pwc-archive/papers-with-abstracts huggingface dataset:
Center Emphasized Visual Saliency and a Contrast-based Full Reference Image Quality Index
Authors: Md Abu Layek, Sanjida Afroz, TaeChoong Chung, Eui-Nam Huh
Date: 2018-12-28 00:00:00
Tasks: Full reference image quality assessment, Full-Reference Image Quality Assessment, Image Quality Assessment

Objective image quality assessment (IQA) is imperative in the current
multimedia-intensive world, in order to assess the visual quality of an image
at close to a human level of ability. Many~parameters such as color intensity,
structure, sharpness, contrast, presence of an object, etc., draw human
attention to an image. Psychological vision research suggests that human vision
is biased to the center area of an image and display screen. As a result, if
the center part contains any visually salient information, it draws human
attention even more and any distortion in that part will be better perceived
than other parts. To the best of our knowledge, previous IQA methods have not
considered this fact. In this paper, we propose a full reference image quality
assessment (FR-IQA) approach using visual saliency and contrast; however, we
give extra attention to the center by increasing the sensitivity of the
similarity maps in that region. We evaluated our method on three large-scale
popular benchmark databases used by most of the current IQA researchers
(TID2008, CSIQ~and LIVE), having a total of 3345 distorted images with
28~different kinds of distortions. Our~method is compared with 13
state-of-the-art approaches. This comparison reveals the stronger correlation
of our method with human-evaluated values. The prediction-of-quality score is
consistent for distortion specific as well as distortion independent cases.
Moreover, faster processing makes it applicable to any real-time application.
The MATLAB code is publicly available to test the algorithm and can be found
online at http://layek.khu.ac.kr/CEQI.
MEETING BOT: Reinforcement Learning for Dialogue Based Meeting Scheduling
Authors: Vishwanath D, Lovekesh Vig, Gautam Shroff, Puneet Agarwal
Date: 2018-12-28 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Scheduling

In this paper we present Meeting Bot, a reinforcement learning based
conversational system that interacts with multiple users to schedule meetings.
The system is able to interpret user utterences and map them to preferred time
slots, which are then fed to a reinforcement learning (RL) system with the goal
of converging on an agreeable time slot. The RL system is able to adapt to user
preferences and environmental changes in meeting arrival rate while still
scheduling effectively. Learning is performed via policy gradient with
exploration, by utilizing an MLP as an approximator of the policy function.
Results demonstrate that the system outperforms standard scheduling algorithms
in terms of overall scheduling efficiency. Additionally, the system is able to
adapt its strategy to situations when users consistently reject or accept
meetings in certain slots (such as Friday afternoon versus Thursday morning),
or when the meeting is called by members who are at a more senior designation.
The Diagrammatic AI Language (DIAL): Version 0.1
Authors: Guy Marshall, André Freitas
Date: 2018-12-28 00:00:00

Currently, there is no consistent model for visually or formally representing
the architecture of AI systems. This lack of representation brings
interpretability, correctness and completeness challenges in the description of
existing models and systems. DIAL (The Diagrammatic AI Language) has been
created with the aspiration of being an "engineering schematic" for AI Systems.
It is presented here as a starting point for a community dialogue towards a
common diagrammatic language for AI Systems.
Artistic Object Recognition by Unsupervised Style Adaptation
Authors: Christopher Thomas, Adriana Kovashka
Date: 2018-12-28 00:00:00
Tasks: Domain Adaptation, Object, Object Recognition, Scene Classification, Style Transfer

Computer vision systems currently lack the ability to reliably recognize
artistically rendered objects, especially when such data is limited. In this
paper, we propose a method for recognizing objects in artistic modalities (such
as paintings, cartoons, or sketches), without requiring any labeled data from
those modalities. Our method explicitly accounts for stylistic domain shifts
between and within domains. To do so, we introduce a complementary training
modality constructed to be similar in artistic style to the target domain, and
enforce that the network learns features that are invariant between the two
training modalities. We show how such artificial labeled source domains can be
generated automatically through the use of style transfer techniques, using
diverse target images to represent the style in the target domain. Unlike
existing methods which require a large amount of unlabeled target data, our
method can work with as few as ten unlabeled images. We evaluate it on a number
of cross-domain object and scene classification tasks and on a new dataset we
release. Our experiments show that our approach, though conceptually simple,
significantly improves the accuracy that existing domain adaptation techniques
obtain for artistic object recognition.
Differential Temporal Difference Learning
Authors: Adithya M. Devraj, Ioannis Kontoyiannis, Sean P. Meyn
Date: 2018-12-28 00:00:00
Tasks: General Reinforcement Learning, Reinforcement Learning

Value functions derived from Markov decision processes arise as a central component of algorithms as well as performance metrics in many statistics and engineering applications of machine learning techniques. Computation of the solution to the associated Bellman equations is challenging in most practical cases of interest. A popular class of approximation techniques, known as Temporal Difference (TD) learning algorithms, are an important sub-class of general reinforcement learning methods. The algorithms introduced in this paper are intended to resolve two well-known difficulties of TD-learning approaches: Their slow convergence due to very high variance, and the fact that, for the problem of computing the relative value function, consistent algorithms exist only in special cases. First we show that the gradients of these value functions admit a representation that lends itself to algorithm design. Based on this result, a new class of differential TD-learning algorithms is introduced. For Markovian models on Euclidean space with smooth dynamics, the algorithms are shown to be consistent under general conditions. Numerical results show dramatic variance reduction when compared to standard methods.
Forecasting Cardiology Admissions from Catheterization Laboratory
Authors: Avishek Choudhury, Sunanda Perumalla
Date: 2018-12-28 00:00:00
Tasks: Management, Time Series, Time Series Analysis, Time Series Forecasting

Emergent and unscheduled cardiology admissions from cardiac catheterization laboratory add complexity to the management of Cardiology and in-patient department. In this article, we sought to study the behavior of cardiology admissions from Catheterization laboratory using time series models. Our research involves retrospective cardiology admission data from March 1, 2012, to November 3, 2016, retrieved from a hospital in Iowa. Autoregressive integrated moving average (ARIMA), Holts method, mean method, na\"ive method, seasonal na\"ive, exponential smoothing, and drift method were implemented to forecast weekly cardiology admissions from Catheterization laboratory. ARIMA (2,0,2) (1,1,1) was selected as the best fit model with the minimum sum of error, Akaike information criterion and Schwartz Bayesian criterion. The model failed to reject the null hypothesis of stationarity, it lacked the evidence of independence, and rejected the null hypothesis of normality. The implication of this study will not only improve catheterization laboratory staff schedule, advocate efficient use of imaging equipment and inpatient telemetry beds but also equip management to proactively tackle inpatient overcrowding, plan for physical capacity expansion and so forth.
Reconciling modern machine learning practice and the bias-variance trade-off
Authors: Mikhail Belkin, Daniel Hsu, Siyuan Ma, Soumik Mandal
Date: 2018-12-28 00:00:00
Tasks: BIG-bench Machine Learning

Breakthroughs in machine learning are rapidly changing science and society, yet our fundamental understanding of this technology has lagged far behind. Indeed, one of the central tenets of the field, the bias-variance trade-off, appears to be at odds with the observed behavior of methods used in the modern machine learning practice. The bias-variance trade-off implies that a model should balance under-fitting and over-fitting: rich enough to express underlying structure in data, simple enough to avoid fitting spurious patterns. However, in the modern practice, very rich models such as neural networks are trained to exactly fit (i.e., interpolate) the data. Classically, such models would be considered over-fit, and yet they often obtain high accuracy on test data. This apparent contradiction has raised questions about the mathematical foundations of machine learning and their relevance to practitioners. In this paper, we reconcile the classical understanding and the modern practice within a unified performance curve. This "double descent" curve subsumes the textbook U-shaped bias-variance trade-off curve by showing how increasing model capacity beyond the point of interpolation results in improved performance. We provide evidence for the existence and ubiquity of double descent for a wide spectrum of models and datasets, and we posit a mechanism for its emergence. This connection between the performance and the structure of machine learning models delineates the limits of classical analyses, and has implications for both the theory and practice of machine learning.
Scalable GAM using sparse variational Gaussian processes
Authors: Vincent Adam, Nicolas Durrande, ST John
Date: 2018-12-28 00:00:00
Tasks: Additive models, Gaussian Processes, Variational Inference

Generalized additive models (GAMs) are a widely used class of models of
interest to statisticians as they provide a flexible way to design
interpretable models of data beyond linear models. We here propose a scalable
and well-calibrated Bayesian treatment of GAMs using Gaussian processes (GPs)
and leveraging recent advances in variational inference. We use sparse GPs to
represent each component and exploit the additive structure of the model to
efficiently represent a Gaussian a posteriori coupling between the components.
Predicting with Proxies: Transfer Learning in High Dimension
Authors: Hamsa Bastani
Date: 2018-12-28 00:00:00
Tasks: Decision Making, Transfer Learning, Vocal Bursts Intensity Prediction

Predictive analytics is increasingly used to guide decision-making in many applications. However, in practice, we often have limited data on the true predictive task of interest, and must instead rely on more abundant data on a closely-related proxy predictive task. For example, e-commerce platforms use abundant customer click data (proxy) to make product recommendations rather than the relatively sparse customer purchase data (true outcome of interest); alternatively, hospitals often rely on medical risk scores trained on a different patient population (proxy) rather than their own patient population (true cohort of interest) to assign interventions. Yet, not accounting for the bias in the proxy can lead to sub-optimal decisions. Using real datasets, we find that this bias can often be captured by a sparse function of the features. Thus, we propose a novel two-step estimator that uses techniques from high-dimensional statistics to efficiently combine a large amount of proxy data and a small amount of true data. We prove upper bounds on the error of our proposed estimator and lower bounds on several heuristics used by data scientists; in particular, our proposed estimator can achieve the same accuracy with exponentially less true data (in the number of features). Our proof relies on a new LASSO tail inequality for approximately sparse vectors. Finally, we demonstrate the effectiveness of our approach on e-commerce and healthcare datasets; in both cases, we achieve significantly better predictive accuracy as well as managerial insights into the nature of the bias in the proxy data.
A Tight Runtime Analysis for the $(μ+ λ)$ EA
Authors: Denis Antipov, Benjamin Doerr
Date: 2018-12-28 00:00:00
Tasks: Evolutionary Algorithms

Despite significant progress in the theory of evolutionary algorithms, the theoretical understanding of evolutionary algorithms which use non-trivial populations remains challenging and only few rigorous results exist. Already for the most basic problem, the determination of the asymptotic runtime of the $(\mu+\lambda)$ evolutionary algorithm on the simple OneMax benchmark function, only the special cases $\mu=1$ and $\lambda=1$ have been solved. In this work, we analyze this long-standing problem and show the asymptotically tight result that the runtime $T$, the number of iterations until the optimum is found, satisfies \[E[T] = \Theta\bigg(\frac{n\log n}{\lambda}+\frac{n}{\lambda / \mu} + \frac{n\log^+\log^+ \lambda/ \mu}{\log^+ \lambda / \mu}\bigg),\] where $\log^+ x := \max\{1, \log x\}$ for all $x > 0$. The same methods allow to improve the previous-best $O(\frac{n \log n}{\lambda} + n \log \lambda)$ runtime guarantee for the $(\lambda+\lambda)$~EA with fair parent selection to a tight $\Theta(\frac{n \log n}{\lambda} + n)$ runtime result.
Image Processing in Quantum Computers
Authors: Aditya Dendukuri, Khoa Luu
Date: 2018-12-28 00:00:00

Quantum Image Processing (QIP)is an exciting new field showing a lot of
promise as a powerful addition to the arsenal of Image Processing techniques.
Representing image pixel by pixel using classical information requires an
enormous amount of computational resources. Hence, exploring methods to
represent images in a different paradigm of information is important. In this
work, we study the representation of images in Quantum Information. The main
motivation for this pursuit is the ability of storing N bits of classical
information in only log(2N) quantum bits (qubits). The promising first step was
the exponentially efficient implementation of the Fourier transform in quantum
computers as compared to Fast Fourier Transform in classical computers. In
addition, images encoded in quantum information could obey unique quantum
properties like superposition or entanglement.
On the Benefit of Width for Neural Networks: Disappearance of Bad Basins
Authors: Dawei Li, Tian Ding, Ruoyu Sun
Date: 2018-12-28 00:00:00

Wide networks are often believed to have a nice optimization landscape, but what rigorous results can we prove? To understand the benefit of width, it is important to identify the difference between wide and narrow networks. In this work, we prove that from narrow to wide networks, there is a phase transition from having sub-optimal basins to no sub-optimal basins. Specifically, we prove two results: on the positive side, for any continuous activation functions, the loss surface of a class of wide networks has no sub-optimal basins, where "basin" is defined as the set-wise strict local minimum; on the negative side, for a large class of networks with width below a threshold, we construct strict local minima that are not global. These two results together show the phase transition from narrow to wide networks.
Exploring Weight Symmetry in Deep Neural Networks
Authors: Xu Shell Hu, Sergey Zagoruyko, Nikos Komodakis
Date: 2018-12-28 00:00:00
Tasks: Language Modeling, Language Modelling

We propose to impose symmetry in neural network parameters to improve
parameter usage and make use of dedicated convolution and matrix multiplication
routines. Due to significant reduction in the number of parameters as a result
of the symmetry constraints, one would expect a dramatic drop in accuracy.
Surprisingly, we show that this is not the case, and, depending on network
size, symmetry can have little or no negative effect on network accuracy,
especially in deep overparameterized networks. We propose several ways to
impose local symmetry in recurrent and convolutional neural networks, and show
that our symmetry parameterizations satisfy universal approximation property
for single hidden layer networks. We extensively evaluate these
parameterizations on CIFAR, ImageNet and language modeling datasets, showing
significant benefits from the use of symmetry. For instance, our ResNet-101
with channel-wise symmetry has almost 25% less parameters and only 0.2%
accuracy loss on ImageNet. Code for our experiments is available at
https://github.com/hushell/deep-symmetry
A continuous-time analysis of distributed stochastic gradient
Authors: Nicholas M. Boffi, Jean-Jacques E. Slotine
Date: 2018-12-28 00:00:00
Tasks: Distributed Optimization

We analyze the effect of synchronization on distributed stochastic gradient algorithms. By exploiting an analogy with dynamical models of biological quorum sensing - where synchronization between agents is induced through communication with a common signal - we quantify how synchronization can significantly reduce the magnitude of the noise felt by the individual distributed agents and by their spatial mean. This noise reduction is in turn associated with a reduction in the smoothing of the loss function imposed by the stochastic gradient approximation. Through simulations on model non-convex objectives, we demonstrate that coupling can stabilize higher noise levels and improve convergence. We provide a convergence analysis for strongly convex functions by deriving a bound on the expected deviation of the spatial mean of the agents from the global minimizer for an algorithm based on quorum sensing, the same algorithm with momentum, and the Elastic Averaging SGD (EASGD) algorithm. We discuss extensions to new algorithms that allow each agent to broadcast its current measure of success and shape the collective computation accordingly. We supplement our theoretical analysis with numerical experiments on convolutional neural networks trained on the CIFAR-10 dataset, where we note a surprising regularizing property of EASGD even when applied to the non-distributed case. This observation suggests alternative second-order in-time algorithms for non-distributed optimization that are competitive with momentum methods.
The role of grammar in transition-probabilities of subsequent words in English text
Authors: Rudolf Hanel, Stefan Thurner
Date: 2018-12-28 00:00:00
Tasks: Sentence, Text Generation

Sentence formation is a highly structured, history-dependent, and
sample-space reducing (SSR) process. While the first word in a sentence can be
chosen from the entire vocabulary, typically, the freedom of choosing
subsequent words gets more and more constrained by grammar and context, as the
sentence progresses. This sample-space reducing property offers a natural
explanation of Zipf's law in word frequencies, however, it fails to capture the
structure of the word-to-word transition probability matrices of English text.
Here we adopt the view that grammatical constraints (such as
subject--predicate--object) locally re-order the word order in sentences that
are sampled with a SSR word generation process. We demonstrate that
superimposing grammatical structure -- as a local word re-ordering
(permutation) process -- on a sample-space reducing process is sufficient to
explain both, word frequencies and word-to-word transition probabilities. We
compare the quality of the grammatically ordered SSR model in reproducing
several test statistics of real texts with other text generation models, such
as the Bernoulli model, the Simon model, and the Monkey typewriting model.
Reasoning About Physical Interactions with Object-Oriented Prediction and Planning
Authors: Michael Janner, Sergey Levine, William T. Freeman, Joshua B. Tenenbaum, Chelsea Finn, Jiajun Wu
Date: 2018-12-28 00:00:00
Tasks: Object, Scene Understanding

Object-based factorizations provide a useful level of abstraction for
interacting with the world. Building explicit object representations, however,
often requires supervisory signals that are difficult to obtain in practice. We
present a paradigm for learning object-centric representations for physical
scene understanding without direct supervision of object properties. Our model,
Object-Oriented Prediction and Planning (O2P2), jointly learns a perception
function to map from image observations to object representations, a pairwise
physics interaction function to predict the time evolution of a collection of
objects, and a rendering function to map objects back to pixels. For
evaluation, we consider not only the accuracy of the physical predictions of
the model, but also its utility for downstream tasks that require an actionable
representation of intuitive physics. After training our model on an image
prediction task, we can use its learned representations to build block towers
more complicated than those observed during training.
TROVE Feature Detection for Online Pose Recovery by Binocular Cameras
Authors: Yuance Liu, Michael Z. Q. Chen
Date: 2018-12-28 00:00:00
Tasks: Position

This paper proposes a new and efficient method to estimate 6-DoF ego-states:
attitudes and positions in real time. The proposed method extract information
of ego-states by observing a feature called "TROVE" (Three Rays and One
VErtex). TROVE features are projected from structures that are ubiquitous on
man-made constructions and objects. The proposed method does not search for
conventional corner-type features nor use Perspective-n-Point (PnP) methods,
and it achieves a real-time estimation of attitudes and positions up to 60 Hz.
The accuracy of attitude estimates can reach 0.3 degrees and that of position
estimates can reach 2 cm in an indoor environment. The result shows a promising
approach for unmanned robots to localize in an environment that is rich in
man-made structures.
A Variational Topological Neural Model for Cascade-based Diffusion in Networks
Authors: Sylvain Lamprier
Date: 2018-12-28 00:00:00

Many works have been proposed in the literature to capture the dynamics of
diffusion in networks. While some of them define graphical markovian models to
extract temporal relationships between node infections in networks, others
consider diffusion episodes as sequences of infections via recurrent neural
models. In this paper we propose a model at the crossroads of these two
extremes, which embeds the history of diffusion in infected nodes as hidden
continuous states. Depending on the trajectory followed by the content before
reaching a given node, the distribution of influence probabilities may vary.
However, content trajectories are usually hidden in the data, which induces
challenging learning problems. We propose a topological recurrent neural model
which exhibits good experimental performances for diffusion modelling and
prediction.
A Precedent Approach to Assigning Access Rights
Authors: S. V. Belim, N. F. Bogachenko, A. N. Kabanov
Date: 2018-12-28 00:00:00

To design a discretionary access control policy, a technique is proposed that
uses the principle of analogies and is based on both the properties of objects
and the properties of subjects. As attributes characterizing these properties,
the values of the security attributes of subjects and objects are chosen. The
concept of precedent is defined as an access rule explicitly specified by the
security administrator. The problem of interpolation of the access matrix is
formulated: the security administrator defines a sequence of precedents, it is
required to automate the process of filling the remaining cells of the access
matrix. On the family of sets of security attributes, a linear order is
introduced. The principles of filling the access matrix on the basis of analogy
with the dominant precedent in accordance with a given order relation are
developed. The analysis of the proposed methodology is performed and its main
advantages are revealed.