pwc-archive/papers-with-abstracts huggingface dataset:
Mind the Gap: A Well Log Data Analysis
Authors: Rui L. Lopes, Alípio Jorge
Date: 2017-05-10 00:00:00
Tasks: Descriptive

The main task in oil and gas exploration is to gain an understanding of the
distribution and nature of rocks and fluids in the subsurface. Well logs are
records of petro-physical data acquired along a borehole, providing direct
information about what is in the subsurface. The data collected by logging
wells can have significant economic consequences, due to the costs inherent to
drilling wells, and the potential return of oil deposits. In this paper, we
describe preliminary work aimed at building a general framework for well log
prediction.
  First, we perform a descriptive and exploratory analysis of the gaps in the
neutron porosity logs of more than a thousand wells in the North Sea. Then, we
generate artificial gaps in the neutron logs that reflect the statistics
collected before. Finally, we compare Artificial Neural Networks, Random
Forests, and three algorithms of Linear Regression in the prediction of missing
gaps on a well-by-well basis.
A Survey of Deep Learning Methods for Relation Extraction
Authors: Shantanu Kumar
Date: 2017-05-10 00:00:00
Tasks: Deep Learning, Relation, Relation Extraction, Survey

Relation Extraction is an important sub-task of Information Extraction which
has the potential of employing deep learning (DL) models with the creation of
large datasets using distant supervision. In this review, we compare the
contributions and pitfalls of the various DL models that have been used for the
task, to help guide the path ahead.
An evidential Markov decision making model
Authors: Zichang He, Wen Jiang
Date: 2017-05-10 00:00:00
Tasks: Decision Making, model

The sure thing principle and the law of total probability are basic laws in
classic probability theory. A disjunction fallacy leads to the violation of
these two classical laws. In this paper, an Evidential Markov (EM) decision
making model based on Dempster-Shafer (D-S) evidence theory and Markov
modelling is proposed to address this issue and model the real human
decision-making process. In an evidential framework, the states are extended by
introducing an uncertain state which represents the hesitance of a decision
maker. The classical Markov model can not produce the disjunction effect, which
assumes that a decision has to be certain at one time. However, the state is
allowed to be uncertain in the EM model before the final decision is made. An
extra uncertainty degree parameter is defined by a belief entropy, named Deng
entropy, to assignment the basic probability assignment of the uncertain state,
which is the key to predict the disjunction effect. A classical categorization
decision-making experiment is used to illustrate the effectiveness and validity
of EM model. The disjunction effect can be well predicted and the free
parameters are less compared with the existing models.
Visible Light-Based Human Visual System Conceptual Model
Authors: Lee Prangnell
Date: 2016-09-15 00:00:00
Tasks: model

There exists a widely accepted set of assertions in the digital image and video coding literature, which are as follows: the Human Visual System (HVS) is more sensitive to luminance (often confused with brightness) than photon energies (often confused with chromaticity and chrominance). Passages similar to the following occur with high frequency in the peer reviewed literature and academic text books: "the HVS is much more sensitive to brightness than colour" and/or "the HVS is much more sensitive to luma than chroma". In this discussion paper, a Visible Light-Based Human Visual System (VL-HVS) conceptual model is discussed. The objectives of VL-HVS are as follows: 1. To provide a deeper theoretical reflection of the fundamental relationship between visible light, the manifestation of colour perception derived from visible light and the physiology of the perception of colour. That is, in terms of the physics of visible light, photobiology and the human subjective interpretation of visible light, it is appropriate to provide comprehensive background information in relation to the natural interactions between visible light, the retinal photoreceptors and the subsequent cortical processing of such. 2. To provide a more wholesome account with respect to colour information in digital image and video processing applications. 3. To recontextualise colour data in the RGB and YCbCr colour spaces, such that novel techniques in digital image and video processing, including quantisation and artifact reduction techniques, may be developed based on both luma and chroma information (not luma data only).
Light Field Compression with Disparity Guided Sparse Coding based on Structural Key Views
Authors: Jie Chen, Junhui Hou, Lap-Pui Chau
Date: 2016-10-12 00:00:00

Recent imaging technologies are rapidly evolving for sampling richer and more
immersive representations of the 3D world. And one of the emerging technologies
are light field (LF) cameras based on micro-lens arrays. To record the
directional information of the light rays, a much larger storage space and
transmission bandwidth are required by a LF image as compared with a
conventional 2D image of similar spatial dimension, and the compression of LF
data becomes a vital part of its application.
  In this paper, we propose a LF codec that fully exploits the intrinsic
geometry between the LF sub-views by first approximating the LF with disparity
guided sparse coding over a perspective shifted light field dictionary. The
sparse coding is only based on several optimized Structural Key Views (SKV);
however the entire LF can be recovered from the coding coefficients. By keeping
the approximation identical between encoder and decoder, only the residuals of
the non-key views, disparity map and the SKVs need to be compressed into the
bit stream. An optimized SKV selection method is proposed such that most LF
spatial information could be preserved. And to achieve optimum dictionary
efficiency, the LF is divided into several Coding Regions (CR), over which the
reconstruction works individually. Experiments and comparisons have been
carried out over benchmark LF dataset, which show that the proposed SC-SKV
codec produces convincing compression results in terms of both rate-distortion
performance and visual quality compared with High Efficiency Video Coding
(HEVC): with 47.87% BD-rate reduction and 1.59 dB BD-PSNR improvement achieved
on average, especially with up to 4 dB improvement for low bit rate scenarios.
Inferring and Executing Programs for Visual Reasoning
Authors: Justin Johnson, Bharath Hariharan, Laurens van der Maaten, Judy Hoffman, Li Fei-Fei, C. Lawrence Zitnick, Ross Girshick
Date: 2017-05-10 00:00:00
Proceeding: ICCV 2017 10
Tasks: Visual Question Answering (VQA), Visual Reasoning

Existing methods for visual reasoning attempt to directly map inputs to
outputs using black-box architectures without explicitly modeling the
underlying reasoning processes. As a result, these black-box models often learn
to exploit biases in the data rather than learning to perform visual reasoning.
Inspired by module networks, this paper proposes a model for visual reasoning
that consists of a program generator that constructs an explicit representation
of the reasoning process to be performed, and an execution engine that executes
the resulting program to produce an answer. Both the program generator and the
execution engine are implemented by neural networks, and are trained using a
combination of backpropagation and REINFORCE. Using the CLEVR benchmark for
visual reasoning, we show that our model significantly outperforms strong
baselines and generalizes better in a variety of settings.
An initialization method for the k-means using the concept of useful nearest centers
Authors: Hassan Ismkhan
Date: 2017-05-10 00:00:00

The aim of the k-means is to minimize squared sum of Euclidean distance from
the mean (SSEDM) of each cluster. The k-means can effectively optimize this
function, but it is too sensitive for initial centers (seeds). This paper
proposed a method for initialization of the k-means using the concept of useful
nearest center for each data point.
Learning RGB-D Salient Object Detection using background enclosure, depth contrast, and top-down features
Authors: Riku Shigematsu, David Feng, ShaoDi You, Nick Barnes
Date: 2017-05-10 00:00:00
Tasks: Object, object-detection, Object Detection, RGB-D Salient Object Detection, RGB Salient Object Detection, Salient Object Detection

Recently, deep Convolutional Neural Networks (CNN) have demonstrated strong
performance on RGB salient object detection. Although, depth information can
help improve detection results, the exploration of CNNs for RGB-D salient
object detection remains limited. Here we propose a novel deep CNN architecture
for RGB-D salient object detection that exploits high-level, mid-level, and low
level features. Further, we present novel depth features that capture the ideas
of background enclosure and depth contrast that are suitable for a learned
approach. We show improved results compared to state-of-the-art RGB-D salient
object detection methods. We also show that the low-level and mid-level depth
features both contribute to improvements in the results. Especially, F-Score of
our method is 0.848 on RGBD1000 dataset, which is 10.7% better than the second
place.
Learning Model Predictive Control for Iterative Tasks: A Computationally Efficient Approach for Linear System
Authors: Ugo Rosolia, Francesco Borrelli
Date: 2017-02-23 00:00:00
Tasks: Model Predictive Control

A Learning Model Predictive Controller (LMPC) for linear system in presented. The proposed controller is an extension of the LMPC [1] and it aims to decrease the computational burden. The control scheme is reference-free and is able to improve its performance by learning from previous iterations. A convex safe set and a terminal cost function are used in order to guarantee recursive feasibility and non-increasing performance at each iteration. The paper presents the control design approach, and shows how to recursively construct the convex terminal set and the terminal cost from state and input trajectories of previous iterations. Simulation results show the effectiveness of the proposed control logic.
Estimating and Controlling the False Discovery Rate for the PC Algorithm Using Edge-Specific P-Values
Authors: Eric V. Strobl, Peter L. Spirtes, Shyam Visweswaran
Date: 2016-07-14 00:00:00

The PC algorithm allows investigators to estimate a complete partially
directed acyclic graph (CPDAG) from a finite dataset, but few groups have
investigated strategies for estimating and controlling the false discovery rate
(FDR) of the edges in the CPDAG. In this paper, we introduce PC with p-values
(PC-p), a fast algorithm which robustly computes edge-specific p-values and
then estimates and controls the FDR across the edges. PC-p specifically uses
the p-values returned by many conditional independence tests to upper bound the
p-values of more complex edge-specific hypothesis tests. The algorithm then
estimates and controls the FDR using the bounded p-values and the
Benjamini-Yekutieli FDR procedure. Modifications to the original PC algorithm
also help PC-p accurately compute the upper bounds despite non-zero Type II
error rates. Experiments show that PC-p yields more accurate FDR estimation and
control across the edges in a variety of CPDAGs compared to alternative
methods.
Phase recovery and holographic image reconstruction using deep learning in neural networks
Authors: Yair Rivenson, Yibo Zhang, Harun Gunaydin, Da Teng, Aydogan Ozcan
Date: 2017-05-10 00:00:00
Tasks: Image Reconstruction

Phase recovery from intensity-only measurements forms the heart of coherent
imaging techniques and holography. Here we demonstrate that a neural network
can learn to perform phase recovery and holographic image reconstruction after
appropriate training. This deep learning-based approach provides an entirely
new framework to conduct holographic imaging by rapidly eliminating twin-image
and self-interference related spatial artifacts. Compared to existing
approaches, this neural network based method is significantly faster to
compute, and reconstructs improved phase and amplitude images of the objects
using only one hologram, i.e., requires less number of measurements in addition
to being computationally faster. We validated this method by reconstructing
phase and amplitude images of various samples, including blood and Pap smears,
and tissue sections. These results are broadly applicable to any phase recovery
problem, and highlight that through machine learning challenging problems in
imaging science can be overcome, providing new avenues to design powerful
computational imaging systems.
Solving Multi-Objective MDP with Lexicographic Preference: An application to stochastic planning with multiple quantile objective
Authors: Yan Li, Zhaohan Sun
Date: 2017-05-10 00:00:00
Tasks: Autonomous Driving

In most common settings of Markov Decision Process (MDP), an agent evaluate a
policy based on expectation of (discounted) sum of rewards. However in many
applications this criterion might not be suitable from two perspective: first,
in risk aversion situation expectation of accumulated rewards is not robust
enough, this is the case when distribution of accumulated reward is heavily
skewed; another issue is that many applications naturally take several
objective into consideration when evaluating a policy, for instance in
autonomous driving an agent needs to balance speed and safety when choosing
appropriate decision. In this paper, we consider evaluating a policy based on a
sequence of quantiles it induces on a set of target states, our idea is to
reformulate the original problem into a multi-objective MDP problem with
lexicographic preference naturally defined. For computation of finding an
optimal policy, we proposed an algorithm \textbf{FLMDP} that could solve
general multi-objective MDP with lexicographic reward preference.
Collaborative Descriptors: Convolutional Maps for Preprocessing
Authors: Hirokatsu Kataoka, Kaori Abe, Akio Nakamura, Yutaka Satoh
Date: 2017-05-10 00:00:00
Tasks: Object Recognition

The paper presents a novel concept for collaborative descriptors between
deeply learned and hand-crafted features. To achieve this concept, we apply
convolutional maps for pre-processing, namely the convovlutional maps are used
as input of hand-crafted features. We recorded an increase in the performance
rate of +17.06 % (multi-class object recognition) and +24.71 % (car detection)
from grayscale input to convolutional maps. Although the framework is
straight-forward, the concept should be inherited for an improved
representation.
Automatic Response Category Combination in Multinomial Logistic Regression
Authors: Bradley S. Price, Charles J. Geyer, Adam J. Rothman
Date: 2017-05-10 00:00:00
Tasks: Model Selection, regression

We propose a penalized likelihood method that simultaneously fits the
multinomial logistic regression model and combines subsets of the response
categories. The penalty is non differentiable when pairs of columns in the
optimization variable are equal. This encourages pairwise equality of these
columns in the estimator, which corresponds to response category combination.
We use an alternating direction method of multipliers algorithm to compute the
estimator and we discuss the algorithm's convergence. Prediction and model
selection are also addressed.
Sparse modeling approach to analytical continuation of imaginary-time quantum Monte Carlo data
Authors: Junya Otsuki, Masayuki Ohzeki, Hiroshi Shinaoka, Kazuyoshi Yoshimi
Date: 2017-02-10 00:00:00

A new approach of solving the ill-conditioned inverse problem for analytical
continuation is proposed. The root of the problem lies in the fact that even
tiny noise of imaginary-time input data has a serious impact on the inferred
real-frequency spectra. By means of a modern regularization technique, we
eliminate redundant degrees of freedom that essentially carry the noise,
leaving only relevant information unaffected by the noise. The resultant
spectrum is represented with minimal bases and thus a stable analytical
continuation is achieved. This framework further provides a tool for analyzing
to what extent the Monte Carlo data need to be accurate to resolve details of
an expected spectral function.
Lie Algebrized Gaussians for Image Representation
Authors: Liyu Gong, Meng Chen, Chunlong Hu
Date: 2013-04-03 00:00:00

We present an image representation method which is derived from analyzing
Gaussian probability density function (\emph{pdf}) space using Lie group
theory. In our proposed method, images are modeled by Gaussian mixture models
(GMMs) which are adapted from a globally trained GMM called universal
background model (UBM). Then we vectorize the GMMs based on two facts: (1)
components of image-specific GMMs are closely grouped together around their
corresponding component of the UBM due to the characteristic of the UBM
adaption procedure; (2) Gaussian \emph{pdf}s form a Lie group, which is a
differentiable manifold rather than a vector space. We map each Gaussian
component to the tangent vector space (named Lie algebra) of Lie group at the
manifold position of UBM. The final feature vector, named Lie algebrized
Gaussians (LAG) is then constructed by combining the Lie algebrized Gaussian
components with mixture weights. We apply LAG features to scene category
recognition problem and observe state-of-the-art performance on 15Scenes
benchmark.
Deep Episodic Value Iteration for Model-based Meta-Reinforcement Learning
Authors: Steven Stenberg Hansen
Date: 2017-05-09 00:00:00
Tasks: Meta Reinforcement Learning, Model-based Reinforcement Learning, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We present a new deep meta reinforcement learner, which we call Deep Episodic
Value Iteration (DEVI). DEVI uses a deep neural network to learn a similarity
metric for a non-parametric model-based reinforcement learning algorithm. Our
model is trained end-to-end via back-propagation. Despite being trained using
the model-free Q-learning objective, we show that DEVI's model-based internal
structure provides `one-shot' transfer to changes in reward and transition
structure, even for tasks with very high-dimensional state spaces.
DeepTingle
Authors: Ahmed Khalifa, Gabriella A. B. Barros, Julian Togelius
Date: 2017-05-09 00:00:00
Tasks: General Classification, Translation

DeepTingle is a text prediction and classification system trained on the
collected works of the renowned fantastic gay erotica author Chuck Tingle.
Whereas the writing assistance tools you use everyday (in the form of
predictive text, translation, grammar checking and so on) are trained on
generic, purportedly "neutral" datasets, DeepTingle is trained on a very
specific, internally consistent but externally arguably eccentric dataset. This
allows us to foreground and confront the norms embedded in data-driven
creativity and productivity assistance tools. As such tools effectively
function as extensions of our cognition into technology, it is important to
identify the norms they embed within themselves and, by extension, us.
DeepTingle is realized as a web application based on LSTM networks and the
GloVe word embedding, implemented in JavaScript with Keras-JS.
Semi-Supervised Deep Learning for Monocular Depth Map Prediction
Authors: Yevhen Kuznietsov, Jörg Stückler, Bastian Leibe
Date: 2017-02-09 00:00:00
Proceeding: CVPR 2017 7
Tasks: Deep Learning, Monocular Depth Estimation, Prediction

Supervised deep learning often suffers from the lack of sufficient training
data. Specifically in the context of monocular depth map prediction, it is
barely possible to determine dense ground truth depth images in realistic
dynamic outdoor environments. When using LiDAR sensors, for instance, noise is
present in the distance measurements, the calibration between sensors cannot be
perfect, and the measurements are typically much sparser than the camera
images. In this paper, we propose a novel approach to depth map prediction from
monocular images that learns in a semi-supervised way. While we use sparse
ground-truth depth for supervised learning, we also enforce our deep network to
produce photoconsistent dense depth maps in a stereo setup using a direct image
alignment loss. In experiments we demonstrate superior performance in depth map
prediction from single images compared to the state-of-the-art methods.
CORe50: a New Dataset and Benchmark for Continuous Object Recognition
Authors: Vincenzo Lomonaco, Davide Maltoni
Date: 2017-05-09 00:00:00
Tasks: Continuous Object Recognition, Lifelong learning, Object, Object Recognition

Continuous/Lifelong learning of high-dimensional data streams is a
challenging research problem. In fact, fully retraining models each time new
data become available is infeasible, due to computational and storage issues,
while na\"ive incremental strategies have been shown to suffer from
catastrophic forgetting. In the context of real-world object recognition
applications (e.g., robotic vision), where continuous learning is crucial, very
few datasets and benchmarks are available to evaluate and compare emerging
techniques. In this work we propose a new dataset and benchmark CORe50,
specifically designed for continuous object recognition, and introduce baseline
approaches for different continuous learning scenarios.
Multi-Scale Spatially Weighted Local Histograms in O(1)
Authors: Mahdieh Poostchi, Ali Shafiekhani, Kannappan Palaniappan, Guna Seetharaman
Date: 2017-05-09 00:00:00

Weighting pixel contribution considering its location is a key feature in
many fundamental image processing tasks including filtering, object modeling
and distance matching. Several techniques have been proposed that incorporate
Spatial information to increase the accuracy and boost the performance of
detection, tracking and recognition systems at the cost of speed. But, it is
still not clear how to efficiently ex- tract weighted local histograms in
constant time using integral histogram. This paper presents a novel algorithm
to compute accurately multi-scale Spatially weighted local histograms in
constant time using Weighted Integral Histogram (SWIH) for fast search. We
applied our spatially weighted integral histogram approach for fast tracking
and obtained more accurate and robust target localization result in comparison
with using plain histogram.
Policy Iterations for Reinforcement Learning Problems in Continuous Time and Space -- Fundamental Theory and Methods
Authors: Jaeyoung Lee, Richard S. Sutton
Date: 2017-05-09 00:00:00
Tasks: Decision Making, Q-Learning, Reinforcement Learning, Reinforcement Learning (RL)

Policy iteration (PI) is a recursive process of policy evaluation and improvement for solving an optimal decision-making/control problem, or in other words, a reinforcement learning (RL) problem. PI has also served as the fundamental for developing RL methods. In this paper, we propose two PI methods, called differential PI (DPI) and integral PI (IPI), and their variants, for a general RL framework in continuous time and space (CTS), where the environment is modeled by a system of ordinary differential equations (ODEs). The proposed methods inherit the current ideas of PI in classical RL and optimal control and theoretically support the existing RL algorithms in CTS: TD-learning and value-gradient-based (VGB) greedy policy update. We also provide case studies including 1) discounted RL and 2) optimal control tasks. Fundamental mathematical properties -- admissibility, uniqueness of the solution to the Bellman equation (BE), monotone improvement, convergence, and optimality of the solution to the Hamilton-Jacobi-Bellman equation (HJBE) -- are all investigated in-depth and improved from the existing theory, along with the general and case studies. Finally, the proposed ones are simulated with an inverted-pendulum model and their model-based and partially model-free implementations to support the theory and further investigate them beyond.