pwc-archive/papers-with-abstracts huggingface dataset:
Learning Optimal Interventions
Authors: Jonas Mueller, David N. Reshef, George Du, Tommi Jaakkola
Date: 2016-06-16 00:00:00

Our goal is to identify beneficial interventions from observational data. We
consider interventions that are narrowly focused (impacting few covariates) and
may be tailored to each individual or globally enacted over a population. For
applications where harmful intervention is drastically worse than proposing no
change, we propose a conservative definition of the optimal intervention.
Assuming the underlying relationship remains invariant under intervention, we
develop efficient algorithms to identify the optimal intervention policy from
limited data and provide theoretical guarantees for our approach in a Gaussian
Process setting. Although our methods assume covariates can be precisely
adjusted, they remain capable of improving outcomes in misspecified settings
where interventions incur unintentional downstream effects. Empirically, our
approach identifies good interventions in two practical applications: gene
perturbation and writing improvement.
When Fashion Meets Big Data: Discriminative Mining of Best Selling Clothing Features
Authors: Kuan-Ting Chen, Jiebo Luo
Date: 2016-11-11 00:00:00
Tasks: Sociology

With the prevalence of e-commence websites and the ease of online shopping,
consumers are embracing huge amounts of various options in products.
Undeniably, shopping is one of the most essential activities in our society and
studying consumer's shopping behavior is important for the industry as well as
sociology and psychology. Indisputable, one of the most popular e-commerce
categories is clothing business. There arises the needs for analysis of popular
and attractive clothing features which could further boost many emerging
applications, such as clothing recommendation and advertising. In this work, we
design a novel system that consists of three major components: 1) exploring and
organizing a large-scale clothing dataset from a online shopping website, 2)
pruning and extracting images of best-selling products in clothing item data
and user transaction history, and 3) utilizing a machine learning based
approach to discovering fine-grained clothing attributes as the representative
and discriminative characteristics of popular clothing style elements. Through
the experiments over a large-scale online clothing shopping dataset, we
demonstrate the effectiveness of our proposed system, and obtain useful
insights on clothing consumption trends and profitable clothing features.
Discussion quality diffuses in the digital public square
Authors: George Berry, Sean J. Taylor
Date: 2017-02-22 00:00:00

Studies of online social influence have demonstrated that friends have
important effects on many types of behavior in a wide variety of settings.
However, we know much less about how influence works among relative strangers
in digital public squares, despite important conversations happening in such
spaces. We present the results of a study on large public Facebook pages where
we randomly used two different methods--most recent and social feedback--to
order comments on posts. We find that the social feedback condition results in
higher quality viewed comments and response comments. After measuring the
average quality of comments written by users before the study, we find that
social feedback has a positive effect on response quality for both low and high
quality commenters. We draw on a theoretical framework of social norms to
explain this empirical result. In order to examine the influence mechanism
further, we measure the similarity between comments viewed and written during
the study, finding that similarity increases for the highest quality
contributors under the social feedback condition. This suggests that, in
addition to norms, some individuals may respond with increased relevance to
high-quality comments.
Context-Aware Prediction of Derivational Word-forms
Authors: Ekaterina Vylomova, Ryan Cotterell, Timothy Baldwin, Trevor Cohn
Date: 2017-02-22 00:00:00
Proceeding: EACL 2017 4
Tasks: Decoder, Form, LEMMA, Prediction, valid

Derivational morphology is a fundamental and complex characteristic of
language. In this paper we propose the new task of predicting the derivational
form of a given base-form lemma that is appropriate for a given context. We
present an encoder--decoder style neural network to produce a derived form
character-by-character, based on its corresponding character-level
representation of the base form and the context. We demonstrate that our model
is able to generate valid context-sensitive derivations from known base forms,
but is less accurate under a lexicon agnostic setting.
Deep Learning with Dynamic Computation Graphs
Authors: Moshe Looks, Marcello Herreshoff, DeLesley Hutchins, Peter Norvig
Date: 2017-02-07 00:00:00
Tasks: Deep Learning

Neural networks that compute over graph structures are a natural fit for
problems in a variety of domains, including natural language (parse trees) and
cheminformatics (molecular graphs). However, since the computation graph has a
different shape and size for every input, such networks do not directly support
batched training or inference. They are also difficult to implement in popular
deep learning libraries, which are based on static data-flow graphs. We
introduce a technique called dynamic batching, which not only batches together
operations between different input graphs of dissimilar shape, but also between
different nodes within a single input graph. The technique allows us to create
static graphs, using popular libraries, that emulate dynamic computation graphs
of arbitrary shape and size. We further present a high-level library of
compositional blocks that simplifies the creation of dynamic graph models.
Using the library, we demonstrate concise and batch-wise parallel
implementations for a variety of models from the literature.
Calculating Probabilities Simplifies Word Learning
Authors: Aida Nematzadeh, Barend Beekhuizen, Shanshan Huang, Suzanne Stevenson
Date: 2017-02-22 00:00:00

Children can use the statistical regularities of their environment to learn
word meanings, a mechanism known as cross-situational learning. We take a
computational approach to investigate how the information present during each
observation in a cross-situational framework can affect the overall acquisition
of word meanings. We do so by formulating various in-the-moment learning
mechanisms that are sensitive to different statistics of the environment, such
as counts and conditional probabilities. Each mechanism introduces a unique
source of competition or mutual exclusivity bias to the model; the mechanism
that maximally uses the model's knowledge of word meanings performs the best.
Moreover, the gap between this mechanism and others is amplified in more
challenging learning scenarios, such as learning from few examples.
Guided Deep List: Automating the Generation of Epidemiological Line Lists from Open Sources
Authors: Saurav Ghosh, Prithwish Chakraborty, Bryan L. Lewis, Maimuna S. Majumder, Emily Cohn, John S. Brownstein, Madhav V. Marathe, Naren Ramakrishnan
Date: 2017-02-22 00:00:00
Tasks: Dependency Parsing, Epidemiology

Real-time monitoring and responses to emerging public health threats rely on
the availability of timely surveillance data. During the early stages of an
epidemic, the ready availability of line lists with detailed tabular
information about laboratory-confirmed cases can assist epidemiologists in
making reliable inferences and forecasts. Such inferences are crucial to
understand the epidemiology of a specific disease early enough to stop or
control the outbreak. However, construction of such line lists requires
considerable human supervision and therefore, difficult to generate in
real-time. In this paper, we motivate Guided Deep List, the first tool for
building automated line lists (in near real-time) from open source reports of
emerging disease outbreaks. Specifically, we focus on deriving epidemiological
characteristics of an emerging disease and the affected population from reports
of illness. Guided Deep List uses distributed vector representations (ala
word2vec) to discover a set of indicators for each line list feature. This
discovery of indicators is followed by the use of dependency parsing based
techniques for final extraction in tabular form. We evaluate the performance of
Guided Deep List against a human annotated line list provided by HealthMap
corresponding to MERS outbreaks in Saudi Arabia. We demonstrate that Guided
Deep List extracts line list features with increased accuracy compared to a
baseline method. We further show how these automatically extracted line list
features can be used for making epidemiological inferences, such as inferring
demographics and symptoms-to-hospitalization period of affected individuals.
An Integer Programming Model for Binary Knapsack Problem with Value-Related Dependencies among Elements
Authors: Davoud Mougouei, David M. W. Powers, Asghar Moeini
Date: 2017-02-22 00:00:00

Binary Knapsack Problem (BKP) is to select a subset of an element (item) set
with the highest value while keeping the total weight within the capacity of
the knapsack. This paper presents an integer programming model for a variation
of BKP where the value of each element may depend on selecting or ignoring
other elements. Strengths of such Value-Related Dependencies are assumed to be
imprecise and hard to specify. To capture this imprecision, we have proposed
modeling value-related dependencies using fuzzy graphs and their algebraic
structure.
Social Learning and Diffusion of Pervasive Goods: An Empirical Study of an African App Store
Authors: Meisam Hejazi Nia, Brian T. Ratchford, Norris Bruce
Date: 2017-02-22 00:00:00
Tasks: counterfactual, Marketing

In this study, the authors develop a structural model that combines a macro
diffusion model with a micro choice model to control for the effect of social
influence on the mobile app choices of customers over app stores. Social
influence refers to the density of adopters within the proximity of other
customers. Using a large data set from an African app store and Bayesian
estimation methods, the authors quantify the effect of social influence and
investigate the impact of ignoring this process in estimating customer choices.
The findings show that customer choices in the app store are explained better
by offline than online density of adopters and that ignoring social influence
in estimations results in biased estimates. Furthermore, the findings show that
the mobile app adoption process is similar to adoption of music CDs, among all
other classic economy goods. A counterfactual analysis shows that the app store
can increase its revenue by 13.6% through a viral marketing policy (e.g., a
sharing with friends and family button).
Lensless Photography with only an image sensor
Authors: Ganghun Kim, Kyle Isaacson, Racheal Palmer, Rajesh Menon
Date: 2017-02-21 00:00:00

Photography usually requires optics in conjunction with a recording device
(an image sensor). Eliminating the optics could lead to new form factors for
cameras. Here, we report a simple demonstration of imaging using a bare CMOS
sensor that utilizes computation. The technique relies on the space variant
point-spread functions resulting from the interaction of a point source in the
field of view with the image sensor. These space-variant point-spread functions
are combined with a reconstruction algorithm in order to image simple objects
displayed on a discrete LED array as well as on an LCD screen. We extended the
approach to video imaging at the native frame rate of the sensor. Finally, we
performed experiments to analyze the parametric impact of the object distance.
Improving the sensor designs and reconstruction algorithms can lead to useful
cameras without optics.
Weakly Supervised PatchNets: Describing and Aggregating Local Patches for Scene Recognition
Authors: Zhe Wang, Li-Min Wang, Yali Wang, Bo-Wen Zhang, Yu Qiao
Date: 2016-09-01 00:00:00
Tasks: Scene Recognition

Traditional feature encoding scheme (e.g., Fisher vector) with local
descriptors (e.g., SIFT) and recent convolutional neural networks (CNNs) are
two classes of successful methods for image recognition. In this paper, we
propose a hybrid representation, which leverages the discriminative capacity of
CNNs and the simplicity of descriptor encoding schema for image recognition,
with a focus on scene recognition. To this end, we make three main
contributions from the following aspects. First, we propose a patch-level and
end-to-end architecture to model the appearance of local patches, called {\em
PatchNet}. PatchNet is essentially a customized network trained in a weakly
supervised manner, which uses the image-level supervision to guide the
patch-level feature extraction. Second, we present a hybrid visual
representation, called {\em VSAD}, by utilizing the robust feature
representations of PatchNet to describe local patches and exploiting the
semantic probabilities of PatchNet to aggregate these local patches into a
global representation. Third, based on the proposed VSAD representation, we
propose a new state-of-the-art scene recognition approach, which achieves an
excellent performance on two standard benchmarks: MIT Indoor67 (86.2\%) and
SUN397 (73.0\%).
Knowledge Guided Disambiguation for Large-Scale Scene Classification with Multi-Resolution CNNs
Authors: Limin Wang, Sheng Guo, Weilin Huang, Yuanjun Xiong, Yu Qiao
Date: 2016-10-04 00:00:00
Tasks: General Classification, Scene Classification, Scene Recognition

Convolutional Neural Networks (CNNs) have made remarkable progress on scene
recognition, partially due to these recent large-scale scene datasets, such as
the Places and Places2. Scene categories are often defined by multi-level
information, including local objects, global layout, and background
environment, thus leading to large intra-class variations. In addition, with
the increasing number of scene categories, label ambiguity has become another
crucial issue in large-scale classification. This paper focuses on large-scale
scene recognition and makes two major contributions to tackle these issues.
First, we propose a multi-resolution CNN architecture that captures visual
content and structure at multiple levels. The multi-resolution CNNs are
composed of coarse resolution CNNs and fine resolution CNNs, which are
complementary to each other. Second, we design two knowledge guided
disambiguation techniques to deal with the problem of label ambiguity. (i) We
exploit the knowledge from the confusion matrix computed on validation data to
merge ambiguous classes into a super category. (ii) We utilize the knowledge of
extra networks to produce a soft label for each image. Then the super
categories or soft labels are employed to guide CNN training on the Places2. We
conduct extensive experiments on three large-scale image datasets (ImageNet,
Places, and Places2), demonstrating the effectiveness of our approach.
Furthermore, our method takes part in two major scene recognition challenges,
and achieves the second place at the Places2 challenge in ILSVRC 2015, and the
first place at the LSUN challenge in CVPR 2016. Finally, we directly test the
learned representations on other scene benchmarks, and obtain the new
state-of-the-art results on the MIT Indoor67 (86.7\%) and SUN397 (72.0\%). We
release the code and models
at~\url{https://github.com/wanglimin/MRCNN-Scene-Recognition}.
DSD: Dense-Sparse-Dense Training for Deep Neural Networks
Authors: Song Han, Jeff Pool, Sharan Narang, Huizi Mao, Enhao Gong, Shijian Tang, Erich Elsen, Peter Vajda, Manohar Paluri, John Tran, Bryan Catanzaro, William J. Dally
Date: 2016-07-15 00:00:00
Tasks: 8k, Caption Generation, image-classification, Image Classification, speech-recognition, Speech Recognition

Modern deep neural networks have a large number of parameters, making them
very hard to train. We propose DSD, a dense-sparse-dense training flow, for
regularizing deep neural networks and achieving better optimization
performance. In the first D (Dense) step, we train a dense network to learn
connection weights and importance. In the S (Sparse) step, we regularize the
network by pruning the unimportant connections with small weights and
retraining the network given the sparsity constraint. In the final D (re-Dense)
step, we increase the model capacity by removing the sparsity constraint,
re-initialize the pruned parameters from zero and retrain the whole dense
network. Experiments show that DSD training can improve the performance for a
wide range of CNNs, RNNs and LSTMs on the tasks of image classification,
caption generation and speech recognition. On ImageNet, DSD improved the Top1
accuracy of GoogLeNet by 1.1%, VGG-16 by 4.3%, ResNet-18 by 1.2% and ResNet-50
by 1.1%, respectively. On the WSJ'93 dataset, DSD improved DeepSpeech and
DeepSpeech2 WER by 2.0% and 1.1%. On the Flickr-8K dataset, DSD improved the
NeuralTalk BLEU score by over 1.7. DSD is easy to use in practice: at training
time, DSD incurs only one extra hyper-parameter: the sparsity ratio in the S
step. At testing time, DSD doesn't change the network architecture or incur any
inference overhead. The consistent and significant performance gain of DSD
experiments shows the inadequacy of the current training methods for finding
the best local optimum, while DSD effectively achieves superior optimization
performance for finding a better solution. DSD models are available to download
at https://songhan.github.io/DSD.
Maximally Correlated Principal Component Analysis
Authors: Soheil Feizi, David Tse
Date: 2017-02-17 00:00:00
Tasks: Dimensionality Reduction

In the era of big data, reducing data dimensionality is critical in many
areas of science. Widely used Principal Component Analysis (PCA) addresses this
problem by computing a low dimensional data embedding that maximally explain
variance of the data. However, PCA has two major weaknesses. Firstly, it only
considers linear correlations among variables (features), and secondly it is
not suitable for categorical data. We resolve these issues by proposing
Maximally Correlated Principal Component Analysis (MCPCA). MCPCA computes
transformations of variables whose covariance matrix has the largest Ky Fan
norm. Variable transformations are unknown, can be nonlinear and are computed
in an optimization. MCPCA can also be viewed as a multivariate extension of
Maximal Correlation. For jointly Gaussian variables we show that the covariance
matrix corresponding to the identity (or the negative of the identity)
transformations majorizes covariance matrices of non-identity functions. Using
this result we characterize global MCPCA optimizers for nonlinear functions of
jointly Gaussian variables for every rank constraint. For categorical variables
we characterize global MCPCA optimizers for the rank one constraint based on
the leading eigenvector of a matrix computed using pairwise joint
distributions. For a general rank constraint we propose a block coordinate
descend algorithm and show its convergence to stationary points of the MCPCA
optimization. We compare MCPCA with PCA and other state-of-the-art
dimensionality reduction methods including Isomap, LLE, multilayer autoencoders
(neural networks), kernel PCA, probabilistic PCA and diffusion maps on several
synthetic and real datasets. We show that MCPCA consistently provides improved
performance compared to other methods.
Active One-shot Learning
Authors: Mark Woodward, Chelsea Finn
Date: 2017-02-21 00:00:00
Tasks: Classification, General Classification, One-Shot Learning, Reinforcement Learning, Reinforcement Learning (RL)

Recent advances in one-shot learning have produced models that can learn from
a handful of labeled examples, for passive classification and regression tasks.
This paper combines reinforcement learning with one-shot learning, allowing the
model to decide, during classification, which examples are worth labeling. We
introduce a classification task in which a stream of images are presented and,
on each time step, a decision must be made to either predict a label or pay to
receive the correct label. We present a recurrent neural network based
action-value function, and demonstrate its ability to learn how and when to
request labels. Through the choice of reward function, the model can achieve a
higher prediction accuracy than a similar model on a purely supervised task, or
trade prediction accuracy for fewer label requests.
Adversarially Learned Inference
Authors: Vincent Dumoulin, Ishmael Belghazi, Ben Poole, Olivier Mastropietro, Alex Lamb, Martin Arjovsky, Aaron Courville
Date: 2016-06-02 00:00:00
Tasks: Image Generation, Image-to-Image Translation

We introduce the adversarially learned inference (ALI) model, which jointly
learns a generation network and an inference network using an adversarial
process. The generation network maps samples from stochastic latent variables
to the data space while the inference network maps training examples in data
space to the space of latent variables. An adversarial game is cast between
these two networks and a discriminative network is trained to distinguish
between joint latent/data-space samples from the generative network and joint
samples from the inference network. We illustrate the ability of the model to
learn mutually coherent inference and generation networks through the
inspections of model samples and reconstructions and confirm the usefulness of
the learned representations by obtaining a performance competitive with
state-of-the-art on the semi-supervised SVHN and CIFAR10 tasks.
Algorithmes de classification et d'optimisation: participation du LIA/ADOC á DEFT'14
Authors: Luis Adrián Cabrera-Diego, Stéphane Huet, Bassam Jabaian, Alejandro Molina, Juan-Manuel Torres-Moreno, Marc El-Bèze, Barthélémy Durette
Date: 2017-02-21 00:00:00
Tasks: Articles, General Classification

This year, the DEFT campaign (D\'efi Fouilles de Textes) incorporates a task
which aims at identifying the session in which articles of previous TALN
conferences were presented. We describe the three statistical systems developed
at LIA/ADOC for this task. A fusion of these systems enables us to obtain
interesting results (micro-precision score of 0.76 measured on the test corpus)
PixelNet: Representation of the pixels, by the pixels, and for the pixels
Authors: Aayush Bansal, Xinlei Chen, Bryan Russell, Abhinav Gupta, Deva Ramanan
Date: 2017-02-21 00:00:00
Tasks: Diversity, Edge Detection, Segmentation, Semantic Segmentation, Surface Normal Estimation

We explore design principles for general pixel-level prediction problems,
from low-level edge detection to mid-level surface normal estimation to
high-level semantic segmentation. Convolutional predictors, such as the
fully-convolutional network (FCN), have achieved remarkable success by
exploiting the spatial redundancy of neighboring pixels through convolutional
processing. Though computationally efficient, we point out that such approaches
are not statistically efficient during learning precisely because spatial
redundancy limits the information learned from neighboring pixels. We
demonstrate that stratified sampling of pixels allows one to (1) add diversity
during batch updates, speeding up learning; (2) explore complex nonlinear
predictors, improving accuracy; and (3) efficiently train state-of-the-art
models tabula rasa (i.e., "from scratch") for diverse pixel-labeling tasks. Our
single architecture produces state-of-the-art results for semantic segmentation
on PASCAL-Context dataset, surface normal estimation on NYUDv2 depth dataset,
and edge detection on BSDS.