pwc-archive/papers-with-abstracts huggingface dataset:
Analysis of Invariance and Robustness via Invertibility of ReLU-Networks
Authors: Jens Behrmann, Sören Dittmer, Pascal Fernsel, Peter Maaß
Date: 2018-06-25 00:00:00
Tasks: Diagnostic

Studying the invertibility of deep neural networks (DNNs) provides a
principled approach to better understand the behavior of these powerful models.
Despite being a promising diagnostic tool, a consistent theory on their
invertibility is still lacking. We derive a theoretically motivated approach to
explore the preimages of ReLU-layers and mechanisms affecting the stability of
the inverse. Using the developed theory, we numerically show how this approach
uncovers characteristic properties of the network.
On consistent estimation of the missing mass
Authors: Fadhel Ayed, Marco Battiston, Federico Camerlenghi, Stefano Favaro
Date: 2018-06-25 00:00:00
Tasks: Vocal Bursts Type Prediction

Given $n$ samples from a population of individuals belonging to different
types with unknown proportions, how do we estimate the probability of
discovering a new type at the $(n+1)$-th draw? This is a classical problem in
statistics, commonly referred to as the missing mass estimation problem. Recent
results by Ohannessian and Dahleh \citet{Oha12} and Mossel and Ohannessian
\citet{Mos15} showed: i) the impossibility of estimating (learning) the missing
mass without imposing further structural assumptions on the type proportions;
ii) the consistency of the Good-Turing estimator for the missing mass under the
assumption that the tail of the type proportions decays to zero as a regularly
varying function with parameter $\alpha\in(0,1)$. In this paper we rely on
tools from Bayesian nonparametrics to provide an alternative, and simpler,
proof of the impossibility of a distribution-free estimation of the missing
mass. Up to our knowledge, the use of Bayesian ideas to study large sample
asymptotics for the missing mass is new, and it could be of independent
interest. Still relying on Bayesian nonparametric tools, we then show that
under regularly varying type proportions the convergence rate of the
Good-Turing estimator is the best rate that any estimator can achieve, up to a
slowly varying function, and that minimax rate must be at least
$n^{-\alpha/2}$. We conclude with a discussion of our results, and by
conjecturing that the Good-Turing estimator is an rate optimal minimax
estimator under regularly varying type proportions.
Why Interpretability in Machine Learning? An Answer Using Distributed Detection and Data Fusion Theory
Authors: Kush R. Varshney, Prashant Khanduri, Pranay Sharma, Shan Zhang, Pramod K. Varshney
Date: 2018-06-25 00:00:00
Tasks: BIG-bench Machine Learning, Decision Making

As artificial intelligence is increasingly affecting all parts of society and
life, there is growing recognition that human interpretability of machine
learning models is important. It is often argued that accuracy or other similar
generalization performance metrics must be sacrificed in order to gain
interpretability. Such arguments, however, fail to acknowledge that the overall
decision-making system is composed of two entities: the learned model and a
human who fuses together model outputs with his or her own information. As
such, the relevant performance criteria should be for the entire system, not
just for the machine learning component. In this work, we characterize the
performance of such two-node tandem data fusion systems using the theory of
distributed detection. In doing so, we work in the population setting and model
interpretable learned models as multi-level quantizers. We prove that under our
abstraction, the overall system of a human with an interpretable classifier
outperforms one with a black box classifier.
Mimic and Classify : A meta-algorithm for Conditional Independence Testing
Authors: Rajat Sen, Karthikeyan Shanmugam, Himanshu Asnani, Arman Rahimzamani, Sreeram Kannan
Date: 2018-06-25 00:00:00

Given independent samples generated from the joint distribution
$p(\mathbf{x},\mathbf{y},\mathbf{z})$, we study the problem of Conditional
Independence (CI-Testing), i.e., whether the joint equals the CI distribution
$p^{CI}(\mathbf{x},\mathbf{y},\mathbf{z})= p(\mathbf{z})
p(\mathbf{y}|\mathbf{z})p(\mathbf{x}|\mathbf{z})$ or not. We cast this problem
under the purview of the proposed, provable meta-algorithm, "Mimic and
Classify", which is realized in two-steps: (a) Mimic the CI distribution close
enough to recover the support, and (b) Classify to distinguish the joint and
the CI distribution. Thus, as long as we have a good generative model and a
good classifier, we potentially have a sound CI Tester. With this modular
paradigm, CI Testing becomes amiable to be handled by state-of-the-art, both
generative and classification methods from the modern advances in Deep
Learning, which in general can handle issues related to curse of dimensionality
and operation in small sample regime. We show intensive numerical experiments
on synthetic and real datasets where new mimic methods such conditional GANs,
Regression with Neural Nets, outperform the current best CI Testing performance
in the literature. Our theoretical results provide analysis on the estimation
of null distribution as well as allow for general measures, i.e., when either
some of the random variables are discrete and some are continuous or when one
or more of them are discrete-continuous mixtures.
Person Re-Identification in Identity Regression Space
Authors: Hanxiao Wang, Xiatian Zhu, Shaogang Gong, Tao Xiang
Date: 2018-06-25 00:00:00
Tasks: Benchmarking, Incremental Learning, Person Re-Identification, regression

Most existing person re-identification (re-id) methods are unsuitable for
real-world deployment due to two reasons: Unscalability to large population
size, and Inadaptability over time. In this work, we present a unified solution
to address both problems. Specifically, we propose to construct an Identity
Regression Space (IRS) based on embedding different training person identities
(classes) and formulate re-id as a regression problem solved by identity
regression in the IRS. The IRS approach is characterised by a closed-form
solution with high learning efficiency and an inherent incremental learning
capability with human-in-the-loop. Extensive experiments on four benchmarking
datasets(VIPeR, CUHK01, CUHK03 and Market-1501) show that the IRS model not
only outperforms state-of-the-art re-id methods, but also is more scalable to
large re-id population size by rapidly updating model and actively selecting
informative samples with reduced human labelling effort.
An Outcome Model Approach to Translating a Randomized Controlled Trial Results to a Target Population
Authors: Benjamin A. Goldstein, Matthew Phelan, Neha J. Pagidipati, Rury R. Holman, Michael J. Pencina Elizabeth A Stuart
Date: 2018-06-25 00:00:00

Participants enrolled into randomized controlled trials (RCTs) often do not
reflect real-world populations. Previous research in how best to translate RCT
results to target populations has focused on weighting RCT data to look like
the target data. Simulation work, however, has suggested that an outcome model
approach may be preferable. Here we describe such an approach using source data
from the 2x2 factorial NAVIGATOR trial which evaluated the impact of valsartan
and nateglinide on cardiovascular outcomes and new-onset diabetes in a
pre-diabetic population. Our target data consisted of people with pre-diabetes
serviced at our institution. We used Random Survival Forests to develop
separate outcome models for each of the 4 treatments, estimating the 5-year
risk difference for progression to diabetes and estimated the treatment effect
in our local patient populations, as well as sub-populations, and the results
compared to the traditional weighting approach. Our models suggested that the
treatment effect for valsartan in our patient population was the same as in the
trial, whereas for nateglinide treatment effect was stronger than observed in
the original trial. Our effect estimates were more efficient than the weighting
approach.
Artificial Quantum Neural Network: quantum neurons, logical elements and tests of convolutional nets
Authors: V. I. Dorozhinsky, O. V. Pavlovsky
Date: 2018-06-25 00:00:00

We consider a model of an artificial neural network that uses
quantum-mechanical particles in a two-humped potential as a neuron. To simulate
such a quantum-mechanical system the Monte-Carlo integration method is used. A
form of the self-potential of a particle and two potentials (exciting and
inhibiting) interaction are proposed. The possibility of implementing the
simplest logical elements, (such as AND, OR and NOT) based on introduced
quantum particles is shown. Further we show implementation of a simplest
convolutional network. Finally we construct a network that recognizes
handwritten symbols, which shows that in the case of simple architectures, it
is possible to transfer weights from a classical network to a quantum one.
Learning what you can do before doing anything
Authors: Oleh Rybkin, Karl Pertsch, Konstantinos G. Derpanis, Kostas Daniilidis, Andrew Jaegle
Date: 2018-06-25 00:00:00
Proceeding: ICLR 2019 5
Tasks: Video Prediction

Intelligent agents can learn to represent the action spaces of other agents
simply by observing them act. Such representations help agents quickly learn to
predict the effects of their own actions on the environment and to plan complex
action sequences. In this work, we address the problem of learning an agent's
action space purely from visual observation. We use stochastic video prediction
to learn a latent variable that captures the scene's dynamics while being
minimally sensitive to the scene's static content. We introduce a loss term
that encourages the network to capture the composability of visual sequences
and show that it leads to representations that disentangle the structure of
actions. We call the full model with composable action representations
Composable Learned Action Space Predictor (CLASP). We show the applicability of
our method to synthetic settings and its potential to capture action spaces in
complex, realistic visual settings. When used in a semi-supervised setting, our
learned representations perform comparably to existing fully supervised methods
on tasks such as action-conditioned video prediction and planning in the
learned action space, while requiring orders of magnitude fewer action labels.
Project website: https://daniilidis-group.github.io/learned_action_spaces
Neural Machine Translation for Low Resource Languages using Bilingual Lexicon Induced from Comparable Corpora
Authors: Sree Harsha Ramesh, Krishna Prasad Sankaranarayanan
Date: 2018-06-25 00:00:00
Proceeding: NAACL 2018 6
Tasks: Articles, Machine Translation, NMT, Sentence, Translation

Resources for the non-English languages are scarce and this paper addresses
this problem in the context of machine translation, by automatically extracting
parallel sentence pairs from the multilingual articles available on the
Internet. In this paper, we have used an end-to-end Siamese bidirectional
recurrent neural network to generate parallel sentences from comparable
multilingual articles in Wikipedia. Subsequently, we have showed that using the
harvested dataset improved BLEU scores on both NMT and phrase-based SMT systems
for the low-resource language pairs: English--Hindi and English--Tamil, when
compared to training exclusively on the limited bilingual corpora collected for
these language pairs.
3D Context Enhanced Region-based Convolutional Neural Network for End-to-End Lesion Detection
Authors: Ke Yan, Mohammadhadi Bagheri, Ronald M. Summers
Date: 2018-06-25 00:00:00
Tasks: Computed Tomography (CT), Lesion Detection, Medical Object Detection

Detecting lesions from computed tomography (CT) scans is an important but
difficult problem because non-lesions and true lesions can appear similar. 3D
context is known to be helpful in this differentiation task. However, existing
end-to-end detection frameworks of convolutional neural networks (CNNs) are
mostly designed for 2D images. In this paper, we propose 3D context enhanced
region-based CNN (3DCE) to incorporate 3D context information efficiently by
aggregating feature maps of 2D images. 3DCE is easy to train and end-to-end in
training and inference. A universal lesion detector is developed to detect all
kinds of lesions in one algorithm using the DeepLesion dataset. Experimental
results on this challenging task prove the effectiveness of 3DCE. We have
released the code of 3DCE in
https://github.com/rsummers11/CADLab/tree/master/lesion_detector_3DCE.
A DCA-Like Algorithm and its Accelerated Version with Application in Data Visualization
Authors: Hoai An Le Thi, Hoai Minh Le, Duy Nhat Phan, Bach Tran
Date: 2018-06-25 00:00:00
Tasks: Data Visualization

In this paper, we present two variants of DCA (Different of Convex functions
Algorithm) to solve the constrained sum of differentiable function and
composite functions minimization problem, with the aim of increasing the
convergence speed of DCA. In the first variant, DCA-Like, we introduce a new
technique to iteratively modify the decomposition of the objective function.
This successive decomposition could lead to a better majorization and
consequently a better convergence speed than the basic DCA. We then incorporate
the Nesterov's acceleration technique into DCA-Like to give rise to the second
variant, named Accelerated DCA-Like. The convergence properties and the
convergence rate under Kudyka-Lojasiewicz assumption of both variants are
rigorously studied. As an application, we investigate our algorithms for the
t-distributed stochastic neighbor embedding. Numerical experiments on several
benchmark datasets illustrate the efficiency of our algorithms.
Sounderfeit: Cloning a Physical Model using a Conditional Adversarial Autoencoder
Authors: Stephen Sinclair
Date: 2018-06-25 00:00:00
Tasks: parameter estimation, Resynthesis

An adversarial autoencoder conditioned on known parameters of a physical
modeling bowed string synthesizer is evaluated for use in parameter estimation
and resynthesis tasks. Latent dimensions are provided to capture variance not
explained by the conditional parameters. Results are compared with and without
the adversarial training, and a system capable of "copying" a given
parameter-signal bidirectional relationship is examined. A real-time synthesis
system built on a generative, conditioned and regularized neural network is
presented, allowing to construct engaging sound synthesizers based purely on
recorded data.
Accuracy-based Curriculum Learning in Deep Reinforcement Learning
Authors: Pierre Fournier, Olivier Sigaud, Mohamed Chetouani, Pierre-Yves Oudeyer
Date: 2018-06-25 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In this paper, we investigate a new form of automated curriculum learning
based on adaptive selection of accuracy requirements, called accuracy-based
curriculum learning. Using a reinforcement learning agent based on the Deep
Deterministic Policy Gradient algorithm and addressing the Reacher environment,
we first show that an agent trained with various accuracy requirements sampled
randomly learns more efficiently than when asked to be very accurate at all
times. Then we show that adaptive selection of accuracy requirements, based on
a local measure of competence progress, automatically generates a curriculum
where difficulty progressively increases, resulting in a better learning
efficiency than sampling randomly.
Probabilistic Inference Using Generators - The Statues Algorithm
Authors: Pierre Denis
Date: 2018-06-24 00:00:00

We present here a new probabilistic inference algorithm that gives exact
results in the domain of discrete probability distributions. This algorithm,
named the Statues algorithm, calculates the marginal probability distribution
on probabilistic models defined as direct acyclic graphs. These models are made
up of well-defined primitives that allow to express, in particular, joint
probability distributions, Bayesian networks, discrete Markov chains,
conditioning and probabilistic arithmetic. The Statues algorithm relies on a
variable binding mechanism based on the generator construct, a special form of
coroutine; being related to the enumeration algorithm, this new algorithm
brings important improvements in terms of efficiency, which makes it valuable
in regard to other exact marginalization algorithms. After introduction of
several definitions, primitives and compositional rules, we present in details
the Statues algorithm. Then, we briefly discuss the interest of this algorithm
compared to others and we present possible extensions. Finally, we introduce
Lea and MicroLea, two Python libraries implementing the Statues algorithm,
along with several use cases. A proof of the correctness of the algorithm is
provided in appendix.
Attention-based Few-Shot Person Re-identification Using Meta Learning
Authors: Alireza Rahimpour, Hairong Qi
Date: 2018-06-24 00:00:00
Tasks: Few-Shot Learning, Meta-Learning, Person Re-Identification

In this paper, we investigate the challenging task of person
re-identification from a new perspective and propose an end-to-end
attention-based architecture for few-shot re-identification through
meta-learning. The motivation for this task lies in the fact that humans, can
usually identify another person after just seeing that given person a few times
(or even once) by attending to their memory. On the other hand, the unique
nature of the person re-identification problem, i.e., only few examples exist
per identity and new identities always appearing during testing, calls for a
few shot learning architecture with the capacity of handling new identities.
Hence, we frame the problem within a meta-learning setting, where a neural
network based meta-learner is trained to optimize a learner i.e., an
attention-based matching function. Another challenge of the person
re-identification problem is the small inter-class difference between different
identities and large intra-class difference of the same identity. In order to
increase the discriminative power of the model, we propose a new
attention-based feature encoding scheme that takes into account the critical
intra-view and cross-view relationship of images. We refer to the proposed
Attention-based Re-identification Metalearning model as ARM. Extensive
evaluations demonstrate the advantages of the ARM as compared to the
state-of-the-art on the challenging PRID2011, CUHK01, CUHK03 and Market1501
datasets.
Predictive Maintenance for Industrial IoT of Vehicle Fleets using Hierarchical Modified Fuzzy Support Vector Machine
Authors: Arindam Chaudhuri
Date: 2018-06-24 00:00:00
Tasks: Management

Connected vehicle fleets are deployed worldwide in several industrial IoT
scenarios. With the gradual increase of machines being controlled and managed
through networked smart devices, the predictive maintenance potential grows
rapidly. Predictive maintenance has the potential of optimizing uptime as well
as performance such that time and labor associated with inspections and
preventive maintenance are reduced. In order to understand the trends of
vehicle faults with respect to important vehicle attributes viz mileage, age,
vehicle type etc this problem is addressed through hierarchical modified fuzzy
support vector machine (HMFSVM). The proposed method is compared with other
commonly used approaches like logistic regression, random forests and support
vector machines. This helps better implementation of telematics data to ensure
preventative management as part of the desired solution. The superiority of the
proposed method is highlighted through several experimental results.
Online Signature Verification using Deep Representation: A new Descriptor
Authors: Mohammad Hajizadeh Saffar, Mohsen Fayyaz, Mohammad Sabokrou, Mahmood Fathy
Date: 2018-06-24 00:00:00
Tasks: One-class classifier

This paper presents an accurate method for verifying online signatures. The
main difficulty of signature verification come from: (1) Lacking enough
training samples (2) The methods must be spatial change invariant. To deal with
these difficulties and modeling the signatures efficiently, we propose a method
that a one-class classifier per each user is built on discriminative features.
First, we pre-train a sparse auto-encoder using a large number of unlabeled
signatures, then we applied the discriminative features, which are learned by
auto-encoder to represent the training and testing signatures as a self-thought
learning method (i.e. we have introduced a signature descriptor). Finally,
user's signatures are modeled and classified using a one-class classifier. The
proposed method is independent on signature datasets thanks to self-taught
learning. The experimental results indicate significant error reduction and
accuracy enhancement in comparison with state-of-the-art methods on SVC2004 and
SUSIG datasets.
Dynamic Spectrum Matching with One-shot Learning
Authors: Jinchao Liu, Stuart J. Gibson, James Mills, Margarita Osadchy
Date: 2018-06-23 00:00:00
Tasks: Binary Classification, Classification, General Classification, Multi-class Classification, One-Shot Learning, Representation Learning

Convolutional neural networks (CNN) have been shown to provide a good
solution for classification problems that utilize data obtained from
vibrational spectroscopy. Moreover, CNNs are capable of identification from
noisy spectra without the need for additional preprocessing. However, their
application in practical spectroscopy is limited due to two shortcomings. The
effectiveness of the classification using CNNs drops rapidly when only a small
number of spectra per substance are available for training (which is a typical
situation in real applications). Additionally, to accommodate new, previously
unseen substance classes, the network must be retrained which is
computationally intensive. Here we address these issues by reformulating a
multi-class classification problem with a large number of classes, but a small
number of samples per class, to a binary classification problem with sufficient
data available for representation learning. Namely, we define the learning task
as identifying pairs of inputs as belonging to the same or different classes.
We achieve this using a Siamese convolutional neural network. A novel sampling
strategy is proposed to address the imbalance problem in training the Siamese
Network. The trained network can effectively classify samples of unseen
substance classes using just a single reference sample (termed as one-shot
learning in the machine learning community). Our results demonstrate better
accuracy than other practical systems to date, while allowing effortless
updates of the system's database with novel substance classes.
Multi-Exposure Image Fusion Based on Exposure Compensation
Authors: Yuma Kinoshita, Taichi Yoshida, Sayaka Shiota, Hitoshi Kiya
Date: 2018-06-23 00:00:00
Tasks: Multi-Exposure Image Fusion

This paper proposes a novel multi-exposure image fusion method based on
exposure compensation. Multi-exposure image fusion is a method to produce
images without color saturation regions, by using photos with different
exposures. However, in conventional works, it is unclear how to determine
appropriate exposure values, and moreover, it is difficult to set appropriate
exposure values at the time of photographing due to time constraints. In the
proposed method, the luminance of the input multi-exposure images is adjusted
on the basis of the relationship between exposure values and pixel values,
where the relationship is obtained by assuming that a digital camera has a
linear response function. The use of a local contrast enhancement method is
also considered to improve input multi-exposure images. The compensated images
are finally combined by one of existing multi-exposure image fusion methods. In
some experiments, the effectiveness of the proposed method are evaluated in
terms of the tone mapped image quality index, statistical naturalness, and
discrete entropy, by comparing the proposed one with conventional ones.