pwc-archive/papers-with-abstracts huggingface dataset:
Aggregation using input-output trade-off
Authors: Aurélie Fischer, Mathilde Mougeot
Date: 2018-03-08 00:00:00

In this paper, we introduce a new learning strategy based on a seminal idea
of Mojirsheibani (1999, 2000, 2002a, 2002b), who proposed a smart method for
combining several classifiers, relying on a consensus notion. In many
aggregation methods, the prediction for a new observation x is computed by
building a linear or convex combination over a collection of basic estimators
r1(x),. .. , rm(x) previously calibrated using a training data set.
Mojirsheibani proposes to compute the prediction associated to a new
observation by combining selected outputs of the training examples. The output
of a training example is selected if some kind of consensus is observed: the
predictions computed for the training example with the different machines have
to be "similar" to the prediction for the new observation. This approach has
been recently extended to the context of regression in Biau et al. (2016). In
the original scheme, the agreement condition is actually required to hold for
all individual estimators, which appears inadequate if there is one bad initial
estimator. In practice, a few disagreements are allowed ; for establishing the
theoretical results, the proportion of estimators satisfying the condition is
required to tend to 1. In this paper, we propose an alternative procedure,
mixing the previous consensus ideas on the predictions with the Euclidean
distance computed between entries. This may be seen as an alternative approach
allowing to reduce the effect of a possibly bad estimator in the initial list,
using a constraint on the inputs. We prove the consistency of our strategy in
classification and in regression. We also provide some numerical experiments on
simulated and real data to illustrate the benefits of this new aggregation
method. On the whole, our practical study shows that our method may perform
much better than the original combination technique, and, in particular,
exhibit far less variance. We also show on simulated examples that this
procedure mixing inputs and outputs is still robust to high dimensional inputs.
A notion of stability for k-means clustering
Authors: Thibaut Le Gouic, Quentin Paris
Date: 2018-01-29 00:00:00
Tasks: Clustering, Quantization

In this paper, we define and study a new notion of stability for the
$k$-means clustering scheme building upon the notion of quantization of a
probability measure. We connect this notion of stability to a geometric feature
of the underlying distribution of the data, named absolute margin condition,
inspired by recent works on the subject.
SentRNA: Improving computational RNA design by incorporating a prior of human design strategies
Authors: Jade Shi, Rhiju Das, Vijay S. Pande
Date: 2018-03-08 00:00:00

Solving the RNA inverse folding problem is a critical prerequisite to RNA
design, an emerging field in bioengineering with a broad range of applications
from reaction catalysis to cancer therapy. Although significant progress has
been made in developing machine-based inverse RNA folding algorithms, current
approaches still have difficulty designing sequences for large or complex
targets. On the other hand, human players of the online RNA design game EteRNA
have consistently shown superior performance in this regard, being able to
readily design sequences for targets that are challenging for machine
algorithms. Here we present a novel approach to the RNA design problem,
SentRNA, a design agent consisting of a fully-connected neural network trained
end-to-end using human-designed RNA sequences. We show that through this
approach, SentRNA can solve complex targets previously unsolvable by any
machine-based approach and achieve state-of-the-art performance on two separate
challenging test sets. Our results demonstrate that incorporating human design
strategies into a design algorithm can significantly boost machine performance
and suggests a new paradigm for machine-based RNA design.
A Deep Q-Network for the Beer Game: A Deep Reinforcement Learning algorithm to Solve Inventory Optimization Problems
Authors: Afshin Oroojlooyjadid, Mohammadreza Nazari, Lawrence Snyder, Martin Takáč
Date: 2017-08-20 00:00:00
Tasks: Deep Reinforcement Learning, Management, Reinforcement Learning, Transfer Learning

The beer game is a widely used in-class game that is played in supply chain management classes to demonstrate the bullwhip effect. The game is a decentralized, multi-agent, cooperative problem that can be modeled as a serial supply chain network in which agents cooperatively attempt to minimize the total cost of the network even though each agent can only observe its own local information. Each agent chooses order quantities to replenish its stock. Under some conditions, a base-stock replenishment policy is known to be optimal. However, in a decentralized supply chain in which some agents (stages) may act irrationally (as they do in the beer game), there is no known optimal policy for an agent wishing to act optimally. We propose a machine learning algorithm, based on deep Q-networks, to optimize the replenishment decisions at a given stage. When playing alongside agents who follow a base-stock policy, our algorithm obtains near-optimal order quantities. It performs much better than a base-stock policy when the other agents use a more realistic model of human ordering behavior. Unlike most other algorithms in the literature, our algorithm does not have any limits on the beer game parameter values. Like any deep learning algorithm, training the algorithm can be computationally intensive, but this can be performed ahead of time; the algorithm executes in real time when the game is played. Moreover, we propose a transfer learning approach so that the training performed for one agent and one set of cost coefficients can be adapted quickly for other agents and costs. Our algorithm can be extended to other decentralized multi-agent cooperative games with partially observed information, which is a common type of situation in real-world supply chain problems.
Physical Layer Communications System Design Over-the-Air Using Adversarial Networks
Authors: Timothy J. O'Shea, Tamoghna Roy, Nathan West, Benjamin C. Hilburn
Date: 2018-03-08 00:00:00

This paper presents a novel method for synthesizing new physical layer
modulation and coding schemes for communications systems using a learning-based
approach which does not require an analytic model of the impairments in the
channel. It extends prior work published on the channel autoencoder to consider
the case where the channel response is not known or can not be easily modeled
in a closed form analytic expression. By adopting an adversarial approach for
channel response approximation and information encoding, we can jointly learn a
good solution to both tasks over a wide range of channel environments. We
describe the operation of the proposed adversarial system, share results for
its training and validation over-the-air, and discuss implications and future
work in the area.
Concise Fuzzy Planar Embedding of Graphs: a Dimensionality Reduction Approach
Authors: Faisal N. Abu-Khzam, Rana H. Mouawi, Amer Hajj Ahmad, Sergio Thoumi
Date: 2018-03-08 00:00:00
Tasks: Dimensionality Reduction

The enormous amount of data to be represented using large graphs exceeds in some cases the resources of a conventional computer. Edges in particular can take up a considerable amount of memory as compared to the number of nodes. However, rigorous edge storage might not always be essential to be able to draw the needed conclusions. A similar problem takes records with many variables and attempts to extract the most discernible features. It is said that the ``dimension'' of this data is reduced. Following an approach with the same objective in mind, we can map a graph representation to a $k$-dimensional space and answer queries of neighboring nodes mainly by measuring Euclidean distances. The accuracy of our answers would decrease but would be compensated for by fuzzy logic which gives an idea about the likelihood of error. This method allows for reasonable representation in memory while maintaining a fair amount of useful information, and allows for concise embedding in $k$-dimensional Euclidean space as well as solving some problems without having to decompress the graph. Of particular interest is the case where $k=2$. Promising highly accurate experimental results are obtained and reported.
Applicability and interpretation of the deterministic weighted cepstral distance
Authors: Oliver Lauwers, Bart De Moor
Date: 2018-03-08 00:00:00
Tasks: BIG-bench Machine Learning, Clustering, Time Series, Time Series Analysis, Time Series Clustering

Quantifying similarity between data objects is an important part of modern
data science. Deciding what similarity measure to use is very application
dependent. In this paper, we combine insights from systems theory and machine
learning, and investigate the weighted cepstral distance, which was previously
defined for signals coming from ARMA models. We provide an extension of this
distance to invertible deterministic linear time invariant single input single
output models, and assess its applicability. We show that it can always be
interpreted in terms of the poles and zeros of the underlying model, and that,
in the case of stable, minimum-phase, or unstable, maximum-phase models, a
geometrical interpretation in terms of subspace angles can be given. We then
devise a method to assess stability and phase-type of the generating models,
using only input/output signal information. In this way, we prove a connection
between the extended weighted cepstral distance and a weighted cepstral model
norm. In this way, we provide a purely data-driven way to assess different
underlying dynamics of input/output signal pairs, without the need for any
system identification step. This can be useful in machine learning tasks such
as time series clustering. An iPython tutorial is published complementary to
this paper, containing implementations of the various methods and algorithms
presented here, as well as some numerical illustrations of the equivalences
proven here.
Stock-out Prediction in Multi-echelon Networks
Authors: Afshin Oroojlooyjadid, Lawrence Snyder, Martin Takáč
Date: 2017-09-20 00:00:00
Tasks: Prediction

In multi-echelon inventory systems the performance of a given node is
affected by events that occur at many other nodes and in many other time
periods. For example, a supply disruption upstream will have an effect on
downstream, customer-facing nodes several periods later as the disruption
"cascades" through the system. There is very little research on stock-out
prediction in single-echelon systems and (to the best of our knowledge) none on
multi-echelon systems. However, in real the world, it is clear that there is
significant interest in techniques for this sort of stock-out prediction.
Therefore, our research aims to fill this gap by using deep neural networks
(DNN) to predict stock-outs in multi-echelon supply chains.
Leveraging Unlabeled Data for Crowd Counting by Learning to Rank
Authors: Xialei Liu, Joost Van de Weijer, Andrew D. Bagdanov
Date: 2018-03-08 00:00:00
Proceeding: CVPR 2018 6
Tasks: Crowd Counting, Image Retrieval, Learning-To-Rank, Retrieval

We propose a novel crowd counting approach that leverages abundantly
available unlabeled crowd imagery in a learning-to-rank framework. To induce a
ranking of cropped images , we use the observation that any sub-image of a
crowded scene image is guaranteed to contain the same number or fewer persons
than the super-image. This allows us to address the problem of limited size of
existing datasets for crowd counting. We collect two crowd scene datasets from
Google using keyword searches and query-by-example image retrieval,
respectively. We demonstrate how to efficiently learn from these unlabeled
datasets by incorporating learning-to-rank in a multi-task network which
simultaneously ranks images and estimates crowd density maps. Experiments on
two of the most challenging crowd counting datasets show that our approach
obtains state-of-the-art results.
Norm matters: efficient and accurate normalization schemes in deep networks
Authors: Elad Hoffer, Ron Banner, Itay Golan, Daniel Soudry
Date: 2018-03-05 00:00:00
Proceeding: NeurIPS 2018 12

Over the past few years, Batch-Normalization has been commonly used in deep
networks, allowing faster training and high performance for a wide variety of
applications. However, the reasons behind its merits remained unanswered, with
several shortcomings that hindered its use for certain tasks. In this work, we
present a novel view on the purpose and function of normalization methods and
weight-decay, as tools to decouple weights' norm from the underlying optimized
objective. This property highlights the connection between practices such as
normalization, weight decay and learning-rate adjustments. We suggest several
alternatives to the widely used $L^2$ batch-norm, using normalization in $L^1$
and $L^\infty$ spaces that can substantially improve numerical stability in
low-precision implementations as well as provide computational and memory
benefits. We demonstrate that such methods enable the first batch-norm
alternative to work for half-precision implementations. Finally, we suggest a
modification to weight-normalization, which improves its performance on
large-scale tasks.
The Enemy Among Us: Detecting Hate Speech with Threats Based 'Othering' Language Embeddings
Authors: Wafa Alorainy, Pete Burnap, Han Liu, Matthew Williams
Date: 2018-01-23 00:00:00

Offensive or antagonistic language targeted at individuals and social groups
based on their personal characteristics (also known as cyber hate speech or
cyberhate) has been frequently posted and widely circulated viathe World Wide
Web. This can be considered as a key risk factor for individual and societal
tension linked toregional instability. Automated Web-based cyberhate detection
is important for observing and understandingcommunity and regional societal
tension - especially in online social networks where posts can be rapidlyand
widely viewed and disseminated. While previous work has involved using
lexicons, bags-of-words orprobabilistic language parsing approaches, they often
suffer from a similar issue which is that cyberhate can besubtle and indirect -
thus depending on the occurrence of individual words or phrases can lead to a
significantnumber of false negatives, providing inaccurate representation of
the trends in cyberhate. This problemmotivated us to challenge thinking around
the representation of subtle language use, such as references toperceived
threats from "the other" including immigration or job prosperity in a hateful
context. We propose anovel framework that utilises language use around the
concept of "othering" and intergroup threat theory toidentify these subtleties
and we implement a novel classification method using embedding learning to
computesemantic distances between parts of speech considered to be part of an
"othering" narrative. To validate ourapproach we conduct several experiments on
different types of cyberhate, namely religion, disability, race andsexual
orientation, with F-measure scores for classifying hateful instances obtained
through applying ourmodel of 0.93, 0.86, 0.97 and 0.98 respectively, providing
a significant improvement in classifier accuracy overthe state-of-the-art
Vision-Aided Absolute Trajectory Estimation Using an Unsupervised Deep Network with Online Error Correction
Authors: E. Jared Shamwell, Sarah Leung, William D. Nothwang
Date: 2018-03-08 00:00:00
Tasks: Simultaneous Localization and Mapping, Visual Odometry

We present an unsupervised deep neural network approach to the fusion of
RGB-D imagery with inertial measurements for absolute trajectory estimation.
Our network, dubbed the Visual-Inertial-Odometry Learner (VIOLearner), learns
to perform visual-inertial odometry (VIO) without inertial measurement unit
(IMU) intrinsic parameters (corresponding to gyroscope and accelerometer bias
or white noise) or the extrinsic calibration between an IMU and camera. The
network learns to integrate IMU measurements and generate hypothesis
trajectories which are then corrected online according to the Jacobians of
scaled image projection errors with respect to a spatial grid of pixel
coordinates. We evaluate our network against state-of-the-art (SOA)
visual-inertial odometry, visual odometry, and visual simultaneous localization
and mapping (VSLAM) approaches on the KITTI Odometry dataset and demonstrate
competitive odometry performance.
Preserving Semantic Relations for Zero-Shot Learning
Authors: Yashas Annadani, Soma Biswas
Date: 2018-03-08 00:00:00
Proceeding: CVPR 2018 6
Tasks: Attribute, Zero-Shot Learning

Zero-shot learning has gained popularity due to its potential to scale
recognition models without requiring additional training data. This is usually
achieved by associating categories with their semantic information like
attributes. However, we believe that the potential offered by this paradigm is
not yet fully exploited. In this work, we propose to utilize the structure of
the space spanned by the attributes using a set of relations. We devise
objective functions to preserve these relations in the embedding space, thereby
inducing semanticity to the embedding space. Through extensive experimental
evaluation on five benchmark datasets, we demonstrate that inducing semanticity
to the embedding space is beneficial for zero-shot learning. The proposed
approach outperforms the state-of-the-art on the standard zero-shot setting as
well as the more realistic generalized zero-shot setting. We also demonstrate
how the proposed approach can be useful for making approximate semantic
inferences about an image belonging to a category for which attribute
information is not available.
Fast Cylinder and Plane Extraction from Depth Cameras for Visual Odometry
Authors: Pedro F. Proença, Yang Gao
Date: 2018-03-06 00:00:00
Tasks: CPU, Visual Odometry

This paper presents CAPE, a method to extract planes and cylinder segments
from organized point clouds, which processes 640x480 depth images on a single
CPU core at an average of 300 Hz, by operating on a grid of planar cells.
While, compared to state-of-the-art plane extraction, the latency of CAPE is
more consistent and 4-10 times faster, depending on the scene, we also
demonstrate empirically that applying CAPE to visual odometry can improve
trajectory estimation on scenes made of cylindrical surfaces (e.g. tunnels),
whereas using a plane extraction approach that is not curve-aware deteriorates
performance on these scenes. To use these geometric primitives in visual
odometry, we propose extending a probabilistic RGB-D odometry framework based
on points, lines and planes to cylinder primitives. Following this framework,
CAPE runs on fused depth maps and the parameters of cylinders are modelled
probabilistically to account for uncertainty and weight accordingly the pose
optimization residuals.
Counterfactual Fairness
Authors: Matt J. Kusner, Joshua R. Loftus, Chris Russell, Ricardo Silva
Date: 2017-03-20 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: BIG-bench Machine Learning, Causal Inference, counterfactual, Fairness

Machine learning can impact people with legal or ethical consequences when it
is used to automate decisions in areas such as insurance, lending, hiring, and
predictive policing. In many of these scenarios, previous decisions have been
made that are unfairly biased against certain subpopulations, for example those
of a particular race, gender, or sexual orientation. Since this past data may
be biased, machine learning predictors must account for this to avoid
perpetuating or creating discriminatory practices. In this paper, we develop a
framework for modeling fairness using tools from causal inference. Our
definition of counterfactual fairness captures the intuition that a decision is
fair towards an individual if it is the same in (a) the actual world and (b) a
counterfactual world where the individual belonged to a different demographic
group. We demonstrate our framework on a real-world problem of fair prediction
of success in law school.
Design of a nickel-base superalloy using a neural network
Authors: B. D. Conduit, N. G. Jones, H. J. Stone, G. J. Conduit
Date: 2018-03-08 00:00:00

A new computational tool has been developed to model, discover, and optimize
new alloys that simultaneously satisfy up to eleven physical criteria. An
artificial neural network is trained from pre-existing materials data that
enables the prediction of individual material properties both as a function of
composition and heat treatment routine, which allows it to optimize the
material properties to search for the material with properties most likely to
exceed a target criteria. We design a new polycrystalline nickel-base
superalloy with the optimal combination of cost, density, gamma' phase content
and solvus, phase stability, fatigue life, yield stress, ultimate tensile
strength, stress rupture, oxidation resistance, and tensile elongation.
Experimental data demonstrates that the proposed alloy fulfills the
computational predictions, possessing multiple physical properties,
particularly oxidation resistance and yield stress, that exceed existing
commercially available alloys.
Insights into the robustness of control point configurations for homography and planar pose estimation
Authors: Raul Acuna, Volker Willert
Date: 2018-03-08 00:00:00
Tasks: Homography Estimation, Pose Estimation

In this paper, we investigate the influence of the spatial configuration of a
number of $n \geq 4$ control points on the accuracy and robustness of space
resection methods, e.g. used by a fiducial marker for pose estimation. We find
robust configurations of control points by minimizing the first order perturbed
solution of the DLT algorithm which is equivalent to minimizing the condition
number of the data matrix. An empirical statistical evaluation is presented
verifying that these optimized control point configurations not only increase
the performance of the DLT homography estimation but also improve the
performance of planar pose estimation methods like IPPE and EPnP, including the
iterative minimization of the reprojection error which is the most accurate
algorithm. We provide the characteristics of stable control point
configurations for real-world noisy camera data that are practically
independent on the camera pose and form certain symmetric patterns dependent on
the number of points. Finally, we present a comparison of optimized
configuration versus the number of control points.
SA-IGA: A Multiagent Reinforcement Learning Method Towards Socially Optimal Outcomes
Authors: Chengwei Zhang, Xiaohong Li, Jianye Hao, Siqi Chen, Karl Tuyls, Wanli Xue
Date: 2018-03-08 00:00:00
Tasks: Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In multiagent environments, the capability of learning is important for an
agent to behave appropriately in face of unknown opponents and dynamic
environment. From the system designer's perspective, it is desirable if the
agents can learn to coordinate towards socially optimal outcomes, while also
avoiding being exploited by selfish opponents. To this end, we propose a novel
gradient ascent based algorithm (SA-IGA) which augments the basic
gradient-ascent algorithm by incorporating social awareness into the policy
update process. We theoretically analyze the learning dynamics of SA-IGA using
dynamical system theory and SA-IGA is shown to have linear dynamics for a wide
range of games including symmetric games. The learning dynamics of two
representative games (the prisoner's dilemma game and the coordination game)
are analyzed in details. Based on the idea of SA-IGA, we further propose a
practical multiagent learning algorithm, called SA-PGA, based on Q-learning
update rule. Simulation results show that SA-PGA agent can achieve higher
social welfare than previous social-optimality oriented Conditional Joint
Action Learner (CJAL) and also is robust against individually rational
opponents by reaching Nash equilibrium solutions.