pwc-archive/papers-with-abstracts huggingface dataset:
LCrowdV: Generating Labeled Videos for Simulation-based Crowd Behavior Learning
Authors: Ernest Cheung, Tsan Kwong Wong, Aniket Bera, Xiaogang Wang, Dinesh Manocha
Date: 2016-06-29 00:00:00
Tasks: General Classification, Pedestrian Detection, Scene Understanding

We present a novel procedural framework to generate an arbitrary number of
labeled crowd videos (LCrowdV). The resulting crowd video datasets are used to
design accurate algorithms or training models for crowded scene understanding.
Our overall approach is composed of two components: a procedural simulation
framework for generating crowd movements and behaviors, and a procedural
rendering framework to generate different videos or images. Each video or image
is automatically labeled based on the environment, number of pedestrians,
density, behavior, flow, lighting conditions, viewpoint, noise, etc.
Furthermore, we can increase the realism by combining synthetically-generated
behaviors with real-world background videos. We demonstrate the benefits of
LCrowdV over prior lableled crowd datasets by improving the accuracy of
pedestrian detection and crowd behavior classification algorithms. LCrowdV
would be released on the WWW.
DeepFool: a simple and accurate method to fool deep neural networks
Authors: Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, Pascal Frossard
Date: 2015-11-14 00:00:00
Proceeding: CVPR 2016 6
Tasks: Adversarial Attack, General Classification, image-classification, Image Classification

State-of-the-art deep neural networks have achieved impressive results on
many image classification tasks. However, these same architectures have been
shown to be unstable to small, well sought, perturbations of the images.
Despite the importance of this phenomenon, no effective methods have been
proposed to accurately compute the robustness of state-of-the-art deep
classifiers to such perturbations on large-scale datasets. In this paper, we
fill this gap and propose the DeepFool algorithm to efficiently compute
perturbations that fool deep networks, and thus reliably quantify the
robustness of these classifiers. Extensive experimental results show that our
approach outperforms recent methods in the task of computing adversarial
perturbations and making classifiers more robust.
CFO: Conditional Focused Neural Question Answering with Large-scale Knowledge Bases
Authors: Zihang Dai, Lei LI, Wei Xu
Date: 2016-06-07 00:00:00
Proceeding: ACL 2016 8
Tasks: Question Answering

How can we enable computers to automatically answer questions like "Who
created the character Harry Potter"? Carefully built knowledge bases provide
rich sources of facts. However, it remains a challenge to answer factoid
questions raised in natural language due to numerous expressions of one
question. In particular, we focus on the most common questions --- ones that
can be answered with a single fact in the knowledge base. We propose CFO, a
Conditional Focused neural-network-based approach to answering factoid
questions with knowledge bases. Our approach first zooms in a question to find
more probable candidate subject mentions, and infers the final answers with a
unified conditional probabilistic framework. Powered by deep recurrent neural
networks and neural embeddings, our proposed CFO achieves an accuracy of 75.7%
on a dataset of 108k questions - the largest public one to date. It outperforms
the current state of the art by an absolute margin of 11.8%.
Coarse2Fine: Two-Layer Fusion For Image Retrieval
Authors: Gaipeng Kong, Le Dong, Wenpu Dong, Liang Zheng, Qi Tian
Date: 2016-07-04 00:00:00
Tasks: Image Retrieval, Retrieval, Vocal Bursts Valence Prediction

This paper addresses the problem of large-scale image retrieval. We propose a
two-layer fusion method which takes advantage of global and local cues and
ranks database images from coarse to fine (C2F). Departing from the previous
methods fusing multiple image descriptors simultaneously, C2F is featured by a
layered procedure composed by filtering and refining. In particular, C2F
consists of three components. 1) Distractor filtering. With holistic
representations, noise images are filtered out from the database, so the number
of candidate images to be used for comparison with the query can be greatly
reduced. 2) Adaptive weighting. For a certain query, the similarity of
candidate images can be estimated by holistic similarity scores in
complementary to the local ones. 3) Candidate refining. Accurate retrieval is
conducted via local features, combining the pre-computed adaptive weights.
Experiments are presented on two benchmarks, \emph{i.e.,} Holidays and Ukbench
datasets. We show that our method outperforms recent fusion methods in terms of
storage consumption and computation complexity, and that the accuracy is
competitive to the state-of-the-arts.
Towards Abstraction from Extraction: Multiple Timescale Gated Recurrent Unit for Summarization
Authors: Minsoo Kim, Moirangthem Dennis Singh, Minho Lee
Date: 2016-07-04 00:00:00
Proceeding: WS 2016 8
Tasks: Abstractive Text Summarization, Articles, Decoder

In this work, we introduce temporal hierarchies to the sequence to sequence
(seq2seq) model to tackle the problem of abstractive summarization of
scientific articles. The proposed Multiple Timescale model of the Gated
Recurrent Unit (MTGRU) is implemented in the encoder-decoder setting to better
deal with the presence of multiple compositionalities in larger texts. The
proposed model is compared to the conventional RNN encoder-decoder, and the
results demonstrate that our model trains faster and shows significant
performance gains. The results also show that the temporal hierarchies help
improve the ability of seq2seq models to capture compositionalities better
without the presence of highly complex architectural hierarchies.
Temporal Topic Analysis with Endogenous and Exogenous Processes
Authors: Baiyang Wang, Diego Klabjan
Date: 2016-07-04 00:00:00
Tasks: Articles

We consider the problem of modeling temporal textual data taking endogenous
and exogenous processes into account. Such text documents arise in real world
applications, including job advertisements and economic news articles, which
are influenced by the fluctuations of the general economy. We propose a
hierarchical Bayesian topic model which imposes a "group-correlated"
hierarchical structure on the evolution of topics over time incorporating both
processes, and show that this model can be estimated from Markov chain Monte
Carlo sampling methods. We further demonstrate that this model captures the
intrinsic relationships between the topic distribution and the time-dependent
factors, and compare its performance with latent Dirichlet allocation (LDA) and
two other related models. The model is applied to two collections of documents
to illustrate its empirical performance: online job advertisements from
DirectEmployers Association and journalists' postings on BusinessInsider.com.
Path planning with Inventory-driven Jump-Point-Search
Authors: Davide Aversa, Sebastian Sardina, Stavros Vassos
Date: 2016-07-04 00:00:00

In many navigational domains the traversability of cells is conditioned on
the path taken. This is often the case in video-games, in which a character may
need to acquire a certain object (i.e., a key or a flying suit) to be able to
traverse specific locations (e.g., doors or high walls). In order for
non-player characters to handle such scenarios we present invJPS, an
"inventory-driven" pathfinding approach based on the highly successful
grid-based Jump-Point-Search (JPS) algorithm. We show, formally and
experimentally, that the invJPS preserves JPS's optimality guarantees and its
symmetry breaking advantages in inventory-based variants of game maps.
A Semi-supervised learning approach to enhance health care Community-based Question Answering: A case study in alcoholism
Authors: Papis Wongchaisuwat, Diego Klabjan, Siddhartha R. Jonnalagadda
Date: 2016-07-04 00:00:00
Tasks: Information Retrieval, Question Answering, Retrieval, valid

Community-based Question Answering (CQA) sites play an important role in
addressing health information needs. However, a significant number of posted
questions remain unanswered. Automatically answering the posted questions can
provide a useful source of information for online health communities. In this
study, we developed an algorithm to automatically answer health-related
questions based on past questions and answers (QA). We also aimed to understand
information embedded within online health content that are good features in
identifying valid answers. Our proposed algorithm uses information retrieval
techniques to identify candidate answers from resolved QA. In order to rank
these candidates, we implemented a semi-supervised leaning algorithm that
extracts the best answer to a question. We assessed this approach on a curated
corpus from Yahoo! Answers and compared against a rule-based string similarity
baseline. On our dataset, the semi-supervised learning algorithm has an
accuracy of 86.2%. UMLS-based (health-related) features used in the model
enhance the algorithm's performance by proximately 8 %. A reasonably high rate
of accuracy is obtained given that the data is considerably noisy. Important
features distinguishing a valid answer from an invalid answer include text
length, number of stop words contained in a test question, a distance between
the test question and other questions in the corpus as well as a number of
overlapping health-related terms between questions. Overall, our automated QA
system based on historical QA pairs is shown to be effective according to the
data set in this case study. It is developed for general use in the health care
domain which can also be applied to other CQA sites.
Can we reach Pareto optimal outcomes using bottom-up approaches?
Authors: Victor Sanchez-Anguix, Reyhan Aydogan, Tim Baarslag, Catholijn M. Jonker
Date: 2016-07-03 00:00:00
Tasks: Decision Making

Traditionally, researchers in decision making have focused on attempting to
reach Pareto Optimality using horizontal approaches, where optimality is
calculated taking into account every participant at the same time. Sometimes,
this may prove to be a difficult task (e.g., conflict, mistrust, no information
sharing, etc.). In this paper, we explore the possibility of achieving Pareto
Optimal outcomes in a group by using a bottom-up approach: discovering Pareto
optimal outcomes by interacting in subgroups. We analytically show that Pareto
optimal outcomes in a subgroup are also Pareto optimal in a supergroup of those
agents in the case of strict, transitive, and complete preferences. Then, we
empirically analyze the prospective usability and practicality of bottom-up
approaches in a variety of decision making domains.
Community Structure in Industrial SAT Instances
Authors: Carlos Ansótegui, Maria Luisa Bonet, Jesús Giráldez-Cru, Jordi Levy, Laurent Simon
Date: 2016-06-10 00:00:00

Modern SAT solvers have experienced a remarkable progress on solving industrial instances. Most of the techniques have been developed after an intensive experimental process. It is believed that these techniques exploit the underlying structure of industrial instances. However, there are few works trying to exactly characterize the main features of this structure. The research community on complex networks has developed techniques of analysis and algorithms to study real-world graphs that can be used by the SAT community. Recently, there have been some attempts to analyze the structure of industrial SAT instances in terms of complex networks, with the aim of explaining the success of SAT solving techniques, and possibly improving them. In this paper, inspired by the results on complex networks, we study the community structure, or modularity, of industrial SAT instances. In a graph with clear community structure, or high modularity, we can find a partition of its nodes into communities such that most edges connect variables of the same community. In our analysis, we represent SAT instances as graphs, and we show that most application benchmarks are characterized by a high modularity. On the contrary, random SAT instances are closer to the classical Erd\"os-R\'enyi random graph model, where no structure can be observed. We also analyze how this structure evolves by the effects of the execution of a CDCL SAT solver. In particular, we use the community structure to detect that new clauses learned by the solver during the search contribute to destroy the original structure of the formula. This is, learned clauses tend to contain variables of distinct communities.
The VGLC: The Video Game Level Corpus
Authors: Adam James Summerville, Sam Snodgrass, Michael Mateas, Santiago Ontañón
Date: 2016-06-23 00:00:00
Tasks: BIG-bench Machine Learning

Levels are a key component of many different video games, and a large body of
work has been produced on how to procedurally generate game levels. Recently,
Machine Learning techniques have been applied to video game level generation
towards the purpose of automatically generating levels that have the properties
of the training corpus. Towards that end we have made available a corpora of
video game levels in an easy to parse format ideal for different machine
learning and other game AI research purposes.
Adaptive Smoothing Algorithms for Nonsmooth Composite Convex Minimization
Authors: Quoc Tran-Dinh
Date: 2015-09-01 00:00:00

We propose an adaptive smoothing algorithm based on Nesterov's smoothing
technique in \cite{Nesterov2005c} for solving "fully" nonsmooth composite
convex optimization problems. Our method combines both Nesterov's accelerated
proximal gradient scheme and a new homotopy strategy for smoothness parameter.
By an appropriate choice of smoothing functions, we develop a new algorithm
that has the $\mathcal{O}\left(\frac{1}{\varepsilon}\right)$-worst-case
iteration-complexity while preserves the same complexity-per-iteration as in
Nesterov's method and allows one to automatically update the smoothness
parameter at each iteration. Then, we customize our algorithm to solve four
special cases that cover various applications. We also specify our algorithm to
solve constrained convex optimization problems and show its convergence
guarantee on a primal sequence of iterates. We demonstrate our algorithm
through three numerical examples and compare it with other related algorithms.
Unsupervised Learning of 3D Structure from Images
Authors: Danilo Jimenez Rezende, S. M. Ali Eslami, Shakir Mohamed, Peter Battaglia, Max Jaderberg, Nicolas Heess
Date: 2016-07-03 00:00:00
Proceeding: NeurIPS 2016 12

A key goal of computer vision is to recover the underlying 3D structure from
2D observations of the world. In this paper we learn strong deep generative
models of 3D structures, and recover these structures from 3D and 2D images via
probabilistic inference. We demonstrate high-quality samples and report
log-likelihoods on several datasets, including ShapeNet [2], and establish the
first benchmarks in the literature. We also show how these models and their
inference networks can be trained end-to-end from 2D images. This demonstrates
for the first time the feasibility of learning to infer 3D representations of
the world in a purely unsupervised manner.
Robust Deep Appearance Models
Authors: Kha Gia Quach, Chi Nhan Duong, Khoa Luu, Tien D. Bui
Date: 2016-07-03 00:00:00

This paper presents a novel Robust Deep Appearance Models to learn the
non-linear correlation between shape and texture of face images. In this
approach, two crucial components of face images, i.e. shape and texture, are
represented by Deep Boltzmann Machines and Robust Deep Boltzmann Machines
(RDBM), respectively. The RDBM, an alternative form of Robust Boltzmann
Machines, can separate corrupted/occluded pixels in the texture modeling to
achieve better reconstruction results. The two models are connected by
Restricted Boltzmann Machines at the top layer to jointly learn and capture the
variations of both facial shapes and appearances. This paper also introduces
new fitting algorithms with occlusion awareness through the mask obtained from
the RDBM reconstruction. The proposed approach is evaluated in various
applications by using challenging face datasets, i.e. Labeled Face Parts in the
Wild (LFPW), Helen, EURECOM and AR databases, to demonstrate its robustness and
capabilities.
A Hybrid POMDP-BDI Agent Architecture with Online Stochastic Planning and Plan Caching
Authors: Gavin Rens, Deshendran Moodley
Date: 2016-07-03 00:00:00
Tasks: Management

This article presents an agent architecture for controlling an autonomous
agent in stochastic environments. The architecture combines the partially
observable Markov decision process (POMDP) model with the
belief-desire-intention (BDI) framework. The Hybrid POMDP-BDI agent
architecture takes the best features from the two approaches, that is, the
online generation of reward-maximizing courses of action from POMDP theory, and
sophisticated multiple goal management from BDI theory. We introduce the
advances made since the introduction of the basic architecture, including (i)
the ability to pursue multiple goals simultaneously and (ii) a plan library for
storing pre-written plans and for storing recently generated plans for future
reuse. A version of the architecture without the plan library is implemented
and is evaluated using simulations. The results of the simulation experiments
indicate that the approach is feasible.
Error-Resilient Machine Learning in Near Threshold Voltage via Classifier Ensemble
Authors: Sai Zhang, Naresh Shanbhag
Date: 2016-07-03 00:00:00
Tasks: BIG-bench Machine Learning

In this paper, we present the design of error-resilient machine learning
architectures by employing a distributed machine learning framework referred to
as classifier ensemble (CE). CE combines several simple classifiers to obtain a
strong one. In contrast, centralized machine learning employs a single complex
block. We compare the random forest (RF) and the support vector machine (SVM),
which are representative techniques from the CE and centralized frameworks,
respectively. Employing the dataset from UCI machine learning repository and
architectural-level error models in a commercial 45 nm CMOS process, it is
demonstrated that RF-based architectures are significantly more robust than SVM
architectures in presence of timing errors due to process variations in
near-threshold voltage (NTV) regions (0.3 V - 0.7 V). In particular, the RF
architecture exhibits a detection accuracy (P_{det}) that varies by 3.2% while
maintaining a median P_{det} > 0.9 at a gate level delay variation of 28.9% .
In comparison, SVM exhibits a P_{det} that varies by 16.8%. Additionally, we
propose an error weighted voting technique that incorporates the timing error
statistics of the NTV circuit fabric to further enhance robustness. Simulation
results confirm that the error weighted voting achieves a P_{det} that varies
by only 1.4%, which is 12X lower compared to SVM.
node2vec: Scalable Feature Learning for Networks
Authors: Aditya Grover, Jure Leskovec
Date: 2016-07-03 00:00:00
Tasks: Link Prediction, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Node Classification, Representation Learning

Prediction tasks over nodes and edges in networks require careful effort in
engineering features used by learning algorithms. Recent research in the
broader field of representation learning has led to significant progress in
automating prediction by learning the features themselves. However, present
feature learning approaches are not expressive enough to capture the diversity
of connectivity patterns observed in networks. Here we propose node2vec, an
algorithmic framework for learning continuous feature representations for nodes
in networks. In node2vec, we learn a mapping of nodes to a low-dimensional
space of features that maximizes the likelihood of preserving network
neighborhoods of nodes. We define a flexible notion of a node's network
neighborhood and design a biased random walk procedure, which efficiently
explores diverse neighborhoods. Our algorithm generalizes prior work which is
based on rigid notions of network neighborhoods, and we argue that the added
flexibility in exploring neighborhoods is the key to learning richer
representations. We demonstrate the efficacy of node2vec over existing
state-of-the-art techniques on multi-label classification and link prediction
in several real-world networks from diverse domains. Taken together, our work
represents a new way for efficiently learning state-of-the-art task-independent
representations in complex networks.
Visualizing Natural Language Descriptions: A Survey
Authors: Kaveh Hassani, Won-Sook Lee
Date: 2016-07-03 00:00:00
Tasks: Survey

A natural language interface exploits the conceptual simplicity and
naturalness of the language to create a high-level user-friendly communication
channel between humans and machines. One of the promising applications of such
interfaces is generating visual interpretations of semantic content of a given
natural language that can be then visualized either as a static scene or a
dynamic animation. This survey discusses requirements and challenges of
developing such systems and reports 26 graphical systems that exploit natural
language interfaces and addresses both artificial intelligence and
visualization aspects. This work serves as a frame of reference to researchers
and to enable further advances in the field.
DropNeuron: Simplifying the Structure of Deep Neural Networks
Authors: Wei Pan, Hao Dong, Yike Guo
Date: 2016-06-23 00:00:00

Deep learning using multi-layer neural networks (NNs) architecture manifests
superb power in modern machine learning systems. The trained Deep Neural
Networks (DNNs) are typically large. The question we would like to address is
whether it is possible to simplify the NN during training process to achieve a
reasonable performance within an acceptable computational time. We presented a
novel approach of optimising a deep neural network through regularisation of
net- work architecture. We proposed regularisers which support a simple
mechanism of dropping neurons during a network training process. The method
supports the construction of a simpler deep neural networks with compatible
performance with its simplified version. As a proof of concept, we evaluate the
proposed method with examples including sparse linear regression, deep
autoencoder and convolutional neural network. The valuations demonstrate
excellent performance.
  The code for this work can be found in
http://www.github.com/panweihit/DropNeuron
A Coarse-to-Fine Indoor Layout Estimation (CFILE) Method
Authors: Yuzhuo Ren, Chen Chen, Shang-Wen Li, C. -C. Jay Kuo
Date: 2016-07-03 00:00:00

The task of estimating the spatial layout of cluttered indoor scenes from a
single RGB image is addressed in this work. Existing solutions to this problems
largely rely on hand-craft features and vanishing lines, and they often fail in
highly cluttered indoor rooms. The proposed coarse-to-fine indoor layout
estimation (CFILE) method consists of two stages: 1) coarse layout estimation;
and 2) fine layout localization. In the first stage, we adopt a fully
convolutional neural network (FCN) to obtain a coarse-scale room layout
estimate that is close to the ground truth globally. The proposed FCN considers
combines the layout contour property and the surface property so as to provide
a robust estimate in the presence of cluttered objects. In the second stage, we
formulate an optimization framework that enforces several constraints such as
layout contour straightness, surface smoothness and geometric constraints for
layout detail refinement. Our proposed system offers the state-of-the-art
performance on two commonly used benchmark datasets.