pwc-archive/papers-with-abstracts huggingface dataset:
Learning Features for Offline Handwritten Signature Verification using Deep Convolutional Neural Networks
Authors: Luiz G. Hafemann, Robert Sabourin, Luiz S. Oliveira
Date: 2017-05-16 00:00:00

Verifying the identity of a person using handwritten signatures is
challenging in the presence of skilled forgeries, where a forger has access to
a person's signature and deliberately attempt to imitate it. In offline
(static) signature verification, the dynamic information of the signature
writing process is lost, and it is difficult to design good feature extractors
that can distinguish genuine signatures and skilled forgeries. This reflects in
a relatively poor performance, with verification errors around 7% in the best
systems in the literature. To address both the difficulty of obtaining good
features, as well as improve system performance, we propose learning the
representations from signature images, in a Writer-Independent format, using
Convolutional Neural Networks. In particular, we propose a novel formulation of
the problem that includes knowledge of skilled forgeries from a subset of users
in the feature learning process, that aims to capture visual cues that
distinguish genuine signatures and forgeries regardless of the user. Extensive
experiments were conducted on four datasets: GPDS, MCYT, CEDAR and Brazilian
PUC-PR datasets. On GPDS-160, we obtained a large improvement in
state-of-the-art performance, achieving 1.72% Equal Error Rate, compared to
6.97% in the literature. We also verified that the features generalize beyond
the GPDS dataset, surpassing the state-of-the-art performance in the other
datasets, without requiring the representation to be fine-tuned to each
particular dataset.
Multiobjective Programming for Type-2 Hierarchical Fuzzy Inference Trees
Authors: Varun Kumar Ojha, Vaclav Snasel, Ajith Abraham
Date: 2017-05-16 00:00:00
Tasks: Classification, feature selection, Time Series Analysis, Time Series Regression, Vocal Bursts Type Prediction

This paper proposes a design of hierarchical fuzzy inference tree (HFIT). An
HFIT produces an optimum treelike structure, i.e., a natural hierarchical
structure that accommodates simplicity by combining several low-dimensional
fuzzy inference systems (FISs). Such a natural hierarchical structure provides
a high degree of approximation accuracy. The construction of HFIT takes place
in two phases. Firstly, a nondominated sorting based multiobjective genetic
programming (MOGP) is applied to obtain a simple tree structure (a low
complexity model) with a high accuracy. Secondly, the differential evolution
algorithm is applied to optimize the obtained tree's parameters. In the derived
tree, each node acquires a different input's combination, where the
evolutionary process governs the input's combination. Hence, HFIT nodes are
heterogeneous in nature, which leads to a high diversity among the rules
generated by the HFIT. Additionally, the HFIT provides an automatic feature
selection because it uses MOGP for the tree's structural optimization that
accepts inputs only relevant to the knowledge contained in data. The HFIT was
studied in the context of both type-1 and type-2 FISs, and its performance was
evaluated through six application problems. Moreover, the proposed
multiobjective HFIT was compared both theoretically and empirically with
recently proposed FISs methods from the literature, such as McIT2FIS,
TSCIT2FNN, SIT2FNN, RIT2FNS-WB, eT2FIS, MRIT2NFS, IT2FNN-SVR, etc. From the
obtained results, it was found that the HFIT provided less complex and highly
accurate models compared to the models produced by the most of other methods.
Hence, the proposed HFIT is an efficient and competitive alternative to the
other FISs for function approximation and feature selection.
Online Article Ranking as a Constrained, Dynamic, Multi-Objective Optimization Problem
Authors: Jeya Balaji Balasubramanian, Akshay Soni, Yashar Mehdad, Nikolay Laptev
Date: 2017-05-16 00:00:00

The content ranking problem in a social news website, is typically a function
that maximizes a scalar metric of interest like dwell-time. However, like in
most real-world applications we are interested in more than one metric---for
instance simultaneously maximizing click-through rate, monetization metrics,
dwell-time---and also satisfy the traffic requirements promised to different
publishers. All this needs to be done on online data and under the settings
where the objective function and the constraints can dynamically change; this
could happen if for instance new publishers are added, some contracts are
adjusted, or if some contracts are over.
  In this paper, we formulate this problem as a constrained, dynamic,
multi-objective optimization problem. We propose a novel framework that extends
a successful genetic optimization algorithm, NSGA-II, to solve this online,
data-driven problem. We design the modules of NSGA-II to suit our problem. We
evaluate optimization performance using Hypervolume and introduce a confidence
interval metric for assessing the practicality of a solution. We demonstrate
the application of this framework on a real-world Article Ranking problem. We
observe that we make considerable improvements in both time and performance
over a brute-force baseline technique that is currently in production.
A decentralized route to the origins of scaling in human language
Authors: Felipe Urbina, Javier Vera
Date: 2017-05-16 00:00:00

The Zipf's law establishes that if the words of a (large) text are ordered by
decreasing frequency, the frequency versus the rank decreases as a power law
with exponent close to $-1$. Previous work has stressed that this pattern
arises from a conflict of interests of the participants of communication. The
challenge here is to define a computational multi-agent language game, mainly
based on a parameter that measures the relative participant's interests.
Numerical simulations suggest that at critical values of the parameter a
human-like vocabulary, exhibiting scaling properties, seems to appear. The
appearance of an intermediate distribution of frequencies at some critical
values of the parameter suggests that on a population of artificial agents the
emergence of scaling partly arises as a self-organized process only from local
interactions between agents.
Estimating individual treatment effect: generalization bounds and algorithms
Authors: Uri Shalit, Fredrik D. Johansson, David Sontag
Date: 2016-06-13 00:00:00
Proceeding: ICML 2017 8
Tasks: Causal Inference, Generalization Bounds, Heterogeneous Treatment Effect Estimation

There is intense interest in applying machine learning to problems of causal
inference in fields such as healthcare, economics and education. In particular,
individual-level causal inference has important applications such as precision
medicine. We give a new theoretical analysis and family of algorithms for
predicting individual treatment effect (ITE) from observational data, under the
assumption known as strong ignorability. The algorithms learn a "balanced"
representation such that the induced treated and control distributions look
similar. We give a novel, simple and intuitive generalization-error bound
showing that the expected ITE estimation error of a representation is bounded
by a sum of the standard generalization-error of that representation and the
distance between the treated and control distributions induced by the
representation. We use Integral Probability Metrics to measure distances
between distributions, deriving explicit bounds for the Wasserstein and Maximum
Mean Discrepancy (MMD) distances. Experiments on real and simulated data show
the new algorithms match or outperform the state-of-the-art.
All-relevant feature selection using multidimensional filters with exhaustive search
Authors: Krzysztof Mnich, Witold R. Rudnicki
Date: 2017-05-16 00:00:00
Tasks: All, feature selection

This paper describes a method for identification of the informative variables
in the information system with discrete decision variables. It is targeted
specifically towards discovery of the variables that are non-informative when
considered alone, but are informative when the synergistic interactions between
multiple variables are considered. To this end, the mutual entropy of all
possible k-tuples of variables with decision variable is computed. Then, for
each variable the maximal information gain due to interactions with other
variables is obtained. For non-informative variables this quantity conforms to
the well known statistical distributions. This allows for discerning truly
informative variables from non-informative ones. For demonstration of the
approach, the method is applied to several synthetic datasets that involve
complex multidimensional interactions between variables. It is capable of
identifying most important informative variables, even in the case when the
dimensionality of the analysis is smaller than the true dimensionality of the
problem. What is more, the high sensitivity of the algorithm allows for
detection of the influence of nuisance variables on the response variable.
Comparison-Based Choices
Authors: Jon Kleinberg, Sendhil Mullainathan, Johan Ugander
Date: 2017-05-16 00:00:00

A broad range of on-line behaviors are mediated by interfaces in which people
make choices among sets of options. A rich and growing line of work in the
behavioral sciences indicate that human choices follow not only from the
utility of alternatives, but also from the choice set in which alternatives are
presented. In this work we study comparison-based choice functions, a simple
but surprisingly rich class of functions capable of exhibiting so-called
choice-set effects. Motivated by the challenge of predicting complex choices,
we study the query complexity of these functions in a variety of settings. We
consider settings that allow for active queries or passive observation of a
stream of queries, and give analyses both at the granularity of individuals or
populations that might exhibit heterogeneous choice behavior. Our main result
is that any comparison-based choice function in one dimension can be inferred
as efficiently as a basic maximum or minimum choice function across many query
contexts, suggesting that choice-set effects need not entail any fundamental
algorithmic barriers to inference. We also introduce a class of choice
functions we call distance-comparison-based functions, and briefly discuss the
analysis of such functions. The framework we outline provides intriguing
connections between human choice behavior and a range of questions in the
theory of sorting.
Efficient Parallel Methods for Deep Reinforcement Learning
Authors: Alfredo V. Clemente, Humberto N. Castejón, Arjun Chandra
Date: 2017-05-13 00:00:00
Tasks: Deep Reinforcement Learning, GPU, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We propose a novel framework for efficient parallelization of deep
reinforcement learning algorithms, enabling these algorithms to learn from
multiple actors on a single machine. The framework is algorithm agnostic and
can be applied to on-policy, off-policy, value based and policy gradient based
algorithms. Given its inherent parallelism, the framework can be efficiently
implemented on a GPU, allowing the usage of powerful models while significantly
reducing training time. We demonstrate the effectiveness of our framework by
implementing an advantage actor-critic algorithm on a GPU, using on-policy
experiences and employing synchronous updates. Our algorithm achieves
state-of-the-art performance on the Atari domain after only a few hours of
training. Our framework thus opens the door for much faster experimentation on
demanding problem domains. Our implementation is open-source and is made public
at https://github.com/alfredvc/paac
Subjective Knowledge Acquisition and Enrichment Powered By Crowdsourcing
Authors: Rui Meng, Hao Xin, Lei Chen, Yangqiu Song
Date: 2017-05-16 00:00:00

Knowledge bases (KBs) have attracted increasing attention due to its great
success in various areas, such as Web and mobile search.Existing KBs are
restricted to objective factual knowledge, such as city population or fruit
shape, whereas,subjective knowledge, such as big city, which is commonly
mentioned in Web and mobile queries, has been neglected. Subjective knowledge
differs from objective knowledge in that it has no documented or observed
ground truth. Instead, the truth relies on people's dominant opinion. Thus, we
can use the crowdsourcing technique to get opinion from the crowd. In our work,
we propose a system, called crowdsourced subjective knowledge acquisition
(CoSKA),for subjective knowledge acquisition powered by crowdsourcing and
existing KBs. The acquired knowledge can be used to enrich existing KBs in the
subjective dimension which bridges the gap between existing objective knowledge
and subjective queries.The main challenge of CoSKA is the conflict between
large scale knowledge facts and limited crowdsourcing resource. To address this
challenge, in this work, we define knowledge inference rules and then select
the seed knowledge judiciously for crowdsourcing to maximize the inference
power under the resource constraint. Our experimental results on real knowledge
base and crowdsourcing platform verify the effectiveness of CoSKA system.
Locally linear representation for image clustering
Authors: Liangli Zhen, Zhang Yi, Xi Peng, Dezhong Peng
Date: 2013-04-24 00:00:00
Tasks: Clustering, Image Clustering

It is a key to construct a similarity graph in graph-oriented subspace
learning and clustering. In a similarity graph, each vertex denotes a data
point and the edge weight represents the similarity between two points. There
are two popular schemes to construct a similarity graph, i.e., pairwise
distance based scheme and linear representation based scheme. Most existing
works have only involved one of the above schemes and suffered from some
limitations. Specifically, pairwise distance based methods are sensitive to the
noises and outliers compared with linear representation based methods. On the
other hand, there is the possibility that linear representation based
algorithms wrongly select inter-subspaces points to represent a point, which
will degrade the performance. In this paper, we propose an algorithm, called
Locally Linear Representation (LLR), which integrates pairwise distance with
linear representation together to address the problems. The proposed algorithm
can automatically encode each data point over a set of points that not only
could denote the objective point with less residual error, but also are close
to the point in Euclidean space. The experimental results show that our
approach is promising in subspace learning and subspace clustering.
Active Control of Camera Parameters for Object Detection Algorithms
Authors: Yulong Wu, John Tsotsos
Date: 2017-05-16 00:00:00
Tasks: Object, object-detection, Object Detection

Camera parameters not only play an important role in determining the visual
quality of perceived images, but also affect the performance of vision
algorithms, for a vision-guided robot. By quantitatively evaluating four object
detection algorithms, with respect to varying ambient illumination, shutter
speed and voltage gain, it is observed that the performance of the algorithms
is highly dependent on these variables. From this observation, a novel active
control of camera parameters method is proposed, to make robot vision more
robust under different light conditions. Experimental results demonstrate the
effectiveness of our proposed approach, which improves the performance of
object detection algorithms, compared with the conventional auto-exposure
algorithm.
Learning Image Relations with Contrast Association Networks
Authors: Yao Lu, Zhirong Yang, Juho Kannala, Samuel Kaski
Date: 2017-05-16 00:00:00
Tasks: Optical Flow Estimation

Inferring the relations between two images is an important class of tasks in
computer vision. Examples of such tasks include computing optical flow and
stereo disparity. We treat the relation inference tasks as a machine learning
problem and tackle it with neural networks. A key to the problem is learning a
representation of relations. We propose a new neural network module, contrast
association unit (CAU), which explicitly models the relations between two sets
of input variables. Due to the non-negativity of the weights in CAU, we adopt a
multiplicative update algorithm for learning these weights. Experiments show
that neural networks with CAUs are more effective in learning five fundamental
image transformations than conventional neural networks.
Modelling serendipity in a computational context
Authors: Joseph Corneli, Anna Jordanous, Christian Guckelsberger, Alison Pease, Simon Colton
Date: 2014-11-03 00:00:00
Tasks: Philosophy, Recommendation Systems

The term serendipity describes a creative process that develops, in context, with the active participation of a creative agent, but not entirely within that agent's control. While a system cannot be made to perform serendipitously on demand, we argue that its $\mathit{serendipity\ potential}$ can be increased by means of a suitable system architecture and other design choices. We distil a unified description of serendipitous occurrences from historical theorisations of serendipity and creativity. This takes the form of a framework with six phases: $\mathit{perception}$, $\mathit{attention}$, $\mathit{interest}$, $\mathit{explanation}$, $\mathit{bridge}$, and $\mathit{valuation}$. We then use this framework to organise a survey of literature in cognitive science, philosophy, and computing, which yields practical definitions of the six phases, along with heuristics for implementation. We use the resulting model to evaluate the serendipity potential of four existing systems developed by others, and two systems previously developed by two of the authors. Most existing research that considers serendipity in a computing context deals with serendipity as a service; here we relate theories of serendipity to the development of autonomous systems and computational creativity practice. We argue that serendipity is not teleologically blind, and outline representative directions for future applications of our model. We conclude that it is feasible to equip computational systems with the potential for serendipity, and that this could be beneficial in varied computational creativity/AI applications, particularly those designed to operate responsively in real-world contexts.
To tune or not to tune the number of trees in random forest?
Authors: Philipp Probst, Anne-Laure Boulesteix
Date: 2017-05-16 00:00:00
Tasks: General Classification

The number of trees T in the random forest (RF) algorithm for supervised
learning has to be set by the user. It is controversial whether T should simply
be set to the largest computationally manageable value or whether a smaller T
may in some cases be better. While the principle underlying bagging is that
"more trees are better", in practice the classification error rate sometimes
reaches a minimum before increasing again for increasing number of trees. The
goal of this paper is four-fold: (i) providing theoretical results showing that
the expected error rate may be a non-monotonous function of the number of trees
and explaining under which circumstances this happens; (ii) providing
theoretical results showing that such non-monotonous patterns cannot be
observed for other performance measures such as the Brier score and the
logarithmic loss (for classification) and the mean squared error (for
regression); (iii) illustrating the extent of the problem through an
application to a large number (n = 306) of datasets from the public database
OpenML; (iv) finally arguing in favor of setting it to a computationally
feasible large number, depending on convergence properties of the desired
performance measure.
WebVision Challenge: Visual Learning and Understanding With Web Data
Authors: Wen Li, Li-Min Wang, Wei Li, Eirikur Agustsson, Jesse Berent, Abhinav Gupta, Rahul Sukthankar, Luc van Gool
Date: 2017-05-16 00:00:00
Tasks: Benchmarking, image-classification, Image Classification, Transfer Learning

We present the 2017 WebVision Challenge, a public image recognition challenge
designed for deep learning based on web images without instance-level human
annotation. Following the spirit of previous vision challenges, such as ILSVRC,
Places2 and PASCAL VOC, which have played critical roles in the development of
computer vision by contributing to the community with large scale annotated
data for model designing and standardized benchmarking, we contribute with this
challenge a large scale web images dataset, and a public competition with a
workshop co-located with CVPR 2017. The WebVision dataset contains more than
$2.4$ million web images crawled from the Internet by using queries generated
from the $1,000$ semantic concepts of the benchmark ILSVRC 2012 dataset. Meta
information is also included. A validation set and test set containing human
annotated images are also provided to facilitate algorithmic development. The
2017 WebVision challenge consists of two tracks, the image classification task
on WebVision test set, and the transfer learning task on PASCAL VOC 2012
dataset. In this paper, we describe the details of data collection and
annotation, highlight the characteristics of the dataset, and introduce the
evaluation metrics.
Text-based Adventures of the Golovin AI Agent
Authors: Bartosz Kostka, Jaroslaw Kwiecien, Jakub Kowalski, Pawel Rychlikowski
Date: 2017-05-16 00:00:00
Tasks: AI Agent

The domain of text-based adventure games has been recently established as a
new challenge of creating the agent that is both able to understand natural
language, and acts intelligently in text-described environments.
  In this paper, we present our approach to tackle the problem. Our agent,
named Golovin, takes advantage of the limited game domain. We use genre-related
corpora (including fantasy books and decompiled games) to create language
models suitable to this domain. Moreover, we embed mechanisms that allow us to
specify, and separately handle, important tasks as fighting opponents, managing
inventory, and navigating on the game map.
  We validated usefulness of these mechanisms, measuring agent's performance on
the set of 50 interactive fiction games. Finally, we show that our agent plays
on a level comparable to the winner of the last year Text-Based Adventure AI
Competition.
Towards a Principled Integration of Multi-Camera Re-Identification and Tracking through Optimal Bayes Filters
Authors: Lucas Beyer, Stefan Breuers, Vitaly Kurin, Bastian Leibe
Date: 2017-05-12 00:00:00

With the rise of end-to-end learning through deep learning, person detectors
and re-identification (ReID) models have recently become very strong.
Multi-camera multi-target (MCMT) tracking has not fully gone through this
transformation yet. We intend to take another step in this direction by
presenting a theoretically principled way of integrating ReID with tracking
formulated as an optimal Bayes filter. This conveniently side-steps the need
for data-association and opens up a direct path from full images to the core of
the tracker. While the results are still sub-par, we believe that this new,
tight integration opens many interesting research opportunities and leads the
way towards full end-to-end tracking from raw pixels.
Research on Bi-mode Biometrics Based on Deep Learning
Authors: Hao Jiang
Date: 2017-05-16 00:00:00
Tasks: Deep Learning

In view of the fact that biological characteristics have excellent
independent distinguishing characteristics,biometric identification technology
involves almost all the relevant areas of human distinction. Fingerprints,
iris, face, voice-print and other biological features have been widely used in
the public security departments to detect detection, mobile equipment unlock,
target tracking and other fields. With the use of electronic devices more and
more widely and the frequency is getting higher and higher. Only the Biometrics
identification technology with excellent recognition rate can guarantee the
long-term development of these fields.