pwc-archive/papers-with-abstracts huggingface dataset:
Adversarial Influence Maximization
Authors: Justin Khim, Varun Jog, Po-Ling Loh
Date: 2016-11-01 00:00:00

We consider the problem of influence maximization in fixed networks for
contagion models in an adversarial setting. The goal is to select an optimal
set of nodes to seed the influence process, such that the number of influenced
nodes at the conclusion of the campaign is as large as possible. We formulate
the problem as a repeated game between a player and adversary, where the
adversary specifies the edges along which the contagion may spread, and the
player chooses sets of nodes to influence in an online fashion. We establish
upper and lower bounds on the minimax pseudo-regret in both undirected and
directed networks.
MetaGrad: Multiple Learning Rates in Online Learning
Authors: Tim van Erven, Wouter M. Koolen
Date: 2016-04-29 00:00:00
Proceeding: NeurIPS 2016 12

In online convex optimization it is well known that certain subclasses of
objective functions are much easier than arbitrary convex functions. We are
interested in designing adaptive methods that can automatically get fast rates
in as many such subclasses as possible, without any manual tuning. Previous
adaptive methods are able to interpolate between strongly convex and general
convex functions. We present a new method, MetaGrad, that adapts to a much
broader class of functions, including exp-concave and strongly convex
functions, but also various types of stochastic and non-stochastic functions
without any curvature. For instance, MetaGrad can achieve logarithmic regret on
the unregularized hinge loss, even though it has no curvature, if the data come
from a favourable probability distribution. MetaGrad's main feature is that it
simultaneously considers multiple learning rates. Unlike previous methods with
provable regret guarantees, however, its learning rates are not monotonically
decreasing over time and are not tuned based on a theoretically derived bound
on the regret. Instead, they are weighted directly proportional to their
empirical performance on the data using a tilted exponential weights master
algorithm.
A Glimpse Far into the Future: Understanding Long-term Crowd Worker Quality
Authors: Kenji Hata, Ranjay Krishna, Li Fei-Fei, Michael S. Bernstein
Date: 2016-09-15 00:00:00

Microtask crowdsourcing is increasingly critical to the creation of extremely
large datasets. As a result, crowd workers spend weeks or months repeating the
exact same tasks, making it necessary to understand their behavior over these
long periods of time. We utilize three large, longitudinal datasets of nine
million annotations collected from Amazon Mechanical Turk to examine claims
that workers fatigue or satisfice over these long periods, producing lower
quality work. We find that, contrary to these claims, workers are extremely
stable in their quality over the entire period. To understand whether workers
set their quality based on the task's requirements for acceptance, we then
perform an experiment where we vary the required quality for a large
crowdsourcing task. Workers did not adjust their quality based on the
acceptance threshold: workers who were above the threshold continued working at
their usual quality level, and workers below the threshold self-selected
themselves out of the task. Capitalizing on this consistency, we demonstrate
that it is possible to predict workers' long-term quality using just a glimpse
of their quality on the first five tasks.
Recurrent Neural Radio Anomaly Detection
Authors: Timothy J. O'Shea, T. Charles Clancy, Robert W. McGwier
Date: 2016-11-01 00:00:00
Tasks: Anomaly Detection, Novelty Detection

We introduce a powerful recurrent neural network based method for novelty
detection to the application of detecting radio anomalies. This approach holds
promise in significantly increasing the ability of naive anomaly detection to
detect small anomalies in highly complex complexity multi-user radio bands. We
demonstrate the efficacy of this approach on a number of common real over the
air radio communications bands of interest and quantify detection performance
in terms of probability of detection an false alarm rates across a range of
interference to band power ratios and compare to baseline methods.
Multi-Label Learning with Provable Guarantee
Authors: Sayantan Dasgupta
Date: 2016-09-12 00:00:00
Tasks: Multi-Label Learning

Here we study the problem of learning labels for large text corpora where
each text can be assigned a variable number of labels. The problem might seem
trivial when the label dimensionality is small and can be easily solved using a
series of one-vs-all classifiers. However, as the label dimensionality
increases to several thousand, the parameter space becomes extremely large, and
it is no longer possible to use the one-vs-all technique. Here we propose a
model based on the factorization of higher order moments of the words in the
corpora, as well as the cross moment between the labels and the words for
multi-label prediction. Our model provides guaranteed convergence bounds on the
estimated parameters. Further, our model takes only three passes through the
training dataset to extract the parameters, resulting in a highly scalable
algorithm that can train on GB's of data consisting of millions of documents
with hundreds of thousands of labels using a nominal resource of a single
processor with 16GB RAM. Our model achieves 10x-15x order of speed-up on
large-scale datasets while producing competitive performance in comparison with
existing benchmark algorithms.
Review of Action Recognition and Detection Methods
Authors: Soo Min Kang, Richard P. Wildes
Date: 2016-10-21 00:00:00
Tasks: Action Detection, Action Recognition, Temporal Action Localization

In computer vision, action recognition refers to the act of classifying an
action that is present in a given video and action detection involves locating
actions of interest in space and/or time. Videos, which contain photometric
information (e.g. RGB, intensity values) in a lattice structure, contain
information that can assist in identifying the action that has been imaged. The
process of action recognition and detection often begins with extracting useful
features and encoding them to ensure that the features are specific to serve
the task of action recognition and detection. Encoded features are then
processed through a classifier to identify the action class and their spatial
and/or temporal locations. In this report, a thorough review of various action
recognition and detection algorithms in computer vision is provided by
analyzing the two-step process of a typical action recognition and detection
algorithm: (i) extraction and encoding of features, and (ii) classifying
features into action classes. In efforts to ensure that computer vision-based
algorithms reach the capabilities that humans have of identifying actions
irrespective of various nuisance variables that may be present within the field
of view, the state-of-the-art methods are reviewed and some remaining problems
are addressed in the final chapter.
Detecting Affordances by Visuomotor Simulation
Authors: Wolfram Schenck, Hendrik Hasenbein, Ralf Möller
Date: 2016-11-01 00:00:00
Tasks: Affordance Detection

The term "affordance" denotes the behavioral meaning of objects. We propose a
cognitive architecture for the detection of affordances in the visual modality.
This model is based on the internal simulation of movement sequences. For each
movement step, the resulting sensory state is predicted by a forward model,
which in turn triggers the generation of a new (simulated) motor command by an
inverse model. Thus, a series of mental images in the sensory and in the motor
domain is evoked. Starting from a real sensory state, a large number of such
sequences is simulated in parallel. Final affordance detection is based on the
generated motor commands. We apply this model to a real-world mobile robot
which is faced with obstacle arrangements some of which are passable (corridor)
and some of which are not (dead ends). The robot's task is to detect the right
affordance ("pass-through-able" or "non-pass-through-able"). The required
internal models are acquired in a hierarchical training process. Afterwards,
the robotic agent is able to distinguish reliably between corridors and dead
ends. This real-world result enhances the validity of the proposed mental
simulation approach. In addition, we compare several key factors in the
simulation process regarding performance and efficiency.
Improving Color Constancy by Discounting the Variation of Camera Spectral Sensitivity
Authors: Shao-Bing Gao, Ming Zhang, Chao-Yi Li, Yong-Jie Li
Date: 2016-09-06 00:00:00
Tasks: Color Constancy, Sensitivity

It is an ill-posed problem to recover the true scene colors from a color
biased image by discounting the effects of scene illuminant and camera spectral
sensitivity (CSS) at the same time. Most color constancy (CC) models have been
designed to first estimate the illuminant color, which is then removed from the
color biased image to obtain an image taken under white light, without the
explicit consideration of CSS effect on CC. This paper first studies the CSS
effect on illuminant estimation arising in the inter-dataset-based CC
(inter-CC), i.e., training a CC model on one dataset and then testing on
another dataset captured by a distinct CSS. We show the clear degradation of
existing CC models for inter-CC application. Then a simple way is proposed to
overcome such degradation by first learning quickly a transform matrix between
the two distinct CSSs (CSS-1 and CSS-2). The learned matrix is then used to
convert the data (including the illuminant ground truth and the color biased
images) rendered under CSS-1 into CSS-2, and then train and apply the CC model
on the color biased images under CSS-2, without the need of burdensome
acquiring of training set under CSS-2. Extensive experiments on synthetic and
real images show that our method can clearly improve the inter-CC performance
for traditional CC algorithms. We suggest that by taking the CSS effect into
account, it is more likely to obtain the truly color constant images invariant
to the changes of both illuminant and camera sensors.
Learning Lexical Entries for Robotic Commands using Crowdsourcing
Authors: Junjie Hu, Jean Oh, Anatole Gershman
Date: 2016-09-08 00:00:00
Tasks: Machine Translation, Translation

Robotic commands in natural language usually contain various spatial
descriptions that are semantically similar but syntactically different. Mapping
such syntactic variants into semantic concepts that can be understood by robots
is challenging due to the high flexibility of natural language expressions. To
tackle this problem, we collect robotic commands for navigation and
manipulation tasks using crowdsourcing. We further define a robot language and
use a generative machine translation model to translate robotic commands from
natural language to robot language. The main purpose of this paper is to
simulate the interaction process between human and robots using crowdsourcing
platforms, and investigate the possibility of translating natural language to
robot language with paraphrases.
Causal Compression
Authors: Aleksander Wieczorek, Volker Roth
Date: 2016-11-01 00:00:00
Tasks: Time Series, Time Series Analysis

We propose a new method of discovering causal relationships in temporal data
based on the notion of causal compression. To this end, we adopt the Pearlian
graph setting and the directed information as an information theoretic tool for
quantifying causality. We introduce chain rule for directed information and use
it to motivate causal sparsity. We show two applications of the proposed
method: causal time series segmentation which selects time points capturing the
incoming and outgoing causal flow between time points belonging to different
signals, and causal bipartite graph recovery. We prove that modelling of
causality in the adopted set-up only requires estimating the copula density of
the data distribution and thus does not depend on its marginals. We evaluate
the method on time resolved gene expression data.
Surrogate-Assisted Partial Order-based Evolutionary Optimisation
Authors: Vanessa Volz, Günter Rudolph, Boris Naujoks
Date: 2016-11-01 00:00:00
Tasks: Evolutionary Algorithms

In this paper, we propose a novel approach (SAPEO) to support the survival
selection process in multi-objective evolutionary algorithms with surrogate
models - it dynamically chooses individuals to evaluate exactly based on the
model uncertainty and the distinctness of the population. We introduce variants
that differ in terms of the risk they allow when doing survival selection.
Here, the anytime performance of different SAPEO variants is evaluated in
conjunction with an SMS-EMOA using the BBOB bi-objective benchmark. We compare
the obtained results with the performance of the regular SMS-EMOA, as well as
another surrogate-assisted approach. The results open up general questions
about the applicability and required conditions for surrogate-assisted
multi-objective evolutionary algorithms to be tackled in the future.
What Objective Does Self-paced Learning Indeed Optimize?
Authors: Deyu Meng, Qian Zhao, Lu Jiang
Date: 2015-11-19 00:00:00

Self-paced learning (SPL) is a recently raised methodology designed through
simulating the learning principle of humans/animals. A variety of SPL
realization schemes have been designed for different computer vision and
pattern recognition tasks, and empirically substantiated to be effective in
these applications. However, the investigation on its theoretical insight is
still a blank. To this issue, this study attempts to provide some new
theoretical understanding under the SPL scheme. Specifically, we prove that the
solving strategy on SPL accords with a majorization minimization algorithm
implemented on a latent objective function. Furthermore, we find that the loss
function contained in this latent objective has a similar configuration with
non-convex regularized penalty (NSPR) known in statistics and machine learning.
Such connection inspires us discovering more intrinsic relationship between SPL
regimes and NSPR forms, like SCAD, LOG and EXP. The robustness insight under
SPL can then be finely explained. We also analyze the capability of SPL on its
easy loss prior embedding property, and provide an insightful interpretation to
the effectiveness mechanism under previous SPL variations. Besides, we design a
group-partial-order loss prior, which is especially useful to weakly labeled
large-scale data processing tasks. Through applying SPL with this loss prior to
the FCVID dataset, which is currently one of the biggest manually annotated
video dataset, our method achieves state-of-the-art performance beyond previous
methods, which further helps supports the proposed theoretical arguments.
Towards Blended Reactive Planning and Acting using Behavior Trees
Authors: Michele Colledanchise, Diogo Almeida, Petter Ögren
Date: 2016-11-01 00:00:00

In this paper, we show how a planning algorithm can be used to automatically
create and update a Behavior Tree (BT), controlling a robot in a dynamic
environment. The planning part of the algorithm is based on the idea of back
chaining. Starting from a goal condition we iteratively select actions to
achieve that goal, and if those actions have unmet preconditions, they are
extended with actions to achieve them in the same way. The fact that BTs are
inherently modular and reactive makes the proposed solution blend acting and
planning in a way that enables the robot to efficiently react to external
disturbances. If an external agent undoes an action the robot reexecutes it
without re-planning, and if an external agent helps the robot, it skips the
corresponding actions, again without replanning. We illustrate our approach in
two different robotics scenarios.
Sliding Dictionary Based Sparse Representation For Action Recognition
Authors: Yashas Annadani, D L Rakshith, Soma Biswas
Date: 2016-11-01 00:00:00
Tasks: Action Recognition, Temporal Action Localization

The task of action recognition has been in the forefront of research, given
its applications in gaming, surveillance and health care. In this work, we
propose a simple, yet very effective approach which works seamlessly for both
offline and online action recognition using the skeletal joints. We construct a
sliding dictionary which has the training data along with their time stamps.
This is used to compute the sparse coefficients of the input action sequence
which is divided into overlapping windows and each window gives a probability
score for each action class. In addition, we compute another simple feature,
which calibrates each of the action sequences to the training sequences, and
models the deviation of the action from the each of the training data. Finally,
a score level fusion of the two heterogeneous but complementary features for
each window is obtained and the scores for the available windows are
successively combined to give the confidence scores of each action class. This
way of combining the scores makes the approach suitable for scenarios where
only part of the sequence is available. Extensive experimental evaluation on
three publicly available datasets shows the effectiveness of the proposed
approach for both offline and online action recognition tasks.
Universality of Bayesian mixture predictors
Authors: Daniil Ryabko
Date: 2016-10-26 00:00:00
Tasks: Time Series, Time Series Analysis

The problem is that of sequential probability forecasting for finite-valued
time series. The data is generated by an unknown probability distribution over
the space of all one-way infinite sequences. It is known that this measure
belongs to a given set C, but the latter is completely arbitrary (uncountably
infinite, without any structure given). The performance is measured with
asymptotic average log loss. In this work it is shown that the minimax
asymptotic performance is always attainable, and it is attained by a convex
combination of a countably many measures from the set C (a Bayesian mixture).
This was previously only known for the case when the best achievable asymptotic
error is 0. This also contrasts previous results that show that in the
non-realizable case all Bayesian mixtures may be suboptimal, while there is a
predictor that achieves the optimal performance.
Towards Lifelong Self-Supervision: A Deep Learning Direction for Robotics
Authors: Jay M. Wong
Date: 2016-11-01 00:00:00
Tasks: Deep Learning, Lifelong learning

Despite outstanding success in vision amongst other domains, many of the
recent deep learning approaches have evident drawbacks for robots. This
manuscript surveys recent work in the literature that pertain to applying deep
learning systems to the robotics domain, either as means of estimation or as a
tool to resolve motor commands directly from raw percepts. These recent
advances are only a piece to the puzzle. We suggest that deep learning as a
tool alone is insufficient in building a unified framework to acquire general
intelligence. For this reason, we complement our survey with insights from
cognitive development and refer to ideas from classical control theory,
producing an integrated direction for a lifelong learning architecture.
Recurrent Neural Network Language Model Adaptation Derived Document Vector
Authors: Wei Li, Brian Kan Wing Mak
Date: 2016-11-01 00:00:00
Tasks: General Classification, Genre classification, Language Modeling, Language Modelling

In many natural language processing (NLP) tasks, a document is commonly
modeled as a bag of words using the term frequency-inverse document frequency
(TF-IDF) vector. One major shortcoming of the frequency-based TF-IDF feature
vector is that it ignores word orders that carry syntactic and semantic
relationships among the words in a document, and they can be important in some
NLP tasks such as genre classification. This paper proposes a novel distributed
vector representation of a document: a simple recurrent-neural-network language
model (RNN-LM) or a long short-term memory RNN language model (LSTM-LM) is
first created from all documents in a task; some of the LM parameters are then
adapted by each document, and the adapted parameters are vectorized to
represent the document. The new document vectors are labeled as DV-RNN and
DV-LSTM respectively. We believe that our new document vectors can capture some
high-level sequential information in the documents, which other current
document representations fail to capture. The new document vectors were
evaluated in the genre classification of documents in three corpora: the Brown
Corpus, the BNC Baby Corpus and an artificially created Penn Treebank dataset.
Their classification performances are compared with the performance of TF-IDF
vector and the state-of-the-art distributed memory model of paragraph vector
(PV-DM). The results show that DV-LSTM significantly outperforms TF-IDF and
PV-DM in most cases, and combinations of the proposed document vectors with
TF-IDF or PV-DM may further improve performance.
Local Subspace-Based Outlier Detection using Global Neighbourhoods
Authors: Bas van Stein, Matthijs van Leeuwen, Thomas Bäck
Date: 2016-11-01 00:00:00
Tasks: Fraud Detection, Outlier Detection

Outlier detection in high-dimensional data is a challenging yet important
task, as it has applications in, e.g., fraud detection and quality control.
State-of-the-art density-based algorithms perform well because they 1) take the
local neighbourhoods of data points into account and 2) consider feature
subspaces. In highly complex and high-dimensional data, however, existing
methods are likely to overlook important outliers because they do not
explicitly take into account that the data is often a mixture distribution of
multiple components.
  We therefore introduce GLOSS, an algorithm that performs local subspace
outlier detection using global neighbourhoods. Experiments on synthetic data
demonstrate that GLOSS more accurately detects local outliers in mixed data
than its competitors. Moreover, experiments on real-world data show that our
approach identifies relevant outliers overlooked by existing methods,
confirming that one should keep an eye on the global perspective even when
doing local outlier detection.
Dual Learning for Machine Translation
Authors: Yingce Xia, Di He, Tao Qin, Li-Wei Wang, Nenghai Yu, Tie-Yan Liu, Wei-Ying Ma
Date: 2016-11-01 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: Language Modeling, Language Modelling, Machine Translation, NMT, Policy Gradient Methods, Reinforcement Learning, Sentence, Translation

While neural machine translation (NMT) is making good progress in the past
two years, tens of millions of bilingual sentence pairs are needed for its
training. However, human labeling is very costly. To tackle this training data
bottleneck, we develop a dual-learning mechanism, which can enable an NMT
system to automatically learn from unlabeled data through a dual-learning game.
This mechanism is inspired by the following observation: any machine
translation task has a dual task, e.g., English-to-French translation (primal)
versus French-to-English translation (dual); the primal and dual tasks can form
a closed loop, and generate informative feedback signals to train the
translation models, even if without the involvement of a human labeler. In the
dual-learning mechanism, we use one agent to represent the model for the primal
task and the other agent to represent the model for the dual task, then ask
them to teach each other through a reinforcement learning process. Based on the
feedback signals generated during this process (e.g., the language-model
likelihood of the output of a model, and the reconstruction error of the
original sentence after the primal and dual translations), we can iteratively
update the two models until convergence (e.g., using the policy gradient
methods). We call the corresponding approach to neural machine translation
\emph{dual-NMT}. Experiments show that dual-NMT works very well on
English$\leftrightarrow$French translation; especially, by learning from
monolingual data (with 10% bilingual data for warm start), it achieves a
comparable accuracy to NMT trained from the full bilingual data for the
French-to-English translation task.