pwc-archive/papers-with-abstracts huggingface dataset:
Extensions of Morse-Smale Regression with Application to Actuarial Science
Authors: Colleen M. Farrelly
Date: 2017-08-17 00:00:00
Tasks: BIG-bench Machine Learning, regression

The problem of subgroups is ubiquitous in scientific research (ex. disease
heterogeneity, spatial distributions in ecology...), and piecewise regression
is one way to deal with this phenomenon. Morse-Smale regression offers a way to
partition the regression function based on level sets of a defined function and
that function's basins of attraction. This topologically-based piecewise
regression algorithm has shown promise in its initial applications, but the
current implementation in the literature has been limited to elastic net and
generalized linear regression. It is possible that nonparametric methods, such
as random forest or conditional inference trees, may provide better prediction
and insight through modeling interaction terms and other nonlinear
relationships between predictors and a given outcome.
  This study explores the use of several machine learning algorithms within a
Morse-Smale piecewise regression framework, including boosted regression with
linear baselearners, homotopy-based LASSO, conditional inference trees, random
forest, and a wide neural network framework called extreme learning machines.
Simulations on Tweedie regression problems with varying Tweedie parameter and
dispersion suggest that many machine learning approaches to Morse-Smale
piecewise regression improve the original algorithm's performance, particularly
for outcomes with lower dispersion and linear or a mix of linear and nonlinear
predictor relationships. On a real actuarial problem, several of these new
algorithms perform as good as or better than the original Morse-Smale
regression algorithm, and most provide information on the nature of predictor
relationships within each partition to provide insight into differences between
dataset partitions.
An Annotated Corpus of Relational Strategies in Customer Service
Authors: Ian Beaver, Cynthia Freeman, Abdullah Mueen
Date: 2017-08-17 00:00:00

We create and release the first publicly available commercial customer
service corpus with annotated relational segments. Human-computer data from
three live customer service Intelligent Virtual Agents (IVAs) in the domains of
travel and telecommunications were collected, and reviewers marked all text
that was deemed unnecessary to the determination of user intention. After
merging the selections of multiple reviewers to create highlighted texts, a
second round of annotation was done to determine the classes of language
present in the highlighted sections such as the presence of Greetings,
Backstory, Justification, Gratitude, Rants, or Emotions. This resulting corpus
is a valuable resource for improving the quality and relational abilities of
IVAs. As well as discussing the corpus itself, we compare the usage of such
language in human-human interactions on TripAdvisor forums. We show that
removal of this language from task-based inputs has a positive effect on IVA
understanding by both an increase in confidence and improvement in responses,
demonstrating the need for automated methods of its discovery.
On Ensuring that Intelligent Machines Are Well-Behaved
Authors: Philip S. Thomas, Bruno Castro da Silva, Andrew G. Barto, Emma Brunskill
Date: 2017-08-17 00:00:00
Tasks: BIG-bench Machine Learning

Machine learning algorithms are everywhere, ranging from simple data analysis
and pattern recognition tools used across the sciences to complex systems that
achieve super-human performance on various tasks. Ensuring that they are
well-behaved---that they do not, for example, cause harm to humans or act in a
racist or sexist way---is therefore not a hypothetical problem to be dealt with
in the future, but a pressing one that we address here. We propose a new
framework for designing machine learning algorithms that simplifies the problem
of specifying and regulating undesirable behaviors. To show the viability of
this new framework, we use it to create new machine learning algorithms that
preclude the sexist and harmful behaviors exhibited by standard machine
learning algorithms in our experiments. Our framework for designing machine
learning algorithms simplifies the safe and responsible application of machine
learning.
Robust Contextual Bandit via the Capped-$\ell_{2}$ norm
Authors: Feiyun Zhu, Xinliang Zhu, Sheng Wang, Jiawen Yao, Junzhou Huang
Date: 2017-08-17 00:00:00
Tasks: Decision Making

This paper considers the actor-critic contextual bandit for the mobile health
(mHealth) intervention. The state-of-the-art decision-making methods in mHealth
generally assume that the noise in the dynamic system follows the Gaussian
distribution. Those methods use the least-square-based algorithm to estimate
the expected reward, which is prone to the existence of outliers. To deal with
the issue of outliers, we propose a novel robust actor-critic contextual bandit
method for the mHealth intervention. In the critic updating, the
capped-$\ell_{2}$ norm is used to measure the approximation error, which
prevents outliers from dominating our objective. A set of weights could be
achieved from the critic updating. Considering them gives a weighted objective
for the actor updating. It provides the badly noised sample in the critic
updating with zero weights for the actor updating. As a result, the robustness
of both actor-critic updating is enhanced. There is a key parameter in the
capped-$\ell_{2}$ norm. We provide a reliable method to properly set it by
making use of one of the most fundamental definitions of outliers in
statistics. Extensive experiment results demonstrate that our method can
achieve almost identical results compared with the state-of-the-art methods on
the dataset without outliers and dramatically outperform them on the datasets
noised by outliers.
General Backpropagation Algorithm for Training Second-order Neural Networks
Authors: Fenglei Fan, Wenxiang Cong, Ge Wang
Date: 2017-08-17 00:00:00
Tasks: BIG-bench Machine Learning

The artificial neural network is a popular framework in machine learning. To
empower individual neurons, we recently suggested that the current type of
neurons could be upgraded to 2nd order counterparts, in which the linear
operation between inputs to a neuron and the associated weights is replaced
with a nonlinear quadratic operation. A single 2nd order neurons already has a
strong nonlinear modeling ability, such as implementing basic fuzzy logic
operations. In this paper, we develop a general backpropagation (BP) algorithm
to train the network consisting of 2nd-order neurons. The numerical studies are
performed to verify of the generalized BP algorithm.
Sequence-to-Label Script Identification for Multilingual OCR
Authors: Yasuhisa Fujii, Karel Driesen, Jonathan Baccash, Ash Hurst, Ashok C. Popat
Date: 2017-08-15 00:00:00
Tasks: Optical Character Recognition (OCR)

We describe a novel line-level script identification method. Previous work
repurposed an OCR model generating per-character script codes, counted to
obtain line-level script identification. This has two shortcomings. First, as a
sequence-to-sequence model it is more complex than necessary for the
sequence-to-label problem of line script identification. This makes it harder
to train and inefficient to run. Second, the counting heuristic may be
suboptimal compared to a learned model. Therefore we reframe line script
identification as a sequence-to-label problem and solve it using two
components, trained end-toend: Encoder and Summarizer. The encoder converts a
line image into a feature sequence. The summarizer aggregates the sequence to
classify the line. We test various summarizers with identical inception-style
convolutional networks as encoders. Experiments on scanned books and photos
containing 232 languages in 30 scripts show 16% reduction of script
identification error rate compared to the baseline. This improved script
identification reduces the character error rate attributable to script
misidentification by 33%.
Power Optimizations in MTJ-based Neural Networks through Stochastic Computing
Authors: Ankit Mondal, Ankur Srivastava
Date: 2017-08-17 00:00:00
Tasks: General Classification, image-classification, Image Classification

Artificial Neural Networks (ANNs) have found widespread applications in tasks
such as pattern recognition and image classification. However, hardware
implementations of ANNs using conventional binary arithmetic units are
computationally expensive, energy-intensive and have large area overheads.
Stochastic Computing (SC) is an emerging paradigm which replaces these
conventional units with simple logic circuits and is particularly suitable for
fault-tolerant applications. Spintronic devices, such as Magnetic Tunnel
Junctions (MTJs), are capable of replacing CMOS in memory and logic circuits.
In this work, we propose an energy-efficient use of MTJs, which exhibit
probabilistic switching behavior, as Stochastic Number Generators (SNGs), which
forms the basis of our NN implementation in the SC domain. Further, error
resilient target applications of NNs allow us to introduce Approximate
Computing, a framework wherein accuracy of computations is traded-off for
substantial reductions in power consumption. We propose approximating the
synaptic weights in our MTJ-based NN implementation, in ways brought about by
properties of our MTJ-SNG, to achieve energy-efficiency. We design an algorithm
that can perform such approximations within a given error tolerance in a
single-layer NN in an optimal way owing to the convexity of the problem
formulation. We then use this algorithm and develop a heuristic approach for
approximating multi-layer NNs. To give a perspective of the effectiveness of
our approach, a 43% reduction in power consumption was obtained with less than
1% accuracy loss on a standard classification problem, with 26% being brought
about by the proposed algorithm.
A Review of Methodologies for Natural-Language-Facilitated Human-Robot Cooperation
Authors: Rui Liu, Xiaoli Zhang
Date: 2017-01-30 00:00:00
Tasks: Autonomous Navigation

Natural-language-facilitated human-robot cooperation (NLC) refers to using
natural language (NL) to facilitate interactive information sharing and task
executions with a common goal constraint between robots and humans. Recently,
NLC research has received increasing attention. Typical NLC scenarios include
robotic daily assistance, robotic health caregiving, intelligent manufacturing,
autonomous navigation, and robot social accompany. However, a thorough review,
that can reveal latest methodologies to use NL to facilitate human-robot
cooperation, is missing. In this review, a comprehensive summary about
methodologies for NLC is presented. NLC research includes three main research
focuses: NL instruction understanding, NL-based execution plan generation, and
knowledge-world mapping. In-depth analyses on theoretical methods,
applications, and model advantages and disadvantages are made. Based on our
paper review and perspective, potential research directions of NLC are
summarized.
Learning to Perform Physics Experiments via Deep Reinforcement Learning
Authors: Misha Denil, Pulkit Agrawal, Tejas D. Kulkarni, Tom Erez, Peter Battaglia, Nando de Freitas
Date: 2016-11-06 00:00:00
Tasks: Deep Reinforcement Learning, Friction, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

When encountering novel objects, humans are able to infer a wide range of
physical properties such as mass, friction and deformability by interacting
with them in a goal driven way. This process of active interaction is in the
same spirit as a scientist performing experiments to discover hidden facts.
Recent advances in artificial intelligence have yielded machines that can
achieve superhuman performance in Go, Atari, natural language processing, and
complex control problems; however, it is not clear that these systems can rival
the scientific intuition of even a young child. In this work we introduce a
basic set of tasks that require agents to estimate properties such as mass and
cohesion of objects in an interactive simulated environment where they can
manipulate the objects and observe the consequences. We found that state of art
deep reinforcement learning methods can learn to perform the experiments
necessary to discover such hidden properties. By systematically manipulating
the problem difficulty and the cost incurred by the agent for performing
experiments, we found that agents learn different strategies that balance the
cost of gathering information against the cost of making mistakes in different
situations.
On a method for Rock Classification using Textural Features and Genetic Optimization
Authors: Manuel Blanco Valentin, Clecio Roque De Bom, Marcio Portes de Albuquerque, Marcelo Portes de Albuquerque, Elisangela Faria, Maury Duarte Correia, Rodrigo Surmas
Date: 2016-07-06 00:00:00
Tasks: Classification, General Classification

In this work we present a method to classify a set of rock textures based on
a Spectral Analysis and the extraction of the texture Features of the resulted
images. Up to 520 features were tested using 4 different filters and all 31
different combinations were verified. The classification process relies on a
Naive Bayes classifier. We performed two kinds of optimizations: statistical
optimization with covariance-based Principal Component Analysis (PCA) and a
genetic optimization, for 10,000 randomly defined samples, achieving a final
maximum classification success of 91% against the original 70% success ratio
(without any optimization nor filters used). After the optimization 9 types of
features emerged as most relevant.
Computer-aided position planning of miniplates to treat facial bone defects
Authors: Jan Egger, Jürgen Wallner, Markus Gall, Xiaojun Chen, Katja Schwenzer-Zimmerer, Knut Reinbacher, Dieter Schmalstieg
Date: 2017-08-17 00:00:00
Tasks: Anatomy, Position

In this contribution, a software system for computer-aided position planning
of miniplates to treat facial bone defects is proposed. The intra-operatively
used bone plates have to be passively adapted on the underlying bone contours
for adequate bone fragment stabilization. However, this procedure can lead to
frequent intra-operatively performed material readjustments especially in
complex surgical cases. Our approach is able to fit a selection of common
implant models on the surgeon's desired position in a 3D computer model. This
happens with respect to the surrounding anatomical structures, always including
the possibility of adjusting both the direction and the position of the used
osteosynthesis material. By using the proposed software, surgeons are able to
pre-plan the out coming implant in its form and morphology with the aid of a
computer-visualized model within a few minutes. Further, the resulting model
can be stored in STL file format, the commonly used format for 3D printing.
Using this technology, surgeons are able to print the virtual generated
implant, or create an individually designed bending tool. This method leads to
adapted osteosynthesis materials according to the surrounding anatomy and
requires further a minimum amount of money and time.
The Promise of Premise: Harnessing Question Premises in Visual Question Answering
Authors: Aroma Mahendru, Viraj Prabhu, Akrit Mohapatra, Dhruv Batra, Stefan Lee
Date: 2017-05-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Question Answering, Relevance Detection, Visual Question Answering, Visual Question Answering (VQA)

In this paper, we make a simple observation that questions about images often
contain premises - objects and relationships implied by the question - and that
reasoning about premises can help Visual Question Answering (VQA) models
respond more intelligently to irrelevant or previously unseen questions. When
presented with a question that is irrelevant to an image, state-of-the-art VQA
models will still answer purely based on learned language biases, resulting in
non-sensical or even misleading answers. We note that a visual question is
irrelevant to an image if at least one of its premises is false (i.e. not
depicted in the image). We leverage this observation to construct a dataset for
Question Relevance Prediction and Explanation (QRPE) by searching for false
premises. We train novel question relevance detection models and show that
models that reason about premises consistently outperform models that do not.
We also find that forcing standard VQA models to reason about premises during
training can lead to improvements on tasks requiring compositional reasoning.
Towards Large-Pose Face Frontalization in the Wild
Authors: Xi Yin, Xiang Yu, Kihyuk Sohn, Xiaoming Liu, Manmohan Chandraker
Date: 2017-04-20 00:00:00
Proceeding: ICCV 2017 10
Tasks: 3D Reconstruction, Face Recognition, Generative Adversarial Network

Despite recent advances in face recognition using deep learning, severe
accuracy drops are observed for large pose variations in unconstrained
environments. Learning pose-invariant features is one solution, but needs
expensively labeled large-scale data and carefully designed feature learning
algorithms. In this work, we focus on frontalizing faces in the wild under
various head poses, including extreme profile views. We propose a novel deep 3D
Morphable Model (3DMM) conditioned Face Frontalization Generative Adversarial
Network (GAN), termed as FF-GAN, to generate neutral head pose face images. Our
framework differs from both traditional GANs and 3DMM based modeling.
Incorporating 3DMM into the GAN structure provides shape and appearance priors
for fast convergence with less training data, while also supporting end-to-end
training. The 3DMM-conditioned GAN employs not only the discriminator and
generator loss but also a new masked symmetry loss to retain visual quality
under occlusions, besides an identity loss to recover high frequency
information. Experiments on face recognition, landmark localization and 3D
reconstruction consistently show the advantage of our frontalization method on
faces in the wild datasets.
Restricted Boltzmann machine to determine the input weights for extreme learning machines
Authors: Andre Pacheco, Renato Krohling, Carlos da Silva
Date: 2017-08-17 00:00:00

The Extreme Learning Machine (ELM) is a single-hidden layer feedforward
neural network (SLFN) learning algorithm that can learn effectively and
quickly. The ELM training phase assigns the input weights and bias randomly and
does not change them in the whole process. Although the network works well, the
random weights in the input layer can make the algorithm less effective and
impact on its performance. Therefore, we propose a new approach to determine
the input weights and bias for the ELM using the restricted Boltzmann machine
(RBM), which we call RBM-ELM. We compare our new approach with a well-known
approach to improve the ELM and a state of the art algorithm to select the
weights for the ELM. The results show that the RBM-ELM outperforms both
methodologies and achieve a better performance than the ELM.
Learning a Multi-View Stereo Machine
Authors: Abhishek Kar, Christian Häne, Jitendra Malik
Date: 2017-08-17 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: 3D geometry, 3D Reconstruction

We present a learnt system for multi-view stereopsis. In contrast to recent
learning based methods for 3D reconstruction, we leverage the underlying 3D
geometry of the problem through feature projection and unprojection along
viewing rays. By formulating these operations in a differentiable manner, we
are able to learn the system end-to-end for the task of metric 3D
reconstruction. End-to-end learning allows us to jointly reason about shape
priors while conforming geometric constraints, enabling reconstruction from
much fewer images (even a single image) than required by classical approaches
as well as completion of unseen surfaces. We thoroughly evaluate our approach
on the ShapeNet dataset and demonstrate the benefits over classical approaches
as well as recent learning based methods.
MirrorFlow: Exploiting Symmetries in Joint Optical Flow and Occlusion Estimation
Authors: Junhwa Hur, Stefan Roth
Date: 2017-08-17 00:00:00
Proceeding: ICCV 2017 10
Tasks: Occlusion Estimation, Optical Flow Estimation

Optical flow estimation is one of the most studied problems in computer
vision, yet recent benchmark datasets continue to reveal problem areas of
today's approaches. Occlusions have remained one of the key challenges. In this
paper, we propose a symmetric optical flow method to address the well-known
chicken-and-egg relation between optical flow and occlusions. In contrast to
many state-of-the-art methods that consider occlusions as outliers, possibly
filtered out during post-processing, we highlight the importance of joint
occlusion reasoning in the optimization and show how to utilize occlusion as an
important cue for estimating optical flow. The key feature of our model is to
fully exploit the symmetry properties that characterize optical flow and
occlusions in the two consecutive images. Specifically through utilizing
forward-backward consistency and occlusion-disocclusion symmetry in the energy,
our model jointly estimates optical flow in both forward and backward
direction, as well as consistent occlusion maps in both views. We demonstrate
significant performance benefits on standard benchmarks, especially from the
occlusion-disocclusion symmetry. On the challenging KITTI dataset we report the
most accurate two-frame results to date.
PixelNN: Example-based Image Synthesis
Authors: Aayush Bansal, Yaser Sheikh, Deva Ramanan
Date: 2017-08-17 00:00:00
Proceeding: ICLR 2018 1
Tasks: Image Generation

We present a simple nearest-neighbor (NN) approach that synthesizes
high-frequency photorealistic images from an "incomplete" signal such as a
low-resolution image, a surface normal map, or edges. Current state-of-the-art
deep generative models designed for such conditional image synthesis lack two
important things: (1) they are unable to generate a large set of diverse
outputs, due to the mode collapse problem. (2) they are not interpretable,
making it difficult to control the synthesized output. We demonstrate that NN
approaches potentially address such limitations, but suffer in accuracy on
small datasets. We design a simple pipeline that combines the best of both
worlds: the first stage uses a convolutional neural network (CNN) to maps the
input to a (overly-smoothed) image, and the second stage uses a pixel-wise
nearest neighbor method to map the smoothed output to multiple high-quality,
high-frequency outputs in a controllable manner. We demonstrate our approach
for various input modalities, and for various domains ranging from human faces
to cats-and-dogs to shoes and handbags.
General AI Challenge - Round One: Gradual Learning
Authors: Jan Feyereisl, Matej Nikl, Martin Poliak, Martin Stransky, Michal Vlasak
Date: 2017-08-17 00:00:00

The General AI Challenge is an initiative to encourage the wider artificial
intelligence community to focus on important problems in building intelligent
machines with more general scope than is currently possible. The challenge
comprises of multiple rounds, with the first round focusing on gradual
learning, i.e. the ability to re-use already learned knowledge for efficiently
learning to solve subsequent problems. In this article, we will present details
of the first round of the challenge, its inspiration and aims. We also outline
a more formal description of the challenge and present a preliminary analysis
of its curriculum, based on ideas from computational mechanics. We believe,
that such formalism will allow for a more principled approach towards
investigating tasks in the challenge, building new curricula and for
potentially improving consequent challenge rounds.