pwc-archive/papers-with-abstracts huggingface dataset:
Proactive Decision Support using Automated Planning
Authors: Satya Gautam Vadlamudi, Tathagata Chakraborti, Yu Zhang, Subbarao Kambhampati
Date: 2016-06-24 00:00:00
Tasks: Decision Making

Proactive decision support (PDS) helps in improving the decision making
experience of human decision makers in human-in-the-loop planning environments.
Here both the quality of the decisions and the ease of making them are
enhanced. In this regard, we propose a PDS framework, named RADAR, based on the
research in Automated Planning in AI, that aids the human decision maker with
her plan to achieve her goals by providing alerts on: whether such a plan can
succeed at all, whether there exist any resource constraints that may foil her
plan, etc. This is achieved by generating and analyzing the landmarks that must
be accomplished by any successful plan on the way to achieving the goals. Note
that, this approach also supports naturalistic decision making which is being
acknowledged as a necessary element in proactive decision support, since it
only aids the human decision maker through suggestions and alerts rather than
enforcing fixed plans or decisions. We demonstrate the utility of the proposed
framework through search-and-rescue examples in a fire-fighting domain.
Modeling Group Dynamics Using Probabilistic Tensor Decompositions
Authors: Lin Li, Ananthram Swami, Anna Scaglione
Date: 2016-06-24 00:00:00
Tasks: parameter estimation

We propose a probabilistic modeling framework for learning the dynamic
patterns in the collective behaviors of social agents and developing profiles
for different behavioral groups, using data collected from multiple information
sources. The proposed model is based on a hierarchical Bayesian process, in
which each observation is a finite mixture of an set of latent groups and the
mixture proportions (i.e., group probabilities) are drawn randomly. Each group
is associated with some distributions over a finite set of outcomes. Moreover,
as time evolves, the structure of these groups also changes; we model the
change in the group structure by a hidden Markov model (HMM) with a fixed
transition probability. We present an efficient inference method based on
tensor decompositions and the expectation-maximization (EM) algorithm for
parameter estimation.
Spatial Aggregation of Holistically-Nested Networks for Automated Pancreas Segmentation
Authors: Holger R. Roth, Le Lu, Amal Farag, Andrew Sohn, Ronald M. Summers
Date: 2016-06-24 00:00:00
Tasks: Automated Pancreas Segmentation, Medical Image Analysis, Organ Segmentation, Pancreas Segmentation, Segmentation

Accurate automatic organ segmentation is an important yet challenging problem
for medical image analysis. The pancreas is an abdominal organ with very high
anatomical variability. This inhibits traditional segmentation methods from
achieving high accuracies, especially compared to other organs such as the
liver, heart or kidneys. In this paper, we present a holistic learning approach
that integrates semantic mid-level cues of deeply-learned organ interior and
boundary maps via robust spatial aggregation using random forest. Our method
generates boundary preserving pixel-wise class labels for pancreas
segmentation. Quantitative evaluation is performed on CT scans of 82 patients
in 4-fold cross-validation. We achieve a (mean $\pm$ std. dev.) Dice Similarity
Coefficient of 78.01% $\pm$ 8.2% in testing which significantly outperforms the
previous state-of-the-art approach of 71.8% $\pm$ 10.7% under the same
evaluation criterion.
Unsupervised Topic Modeling Approaches to Decision Summarization in Spoken Meetings
Authors: Lu Wang, Claire Cardie
Date: 2016-06-24 00:00:00
Proceeding: WS 2012 7
Tasks: Decision Making, Topic Models

We present a token-level decision summarization framework that utilizes the
latent topic structures of utterances to identify "summary-worthy" words.
Concretely, a series of unsupervised topic models is explored and experimental
results show that fine-grained topic models, which discover topics at the
utterance-level rather than the document-level, can better identify the gist of
the decision-making process. Moreover, our proposed token-level summarization
approach, which is able to remove redundancies within utterances, outperforms
existing utterance ranking based summarization methods. Finally, context
information is also investigated to add additional relevant information to the
summary.
Modeling and Inferring Human Intents and Latent Functional Objects for Trajectory Prediction
Authors: Dan Xie, Tianmin Shu, Sinisa Todorovic, Song-Chun Zhu
Date: 2016-06-24 00:00:00
Tasks: Clustering, Trajectory Prediction

This paper is about detecting functional objects and inferring human
intentions in surveillance videos of public spaces. People in the videos are
expected to intentionally take shortest paths toward functional objects subject
to obstacles, where people can satisfy certain needs (e.g., a vending machine
can quench thirst), by following one of three possible intent behaviors: reach
a single functional object and stop, or sequentially visit several functional
objects, or initially start moving toward one goal but then change the intent
to move toward another. Since detecting functional objects in low-resolution
surveillance videos is typically unreliable, we call them "dark matter"
characterized by the functionality to attract people. We formulate the
Agent-based Lagrangian Mechanics wherein human trajectories are
probabilistically modeled as motions of agents in many layers of "dark-energy"
fields, where each agent can select a particular force field to affect its
motions, and thus define the minimum-energy Dijkstra path toward the
corresponding source "dark matter". For evaluation, we compiled and annotated a
new dataset. The results demonstrate our effectiveness in predicting human
intent behaviors and trajectories, and localizing functional objects, as well
as discovering distinct functional classes of objects by clustering human
motion behavior in the vicinity of functional objects.
Efficient Parallel Learning of Word2Vec
Authors: Jeroen B. P. Vuurens, Carsten Eickhoff, Arjen P. de Vries
Date: 2016-06-24 00:00:00

Since its introduction, Word2Vec and its variants are widely used to learn
semantics-preserving representations of words or entities in an embedding
space, which can be used to produce state-of-art results for various Natural
Language Processing tasks. Existing implementations aim to learn efficiently by
running multiple threads in parallel while operating on a single model in
shared memory, ignoring incidental memory update collisions. We show that these
collisions can degrade the efficiency of parallel learning, and propose a
straightforward caching strategy that improves the efficiency by a factor of 4.
Wide & Deep Learning for Recommender Systems
Authors: Heng-Tze Cheng, Levent Koc, Jeremiah Harmsen, Tal Shaked, Tushar Chandra, Hrishi Aradhye, Glen Anderson, Greg Corrado, Wei Chai, Mustafa Ispir, Rohan Anil, Zakaria Haque, Lichan Hong, Vihan Jain, Xiaobing Liu, Hemal Shah
Date: 2016-06-24 00:00:00
Tasks: Click-Through Rate Prediction, Deep Learning, Feature Engineering, Memorization, News Recommendation, Recommendation Systems

Generalized linear models with nonlinear feature transformations are widely
used for large-scale regression and classification problems with sparse inputs.
Memorization of feature interactions through a wide set of cross-product
feature transformations are effective and interpretable, while generalization
requires more feature engineering effort. With less feature engineering, deep
neural networks can generalize better to unseen feature combinations through
low-dimensional dense embeddings learned for the sparse features. However, deep
neural networks with embeddings can over-generalize and recommend less relevant
items when the user-item interactions are sparse and high-rank. In this paper,
we present Wide & Deep learning---jointly trained wide linear models and deep
neural networks---to combine the benefits of memorization and generalization
for recommender systems. We productionized and evaluated the system on Google
Play, a commercial mobile app store with over one billion active users and over
one million apps. Online experiment results show that Wide & Deep significantly
increased app acquisitions compared with wide-only and deep-only models. We
have also open-sourced our implementation in TensorFlow.
Sequential Convolutional Neural Networks for Slot Filling in Spoken Language Understanding
Authors: Ngoc Thang Vu
Date: 2016-06-24 00:00:00
Tasks: General Classification, slot-filling, Slot Filling, Spoken Language Understanding

We investigate the usage of convolutional neural networks (CNNs) for the slot
filling task in spoken language understanding. We propose a novel CNN
architecture for sequence labeling which takes into account the previous
context words with preserved order information and pays special attention to
the current word with its surrounding context. Moreover, it combines the
information from the past and the future words for classification. Our proposed
CNN architecture outperforms even the previously best ensembling recurrent
neural network model and achieves state-of-the-art results with an F1-score of
95.61% on the ATIS benchmark dataset without using any additional linguistic
knowledge and resources.
Harnessing the Power of the Crowd to Increase Capacity for Data Science in the Social Sector
Authors: Peter Bull, Isaac Slavitt, Greg Lipstein
Date: 2016-06-24 00:00:00
Tasks: TAG

We present three case studies of organizations using a data science
competition to answer a pressing question. The first is in education where a
nonprofit that creates smart school budgets wanted to automatically tag budget
line items. The second is in public health, where a low-cost, nonprofit women's
health care provider wanted to understand the effect of demographic and
behavioral questions on predicting which services a woman would need. The third
and final example is in government innovation: using online restaurant reviews
from Yelp, competitors built models to forecast which restaurants were most
likely to have hygiene violations when visited by health inspectors. Finally,
we reflect on the unique benefits of the open, public competition model.
A Taxonomy and Library for Visualizing Learned Features in Convolutional Neural Networks
Authors: Felix Grün, Christian Rupprecht, Nassir Navab, Federico Tombari
Date: 2016-06-24 00:00:00

Over the last decade, Convolutional Neural Networks (CNN) saw a tremendous
surge in performance. However, understanding what a network has learned still
proves to be a challenging task. To remedy this unsatisfactory situation, a
number of groups have recently proposed different methods to visualize the
learned models. In this work we suggest a general taxonomy to classify and
compare these methods, subdividing the literature into three main categories
and providing researchers with a terminology to base their works on.
Furthermore, we introduce the FeatureVis library for MatConvNet: an extendable,
easy to use open source library for visualizing CNNs. It contains
implementations from each of the three main classes of visualization methods
and serves as a useful tool for an enhanced understanding of the features
learned by intermediate layers, as well as for the analysis of why a network
might fail for certain examples.
Issues in evaluating semantic spaces using word analogies
Authors: Tal Linzen
Date: 2016-06-24 00:00:00
Proceeding: WS 2016 8

The offset method for solving word analogies has become a standard evaluation
tool for vector-space semantic models: it is considered desirable for a space
to represent semantic relations as consistent vector offsets. We show that the
method's reliance on cosine similarity conflates offset consistency with
largely irrelevant neighborhood structure, and propose simple baselines that
should be used to improve the utility of the method in vector space evaluation.
Neural Network Based Next-Song Recommendation
Authors: Kai-Chun Hsu, Szu-Yu Chou, Yi-Hsuan Yang, Tai-Shih Chi
Date: 2016-06-24 00:00:00
Tasks: Recommendation Systems

Recently, the next-item/basket recommendation system, which considers the
sequential relation between bought items, has drawn attention of researchers.
The utilization of sequential patterns has boosted performance on several kinds
of recommendation tasks. Inspired by natural language processing (NLP)
techniques, we propose a novel neural network (NN) based next-song recommender,
CNN-rec, in this paper. Then, we compare the proposed system with several NN
based and classic recommendation systems on the next-song recommendation task.
Verification results indicate the proposed system outperforms classic systems
and has comparable performance with the state-of-the-art system.
Collective Semi-Supervised Learning for User Profiling in Social Media
Authors: Richard J. Oentaryo, Ee-Peng Lim, Freddy Chong Tat Chua, Jia-Wei Low, David Lo
Date: 2016-06-24 00:00:00

The abundance of user-generated data in social media has incentivized the
development of methods to infer the latent attributes of users, which are
crucially useful for personalization, advertising and recommendation. However,
the current user profiling approaches have limited success, due to the lack of
a principled way to integrate different types of social relationships of a
user, and the reliance on scarcely-available labeled data in building a
prediction model. In this paper, we present a novel solution termed Collective
Semi-Supervised Learning (CSL), which provides a principled means to integrate
different types of social relationship and unlabeled data under a unified
computational framework. The joint learning from multiple relationships and
unlabeled data yields a computationally sound and accurate approach to model
user attributes in social media. Extensive experiments using Twitter data have
demonstrated the efficacy of our CSL approach in inferring user attributes such
as account type and marital status. We also show how CSL can be used to
determine important user features, and to make inference on a larger user
population.
Text Understanding with the Attention Sum Reader Network
Authors: Rudolf Kadlec, Martin Schmid, Ondrej Bajgar, Jan Kleindienst
Date: 2016-03-04 00:00:00
Proceeding: ACL 2016 8
Tasks: Machine Reading Comprehension, Open-Domain Question Answering, Question Answering, Reading Comprehension

Several large cloze-style context-question-answer datasets have been
introduced recently: the CNN and Daily Mail news data and the Children's Book
Test. Thanks to the size of these datasets, the associated text comprehension
task is well suited for deep-learning techniques that currently seem to
outperform all alternative approaches. We present a new, simple model that uses
attention to directly pick the answer from the context as opposed to computing
the answer using a blended representation of words in the document as is usual
in similar models. This makes the model particularly suitable for
question-answering problems where the answer is a single word from the
document. Ensemble of our models sets new state of the art on all evaluated
datasets.
Evaluation method of word embedding by roots and affixes
Authors: KeBin Peng
Date: 2016-06-24 00:00:00

Word embedding has been shown to be remarkably effective in a lot of Natural
Language Processing tasks. However, existing models still have a couple of
limitations in interpreting the dimensions of word vector. In this paper, we
provide a new approach---roots and affixes model(RAAM)---to interpret it from
the intrinsic structures of natural language. Also it can be used as an
evaluation measure of the quality of word embedding. We introduce the
information entropy into our model and divide the dimensions into two
categories, just like roots and affixes in lexical semantics. Then considering
each category as a whole rather than individually. We experimented with English
Wikipedia corpus. Our result show that there is a negative linear relation
between the two attributes and a high positive correlation between our model
and downstream semantic evaluation tasks.
Combining local regularity estimation and total variation optimization for scale-free texture segmentation
Authors: Nelly Pustelnik, Herwig Wendt, Patrice Abry, Nicolas Dobigeon
Date: 2015-04-22 00:00:00
Tasks: Segmentation

Texture segmentation constitutes a standard image processing task, crucial to
many applications. The present contribution focuses on the particular subset of
scale-free textures and its originality resides in the combination of three key
ingredients: First, texture characterization relies on the concept of local
regularity ; Second, estimation of local regularity is based on new multiscale
quantities referred to as wavelet leaders ; Third, segmentation from local
regularity faces a fundamental bias variance trade-off: In nature, local
regularity estimation shows high variability that impairs the detection of
changes, while a posteriori smoothing of regularity estimates precludes from
locating correctly changes. Instead, the present contribution proposes several
variational problem formulations based on total variation and proximal
resolutions that effectively circumvent this trade-off. Estimation and
segmentation performance for the proposed procedures are quantified and
compared on synthetic as well as on real-world textures.
Regression Trees and Random forest based feature selection for malaria risk exposure prediction
Authors: Bienvenue Kouwayè
Date: 2016-06-24 00:00:00
Tasks: BIG-bench Machine Learning, CPU, feature selection, Malaria Risk Exposure Prediction, Prediction, regression

This paper deals with prediction of anopheles number, the main vector of
malaria risk, using environmental and climate variables. The variables
selection is based on an automatic machine learning method using regression
trees, and random forests combined with stratified two levels cross validation.
The minimum threshold of variables importance is accessed using the quadratic
distance of variables importance while the optimal subset of selected variables
is used to perform predictions. Finally the results revealed to be
qualitatively better, at the selection, the prediction , and the CPU time point
of view than those obtained by GLM-Lasso method.
Multipartite Ranking-Selection of Low-Dimensional Instances by Supervised Projection to High-Dimensional Space
Authors: Arash Shahriari
Date: 2016-06-24 00:00:00

Pruning of redundant or irrelevant instances of data is a key to every
successful solution for pattern recognition. In this paper, we present a novel
ranking-selection framework for low-length but highly correlated instances.
Instead of working in the low-dimensional instance space, we learn a supervised
projection to high-dimensional space spanned by the number of classes in the
dataset under study. Imposing higher distinctions via exposing the notion of
labels to the instances, lets to deploy one versus all ranking for each
individual classes and selecting quality instances via adaptive thresholding of
the overall scores. To prove the efficiency of our paradigm, we employ it for
the purpose of texture understanding which is a hard recognition challenge due
to high similarity of texture pixels and low dimensionality of their color
features. Our experiments show considerable improvements in recognition
performance over other local descriptors on several publicly available
datasets.
Adaptability of Neural Networks on Varying Granularity IR Tasks
Authors: Daniel Cohen, Qingyao Ai, W. Bruce Croft
Date: 2016-06-24 00:00:00
Tasks: Information Retrieval, Retrieval

Recent work in Information Retrieval (IR) using Deep Learning models has
yielded state of the art results on a variety of IR tasks. Deep neural networks
(DNN) are capable of learning ideal representations of data during the training
process, removing the need for independently extracting features. However, the
structures of these DNNs are often tailored to perform on specific datasets. In
addition, IR tasks deal with text at varying levels of granularity from single
factoids to documents containing thousands of words. In this paper, we examine
the role of the granularity on the performance of common state of the art DNN
structures in IR.
Learning Communities in the Presence of Errors
Authors: Konstantin Makarychev, Yury Makarychev, Aravindan Vijayaraghavan
Date: 2015-11-10 00:00:00
Tasks: Community Detection, graph partitioning, Open-Ended Question Answering, Stochastic Block Model

We study the problem of learning communities in the presence of modeling
errors and give robust recovery algorithms for the Stochastic Block Model
(SBM). This model, which is also known as the Planted Partition Model, is
widely used for community detection and graph partitioning in various fields,
including machine learning, statistics, and social sciences. Many algorithms
exist for learning communities in the Stochastic Block Model, but they do not
work well in the presence of errors.
  In this paper, we initiate the study of robust algorithms for partial
recovery in SBM with modeling errors or noise. We consider graphs generated
according to the Stochastic Block Model and then modified by an adversary. We
allow two types of adversarial errors, Feige---Kilian or monotone errors, and
edge outlier errors. Mossel, Neeman and Sly (STOC 2015) posed an open question
about whether an almost exact recovery is possible when the adversary is
allowed to add $o(n)$ edges. Our work answers this question affirmatively even
in the case of $k>2$ communities.
  We then show that our algorithms work not only when the instances come from
SBM, but also work when the instances come from any distribution of graphs that
is $\epsilon m$ close to SBM in the Kullback---Leibler divergence. This result
also works in the presence of adversarial errors. Finally, we present almost
tight lower bounds for two communities.
A Sentence Compression Based Framework to Query-Focused Multi-Document Summarization
Authors: Lu Wang, Hema Raghavan, Vittorio Castelli, Radu Florian, Claire Cardie
Date: 2016-06-24 00:00:00
Proceeding: ACL 2013 8
Tasks: Decoder, Document Summarization, Multi-Document Summarization, Sentence, Sentence Compression

We consider the problem of using sentence compression techniques to
facilitate query-focused multi-document summarization. We present a
sentence-compression-based framework for the task, and design a series of
learning-based compression models built on parse trees. An innovative beam
search decoder is proposed to efficiently find highly probable compressions.
Under this framework, we show how to integrate various indicative metrics such
as linguistic motivation and query relevance into the compression process by
deriving a novel formulation of a compression scoring function. Our best model
achieves statistically significant improvement over the state-of-the-art
systems on several metrics (e.g. 8.0% and 5.4% improvements in ROUGE-2
respectively) for the DUC 2006 and 2007 summarization task.
Interactive Semantic Featuring for Text Classification
Authors: Camille Jandot, Patrice Simard, Max Chickering, David Grangier, Jina Suh
Date: 2016-06-24 00:00:00
Tasks: Classification, General Classification, text-classification, Text Classification

In text classification, dictionaries can be used to define
human-comprehensible features. We propose an improvement to dictionary features
called smoothed dictionary features. These features recognize document contexts
instead of n-grams. We describe a principled methodology to solicit dictionary
features from a teacher, and present results showing that models built using
these human-comprehensible features are competitive with models trained with
Bag of Words features.