pwc-archive/papers-with-abstracts huggingface dataset:
GP CaKe: Effective brain connectivity with causal kernels
Authors: Luca Ambrogioni, Max Hinne, Marcel van Gerven, Eric Maris
Date: 2017-05-16 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Causal Inference

A fundamental goal in network neuroscience is to understand how activity in
one region drives activity elsewhere, a process referred to as effective
connectivity. Here we propose to model this causal interaction using
integro-differential equations and causal kernels that allow for a rich
analysis of effective connectivity. The approach combines the tractability and
flexibility of autoregressive modeling with the biophysical interpretability of
dynamic causal modeling. The causal kernels are learned nonparametrically using
Gaussian process regression, yielding an efficient framework for causal
inference. We construct a novel class of causal covariance functions that
enforce the desired properties of the causal kernels, an approach which we call
GP CaKe. By construction, the model and its hyperparameters have biophysical
meaning and are therefore easily interpretable. We demonstrate the efficacy of
GP CaKe on a number of simulations and give an example of a realistic
application on magnetoencephalography (MEG) data.
Identifying hazardousness of sewer pipeline gas mixture using classification methods: a comparative study
Authors: Varun Kumar Ojha, Paramartha Dutta, Atal Chaudhuri
Date: 2017-05-16 00:00:00
Tasks: General Classification

In this work, we formulated a real-world problem related to sewer pipeline
gas detection using the classification-based approaches. The primary goal of
this work was to identify the hazardousness of sewer pipeline to offer safe and
non-hazardous access to sewer pipeline workers so that the human fatalities,
which occurs due to the toxic exposure of sewer gas components, can be avoided.
The dataset acquired through laboratory tests, experiments, and various
literature sources was organized to design a predictive model that was able to
identify/classify hazardous and non-hazardous situation of sewer pipeline. To
design such prediction model, several classification algorithms were used and
their performances were evaluated and compared, both empirically and
statistically, over the collected dataset. In addition, the performances of
several ensemble methods were analyzed to understand the extent of improvement
offered by these methods. The result of this comprehensive study showed that
the instance-based learning algorithm performed better than many other
algorithms such as multilayer perceptron, radial basis function network,
support vector machine, reduced pruning tree. Similarly, it was observed that
multi-scheme ensemble approach enhanced the performance of base predictors.
Predictive modeling of die filling of the pharmaceutical granules using the flexible neural tree
Authors: Varun Kumar Ojha, Serena Schiano, Chuan-Yu Wu, Václav Snášel, Ajith Abraham
Date: 2017-05-16 00:00:00

In this work, a computational intelligence (CI) technique named flexible
neural tree (FNT) was developed to predict die filling performance of
pharmaceutical granules and to identify significant die filling process
variables. FNT resembles feedforward neural network, which creates a tree-like
structure by using genetic programming. To improve accuracy, FNT parameters
were optimized by using differential evolution algorithm. The performance of
the FNT-based CI model was evaluated and compared with other CI techniques:
multilayer perceptron, Gaussian process regression, and reduced error pruning
tree. The accuracy of the CI model was evaluated experimentally using die
filling as a case study. The die filling experiments were performed using a
model shoe system and three different grades of microcrystalline cellulose
(MCC) powders (MCC PH 101, MCC PH 102, and MCC DG). The feed powders were
roll-compacted and milled into granules. The granules were then sieved into
samples of various size classes. The mass of granules deposited into the die at
different shoe speeds was measured. From these experiments, a dataset
consisting true density, mean diameter (d50), granule size, and shoe speed as
the inputs and the deposited mass as the output was generated. Cross-validation
(CV) methods such as 10FCV and 5x2FCV were applied to develop and to validate
the predictive models. It was found that the FNT-based CI model (for both CV
methods) performed much better than other CI models. Additionally, it was
observed that process variables such as the granule size and the shoe speed had
a higher impact on the predictability than that of the powder property such as
d50. Furthermore, validation of model prediction with experimental data showed
that the die filling behavior of coarse granules could be better predicted than
that of fine granules.
Ensemble of heterogeneous flexible neural trees using multiobjective genetic programming
Authors: Varun Kumar Ojha, Ajith Abraham, Václav Snášel
Date: 2017-05-16 00:00:00
Tasks: Classification, Diversity, Time Series, Time Series Analysis, Time Series Regression

Machine learning algorithms are inherently multiobjective in nature, where
approximation error minimization and model's complexity simplification are two
conflicting objectives. We proposed a multiobjective genetic programming (MOGP)
for creating a heterogeneous flexible neural tree (HFNT), tree-like flexible
feedforward neural network model. The functional heterogeneity in neural tree
nodes was introduced to capture a better insight of data during learning
because each input in a dataset possess different features. MOGP guided an
initial HFNT population towards Pareto-optimal solutions, where the final
population was used for making an ensemble system. A diversity index measure
along with approximation error and complexity was introduced to maintain
diversity among the candidates in the population. Hence, the ensemble was
created by using accurate, structurally simple, and diverse candidates from
MOGP final population. Differential evolution algorithm was applied to
fine-tune the underlying parameters of the selected candidates. A comprehensive
test over classification, regression, and time-series datasets proved the
efficiency of the proposed algorithm over other available prediction methods.
Moreover, the heterogeneous creation of HFNT proved to be efficient in making
ensemble system from the final population.
Learning Convex Regularizers for Optimal Bayesian Denoising
Authors: Ha Q. Nguyen, Emrah Bostan, Michael Unser
Date: 2017-05-16 00:00:00
Tasks: Denoising

We propose a data-driven algorithm for the maximum a posteriori (MAP)
estimation of stochastic processes from noisy observations. The primary
statistical properties of the sought signal is specified by the penalty
function (i.e., negative logarithm of the prior probability density function).
Our alternating direction method of multipliers (ADMM)-based approach
translates the estimation task into successive applications of the proximal
mapping of the penalty function. Capitalizing on this direct link, we define
the proximal operator as a parametric spline curve and optimize the spline
coefficients by minimizing the average reconstruction error for a given
training set. The key aspects of our learning method are that the associated
penalty function is constrained to be convex and the convergence of the ADMM
iterations is proven. As a result of these theoretical guarantees, adaptation
of the proposed framework to different levels of measurement noise is extremely
simple and does not require any retraining. We apply our method to estimation
of both sparse and non-sparse models of L\'{e}vy processes for which the
minimum mean square error (MMSE) estimators are available. We carry out a
single training session and perform comparisons at various signal-to-noise
ratio (SNR) values. Simulations illustrate that the performance of our
algorithm is practically identical to the one of the MMSE estimator
irrespective of the noise power.
Metaheuristic Design of Feedforward Neural Networks: A Review of Two Decades of Research
Authors: Varun Kumar Ojha, Ajith Abraham, Václav Snášel
Date: 2017-05-16 00:00:00
Tasks: Evolutionary Algorithms

Over the past two decades, the feedforward neural network (FNN) optimization
has been a key interest among the researchers and practitioners of multiple
disciplines. The FNN optimization is often viewed from the various
perspectives: the optimization of weights, network architecture, activation
nodes, learning parameters, learning environment, etc. Researchers adopted such
different viewpoints mainly to improve the FNN's generalization ability. The
gradient-descent algorithm such as backpropagation has been widely applied to
optimize the FNNs. Its success is evident from the FNN's application to
numerous real-world problems. However, due to the limitations of the
gradient-based optimization methods, the metaheuristic algorithms including the
evolutionary algorithms, swarm intelligence, etc., are still being widely
explored by the researchers aiming to obtain generalized FNN for a given
problem. This article attempts to summarize a broad spectrum of FNN
optimization methodologies including conventional and metaheuristic approaches.
This article also tries to connect various research directions emerged out of
the FNN optimization practices, such as evolving neural network (NN),
cooperative coevolution NN, complex-valued NN, deep learning, extreme learning
machine, quantum NN, etc. Additionally, it provides interesting research
challenges for future research to cope-up with the present information
processing era.
Communications that Emerge through Reinforcement Learning Using a (Recurrent) Neural Network
Authors: Katsunari Shibata
Date: 2017-03-10 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Communication is not only an action of choosing a signal, but needs to
consider the context and sensor signals. It also needs to decide what
information is communicated and how it is represented in or understood from
signals. Therefore, communication should be realized comprehensively together
with its purpose and other functions.
  The recent successful results in end-to-end reinforcement learning (RL) show
the importance of comprehensive learning and the usefulness of end-to-end RL.
Although little is known, we have shown that a variety of communications emerge
through RL using a (recurrent) neural network (NN). Here, three of them are
introduced.
  In the 1st one, negotiation to avoid conflicts among 4 randomly-picked agents
was learned. Each agent generates a binary signal from the output of its
recurrent NN (RNN), and receives 4 signals from the agents three times. After
learning, each agent made an appropriate final decision after negotiation for
any combination of 4 agents. Differentiation of individuality among the agents
also could be seen.
  The 2nd one focused on discretization of communication signal. A sender agent
perceives the receiver's location and generates a continuous signal twice by
its RNN. A receiver agent receives them sequentially, and moves according to
its RNN's output to reach the sender's location. When noises were added to the
signal, it was binarized through learning and 2-bit communication was
established.
  The 3rd one focused on end-to-end comprehensive communication. A sender
receives 1,785-pixel real camera image on which a real robot can be seen, and
sends two sounds whose frequencies are computed by its NN. A receiver receives
them, and two motion commands for the robot are generated by its NN. After
learning, though some preliminary learning was necessary for the sender, the
robot could reach the goal from any initial location.
Functions that Emerge through End-to-End Reinforcement Learning - The Direction for Artificial General Intelligence -
Authors: Katsunari Shibata
Date: 2017-03-07 00:00:00
Tasks: Color Constancy, Game of Go, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Transfer Learning

Recently, triggered by the impressive results in TV-games or game of Go by
Google DeepMind, end-to-end reinforcement learning (RL) is collecting
attentions. Although little is known, the author's group has propounded this
framework for around 20 years and already has shown various functions that
emerge in a neural network (NN) through RL. In this paper, they are introduced
again at this timing.
  "Function Modularization" approach is deeply penetrated subconsciously. The
inputs and outputs for a learning system can be raw sensor signals and motor
commands. "State space" or "action space" generally used in RL show the
existence of functional modules. That has limited reinforcement learning to
learning only for the action-planning module. In order to extend reinforcement
learning to learning of the entire function on a huge degree of freedom of a
massively parallel learning system and to explain or develop human-like
intelligence, the author has believed that end-to-end RL from sensors to motors
using a recurrent NN (RNN) becomes an essential key. Especially in the higher
functions, this approach is very effective by being free from the need to
decide their inputs and outputs.
  The functions that emerge, we have confirmed, through RL using a NN cover a
broad range from real robot learning with raw camera pixel inputs to
acquisition of dynamic functions in a RNN. Those are (1)image recognition,
(2)color constancy (optical illusion), (3)sensor motion (active recognition),
(4)hand-eye coordination and hand reaching movement, (5)explanation of brain
activities, (6)communication, (7)knowledge transfer, (8)memory, (9)selective
attention, (10)prediction, (11)exploration. The end-to-end RL enables the
emergence of very flexible comprehensive functions that consider many things in
parallel although it is difficult to give the boundary of each function
clearly.
IAN: The Individual Aggregation Network for Person Search
Authors: Jimin Xiao, Yanchun Xie, Tammam Tillo, Kai-Zhu Huang, Yunchao Wei, Jiashi Feng
Date: 2017-05-16 00:00:00
Tasks: object-detection, Object Detection, Person Search

Person search in real-world scenarios is a new challenging computer version
task with many meaningful applications. The challenge of this task mainly comes
from: (1) unavailable bounding boxes for pedestrians and the model needs to
search for the person over the whole gallery images; (2) huge variance of
visual appearance of a particular person owing to varying poses, lighting
conditions, and occlusions. To address these two critical issues in modern
person search applications, we propose a novel Individual Aggregation Network
(IAN) that can accurately localize persons by learning to minimize intra-person
feature variations. IAN is built upon the state-of-the-art object detection
framework, i.e., faster R-CNN, so that high-quality region proposals for
pedestrians can be produced in an online manner. In addition, to relieve the
negative effect caused by varying visual appearances of the same individual,
IAN introduces a novel center loss that can increase the intra-class
compactness of feature representations. The engaged center loss encourages
persons with the same identity to have similar feature characteristics.
Extensive experimental results on two benchmarks, i.e., CUHK-SYSU and PRW, well
demonstrate the superiority of the proposed model. In particular, IAN achieves
77.23% mAP and 80.45% top-1 accuracy on CUHK-SYSU, which outperform the
state-of-the-art by 1.7% and 1.85%, respectively.
New Reinforcement Learning Using a Chaotic Neural Network for Emergence of "Thinking" - "Exploration" Grows into "Thinking" through Learning -
Authors: Katsunari Shibata, Yuki Goto
Date: 2017-05-16 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Expectation for the emergence of higher functions is getting larger in the
framework of end-to-end reinforcement learning using a recurrent neural
network. However, the emergence of "thinking" that is a typical higher function
is difficult to realize because "thinking" needs non fixed-point, flow-type
attractors with both convergence and transition dynamics. Furthermore, in order
to introduce "inspiration" or "discovery" in "thinking", not completely random
but unexpected transition should be also required.
  By analogy to "chaotic itinerancy", we have hypothesized that "exploration"
grows into "thinking" through learning by forming flow-type attractors on
chaotic random-like dynamics. It is expected that if rational dynamics are
learned in a chaotic neural network (ChNN), coexistence of rational state
transition, inspiration-like state transition and also random-like exploration
for unknown situation can be realized.
  Based on the above idea, we have proposed new reinforcement learning using a
ChNN as an actor. The positioning of exploration is completely different from
the conventional one. The chaotic dynamics inside the ChNN produces exploration
factors by itself. Since external random numbers for stochastic action
selection are not used, exploration factors cannot be isolated from the output.
Therefore, the learning method is also completely different from the
conventional one.
  At each non-feedback connection, one variable named causality trace takes in
and maintains the input through the connection according to the change in its
output. Using the trace and TD error, the weight is updated.
  In this paper, as the result of a recent simple task to see whether the new
learning works or not, it is shown that a robot with two wheels and two visual
sensors reaches a target while avoiding an obstacle after learning though there
are still many rooms for improvement.
Constrained Multi-Slot Optimization for Ranking Recommendations
Authors: Kinjal Basu, Shaunak Chatterjee, Ankan Saha
Date: 2016-02-13 00:00:00

Ranking items to be recommended to users is one of the main problems in large
scale social media applications. This problem can be set up as a
multi-objective optimization problem to allow for trading off multiple,
potentially conflicting objectives (that are driven by those items) against
each other. Most previous approaches to this problem optimize for a single slot
without considering the interaction effect of these items on one another.
  In this paper, we develop a constrained multi-slot optimization formulation,
which allows for modeling interactions among the items on the different slots.
We characterize the solution in terms of problem parameters and identify
conditions under which an efficient solution is possible. The problem
formulation results in a quadratically constrained quadratic program (QCQP). We
provide an algorithm that gives us an efficient solution by relaxing the
constraints of the QCQP minimally. Through simulated experiments, we show the
benefits of modeling interactions in a multi-slot ranking context, and the
speed and accuracy of our QCQP approximate solver against other state of the
art methods.
Colors in Context: A Pragmatic Neural Model for Grounded Language Understanding
Authors: Will Monroe, Robert X. D. Hawkins, Noah D. Goodman, Christopher Potts
Date: 2017-03-29 00:00:00
Proceeding: TACL 2017 1
Tasks: Referring Expression

We present a model of pragmatic referring expression interpretation in a
grounded communication task (identifying colors from descriptions) that draws
upon predictions from two recurrent neural network classifiers, a speaker and a
listener, unified by a recursive pragmatic reasoning framework. Experiments
show that this combined pragmatic model interprets color descriptions more
accurately than the classifiers from which it is built, and that much of this
improvement results from combining the speaker and listener perspectives. We
observe that pragmatic reasoning helps primarily in the hardest cases: when the
model must distinguish very similar colors, or when few utterances adequately
express the target color. Our findings make use of a newly-collected corpus of
human utterances in color reference games, which exhibit a variety of pragmatic
behaviors. We also show that the embedded speaker model reproduces many of
these pragmatic behaviors.
A Method for Determining Weights of Criterias and Alternative of Fuzzy Group Decision Making Problem
Authors: Jon JaeGyong, Mun JongHui, Ryang GyongIl
Date: 2017-05-16 00:00:00
Tasks: Decision Making

In this paper, we constructed a model to determine weights of criterias and
presented a solution for determining the optimal alternative by using the
constructed model and relationship analysis between criterias in fuzzy group
decision-making problem with different forms of preference information of
decision makers on criterias.
Cooperative Learning with Visual Attributes
Authors: Tanmay Batra, Devi Parikh
Date: 2017-05-16 00:00:00

Learning paradigms involving varying levels of supervision have received a
lot of interest within the computer vision and machine learning communities.
The supervisory information is typically considered to come from a human
supervisor -- a "teacher" figure. In this paper, we consider an alternate
source of supervision -- a "peer" -- i.e. a different machine. We introduce
cooperative learning, where two agents trying to learn the same visual
concepts, but in potentially different environments using different sources of
data (sensors), communicate their current knowledge of these concepts to each
other. Given the distinct sources of data in both agents, the mode of
communication between the two agents is not obvious. We propose the use of
visual attributes -- semantic mid-level visual properties such as furry,
wooden, etc.-- as the mode of communication between the agents. Our experiments
in three domains -- objects, scenes, and animals -- demonstrate that our
proposed cooperative learning approach improves the performance of both agents
as compared to their performance if they were to learn in isolation. Our
approach is particularly applicable in scenarios where privacy, security and/or
bandwidth constraints restrict the amount and type of information the two
agents can exchange.
Automated Body Structure Extraction from Arbitrary 3D Mesh
Authors: Yong Khoo, Sang Chung
Date: 2017-05-16 00:00:00

This paper presents an automated method for 3D character skeleton extraction
that can be applied for generic 3D shapes. Our work is motivated by the
skeleton-based prior work on automatic rigging focused on skeleton extraction
and can automatically aligns the extracted structure to fit the 3D shape of the
given 3D mesh. The body mesh can be subsequently skinned based on the extracted
skeleton and thus enables rigging process. In the experiment, we apply public
dataset to drive the estimated skeleton from different body shapes, as well as
the real data obtained from 3D scanning systems. Satisfactory results are
obtained compared to the existing approaches.
Joint Geometrical and Statistical Alignment for Visual Domain Adaptation
Authors: Jing Zhang, Wanqing Li, Philip Ogunbona
Date: 2017-05-16 00:00:00
Proceeding: CVPR 2017 7
Tasks: Domain Adaptation, Unsupervised Domain Adaptation

This paper presents a novel unsupervised domain adaptation method for
cross-domain visual recognition. We propose a unified framework that reduces
the shift between domains both statistically and geometrically, referred to as
Joint Geometrical and Statistical Alignment (JGSA). Specifically, we learn two
coupled projections that project the source domain and target domain data into
low dimensional subspaces where the geometrical shift and distribution shift
are reduced simultaneously. The objective function can be solved efficiently in
a closed form. Extensive experiments have verified that the proposed method
significantly outperforms several state-of-the-art domain adaptation methods on
a synthetic dataset and three different real world cross-domain visual
recognition tasks.