pwc-archive/papers-with-abstracts huggingface dataset:
Deep Kinematic Pose Regression
Authors: Xingyi Zhou, Xiao Sun, Wei zhang, Shuang Liang, Yichen Wei
Date: 2016-09-17 00:00:00
Tasks: 3D Human Pose Estimation, Object, Pose Estimation, regression, valid

Learning articulated object pose is inherently difficult because the pose is
high dimensional but has many structural constraints. Most existing work do not
model such constraints and does not guarantee the geometric validity of their
pose estimation, therefore requiring a post-processing to recover the correct
geometry if desired, which is cumbersome and sub-optimal. In this work, we
propose to directly embed a kinematic object model into the deep neutral
network learning for general articulated object pose estimation. The kinematic
function is defined on the appropriately parameterized object motion variables.
It is differentiable and can be used in the gradient descent based optimization
in network training. The prior knowledge on the object geometric model is fully
exploited and the structure is guaranteed to be valid. We show convincing
experiment results on a toy example and the 3D human pose estimation problem.
For the latter we achieve state-of-the-art result on Human3.6M dataset.
NPCs Vote! Changing Voter Reactions Over Time Using the Extreme AI Personality Engine
Authors: Jeffrey Georgeson
Date: 2016-09-17 00:00:00

Can non-player characters have human-realistic personalities, changing over
time depending on input from those around them? And can they have different
reactions and thoughts about different people? Using Extreme AI, a
psychology-based personality engine using the Five Factor model of personality,
I answer these questions by creating personalities for 100 voters and allowing
them to react to two politicians to see if the NPC voters' choice of candidate
develops in a realistic-seeming way, based on initial and changing personality
facets and on their differing feelings toward the politicians (in this case,
across liking, trusting, and feeling affiliated with the candidates). After 16
test runs, the voters did indeed change their attitudes and feelings toward the
candidates in different and yet generally realistic ways, and even changed
their attitudes about other issues based on what a candidate extolled.
Development of a Fuzzy Expert System based Liveliness Detection Scheme for Biometric Authentication
Authors: Avinash Kumar Singh, Piyush Joshi, G. C. Nandi
Date: 2016-09-17 00:00:00

Liveliness detection acts as a safe guard against spoofing attacks. Most of
the researchers used vision based techniques to detect liveliness of the user,
but they are highly sensitive to illumination effects. Therefore it is very
hard to design a system, which will work robustly under all circumstances.
Literature shows that most of the research utilize eye blink or mouth movement
to detect the liveliness, while the other group used face texture to
distinguish between real and imposter. The classification results of all these
approaches decreases drastically in variable light conditions. Hence in this
paper we are introducing fuzzy expert system which is sufficient enough to
handle most of the cases comes in real time. We have used two testing
parameters, (a) under bad illumination and (b) less movement in eyes and mouth
in case of real user to evaluate the performance of the system. The system is
behaving well in all, while in first case its False Rejection Rate (FRR) is
0.28, and in second case its FRR is 0.4.
GeThR-Net: A Generalized Temporally Hybrid Recurrent Neural Network for Multimodal Information Fusion
Authors: Ankit Gandhi, Arjun Sharma, Arijit Biswas, Om Deshmukh
Date: 2016-09-17 00:00:00

Data generated from real world events are usually temporal and contain
multimodal information such as audio, visual, depth, sensor etc. which are
required to be intelligently combined for classification tasks. In this paper,
we propose a novel generalized deep neural network architecture where temporal
streams from multiple modalities are combined. There are total M+1 (M is the
number of modalities) components in the proposed network. The first component
is a novel temporally hybrid Recurrent Neural Network (RNN) that exploits the
complimentary nature of the multimodal temporal information by allowing the
network to learn both modality specific temporal dynamics as well as the
dynamics in a multimodal feature space. M additional components are added to
the network which extract discriminative but non-temporal cues from each
modality. Finally, the predictions from all of these components are linearly
combined using a set of automatically learned weights. We perform exhaustive
experiments on three different datasets spanning four modalities. The proposed
network is relatively 3.5%, 5.7% and 2% better than the best performing
temporal multimodal baseline for UCF-101, CCV and Multimodal Gesture datasets
respectively.
Deep Reinforcement Learning with a Combinatorial Action Space for Predicting Popular Reddit Threads
Authors: Ji He, Mari Ostendorf, Xiaodong He, Jianshu Chen, Jianfeng Gao, Lihong Li, Li Deng
Date: 2016-06-12 00:00:00
Proceeding: EMNLP 2016 11
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We introduce an online popularity prediction and tracking task as a benchmark
task for reinforcement learning with a combinatorial, natural language action
space. A specified number of discussion threads predicted to be popular are
recommended, chosen from a fixed window of recent comments to track. Novel deep
reinforcement learning architectures are studied for effective modeling of the
value function associated with actions comprised of interdependent sub-actions.
The proposed model, which represents dependence between sub-actions through a
bi-directional LSTM, gives the best performance across different experimental
configurations and domains, and it also generalizes well with varying numbers
of recommendation requests.
A convolutional approach to reflection symmetry
Authors: Marcelo Cicconet, Vighnesh Birodkar, Mads Lund, Michael Werman, Davi Geiger
Date: 2016-09-17 00:00:00
Tasks: Symmetry Detection

We present a convolutional approach to reflection symmetry detection in 2D.
Our model, built on the products of complex-valued wavelet convolutions,
simplifies previous edge-based pairwise methods. Being parameter-centered, as
opposed to feature-centered, it has certain computational advantages when the
object sizes are known a priori, as demonstrated in an ellipse detection
application. The method outperforms the best-performing algorithm on the CVPR
2013 Symmetry Detection Competition Database in the single-symmetry case. Code
and a new database for 2D symmetry detection is available.
Are Facial Attributes Adversarially Robust?
Authors: Andras Rozsa, Manuel Günther, Ethan M. Rudd, Terrance E. Boult
Date: 2016-05-18 00:00:00
Tasks: Attribute, Attribute Extraction, Facial Attribute Classification, General Classification

Facial attributes are emerging soft biometrics that have the potential to
reject non-matches, for example, based on mismatching gender. To be usable in
stand-alone systems, facial attributes must be extracted from images
automatically and reliably. In this paper, we propose a simple yet effective
solution for automatic facial attribute extraction by training a deep
convolutional neural network (DCNN) for each facial attribute separately,
without using any pre-training or dataset augmentation, and we obtain new
state-of-the-art facial attribute classification results on the CelebA
benchmark. To test the stability of the networks, we generated adversarial
images -- formed by adding imperceptible non-random perturbations to original
inputs which result in classification errors -- via a novel fast flipping
attribute (FFA) technique. We show that FFA generates more adversarial examples
than other related algorithms, and that DCNNs for certain attributes are
generally robust to adversarial inputs, while DCNNs for other attributes are
not. This result is surprising because no DCNNs tested to date have exhibited
robustness to adversarial images without explicit augmentation in the training
procedure to account for adversarial examples. Finally, we introduce the
concept of natural adversarial samples, i.e., images that are misclassified but
can be easily turned into correctly classified images by applying small
perturbations. We demonstrate that natural adversarial samples commonly occur,
even within the training set, and show that many of these images remain
misclassified even with additional training epochs. This phenomenon is
surprising because correcting the misclassification, particularly when guided
by training data, should require only a small adjustment to the DCNN
parameters.
Review of Fall Detection Techniques: A Data Availability Perspective
Authors: Shehroz S. Khan, Jesse Hoey
Date: 2016-05-30 00:00:00

A fall is an abnormal activity that occurs rarely; however, missing to
identify falls can have serious health and safety implications on an
individual. Due to the rarity of occurrence of falls, there may be insufficient
or no training data available for them. Therefore, standard supervised machine
learning methods may not be directly applied to handle this problem. In this
paper, we present a taxonomy for the study of fall detection from the
perspective of availability of fall data. The proposed taxonomy is independent
of the type of sensors used and specific feature extraction/selection methods.
The taxonomy identifies different categories of classification methods for the
study of fall detection based on the availability of their data during training
the classifiers. Then, we present a comprehensive literature review within
those categories and identify the approach of treating a fall as an abnormal
activity to be a plausible research direction. We conclude our paper by
discussing several open research problems in the field and pointers for future
research.
Interactive Spoken Content Retrieval by Deep Reinforcement Learning
Authors: Yen-chen Wu, Tzu-Hsiang Lin, Yang-De Chen, Hung-Yi Lee, Lin-shan Lee
Date: 2016-09-16 00:00:00
Tasks: Deep Reinforcement Learning, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Retrieval, speech-recognition, Speech Recognition

User-machine interaction is important for spoken content retrieval. For text
content retrieval, the user can easily scan through and select on a list of
retrieved item. This is impossible for spoken content retrieval, because the
retrieved items are difficult to show on screen. Besides, due to the high
degree of uncertainty for speech recognition, the retrieval results can be very
noisy. One way to counter such difficulties is through user-machine
interaction. The machine can take different actions to interact with the user
to obtain better retrieval results before showing to the user. The suitable
actions depend on the retrieval status, for example requesting for extra
information from the user, returning a list of topics for user to select, etc.
In our previous work, some hand-crafted states estimated from the present
retrieval results are used to determine the proper actions. In this paper, we
propose to use Deep-Q-Learning techniques instead to determine the machine
actions for interactive spoken content retrieval. Deep-Q-Learning bypasses the
need for estimation of the hand-crafted states, and directly determine the best
action base on the present retrieval status even without any human knowledge.
It is shown to achieve significantly better performance compared with the
previous hand-crafted states.
Detecting weak changes in dynamic events over networks
Authors: Shuang Li, Yao Xie, Mehrdad Farajtabar, Apurv Verma, Le Song
Date: 2016-03-29 00:00:00
Tasks: Change Point Detection, Point Processes

Large volume of networked streaming event data are becoming increasingly
available in a wide variety of applications, such as social network analysis,
Internet traffic monitoring and healthcare analytics. Streaming event data are
discrete observation occurred in continuous time, and the precise time interval
between two events carries a great deal of information about the dynamics of
the underlying systems. How to promptly detect changes in these dynamic systems
using these streaming event data? In this paper, we propose a novel
change-point detection framework for multi-dimensional event data over
networks. We cast the problem into sequential hypothesis test, and derive the
likelihood ratios for point processes, which are computed efficiently via an
EM-like algorithm that is parameter-free and can be computed in a distributed
fashion. We derive a highly accurate theoretical characterization of the
false-alarm-rate, and show that it can achieve weak signal detection by
aggregating local statistics over time and networks. Finally, we demonstrate
the good performance of our algorithm on numerical examples and real-world
datasets from twitter and Memetracker.
Gradient Descent Learns Linear Dynamical Systems
Authors: Moritz Hardt, Tengyu Ma, Benjamin Recht
Date: 2016-09-16 00:00:00

We prove that stochastic gradient descent efficiently converges to the global
optimizer of the maximum likelihood objective of an unknown linear
time-invariant dynamical system from a sequence of noisy observations generated
by the system. Even though the objective function is non-convex, we provide
polynomial running time and sample complexity bounds under strong but natural
assumptions. Linear systems identification has been studied for many decades,
yet, to the best of our knowledge, these are the first polynomial guarantees
for the problem we consider.
Information Theoretic Limits of Data Shuffling for Distributed Learning
Authors: Mohamed Attia, Ravi Tandon
Date: 2016-09-16 00:00:00

Data shuffling is one of the fundamental building blocks for distributed
learning algorithms, that increases the statistical gain for each step of the
learning process. In each iteration, different shuffled data points are
assigned by a central node to a distributed set of workers to perform local
computations, which leads to communication bottlenecks. The focus of this paper
is on formalizing and understanding the fundamental information-theoretic
trade-off between storage (per worker) and the worst-case communication
overhead for the data shuffling problem. We completely characterize the
information theoretic trade-off for $K=2$, and $K=3$ workers, for any value of
storage capacity, and show that increasing the storage across workers can
reduce the communication overhead by leveraging coding. We propose a novel and
systematic data delivery and storage update strategy for each data shuffle
iteration, which preserves the structural properties of the storage across the
workers, and aids in minimizing the communication overhead in subsequent data
shuffling iterations.
Should Terminology Principles be re-examined?
Authors: Christophe Roche
Date: 2016-09-16 00:00:00

Operationalization of terminology for IT applications has revived the
Wusterian approach. The conceptual dimension once more prevails after taking
back seat to specialised lexicography. This is demonstrated by the emergence of
ontology in terminology. While the Terminology Principles as defined in Felber
manual and the ISO standards remain at the core of traditional terminology,
their computational implementation raises some issues. In this article, while
reiterating their importance, we will be re-examining these Principles from a
dual perspective: that of logic in the mathematical sense of the term and that
of epistemology as in the theory of knowledge. We will thus be clarifying and
describing some of them so as to take into account advances in knowledge
engineering (ontology) and formal systems (logic). The notion of
ontoterminology, terminology whose conceptual system is a formal ontology,
results from this approach.
Rule Extraction Algorithm for Deep Neural Networks: A Review
Authors: Tameru Hailesilassie
Date: 2016-09-16 00:00:00
Tasks: Classification, General Classification

Despite the highest classification accuracy in wide varieties of application
areas, artificial neural network has one disadvantage. The way this Network
comes to a decision is not easily comprehensible. The lack of explanation
ability reduces the acceptability of neural network in data mining and decision
system. This drawback is the reason why researchers have proposed many rule
extraction algorithms to solve the problem. Recently, Deep Neural Network (DNN)
is achieving a profound result over the standard neural network for
classification and recognition problems. It is a hot machine learning area
proven both useful and innovative. This paper has thoroughly reviewed various
rule extraction algorithms, considering the classification scheme:
decompositional, pedagogical, and eclectics. It also presents the evaluation of
these algorithms based on the neural network structure with which the algorithm
is intended to work. The main contribution of this review is to show that there
is a limited study of rule extraction algorithm from DNN.
No-Regret Replanning under Uncertainty
Authors: Wen Sun, Niteesh Sood, Debadeepta Dey, Gireeja Ranade, Siddharth Prakash, Ashish Kapoor
Date: 2016-09-16 00:00:00
Tasks: Gaussian Processes

This paper explores the problem of path planning under uncertainty.
Specifically, we consider online receding horizon based planners that need to
operate in a latent environment where the latent information can be modeled via
Gaussian Processes. Online path planning in latent environments is challenging
since the robot needs to explore the environment to get a more accurate model
of latent information for better planning later and also achieves the task as
quick as possible. We propose UCB style algorithms that are popular in the
bandit settings and show how those analyses can be adapted to the online
robotic path planning problems. The proposed algorithm trades-off exploration
and exploitation in near-optimal manner and has appealing no-regret properties.
We demonstrate the efficacy of the framework on the application of aircraft
flight path planning when the winds are partially observed.
Style Imitation and Chord Invention in Polyphonic Music with Exponential Families
Authors: Gaëtan Hadjeres, Jason Sakellariou, François Pachet
Date: 2016-09-16 00:00:00
Tasks: Music Generation

Modeling polyphonic music is a particularly challenging task because of the
intricate interplay between melody and harmony. A good model should satisfy
three requirements: statistical accuracy (capturing faithfully the statistics
of correlations at various ranges, horizontally and vertically), flexibility
(coping with arbitrary user constraints), and generalization capacity
(inventing new material, while staying in the style of the training corpus).
Models proposed so far fail on at least one of these requirements. We propose a
statistical model of polyphonic music, based on the maximum entropy principle.
This model is able to learn and reproduce pairwise statistics between
neighboring note events in a given corpus. The model is also able to invent new
chords and to harmonize unknown melodies. We evaluate the invention capacity of
the model by assessing the amount of cited, re-discovered, and invented chords
on a corpus of Bach chorales. We discuss how the model enables the user to
specify and enforce user-defined constraints, which makes it useful for
style-based, interactive music generation.
Target-driven Visual Navigation in Indoor Scenes using Deep Reinforcement Learning
Authors: Yuke Zhu, Roozbeh Mottaghi, Eric Kolve, Joseph J. Lim, Abhinav Gupta, Li Fei-Fei, Ali Farhadi
Date: 2016-09-16 00:00:00
Tasks: 3D Reconstruction, Deep Reinforcement Learning, Feature Engineering, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Visual Navigation

Two less addressed issues of deep reinforcement learning are (1) lack of
generalization capability to new target goals, and (2) data inefficiency i.e.,
the model requires several (and often costly) episodes of trial and error to
converge, which makes it impractical to be applied to real-world scenarios. In
this paper, we address these two issues and apply our model to the task of
target-driven visual navigation. To address the first issue, we propose an
actor-critic model whose policy is a function of the goal as well as the
current state, which allows to better generalize. To address the second issue,
we propose AI2-THOR framework, which provides an environment with high-quality
3D scenes and physics engine. Our framework enables agents to take actions and
interact with objects. Hence, we can collect a huge number of training samples
efficiently.
  We show that our proposed method (1) converges faster than the
state-of-the-art deep reinforcement learning methods, (2) generalizes across
targets and across scenes, (3) generalizes to a real robot scenario with a
small amount of fine-tuning (although the model is trained in simulation), (4)
is end-to-end trainable and does not need feature engineering, feature matching
between frames or 3D reconstruction of the environment.
  The supplementary video can be accessed at the following link:
https://youtu.be/SmBxMDiOrvs.
Learning Opposites Using Neural Networks
Authors: Shivam Kalra, Aditya Sriram, Shahryar Rahnamayan, H. R. Tizhoosh
Date: 2016-09-16 00:00:00
Tasks: Evolutionary Algorithms, Vocal Bursts Type Prediction

Many research works have successfully extended algorithms such as
evolutionary algorithms, reinforcement agents and neural networks using
"opposition-based learning" (OBL). Two types of the "opposites" have been
defined in the literature, namely \textit{type-I} and \textit{type-II}. The
former are linear in nature and applicable to the variable space, hence easy to
calculate. On the other hand, type-II opposites capture the "oppositeness" in
the output space. In fact, type-I opposites are considered a special case of
type-II opposites where inputs and outputs have a linear relationship. However,
in many real-world problems, inputs and outputs do in fact exhibit a nonlinear
relationship. Therefore, type-II opposites are expected to be better in
capturing the sense of "opposition" in terms of the input-output relation. In
the absence of any knowledge about the problem at hand, there seems to be no
intuitive way to calculate the type-II opposites. In this paper, we introduce
an approach to learn type-II opposites from the given inputs and their outputs
using the artificial neural networks (ANNs). We first perform \emph{opposition
mining} on the sample data, and then use the mined data to learn the
relationship between input $x$ and its opposite $\breve{x}$. We have validated
our algorithm using various benchmark functions to compare it against an
evolving fuzzy inference approach that has been recently introduced. The
results show the better performance of a neural approach to learn the
opposites. This will create new possibilities for integrating oppositional
schemes within existing algorithms promising a potential increase in
convergence speed and/or accuracy.
Deep Impression: Audiovisual Deep Residual Networks for Multimodal Apparent Personality Trait Recognition
Authors: Yağmur Güçlütürk, Umut Güçlü, Marcel A. J. van Gerven, Rob Van Lier
Date: 2016-09-16 00:00:00
Tasks: Facial Expression Recognition, Feature Engineering, Personality Trait Recognition

Here, we develop an audiovisual deep residual network for multimodal apparent
personality trait recognition. The network is trained end-to-end for predicting
the Big Five personality traits of people from their videos. That is, the
network does not require any feature engineering or visual analysis such as
face detection, face landmark alignment or facial expression recognition.
Recently, the network won the third place in the ChaLearn First Impressions
Challenge with a test accuracy of 0.9109.