pwc-archive/papers-with-abstracts huggingface dataset:
Adversarial Multi-Criteria Learning for Chinese Word Segmentation
Authors: Xinchi Chen, Zhan Shi, Xipeng Qiu, Xuanjing Huang
Date: 2017-04-25 00:00:00
Proceeding: ACL 2017 7
Tasks: Chinese Word Segmentation, Segmentation

Different linguistic perspectives causes many diverse segmentation criteria
for Chinese word segmentation (CWS). Most existing methods focus on improve the
performance for each single criterion. However, it is interesting to exploit
these different criteria and mining their common underlying knowledge. In this
paper, we propose adversarial multi-criteria learning for CWS by integrating
shared knowledge from multiple heterogeneous segmentation criteria. Experiments
on eight corpora with heterogeneous segmentation criteria show that the
performance of each corpus obtains a significant improvement, compared to
single-criterion learning. Source codes of this paper are available on Github.
Semi-supervised Bayesian Deep Multi-modal Emotion Recognition
Authors: Changde Du, Changying Du, Jinpeng Li, Wei-Long Zheng, Bao-liang Lu, Huiguang He
Date: 2017-04-25 00:00:00
Tasks: Emotion Recognition, Imputation

In emotion recognition, it is difficult to recognize human's emotional states
using just a single modality. Besides, the annotation of physiological
emotional data is particularly expensive. These two aspects make the building
of effective emotion recognition model challenging. In this paper, we first
build a multi-view deep generative model to simulate the generative process of
multi-modality emotional data. By imposing a mixture of Gaussians assumption on
the posterior approximation of the latent variables, our model can learn the
shared deep representation from multiple modalities. To solve the
labeled-data-scarcity problem, we further extend our multi-view model to
semi-supervised learning scenario by casting the semi-supervised classification
problem as a specialized missing data imputation task. Our semi-supervised
multi-view deep generative framework can leverage both labeled and unlabeled
data from multiple modalities, where the weight factor for each modality can be
learned automatically. Compared with previous emotion recognition methods, our
method is more robust and flexible. The experiments conducted on two real
multi-modal emotion datasets have demonstrated the superiority of our framework
over a number of competitors.
Get To The Point: Summarization with Pointer-Generator Networks
Authors: Abigail See, Peter J. Liu, Christopher D. Manning
Date: 2017-04-14 00:00:00
Proceeding: ACL 2017 7
Tasks: Abstractive Text Summarization, Document Summarization, Extractive Text Summarization, Text Summarization

Neural sequence-to-sequence models have provided a viable new approach for
abstractive text summarization (meaning they are not restricted to simply
selecting and rearranging passages from the original text). However, these
models have two shortcomings: they are liable to reproduce factual details
inaccurately, and they tend to repeat themselves. In this work we propose a
novel architecture that augments the standard sequence-to-sequence attentional
model in two orthogonal ways. First, we use a hybrid pointer-generator network
that can copy words from the source text via pointing, which aids accurate
reproduction of information, while retaining the ability to produce novel words
through the generator. Second, we use coverage to keep track of what has been
summarized, which discourages repetition. We apply our model to the CNN / Daily
Mail summarization task, outperforming the current abstractive state-of-the-art
by at least 2 ROUGE points.
Path Planning with Kinematic Constraints for Robot Groups
Authors: Wolfgang Hönig, T. K. Satish Kumar, Liron Cohen, Hang Ma, Sven Koenig, Nora Ayanian
Date: 2017-04-25 00:00:00

Path planning for multiple robots is well studied in the AI and robotics
communities. For a given discretized environment, robots need to find
collision-free paths to a set of specified goal locations. Robots can be fully
anonymous, non-anonymous, or organized in groups. Although powerful solvers for
this abstract problem exist, they make simplifying assumptions by ignoring
kinematic constraints, making it difficult to use the resulting plans on actual
robots. In this paper, we present a solution which takes kinematic constraints,
such as maximum velocities, into account, while guaranteeing a user-specified
minimum safety distance between robots. We demonstrate our approach in
simulation and on real robots in 2D and 3D environments.
Abstract Syntax Networks for Code Generation and Semantic Parsing
Authors: Maxim Rabinovich, Mitchell Stern, Dan Klein
Date: 2017-04-25 00:00:00
Proceeding: ACL 2017 7
Tasks: Code Generation, Decoder, Semantic Parsing

Tasks like code generation and semantic parsing require mapping unstructured
(or partially structured) inputs to well-formed, executable outputs. We
introduce abstract syntax networks, a modeling framework for these problems.
The outputs are represented as abstract syntax trees (ASTs) and constructed by
a decoder with a dynamically-determined modular structure paralleling the
structure of the output tree. On the benchmark Hearthstone dataset for code
generation, our model obtains 79.2 BLEU and 22.7% exact match accuracy,
compared to previous state-of-the-art values of 67.1 and 6.1%. Furthermore, we
perform competitively on the Atis, Jobs, and Geo semantic parsing datasets with
no task-specific engineering.
SREFI: Synthesis of Realistic Example Face Images
Authors: Sandipan Banerjee, John S. Bernhard, Walter J. Scheirer, Kevin W. Bowyer, Patrick J. Flynn
Date: 2017-04-21 00:00:00
Tasks: Face Generation, Face Recognition

In this paper, we propose a novel face synthesis approach that can generate
an arbitrarily large number of synthetic images of both real and synthetic
identities. Thus a face image dataset can be expanded in terms of the number of
identities represented and the number of images per identity using this
approach, without the identity-labeling and privacy complications that come
from downloading images from the web. To measure the visual fidelity and
uniqueness of the synthetic face images and identities, we conducted face
matching experiments with both human participants and a CNN pre-trained on a
dataset of 2.6M real face images. To evaluate the stability of these synthetic
faces, we trained a CNN model with an augmented dataset containing close to
200,000 synthetic faces. We used a snapshot of this trained CNN to recognize
extremely challenging frontal (real) face images. Experiments showed training
with the augmented faces boosted the face recognition performance of the CNN.
Speed/accuracy trade-offs for modern convolutional object detectors
Authors: Jonathan Huang, Vivek Rathod, Chen Sun, Menglong Zhu, Anoop Korattikara, Alireza Fathi, Ian Fischer, Zbigniew Wojna, Yang song, Sergio Guadarrama, Kevin Murphy
Date: 2016-11-30 00:00:00
Proceeding: CVPR 2017 7
Tasks: Object, object-detection, Object Detection

The goal of this paper is to serve as a guide for selecting a detection
architecture that achieves the right speed/memory/accuracy balance for a given
application and platform. To this end, we investigate various ways to trade
accuracy for speed and memory usage in modern convolutional object detection
systems. A number of successful systems have been proposed in recent years, but
apples-to-apples comparisons are difficult due to different base feature
extractors (e.g., VGG, Residual Networks), different default image resolutions,
as well as different hardware and software platforms. We present a unified
implementation of the Faster R-CNN [Ren et al., 2015], R-FCN [Dai et al., 2016]
and SSD [Liu et al., 2015] systems, which we view as "meta-architectures" and
trace out the speed/accuracy trade-off curve created by using alternative
feature extractors and varying other critical parameters such as image size
within each of these meta-architectures. On one extreme end of this spectrum
where speed and memory are critical, we present a detector that achieves real
time speeds and can be deployed on a mobile device. On the opposite end in
which accuracy is critical, we present a detector that achieves
state-of-the-art performance measured on the COCO detection task.
Some Like it Hoax: Automated Fake News Detection in Social Networks
Authors: Eugenio Tacchini, Gabriele Ballarin, Marco L. Della Vedova, Stefano Moret, Luca de Alfaro
Date: 2017-04-25 00:00:00
Tasks: Fake News Detection, General Classification, Misinformation

In recent years, the reliability of information on the Internet has emerged
as a crucial issue of modern society. Social network sites (SNSs) have
revolutionized the way in which information is spread by allowing users to
freely share content. As a consequence, SNSs are also increasingly used as
vectors for the diffusion of misinformation and hoaxes. The amount of
disseminated information and the rapidity of its diffusion make it practically
impossible to assess reliability in a timely manner, highlighting the need for
automatic hoax detection systems.
  As a contribution towards this objective, we show that Facebook posts can be
classified with high accuracy as hoaxes or non-hoaxes on the basis of the users
who "liked" them. We present two classification techniques, one based on
logistic regression, the other on a novel adaptation of boolean crowdsourcing
algorithms. On a dataset consisting of 15,500 Facebook posts and 909,236 users,
we obtain classification accuracies exceeding 99% even when the training set
contains less than 1% of the posts. We further show that our techniques are
robust: they work even when we restrict our attention to the users who like
both hoax and non-hoax posts. These results suggest that mapping the diffusion
pattern of information can be a useful component of automatic hoax detection
systems.
Dynamic Model Selection for Prediction Under a Budget
Authors: Feng Nan, Venkatesh Saligrama
Date: 2017-04-25 00:00:00
Tasks: model, Model Selection, Prediction

We present a dynamic model selection approach for resource-constrained
prediction. Given an input instance at test-time, a gating function identifies
a prediction model for the input among a collection of models. Our objective is
to minimize overall average cost without sacrificing accuracy. We learn gating
and prediction models on fully labeled training data by means of a bottom-up
strategy. Our novel bottom-up method is a recursive scheme whereby a
high-accuracy complex model is first trained. Then a low-complexity gating and
prediction model are subsequently learnt to adaptively approximate the
high-accuracy model in regions where low-cost models are capable of making
highly accurate predictions. We pose an empirical loss minimization problem
with cost constraints to jointly train gating and prediction models. On a
number of benchmark datasets our method outperforms state-of-the-art achieving
higher accuracy for the same cost.
Learning of Human-like Algebraic Reasoning Using Deep Feedforward Neural Networks
Authors: Cheng-Hao Cai, Dengfeng Ke, Yanyan Xu, Kaile Su
Date: 2017-04-25 00:00:00

There is a wide gap between symbolic reasoning and deep learning. In this
research, we explore the possibility of using deep learning to improve symbolic
reasoning. Briefly, in a reasoning system, a deep feedforward neural network is
used to guide rewriting processes after learning from algebraic reasoning
examples produced by humans. To enable the neural network to recognise patterns
of algebraic expressions with non-deterministic sizes, reduced partial trees
are used to represent the expressions. Also, to represent both top-down and
bottom-up information of the expressions, a centralisation technique is used to
improve the reduced partial trees. Besides, symbolic association vectors and
rule application records are used to improve the rewriting processes.
Experimental results reveal that the algebraic reasoning examples can be
accurately learnt only if the feedforward neural network has enough hidden
layers. Also, the centralisation technique, the symbolic association vectors
and the rule application records can reduce error rates of reasoning. In
particular, the above approaches have led to 4.6% error rate of reasoning on a
dataset of linear equations, differentials and integrals.
PPMF: A Patient-based Predictive Modeling Framework for Early ICU Mortality Prediction
Authors: Mohammad Amin Morid, Olivia R. Liu Sheng, Samir AbdelRahman
Date: 2017-04-25 00:00:00
Tasks: feature selection, ICU Admission, ICU Mortality, Mortality Prediction, Time Series, Time Series Analysis

To date, developing a good model for early intensive care unit (ICU)
mortality prediction is still challenging. This paper presents a patient based
predictive modeling framework (PPMF) to improve the performance of ICU
mortality prediction using data collected during the first 48 hours of ICU
admission. PPMF consists of three main components verifying three related
research hypotheses. The first component captures dynamic changes of patients
status in the ICU using their time series data (e.g., vital signs and
laboratory tests). The second component is a local approximation algorithm that
classifies patients based on their similarities. The third component is a
Gradient Decent wrapper that updates feature weights according to the
classification feedback. Experiments using data from MIMICIII show that PPMF
significantly outperforms: (1) the severity score systems, namely SASP III,
APACHE IV, and MPM0III, (2) the aggregation based classifiers that utilize
summarized time series, and (3) baseline feature selection methods.
A Context Aware and Video-Based Risk Descriptor for Cyclists
Authors: Miguel Costa, Beatriz Quintino Ferreira, Manuel Marques
Date: 2017-04-24 00:00:00
Tasks: object-detection, Object Detection

Aiming to reduce pollutant emissions, bicycles are regaining popularity
specially in urban areas. However, the number of cyclists' fatalities is not
showing the same decreasing trend as the other traffic groups. Hence,
monitoring cyclists' data appears as a keystone to foster urban cyclists'
safety by helping urban planners to design safer cyclist routes. In this work,
we propose a fully image-based framework to assess the rout risk from the
cyclist perspective. From smartphone sequences of images, this generic
framework is able to automatically identify events considering different risk
criteria based on the cyclist's motion and object detection. Moreover, since it
is entirely based on images, our method provides context on the situation and
is independent from the expertise level of the cyclist. Additionally, we build
on an existing platform and introduce several improvements on its mobile app to
acquire smartphone sensor data, including video. From the inertial sensor data,
we automatically detect the route segments performed by bicycle, applying
behavior analysis techniques. We test our methods on real data, attaining very
promising results in terms of risk classification, according to two different
criteria, and behavior analysis accuracy.
Spatio-Temporal Facial Expression Recognition Using Convolutional Neural Networks and Conditional Random Fields
Authors: Behzad Hasani, Mohammad H. Mahoor
Date: 2017-03-20 00:00:00
Tasks: Facial Expression Recognition, Facial Expression Recognition (FER), Object Recognition, Optical Flow Estimation

Automated Facial Expression Recognition (FER) has been a challenging task for
decades. Many of the existing works use hand-crafted features such as LBP, HOG,
LPQ, and Histogram of Optical Flow (HOF) combined with classifiers such as
Support Vector Machines for expression recognition. These methods often require
rigorous hyperparameter tuning to achieve good results. Recently Deep Neural
Networks (DNN) have shown to outperform traditional methods in visual object
recognition. In this paper, we propose a two-part network consisting of a
DNN-based architecture followed by a Conditional Random Field (CRF) module for
facial expression recognition in videos. The first part captures the spatial
relation within facial images using convolutional layers followed by three
Inception-ResNet modules and two fully-connected layers. To capture the
temporal relation between the image frames, we use linear chain CRF in the
second part of our network. We evaluate our proposed network on three publicly
available databases, viz. CK+, MMI, and FERA. Experiments are performed in
subject-independent and cross-database manners. Our experimental results show
that cascading the deep network architecture with the CRF module considerably
increases the recognition of facial expressions in videos and in particular it
outperforms the state-of-the-art methods in the cross-database experiments and
yields comparable results in the subject-independent experiments.
Bootstrapping Graph Convolutional Neural Networks for Autism Spectrum Disorder Classification
Authors: Rushil Anirudh, Jayaraman J. Thiagarajan
Date: 2017-04-24 00:00:00
Tasks: Classification, General Classification, graph construction

Using predictive models to identify patterns that can act as biomarkers for
different neuropathoglogical conditions is becoming highly prevalent. In this
paper, we consider the problem of Autism Spectrum Disorder (ASD) classification
where previous work has shown that it can be beneficial to incorporate a wide
variety of meta features, such as socio-cultural traits, into predictive
modeling. A graph-based approach naturally suits these scenarios, where a
contextual graph captures traits that characterize a population, while the
specific brain activity patterns are utilized as a multivariate signal at the
nodes. Graph neural networks have shown improvements in inferencing with
graph-structured data. Though the underlying graph strongly dictates the
overall performance, there exists no systematic way of choosing an appropriate
graph in practice, thus making predictive models non-robust. To address this,
we propose a bootstrapped version of graph convolutional neural networks
(G-CNNs) that utilizes an ensemble of weakly trained G-CNNs, and reduce the
sensitivity of models on the choice of graph construction. We demonstrate its
effectiveness on the challenging Autism Brain Imaging Data Exchange (ABIDE)
dataset and show that our approach improves upon recently proposed graph-based
neural networks. We also show that our method remains more robust to noisy
graphs.
Continuously Differentiable Exponential Linear Units
Authors: Jonathan T. Barron
Date: 2017-04-24 00:00:00

Exponential Linear Units (ELUs) are a useful rectifier for constructing deep
learning architectures, as they may speed up and otherwise improve learning by
virtue of not have vanishing gradients and by having mean activations near
zero. However, the ELU activation as parametrized in [1] is not continuously
differentiable with respect to its input when the shape parameter alpha is not
equal to 1. We present an alternative parametrization which is C1 continuous
for all values of alpha, making the rectifier easier to reason about and making
alpha easier to tune. This alternative parametrization has several other useful
properties that the original parametrization of ELU does not: 1) its derivative
with respect to x is bounded, 2) it contains both the linear transfer function
and ReLU as special cases, and 3) it is scale-similar with respect to alpha.
Perishability of Data: Dynamic Pricing under Varying-Coefficient Models
Authors: Adel Javanmard
Date: 2017-01-13 00:00:00

We consider a firm that sells a large number of products to its customers in
an online fashion. Each product is described by a high dimensional feature
vector, and the market value of a product is assumed to be linear in the values
of its features. Parameters of the valuation model are unknown and can change
over time. The firm sequentially observes a product's features and can use the
historical sales data (binary sale/no sale feedbacks) to set the price of
current product, with the objective of maximizing the collected revenue. We
measure the performance of a dynamic pricing policy via regret, which is the
expected revenue loss compared to a clairvoyant that knows the sequence of
model parameters in advance.
  We propose a pricing policy based on projected stochastic gradient descent
(PSGD) and characterize its regret in terms of time $T$, features dimension
$d$, and the temporal variability in the model parameters, $\delta_t$. We
consider two settings. In the first one, feature vectors are chosen
antagonistically by nature and we prove that the regret of PSGD pricing policy
is of order $O(\sqrt{T} + \sum_{t=1}^T \sqrt{t}\delta_t)$. In the second
setting (referred to as stochastic features model), the feature vectors are
drawn independently from an unknown distribution. We show that in this case,
the regret of PSGD pricing policy is of order $O(d^2 \log T + \sum_{t=1}^T
t\delta_t/d)$.
Learning from Ontology Streams with Semantic Concept Drift
Authors: Freddy Lecue, Jiaoyan Chen, Jeff Pan, Huajun Chen
Date: 2017-04-24 00:00:00

Data stream learning has been largely studied for extracting knowledge
structures from continuous and rapid data records. In the semantic Web, data is
interpreted in ontologies and its ordered sequence is represented as an
ontology stream. Our work exploits the semantics of such streams to tackle the
problem of concept drift i.e., unexpected changes in data distribution, causing
most of models to be less accurate as time passes. To this end we revisited (i)
semantic inference in the context of supervised stream learning, and (ii)
models with semantic embeddings. The experiments show accurate prediction with
data from Dublin and Beijing.
Streaming Word Embeddings with the Space-Saving Algorithm
Authors: Chandler May, Kevin Duh, Benjamin Van Durme, Ashwin Lall
Date: 2017-04-24 00:00:00
Tasks: Word Embeddings

We develop a streaming (one-pass, bounded-memory) word embedding algorithm
based on the canonical skip-gram with negative sampling algorithm implemented
in word2vec. We compare our streaming algorithm to word2vec empirically by
measuring the cosine similarity between word pairs under each algorithm and by
applying each algorithm in the downstream task of hashtag prediction on a
two-month interval of the Twitter sample stream. We then discuss the results of
these experiments, concluding they provide partial validation of our approach
as a streaming replacement for word2vec. Finally, we discuss potential failure
modes and suggest directions for future work.
Denoising Linear Models with Permuted Data
Authors: Ashwin Pananjady, Martin J. Wainwright, Thomas A. Courtade
Date: 2017-04-24 00:00:00
Tasks: Denoising, regression

The multivariate linear regression model with shuffled data and additive
Gaussian noise arises in various correspondence estimation and matching
problems. Focusing on the denoising aspect of this problem, we provide a
characterization the minimax error rate that is sharp up to logarithmic
factors. We also analyze the performance of two versions of a computationally
efficient estimator, and establish their consistency for a large range of input
parameters. Finally, we provide an exact algorithm for the noiseless problem
and demonstrate its performance on an image point-cloud matching task. Our
analysis also extends to datasets with outliers.