning on
graph-structured data that is based on an efficient variant of convolutional
neural networks which operate directly on graphs. We motivate the choice of our
convolutional architecture via a localized first-order approximation of
spectral graph convolutions. Our model scales linearly in the number of graph
edges and learns hidden layer representations that encode both local graph
structure and features of nodes. In a number of experiments on citation
networks and on a knowledge graph dataset we demonstrate that our approach
outperforms related methods by a significant margin.
3D Reconstruction of Temples in the Special Region of Yogyakarta By Using Close-Range Photogrammetry
Authors: Adityo Priyandito Utomo, Canggih Puspo Wibowo
Date: 2017-02-22 00:00:00
Tasks: 3D Reconstruction, Object Reconstruction, SSIM

Object reconstruction is one of the main problems in cultural heritage
preservation. This problem is due to lack of data in documentation. Thus in
this research we presented a method of 3D reconstruction using close-range
photogrammetry. We collected 1319 photos from five temples in Yogyakarta. Using
A-KAZE algorithm, keypoints of each image were obtained. Then we employed LIOP
to create feature descriptor from it. After performing feature matching, L1RA
was utilized to create sparse point clouds. In order to generate the geometry
shape, MVS was used. Finally, FSSR and Large Scale Texturing were employed to
deal with the surface and texture of the object. The quality of the
reconstructed 3D model was measured by comparing the 3D images of the model
with the original photos utilizing SSIM. The results showed that in terms of
quality, our method was on par with other commercial method such as
PhotoModeler and PhotoScan.
Ensembles of Randomized Time Series Shapelets Provide Improved Accuracy while Reducing Computational Costs
Authors: Atif Raza, Stefan Kramer
Date: 2017-02-22 00:00:00
Tasks: Classification, Diversity, General Classification, Time Series, Time Series Analysis

Shapelets are discriminative time series subsequences that allow generation
of interpretable classification models, which provide faster and generally
better classification than the nearest neighbor approach. However, the shapelet
discovery process requires the evaluation of all possible subsequences of all
time series in the training set, making it extremely computation intensive.
Consequently, shapelet discovery for large time series datasets quickly becomes
intractable. A number of improvements have been proposed to reduce the training
time. These techniques use approximation or discretization and often lead to
reduced classification accuracy compared to the exact method.
  We are proposing the use of ensembles of shapelet-based classifiers obtained
using random sampling of the shapelet candidates. Using random sampling reduces
the number of evaluated candidates and consequently the required computational
cost, while the classification accuracy of the resulting models is also not
significantly different than that of the exact algorithm. The combination of
randomized classifiers rectifies the inaccuracies of individual models because
of the diversity of the solutions. Based on the experiments performed, it is
shown that the proposed approach of using an ensemble of inexpensive
classifiers provides better classification accuracy compared to the exact
method at a significantly lesser computational cost.
Fine-Grained Entity Type Classification by Jointly Learning Representations and Label Embeddings
Authors: abhishek, Ashish Anand, Amit Awekar
Date: 2017-02-22 00:00:00
Proceeding: EACL 2017 4
Tasks: General Classification

Fine-grained entity type classification (FETC) is the task of classifying an
entity mention to a broad set of types. Distant supervision paradigm is
extensively used to generate training data for this task. However, generated
training data assigns same set of labels to every mention of an entity without
considering its local context. Existing FETC systems have two major drawbacks:
assuming training data to be noise free and use of hand crafted features. Our
work overcomes both drawbacks. We propose a neural network model that jointly
learns entity mentions and their context representation to eliminate use of
hand crafted features. Our model treats training data as noisy and uses
non-parametric variant of hinge loss function. Experiments show that the
proposed model outperforms previous state-of-the-art methods on two publicly
available datasets, namely FIGER (GOLD) and BBN with an average relative
improvement of 2.69% in micro-F1 score. Knowledge learnt by our model on one
dataset can be transferred to other datasets while using same model or other
FETC systems. These approaches of transferring knowledge further improve the
performance of respective models.
A metric on the space of finite sets of trajectories for evaluation of multi-target tracking algorithms
Authors: Ángel F. García-Fernández, Abu Sajana Rahmathullah, Lennart Svensson
Date: 2016-05-04 00:00:00

In this paper, we propose a metric on the space of finite sets of trajectories for assessing multi-target tracking algorithms in a mathematically sound way. The main use of the metric is to compare estimates of trajectories from different algorithms with the ground truth of trajectories. The proposed metric includes intuitive costs associated to localization error for properly detected targets, missed and false targets and track switches at each time step. The metric computation is based on solving a multi-dimensional assignment problem. We also propose a lower bound for the metric, which is also a metric for sets of trajectories and is computable in polynomial time using linear programming. We also extend the proposed metrics on sets of trajectories to random finite sets of trajectories.
Data Distillation for Controlling Specificity in Dialogue Generation
Authors: Jiwei Li, Will Monroe, Dan Jurafsky
Date: 2017-02-22 00:00:00
Tasks: Dialogue Generation, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Specificity

People speak at different levels of specificity in different situations.
Depending on their knowledge, interlocutors, mood, etc.} A conversational agent
should have this ability and know when to be specific and when to be general.
We propose an approach that gives a neural network--based conversational agent
this ability. Our approach involves alternating between \emph{data
distillation} and model training : removing training examples that are closest
to the responses most commonly produced by the model trained from the last
round and then retrain the model on the remaining dataset. Dialogue generation
models trained with different degrees of data distillation manifest different
levels of specificity.
  We then train a reinforcement learning system for selecting among this pool
of generation models, to choose the best level of specificity for a given
input. Compared to the original generative model trained without distillation,
the proposed system is capable of generating more interesting and
higher-quality responses, in addition to appropriately adjusting specificity
depending on the context.
  Our research constitutes a specific case of a broader approach involving
training multiple subsystems from a single dataset distinguished by differences
in a specific property one wishes to model. We show that from such a set of
subsystems, one can use reinforcement learning to build a system that tailors
its output to different input contexts at test time.
Task-driven Visual Saliency and Attention-based Visual Question Answering
Authors: Yuetan Lin, Zhangyang Pang, Donghui Wang, Yueting Zhuang
Date: 2017-02-22 00:00:00
Tasks: Question Answering, Visual Question Answering, Visual Question Answering (VQA)

Visual question answering (VQA) has witnessed great progress since May, 2015
as a classic problem unifying visual and textual data into a system. Many
enlightening VQA works explore deep into the image and question encodings and
fusing methods, of which attention is the most effective and infusive
mechanism. Current attention based methods focus on adequate fusion of visual
and textual features, but lack the attention to where people focus to ask
questions about the image. Traditional attention based methods attach a single
value to the feature at each spatial location, which losses many useful
information. To remedy these problems, we propose a general method to perform
saliency-like pre-selection on overlapped region features by the interrelation
of bidirectional LSTM (BiLSTM), and use a novel element-wise multiplication
based attention method to capture more competent correlation information
between visual and textual features. We conduct experiments on the large-scale
COCO-VQA dataset and analyze the effectiveness of our model demonstrated by
strong empirical results.
BanglaLekha-Isolated: A Comprehensive Bangla Handwritten Character Dataset
Authors: Mithun Biswas, Rafiqul Islam, Gautam Kumar Shom, Md. Shopon, Nabeel Mohammed, Sifat Momen, Md Anowarul Abedin
Date: 2017-02-22 00:00:00
Tasks: General Classification, Handwriting Recognition

Bangla handwriting recognition is becoming a very important issue nowadays.
It is potentially a very important task specially for Bangla speaking
population of Bangladesh and West Bengal. By keeping that in our mind we are
introducing a comprehensive Bangla handwritten character dataset named
BanglaLekha-Isolated. This dataset contains Bangla handwritten numerals, basic
characters and compound characters. This dataset was collected from multiple
geographical location within Bangladesh and includes sample collected from a
variety of aged groups. This dataset can also be used for other classification
problems i.e: gender, age, district. This is the largest dataset on Bangla
handwritten characters yet.
One Representation per Word - Does it make Sense for Composition?
Authors: Thomas Kober, Julie Weeds, John Wilkie, Jeremy Reffin, David Weir
Date: 2017-02-22 00:00:00
Proceeding: WS 2017 4

In this paper, we investigate whether an a priori disambiguation of word
senses is strictly necessary or whether the meaning of a word in context can be
disambiguated through composition alone. We evaluate the performance of
off-the-shelf single-vector and multi-sense vector models on a benchmark phrase
similarity task and a novel task for word-sense discrimination. We find that
single-sense vector models perform as well or better than multi-sense vector
models despite arguably less clean elementary representations. Our findings
furthermore show that simple composition functions such as pointwise addition
are able to recover sense specific information from a single-sense vector model
remarkably well.
Preconditioned Stochastic Gradient Descent
Authors: Xi-Lin Li
Date: 2015-12-14 00:00:00
Tasks: 2D Human Pose Estimation

Stochastic gradient descent (SGD) still is the workhorse for many practical
problems. However, it converges slow, and can be difficult to tune. It is
possible to precondition SGD to accelerate its convergence remarkably. But many
attempts in this direction either aim at solving specialized problems, or
result in significantly more complicated methods than SGD. This paper proposes
a new method to estimate a preconditioner such that the amplitudes of
perturbations of preconditioned stochastic gradient match that of the
perturbations of parameters to be optimized in a way comparable to Newton
method for deterministic optimization. Unlike the preconditioners based on
secant equation fitting as done in deterministic quasi-Newton methods, which
assume positive definite Hessian and approximate its inverse, the new
preconditioner works equally well for both convex and non-convex optimizations
with exact or noisy gradients. When stochastic gradient is used, it can
naturally damp the gradient noise to stabilize SGD. Efficient preconditioner
estimation methods are developed, and with reasonable simplifications, they are
applicable to large scaled problems. Experimental results demonstrate that
equipped with the new preconditioner, without any tuning effort, preconditioned
SGD can efficiently solve many challenging problems like the training of a deep
neural network or a recurrent neural network requiring extremely long term
memories.