pwc-archive/papers-with-abstracts huggingface dataset:
A* Tree Search for Portfolio Management
Authors: Xiaojie Gao, Shikui Tu, Lei Xu
Date: 2019-01-07 00:00:00
Tasks: Deep Reinforcement Learning, Management, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We propose a planning-based method to teach an agent to manage portfolio from
scratch. Our approach combines deep reinforcement learning techniques with
search techniques like AlphaGo. By uniting the advantages in A* search
algorithm with Monte Carlo tree search, we come up with a new algorithm named
A* tree search in which best information is returned to guide next search.
Also, the expansion mode of Monte Carlo tree is improved for a higher
utilization of the neural network. The suggested algorithm can also optimize
non-differentiable utility function by combinatorial search. This technique is
then used in our trading system. The major component is a neural network that
is trained by trading experiences from tree search and outputs prior
probability to guide search by pruning away branches in turn. Experimental
results on simulated and real financial data verify the robustness of the
proposed trading system and the trading system produces better strategies than
several approaches based on reinforcement learning.
Interactive Matching Network for Multi-Turn Response Selection in Retrieval-Based Chatbots
Authors: Jia-Chen Gu, Zhen-Hua Ling, Quan Liu
Date: 2019-01-07 00:00:00
Tasks: Conversational Response Selection, Descriptive, Retrieval

In this paper, we propose an interactive matching network (IMN) for the multi-turn response selection task. First, IMN constructs word representations from three aspects to address the challenge of out-of-vocabulary (OOV) words. Second, an attentive hierarchical recurrent encoder (AHRE), which is capable of encoding sentences hierarchically and generating more descriptive representations by aggregating with an attention mechanism, is designed. Finally, the bidirectional interactions between whole multi-turn contexts and response candidates are calculated to derive the matching information between them. Experiments on four public datasets show that IMN outperforms the baseline models on all metrics, achieving a new state-of-the-art performance and demonstrating compatibility across domains for multi-turn response selection.
A dual mode adaptive basal-bolus advisor based on reinforcement learning
Authors: Qingnan Sun, Marko V. Jankovic, João Budzinski, Brett Moore, Peter Diem, Christoph Stettler, Stavroula G. Mougiakakou
Date: 2019-01-07 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Self-monitoring of blood glucose (SMBG) and continuous glucose monitoring
(CGM) are commonly used by type 1 diabetes (T1D) patients to measure glucose
concentrations. The proposed adaptive basal-bolus algorithm (ABBA) supports
inputs from either SMBG or CGM devices to provide personalised suggestions for
the daily basal rate and prandial insulin doses on the basis of the patients'
glucose level on the previous day. The ABBA is based on reinforcement learning
(RL), a type of artificial intelligence, and was validated in silico with an
FDA-accepted population of 100 adults under different realistic scenarios
lasting three simulated months. The scenarios involve three main meals and one
bedtime snack per day, along with different variabilities and uncertainties for
insulin sensitivity, mealtime, carbohydrate amount, and glucose measurement
time. The results indicate that the proposed approach achieves comparable
performance with CGM or SMBG as input signals, without influencing the total
daily insulin dose. The results are a promising indication that AI algorithmic
approaches can provide personalised adaptive insulin optimisation and achieve
glucose control - independently of the type of glucose monitoring technology.
Fusing Body Posture with Facial Expressions for Joint Recognition of Affect in Child-Robot Interaction
Authors: Panagiotis P. Filntisis, Niki Efthymiou, Petros Koutras, Gerasimos Potamianos, Petros Maragos
Date: 2019-01-07 00:00:00

In this paper we address the problem of multi-cue affect recognition in challenging scenarios such as child-robot interaction. Towards this goal we propose a method for automatic recognition of affect that leverages body expressions alongside facial ones, as opposed to traditional methods that typically focus only on the latter. Our deep-learning based method uses hierarchical multi-label annotations and multi-stage losses, can be trained both jointly and separately, and offers us computational models for both individual modalities, as well as for the whole body emotion. We evaluate our method on a challenging child-robot interaction database of emotional expressions collected by us, as well as on the GEMEP public database of acted emotions by adults, and show that the proposed method achieves significantly better results than facial-only expression baselines.
Stochastic Approximation Algorithms for Principal Component Analysis
Authors: Jian Vora
Date: 2019-01-07 00:00:00
Tasks: Computational Efficiency, Dimensionality Reduction, Stochastic Optimization

Principal Component Analysis is a novel way of of dimensionality reduction.
This problem essentially boils down to finding the top k eigen vectors of the
data covariance matrix. A considerable amount of literature is found on
algorithms meant to do so such as an online method be Warmuth and Kuzmin,
Matrix Stochastic Gradient by Arora, Oja's method and many others. In this
paper we see some of these stochastic approaches to the PCA optimization
problem and comment on their convergence and runtime to obtain an epsilon
sub-optimal solution. We revisit convex relaxation based methods for stochastic
optimization of principal component analysis. While methods that directly solve
the non convex problem have been shown to be optimal in terms of statistical
and computational efficiency, the methods based on convex relaxation have been
shown to enjoy comparable, or even superior, empirical performance. This
motivates the need for a deeper formal understanding of the latter.
Understanding partition comparison indices based on counting object pairs
Authors: Matthijs J. Warrens, Hanneke van der Hoef
Date: 2019-01-07 00:00:00
Tasks: Object

In unsupervised machine learning, agreement between partitions is commonly
assessed with so-called external validity indices. Researchers tend to use and
report indices that quantify agreement between two partitions for all clusters
simultaneously. Commonly used examples are the Rand index and the adjusted Rand
index. Since these overall measures give a general notion of what is going on,
their values are usually hard to interpret.
  Three families of indices based on counting object pairs are analyzed. It is
shown that the overall indices can be decomposed into indices that reflect the
degree of agreement on the level of individual clusters. The overall indices
based on the pair-counting approach are sensitive to cluster size imbalance:
they tend to reflect the degree of agreement on the large clusters and provide
little to no information on smaller clusters. Furthermore, the value of
Rand-like indices is determined to a large extent by the number of pairs of
objects that are not joined in either of the partitions.
Location-Centered House Price Prediction: A Multi-Task Learning Approach
Authors: Guangliang Gao, Zhifeng Bao, Jie Cao, A. K. Qin, Timos Sellis, Fellow, IEEE, Zhiang Wu
Date: 2019-01-07 00:00:00
Tasks: Multi-Task Learning, Prediction

Accurate house prediction is of great significance to various real estate
stakeholders such as house owners, buyers, investors, and agents. We propose a
location-centered prediction framework that differs from existing work in terms
of data profiling and prediction model. Regarding data profiling, we define and
capture a fine-grained location profile powered by a diverse range of location
data sources, such as transportation profile (e.g., distance to nearest train
station), education profile (e.g., school zones and ranking), suburb profile
based on census data, facility profile (e.g., nearby hospitals, supermarkets).
Regarding the choice of prediction model, we observe that a variety of
approaches either consider the entire house data for modeling, or split the
entire data and model each partition independently. However, such modeling
ignores the relatedness between partitions, and for all prediction scenarios,
there may not be sufficient training samples per partition for the latter
approach. We address this problem by conducting a careful study of exploiting
the Multi-Task Learning (MTL) model. Specifically, we map the strategies for
splitting the entire house data to the ways the tasks are defined in MTL, and
each partition obtained is aligned with a task. Furthermore, we select specific
MTL-based methods with different regularization terms to capture and exploit
the relatedness between tasks. Based on real-world house transaction data
collected in Melbourne, Australia. We design extensive experimental
evaluations, and the results indicate a significant superiority of MTL-based
methods over state-of-the-art approaches. Meanwhile, we conduct an in-depth
analysis on the impact of task definitions and method selections in MTL on the
prediction performance, and demonstrate that the impact of task definitions on
prediction performance far exceeds that of method selections.
Credit Assignment Techniques in Stochastic Computation Graphs
Authors: Théophane Weber, Nicolas Heess, Lars Buesing, David Silver
Date: 2019-01-07 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Stochastic computation graphs (SCGs) provide a formalism to represent
structured optimization problems arising in artificial intelligence, including
supervised, unsupervised, and reinforcement learning. Previous work has shown
that an unbiased estimator of the gradient of the expected loss of SCGs can be
derived from a single principle. However, this estimator often has high
variance and requires a full model evaluation per data point, making this
algorithm costly in large graphs. In this work, we address these problems by
generalizing concepts from the reinforcement learning literature. We introduce
the concepts of value functions, baselines and critics for arbitrary SCGs, and
show how to use them to derive lower-variance gradient estimates from partial
model evaluations, paving the way towards general and efficient credit
assignment for gradient-based optimization. In doing so, we demonstrate how our
results unify recent advances in the probabilistic inference and reinforcement
learning literature.
Human Pose Estimation with Spatial Contextual Information
Authors: Hong Zhang, Hao Ouyang, Shu Liu, Xiaojuan Qi, Xiaoyong Shen, Ruigang Yang, Jiaya Jia
Date: 2019-01-07 00:00:00
Tasks: Graph Neural Network, Pose Estimation, Prediction

We explore the importance of spatial contextual information in human pose
estimation. Most state-of-the-art pose networks are trained in a multi-stage
manner and produce several auxiliary predictions for deep supervision. With
this principle, we present two conceptually simple and yet computational
efficient modules, namely Cascade Prediction Fusion (CPF) and Pose Graph Neural
Network (PGNN), to exploit underlying contextual information. Cascade
prediction fusion accumulates prediction maps from previous stages to extract
informative signals. The resulting maps also function as a prior to guide
prediction at following stages. To promote spatial correlation among joints,
our PGNN learns a structured representation of human pose as a graph. Direct
message passing between different joints is enabled and spatial relation is
captured. These two modules require very limited computational complexity.
Experimental results demonstrate that our method consistently outperforms
previous methods on MPII and LSP benchmark.
Estimating physical properties from liquid crystal textures via machine learning and complexity-entropy methods
Authors: H. Y. D. Sigaki, R. F. de Souza, R. T. de Souza, R. S. Zola, H. V. Ribeiro
Date: 2019-01-07 00:00:00
Tasks: BIG-bench Machine Learning

Imaging techniques are essential tools for inquiring a number of properties
from different materials. Liquid crystals are often investigated via optical
and image processing methods. In spite of that, considerably less attention has
been paid to the problem of extracting physical properties of liquid crystals
directly from textures images of these materials. Here we present an approach
that combines two physics-inspired image quantifiers (permutation entropy and
statistical complexity) with machine learning techniques for extracting
physical properties of nematic and cholesteric liquid crystals directly from
their textures images. We demonstrate the usefulness and accuracy of our
approach in a series of applications involving simulated and experimental
textures, in which physical properties of these materials (namely: average
order parameter, sample temperature, and cholesteric pitch length) are
predicted with significant precision. Finally, we believe our approach can be
useful in more complex liquid crystal experiments as well as for probing
physical properties of other materials that are investigated via imaging
techniques.
Paired Open-Ended Trailblazer (POET): Endlessly Generating Increasingly Complex and Diverse Learning Environments and Their Solutions
Authors: Rui Wang, Joel Lehman, Jeff Clune, Kenneth O. Stanley
Date: 2019-01-07 00:00:00

While the history of machine learning so far largely encompasses a series of
problems posed by researchers and algorithms that learn their solutions, an
important question is whether the problems themselves can be generated by the
algorithm at the same time as they are being solved. Such a process would in
effect build its own diverse and expanding curricula, and the solutions to
problems at various stages would become stepping stones towards solving even
more challenging problems later in the process. The Paired Open-Ended
Trailblazer (POET) algorithm introduced in this paper does just that: it pairs
the generation of environmental challenges and the optimization of agents to
solve those challenges. It simultaneously explores many different paths through
the space of possible problems and solutions and, critically, allows these
stepping-stone solutions to transfer between problems if better, catalyzing
innovation. The term open-ended signifies the intriguing potential for
algorithms like POET to continue to create novel and increasingly complex
capabilities without bound. Our results show that POET produces a diverse range
of sophisticated behaviors that solve a wide range of environmental challenges,
many of which cannot be solved by direct optimization alone, or even through a
direct-path curriculum-building control algorithm introduced to highlight the
critical role of open-endedness in solving ambitious challenges. The ability to
transfer solutions from one environment to another proves essential to
unlocking the full potential of the system as a whole, demonstrating the
unpredictable nature of fortuitous stepping stones. We hope that POET will
inspire a new push towards open-ended discovery across many domains, where
algorithms like POET can blaze a trail through their interesting possible
manifestations and solutions.
Generative Adversarial Networks for Financial Trading Strategies Fine-Tuning and Combination
Authors: Adriano Koshiyama, Nick Firoozye, Philip Treleaven
Date: 2019-01-07 00:00:00
Tasks: Time Series, Time Series Analysis

Systematic trading strategies are algorithmic procedures that allocate assets
aiming to optimize a certain performance criterion. To obtain an edge in a
highly competitive environment, the analyst needs to proper fine-tune its
strategy, or discover how to combine weak signals in novel alpha creating
manners. Both aspects, namely fine-tuning and combination, have been
extensively researched using several methods, but emerging techniques such as
Generative Adversarial Networks can have an impact into such aspects.
Therefore, our work proposes the use of Conditional Generative Adversarial
Networks (cGANs) for trading strategies calibration and aggregation. To this
purpose, we provide a full methodology on: (i) the training and selection of a
cGAN for time series data; (ii) how each sample is used for strategies
calibration; and (iii) how all generated samples can be used for ensemble
modelling. To provide evidence that our approach is well grounded, we have
designed an experiment with multiple trading strategies, encompassing 579
assets. We compared cGAN with an ensemble scheme and model validation methods,
both suited for time series. Our results suggest that cGANs are a suitable
alternative for strategies calibration and combination, providing
outperformance when the traditional techniques fail to generate any alpha.
Variational bridge constructs for approximate Gaussian process regression
Authors: Wil O. C. Ward, Mauricio A. Álvarez
Date: 2019-01-07 00:00:00
Tasks: regression, Variational Inference

This paper introduces a method to approximate Gaussian process regression by
representing the problem as a stochastic differential equation and using
variational inference to approximate solutions. The approximations are compared
with full GP regression and generated paths are demonstrated to be
indistinguishable from GP samples. We show that the approach extends easily to
non-linear dynamics and discuss extensions to which the approach can be easily
applied.
Deep Network Embedding for Graph Representation Learning in Signed Networks
Authors: Xiao Shen, Fu-Lai Chung
Date: 2019-01-07 00:00:00
Tasks: Community Detection, Graph Mining, Graph Representation Learning, Link Sign Prediction, Network Embedding, Representation Learning

Network embedding has attracted an increasing attention over the past few
years. As an effective approach to solve graph mining problems, network
embedding aims to learn a low-dimensional feature vector representation for
each node of a given network. The vast majority of existing network embedding
algorithms, however, are only designed for unsigned networks, and the signed
networks containing both positive and negative links, have pretty distinct
properties from the unsigned counterpart. In this paper, we propose a deep
network embedding model to learn the low-dimensional node vector
representations with structural balance preservation for the signed networks.
The model employs a semi-supervised stacked auto-encoder to reconstruct the
adjacency connections of a given signed network. As the adjacency connections
are overwhelmingly positive in the real-world signed networks, we impose a
larger penalty to make the auto-encoder focus more on reconstructing the scarce
negative links than the abundant positive links. In addition, to preserve the
structural balance property of signed networks, we design the pairwise
constraints to make the positively connected nodes much closer than the
negatively connected nodes in the embedding space. Based on the network
representations learned by the proposed model, we conduct link sign prediction
and community detection in signed networks. Extensive experimental results in
real-world datasets demonstrate the superiority of the proposed model over the
state-of-the-art network embedding algorithms for graph representation learning
in signed networks.
Double Weighted Truncated Nuclear Norm Regularization for Low-Rank Matrix Completion
Authors: Shengke Xue, Wenyuan Qiu, Fan Liu, Xinyu Jin
Date: 2019-01-07 00:00:00
Tasks: Low-Rank Matrix Completion, Matrix Completion

Matrix completion focuses on recovering a matrix from a small subset of its
observed elements, and has already gained cumulative attention in computer
vision. Many previous approaches formulate this issue as a low-rank matrix
approximation problem. Recently, a truncated nuclear norm has been presented as
a surrogate of traditional nuclear norm, for better estimation to the rank of a
matrix. The truncated nuclear norm regularization (TNNR) method is applicable
in real-world scenarios. However, it is sensitive to the selection of the
number of truncated singular values and requires numerous iterations to
converge. Hereby, this paper proposes a revised approach called the double
weighted truncated nuclear norm regularization (DW-TNNR), which assigns
different weights to the rows and columns of a matrix separately, to accelerate
the convergence with acceptable performance. The DW-TNNR is more robust to the
number of truncated singular values than the TNNR. Instead of the iterative
updating scheme in the second step of TNNR, this paper devises an efficient
strategy that uses a gradient descent manner in a concise form, with a
theoretical guarantee in optimization. Sufficient experiments conducted on real
visual data prove that DW-TNNR has promising performance and holds the
superiority in both speed and accuracy for matrix completion.
Post-mortem Iris Recognition with Deep-Learning-based Image Segmentation
Authors: Mateusz Trokielewicz, Adam Czajka, Piotr Maciejewicz
Date: 2019-01-07 00:00:00
Tasks: Deep Learning, Image Segmentation, Iris Recognition, Iris Segmentation, Segmentation, Semantic Segmentation

This paper proposes the first known to us iris recognition methodology designed specifically for post-mortem samples. We propose to use deep learning-based iris segmentation models to extract highly irregular iris texture areas in post-mortem iris images. We show how to use segmentation masks predicted by neural networks in conventional, Gabor-based iris recognition method, which employs circular approximations of the pupillary and limbic iris boundaries. As a whole, this method allows for a significant improvement in post-mortem iris recognition accuracy over the methods designed only for ante-mortem irises, including the academic OSIRIS and commercial IriCore implementations. The proposed method reaches the EER less than 1% for samples collected up to 10 hours after death, when compared to 16.89% and 5.37% of EER observed for OSIRIS and IriCore, respectively. For samples collected up to 369 hours post-mortem, the proposed method achieves the EER 21.45%, while 33.59% and 25.38% are observed for OSIRIS and IriCore, respectively. Additionally, the method is tested on a database of iris images collected from ophthalmology clinic patients, for which it also offers an advantage over the two other algorithms. This work is the first step towards post-mortem-specific iris recognition, which increases the chances of identification of deceased subjects in forensic investigations. The new database of post-mortem iris images acquired from 42 subjects, as well as the deep learning-based segmentation models are made available along with the paper, to ensure all the results presented in this manuscript are reproducible.
Universal Deep Beamformer for Variable Rate Ultrasound Imaging
Authors: Shujaat Khan, Jaeyoung Huh, Jong Chul Ye
Date: 2019-01-07 00:00:00

Ultrasound (US) imaging is based on the time-reversal principle, in which
individual channel RF measurements are back-propagated and accumulated to form
an image after applying specific delays. While this time reversal is usually
implemented as a delay-and-sum (DAS) beamformer, the image quality quickly
degrades as the number of measurement channels decreases. To address this
problem, various types of adaptive beamforming techniques have been proposed
using predefined models of the signals. However, the performance of these
adaptive beamforming approaches degrade when the underlying model is not
sufficiently accurate. Here, we demonstrate for the first time that a single
universal deep beamformer trained using a purely data-driven way can generate
significantly improved images over widely varying aperture and channel
subsampling patterns. In particular, we design an end-to-end deep learning
framework that can directly process sub-sampled RF data acquired at different
subsampling rate and detector configuration to generate high quality ultrasound
images using a single beamformer. Experimental results using B-mode focused
ultrasound confirm the efficacy of the proposed methods.