pwc-archive/papers-with-abstracts huggingface dataset:
Learning Adversary Behavior in Security Games: A PAC Model Perspective
Authors: Arunesh Sinha, Debarun Kar, Milind Tambe
Date: 2015-10-30 00:00:00

Recent applications of Stackelberg Security Games (SSG), from wildlife crime
to urban crime, have employed machine learning tools to learn and predict
adversary behavior using available data about defender-adversary interactions.
Given these recent developments, this paper commits to an approach of directly
learning the response function of the adversary. Using the PAC model, this
paper lays a firm theoretical foundation for learning in SSGs (e.g.,
theoretically answer questions about the numbers of samples required to learn
adversary behavior) and provides utility guarantees when the learned adversary
model is used to plan the defender's strategy. The paper also aims to answer
practical questions such as how much more data is needed to improve an
adversary model's accuracy. Additionally, we explain a recently observed
phenomenon that prediction accuracy of learned adversary behavior is not enough
to discover the utility maximizing defender strategy. We provide four main
contributions: (1) a PAC model of learning adversary response functions in
SSGs; (2) PAC-model analysis of the learning of key, existing bounded
rationality models in SSGs; (3) an entirely new approach to adversary modeling
based on a non-parametric class of response functions with PAC-model analysis
and (4) identification of conditions under which computing the best defender
strategy against the learned adversary behavior is indeed the optimal strategy.
Finally, we conduct experiments with real-world data from a national park in
Uganda, showing the benefit of our new adversary modeling approach and
verification of our PAC model predictions.
WIDER FACE: A Face Detection Benchmark
Authors: Shuo Yang, Ping Luo, Chen Change Loy, Xiaoou Tang
Date: 2015-11-20 00:00:00
Proceeding: CVPR 2016 6
Tasks: Face Detection

Face detection is one of the most studied topics in the computer vision
community. Much of the progresses have been made by the availability of face
detection benchmark datasets. We show that there is a gap between current face
detection performance and the real world requirements. To facilitate future
face detection research, we introduce the WIDER FACE dataset, which is 10 times
larger than existing datasets. The dataset contains rich annotations, including
occlusions, poses, event categories, and face bounding boxes. Faces in the
proposed dataset are extremely challenging due to large variations in scale,
pose and occlusion, as shown in Fig. 1. Furthermore, we show that WIDER FACE
dataset is an effective training source for face detection. We benchmark
several representative detection systems, providing an overview of
state-of-the-art performance and propose a solution to deal with large scale
variation. Finally, we discuss common failure cases that worth to be further
investigated. Dataset can be downloaded at:
mmlab.ie.cuhk.edu.hk/projects/WIDERFace
A Distribution Adaptive Framework for Prediction Interval Estimation Using Nominal Variables
Authors: Ameen Eetemadi, Ilias Tagkopoulos
Date: 2015-11-18 00:00:00
Tasks: Prediction, Prediction Intervals

Proposed methods for prediction interval estimation so far focus on cases
where input variables are numerical. In datasets with solely nominal input
variables, we observe records with the exact same input $x^u$, but different
real valued outputs due to the inherent noise in the system. Existing
prediction interval estimation methods do not use representations that can
accurately model such inherent noise in the case of nominal inputs. We propose
a new prediction interval estimation method tailored for this type of data,
which is prevalent in biology and medicine. We call this method Distribution
Adaptive Prediction Interval Estimation given Nominal inputs (DAPIEN) and has
four main phases. First, we select a distribution function that can best
represent the inherent noise of the system for all unique inputs. Then we infer
the parameters $\theta_i$ (e.g. $\theta_i=[mean_i, variance_i]$) of the
selected distribution function for all unique input vectors $x^u_i$ and
generate a new corresponding training set using pairs of $x^u_i, \theta_i$.
III). Then, we train a model to predict $\theta$ given a new $x_u$. Finally, we
calculate the prediction interval for a new sample using the inverse of the
cumulative distribution function once the parameters $\theta$ is predicted by
the trained model. We compared DAPIEN to the commonly used Bootstrap method on
three synthetic datasets. Our results show that DAPIEN provides tighter
prediction intervals while preserving the requested coverage when compared to
Bootstrap. This work can facilitate broader usage of regression methods in
medicine and biology where it is necessary to provide tight prediction
intervals while preserving coverage when input variables are nominal.
Flattened Convolutional Neural Networks for Feedforward Acceleration
Authors: Jonghoon Jin, Aysegul Dundar, Eugenio Culurciello
Date: 2014-12-17 00:00:00

We present flattened convolutional neural networks that are designed for fast
feedforward execution. The redundancy of the parameters, especially weights of
the convolutional filters in convolutional neural networks has been extensively
studied and different heuristics have been proposed to construct a low rank
basis of the filters after training. In this work, we train flattened networks
that consist of consecutive sequence of one-dimensional filters across all
directions in 3D space to obtain comparable performance as conventional
convolutional networks. We tested flattened model on different datasets and
found that the flattened layer can effectively substitute for the 3D filters
without loss of accuracy. The flattened convolution pipelines provide around
two times speed-up during feedforward pass compared to the baseline model due
to the significant reduction of learning parameters. Furthermore, the proposed
method does not require efforts in manual tuning or post processing once the
model is trained.
Bidirectional Warping of Active Appearance Model
Authors: Ali Mollahosseini, Mohammad H. Mahoor
Date: 2015-11-20 00:00:00
Tasks: Face Identification, Facial Expression Recognition, Facial Expression Recognition (FER), model

Active Appearance Model (AAM) is a commonly used method for facial image
analysis with applications in face identification and facial expression
recognition. This paper proposes a new approach based on image alignment for
AAM fitting called bidirectional warping. Previous approaches warp either the
input image or the appearance template. We propose to warp both the input
image, using incremental update by an affine transformation, and the appearance
template, using an inverse compositional approach. Our experimental results on
Multi-PIE face database show that the bidirectional approach outperforms
state-of-the-art inverse compositional fitting approaches in extracting
landmark points of faces with shape and pose variations.
A Simple Hierarchical Pooling Data Structure for Loop Closure
Authors: Xiaohan Fei, Konstantine Tsotsos, Stefano Soatto
Date: 2015-11-20 00:00:00

We propose a data structure obtained by hierarchically averaging bag-of-word
descriptors during a sequence of views that achieves average speedups in
large-scale loop closure applications ranging from 4 to 20 times on benchmark
datasets. Although simple, the method works as well as sophisticated
agglomerative schemes at a fraction of the cost with minimal loss of
performance.
Fast Multi-class Dictionaries Learning with Geometrical Directions in MRI Reconstruction
Authors: Zhifang Zhan, Jian-Feng Cai, Di Guo, Yunsong Liu, Zhong Chen, Xiaobo Qu
Date: 2015-03-10 00:00:00
Tasks: Dictionary Learning, Image Reconstruction, MRI Reconstruction

Objective: Improve the reconstructed image with fast and multi-class
dictionaries learning when magnetic resonance imaging is accelerated by
undersampling the k-space data. Methods: A fast orthogonal dictionary learning
method is introduced into magnetic resonance image reconstruction to providing
adaptive sparse representation of images. To enhance the sparsity, image is
divided into classified patches according to the same geometrical direction and
dictionary is trained within each class. A new sparse reconstruction model with
the multi-class dictionaries is proposed and solved using a fast alternating
direction method of multipliers. Results: Experiments on phantom and brain
imaging data with acceleration factor up to 10 and various undersampling
patterns are conducted. The proposed method is compared with state-of-the-art
magnetic resonance image reconstruction methods. Conclusion: Artifacts are
better suppressed and image edges are better preserved than the compared
methods. Besides, the computation of the proposed approach is much faster than
the typical K-SVD dictionary learning method in magnetic resonance image
reconstruction. Significance: The proposed method can be exploited in
undersapmled magnetic resonance imaging to reduce data acquisition time and
reconstruct images with better image quality.
Tract Orientation and Angular Dispersion Deviation Indicator (TOADDI): A framework for single-subject analysis in diffusion tensor imaging
Authors: Cheng Guan Koay, Ping-Hong Yeh, John M. Ollinger, M. Okan İrfanoğlu, Carlo Pierpaoli, Peter J. Basser, Terrence R. Oakes, Gerard Riedy
Date: 2015-10-10 00:00:00

The purpose of this work is to develop a framework for single-subject
analysis of diffusion tensor imaging (DTI) data. This framework (termed TOADDI)
is capable of testing whether an individual tract as represented by the major
eigenvector of the diffusion tensor and its corresponding angular dispersion
are significantly different from a group of tracts on a voxel-by-voxel basis.
This work develops two complementary statistical tests based on the elliptical
cone of uncertainty (COU), which is a model of uncertainty or dispersion of the
major eigenvector of the diffusion tensor. The orientation deviation test
examines whether the major eigenvector from a single subject is within the
average elliptical COU formed by a collection of elliptical COUs. The shape
deviation test is based on the two-tailed Wilcoxon-Mann-Whitney two-sample test
between the normalized shape measures (area and circumference) of the
elliptical cones of uncertainty of the single subject against a group of
controls. The False Discovery Rate (FDR) and False Non-discovery Rate (FNR)
were incorporated in the orientation deviation test. The shape deviation test
uses FDR only. TOADDI was found to be numerically accurate and statistically
effective. Clinical data from two Traumatic Brain Injury (TBI) patients and one
non-TBI subject were tested against the data obtained from a group of 45
non-TBI controls to illustrate the application of the proposed framework in
single-subject analysis. The frontal portion of the superior longitudinal
fasciculus seemed to be implicated in both tests as significantly different
from that of the control group. The TBI patients and the single non-TBI subject
were well separated under the shape deviation test at the chosen FDR level of
0.0005. TOADDI is a simple but novel geometrically based statistical framework
for analyzing DTI data.
Joint Inverse Covariances Estimation with Mutual Linear Structure
Authors: Ilya Soloveychik, Ami Wiesel
Date: 2015-11-20 00:00:00

We consider the problem of joint estimation of structured inverse covariance
matrices. We perform the estimation using groups of measurements with different
covariances of the same unknown structure. Assuming the inverse covariances to
span a low dimensional linear subspace in the space of symmetric matrices, our
aim is to determine this structure. It is then utilized to improve the
estimation of the inverse covariances. We propose a novel optimization
algorithm discovering and exploiting the underlying structure and provide its
efficient implementation. Numerical simulations are presented to illustrate the
performance benefits of the proposed algorithm.
Why are deep nets reversible: A simple theory, with implications for training
Authors: Sanjeev Arora, YIngyu Liang, Tengyu Ma
Date: 2015-11-18 00:00:00
Tasks: Denoising

Generative models for deep learning are promising both to improve
understanding of the model, and yield training methods requiring fewer labeled
samples.
  Recent works use generative model approaches to produce the deep net's input
given the value of a hidden layer several levels above. However, there is no
accompanying "proof of correctness" for the generative model, showing that the
feedforward deep net is the correct inference method for recovering the hidden
layer given the input. Furthermore, these models are complicated.
  The current paper takes a more theoretical tack. It presents a very simple
generative model for RELU deep nets, with the following characteristics: (i)
The generative model is just the reverse of the feedforward net: if the forward
transformation at a layer is $A$ then the reverse transformation is $A^T$.
(This can be seen as an explanation of the old weight tying idea for denoising
autoencoders.) (ii) Its correctness can be proven under a clean theoretical
assumption: the edge weights in real-life deep nets behave like random numbers.
Under this assumption ---which is experimentally tested on real-life nets like
AlexNet--- it is formally proved that feed forward net is a correct inference
method for recovering the hidden layer.
  The generative model suggests a simple modification for training: use the
generative model to produce synthetic data with labels and include it in the
training set. Experiments are shown to support this theory of random-like deep
nets; and that it helps the training.
No-Regret Learning in Bayesian Games
Authors: Jason Hartline, Vasilis Syrgkanis, Eva Tardos
Date: 2015-07-02 00:00:00
Proceeding: NeurIPS 2015 12

Recent price-of-anarchy analyses of games of complete information suggest
that coarse correlated equilibria, which characterize outcomes resulting from
no-regret learning dynamics, have near-optimal welfare. This work provides two
main technical results that lift this conclusion to games of incomplete
information, a.k.a., Bayesian games. First, near-optimal welfare in Bayesian
games follows directly from the smoothness-based proof of near-optimal welfare
in the same game when the private information is public. Second, no-regret
learning dynamics converge to Bayesian coarse correlated equilibrium in these
incomplete information games. These results are enabled by interpretation of a
Bayesian game as a stochastic game of complete information.
Deep Metric Learning via Lifted Structured Feature Embedding
Authors: Hyun Oh Song, Yu Xiang, Stefanie Jegelka, Silvio Savarese
Date: 2015-11-19 00:00:00
Proceeding: CVPR 2016 6
Tasks: Metric Learning, Structured Prediction

Learning the distance metric between pairs of examples is of great importance
for learning and visual recognition. With the remarkable success from the state
of the art convolutional neural networks, recent works have shown promising
results on discriminatively training the networks to learn semantic feature
embeddings where similar examples are mapped close to each other and dissimilar
examples are mapped farther apart. In this paper, we describe an algorithm for
taking full advantage of the training batches in the neural network training by
lifting the vector of pairwise distances within the batch to the matrix of
pairwise distances. This step enables the algorithm to learn the state of the
art feature embedding by optimizing a novel structured prediction objective on
the lifted problem. Additionally, we collected Online Products dataset: 120k
images of 23k classes of online products for metric learning. Our experiments
on the CUB-200-2011, CARS196, and Online Products datasets demonstrate
significant improvement over existing deep feature embedding methods on all
experimented embedding sizes with the GoogLeNet network.
Learning to Represent Words in Context with Multilingual Supervision
Authors: Kazuya Kawakami, Chris Dyer
Date: 2015-11-14 00:00:00
Tasks: Machine Translation, Translation

We present a neural network architecture based on bidirectional LSTMs to
compute representations of words in the sentential contexts. These
context-sensitive word representations are suitable for, e.g., distinguishing
different word senses and other context-modulated variations in meaning. To
learn the parameters of our model, we use cross-lingual supervision,
hypothesizing that a good representation of a word in context will be one that
is sufficient for selecting the correct translation into a second language. We
evaluate the quality of our representations as features in three downstream
tasks: prediction of semantic supersenses (which assign nouns and verbs into a
few dozen semantic classes), low resource machine translation, and a lexical
substitution task, and obtain state-of-the-art results on all of these.
Joint Word Representation Learning using a Corpus and a Semantic Lexicon
Authors: Danushka Bollegala, Alsuhaibani Mohammed, Takanori Maehara, Ken-ichi Kawarabayashi
Date: 2015-11-19 00:00:00
Tasks: Representation Learning, Semantic Similarity, Semantic Textual Similarity, Sentence

Methods for learning word representations using large text corpora have
received much attention lately due to their impressive performance in numerous
natural language processing (NLP) tasks such as, semantic similarity
measurement, and word analogy detection. Despite their success, these
data-driven word representation learning methods do not consider the rich
semantic relational structure between words in a co-occurring context. On the
other hand, already much manual effort has gone into the construction of
semantic lexicons such as the WordNet that represent the meanings of words by
defining the various relationships that exist among the words in a language. We
consider the question, can we improve the word representations learnt using a
corpora by integrating the knowledge from semantic lexicons?. For this purpose,
we propose a joint word representation learning method that simultaneously
predicts the co-occurrences of two words in a sentence subject to the
relational constrains given by the semantic lexicon. We use relations that
exist between words in the lexicon to regularize the word representations
learnt from the corpus. Our proposed method statistically significantly
outperforms previously proposed methods for incorporating semantic lexicons
into word representations on several benchmark datasets for semantic similarity
and word analogy.
AUC-maximized Deep Convolutional Neural Fields for Sequence Labeling
Authors: Sheng Wang, Siqi Sun, Jinbo Xu
Date: 2015-11-17 00:00:00
Tasks: Prediction

Deep Convolutional Neural Networks (DCNN) has shown excellent performance in
a variety of machine learning tasks. This manuscript presents Deep
Convolutional Neural Fields (DeepCNF), a combination of DCNN with Conditional
Random Field (CRF), for sequence labeling with highly imbalanced label
distribution. The widely-used training methods, such as maximum-likelihood and
maximum labelwise accuracy, do not work well on highly imbalanced data. To
handle this, we present a new training algorithm called maximum-AUC for
DeepCNF. That is, we train DeepCNF by directly maximizing the empirical Area
Under the ROC Curve (AUC), which is an unbiased measurement for imbalanced
data. To fulfill this, we formulate AUC in a pairwise ranking framework,
approximate it by a polynomial function and then apply a gradient-based
procedure to optimize it. We then test our AUC-maximized DeepCNF on three very
different protein sequence labeling tasks: solvent accessibility prediction,
8-state secondary structure prediction, and disorder prediction. Our
experimental results confirm that maximum-AUC greatly outperforms the other two
training methods on 8-state secondary structure prediction and disorder
prediction since their label distributions are highly imbalanced and also have
similar performance as the other two training methods on the solvent
accessibility prediction problem which has three equally-distributed labels.
Furthermore, our experimental results also show that our AUC-trained DeepCNF
models greatly outperform existing popular predictors of these three tasks.
Incentivizing Exploration In Reinforcement Learning With Deep Predictive Models
Authors: Bradly C. Stadie, Sergey Levine, Pieter Abbeel
Date: 2015-07-03 00:00:00
Tasks: Atari Games, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Thompson Sampling

Achieving efficient and scalable exploration in complex domains poses a major
challenge in reinforcement learning. While Bayesian and PAC-MDP approaches to
the exploration problem offer strong formal guarantees, they are often
impractical in higher dimensions due to their reliance on enumerating the
state-action space. Hence, exploration in complex domains is often performed
with simple epsilon-greedy methods. In this paper, we consider the challenging
Atari games domain, which requires processing raw pixel inputs and delayed
rewards. We evaluate several more sophisticated exploration strategies,
including Thompson sampling and Boltzman exploration, and propose a new
exploration method based on assigning exploration bonuses from a concurrently
learned model of the system dynamics. By parameterizing our learned model with
a neural network, we are able to develop a scalable and efficient approach to
exploration bonuses that can be applied to tasks with complex, high-dimensional
state spaces. In the Atari domain, our method provides the most consistent
improvement across a range of games that pose a major challenge for prior
methods. In addition to raw game-scores, we also develop an AUC-100 metric for
the Atari Learning domain to evaluate the impact of exploration on this
benchmark.
ParseNet: Looking Wider to See Better
Authors: Wei Liu, Andrew Rabinovich, Alexander C. Berg
Date: 2015-06-15 00:00:00
Tasks: Segmentation, Semantic Segmentation

We present a technique for adding global context to deep convolutional
networks for semantic segmentation. The approach is simple, using the average
feature for a layer to augment the features at each location. In addition, we
study several idiosyncrasies of training, significantly increasing the
performance of baseline networks (e.g. from FCN). When we add our proposed
global feature, and a technique for learning normalization parameters, accuracy
increases consistently even over our improved versions of the baselines. Our
proposed approach, ParseNet, achieves state-of-the-art performance on SiftFlow
and PASCAL-Context with small additional computational cost over baselines, and
near current state-of-the-art performance on PASCAL VOC 2012 semantic
segmentation with a simple approach. Code is available at
https://github.com/weiliu89/caffe/tree/fcn .