pwc-archive/papers-with-abstracts huggingface dataset:
Multilevel Wavelet Decomposition Network for Interpretable Time Series Analysis
Authors: Jingyuan Wang, Ze Wang, Jianfeng Li, Junjie Wu
Date: 2018-06-23 00:00:00
Tasks: Deep Learning, General Classification, Time Series, Time Series Analysis, Time Series Classification

Recent years have witnessed the unprecedented rising of time series from
almost all kindes of academic and industrial fields. Various types of deep
neural network models have been introduced to time series analysis, but the
important frequency information is yet lack of effective modeling. In light of
this, in this paper we propose a wavelet-based neural network structure called
multilevel Wavelet Decomposition Network (mWDN) for building frequency-aware
deep learning models for time series analysis. mWDN preserves the advantage of
multilevel discrete wavelet decomposition in frequency learning while enables
the fine-tuning of all parameters under a deep neural network framework. Based
on mWDN, we further propose two deep learning models called Residual
Classification Flow (RCF) and multi-frequecy Long Short-Term Memory (mLSTM) for
time series classification and forecasting, respectively. The two models take
all or partial mWDN decomposed sub-series in different frequencies as input,
and resort to the back propagation algorithm to learn all the parameters
globally, which enables seamless embedding of wavelet-based frequency analysis
into deep learning frameworks. Extensive experiments on 40 UCR datasets and a
real-world user volume dataset demonstrate the excellent performance of our
time series models based on mWDN. In particular, we propose an importance
analysis method to mWDN based models, which successfully identifies those
time-series elements and mWDN layers that are crucially important to time
series analysis. This indeed indicates the interpretability advantage of mWDN,
and can be viewed as an indepth exploration to interpretable deep learning.
A Recursive PLS (Partial Least Squares) based Approach for Enterprise Threat Management
Authors: Janardan Misra
Date: 2018-06-23 00:00:00
Tasks: Management

Most of the existing solutions to enterprise threat management are preventive
approaches prescribing means to prevent policy violations with varying degrees
of success. In this paper we consider the complementary scenario where a number
of security violations have already occurred, or security threats, or
vulnerabilities have been reported and a security administrator needs to
generate optimal response to these security events. We present a principled
approach to study and model the human expertise in responding to the emergent
threats owing to these security events. A recursive Partial Least Squares based
adaptive learning model is defined using a factorial analysis of the security
events together with a method for estimating the effect of global context
dependent semantic information used by the security administrators. Presented
model is theoretically optimal and operationally recursive in nature to deal
with the set of security events being generated continuously. We discuss the
underlying challenges and ways in which the model could be operationalized in
centralized versus decentralized, and real-time versus batch processing modes.
An Inductive Formalization of Self Reproduction in Dynamical Hierarchies
Authors: Janardan Misra
Date: 2018-06-23 00:00:00
Tasks: Artificial Life

Formalizing self reproduction in dynamical hierarchies is one of the
important problems in Artificial Life (AL) studies. We study, in this paper, an
inductively defined algebraic framework for self reproduction on macroscopic
organizational levels under dynamical system setting for simulated AL models
and explore some existential results. Starting with defining self reproduction
for atomic entities we define self reproduction with possible mutations on
higher organizational levels in terms of hierarchical sets and the
corresponding inductively defined `meta' - reactions. We introduce constraints
to distinguish a collection of entities from genuine cases of emergent
organizational structures.
DALEX: explainers for complex predictive models
Authors: Przemyslaw Biecek
Date: 2018-06-23 00:00:00

Predictive modeling is invaded by elastic, yet complex methods such as neural
networks or ensembles (model stacking, boosting or bagging). Such methods are
usually described by a large number of parameters or hyper parameters - a price
that one needs to pay for elasticity. The very number of parameters makes
models hard to understand. This paper describes a consistent collection of
explainers for predictive models, a.k.a. black boxes. Each explainer is a
technique for exploration of a black box model. Presented approaches are
model-agnostic, what means that they extract useful information from any
predictive method despite its internal structure. Each explainer is linked with
a specific aspect of a model. Some are useful in decomposing predictions, some
serve better in understanding performance, while others are useful in
understanding importance and conditional responses of a particular variable.
Every explainer presented in this paper works for a single model or for a
collection of models. In the latter case, models can be compared against each
other. Such comparison helps to find strengths and weaknesses of different
approaches and gives additional possibilities for model validation. Presented
explainers are implemented in the DALEX package for R. They are based on a
uniform standardized grammar of model exploration which may be easily extended.
The current implementation supports the most popular frameworks for
classification and regression.
Overlapping Sliced Inverse Regression for Dimension Reduction
Authors: Ning Zhang, Zhou Yu, Qiang Wu
Date: 2018-06-23 00:00:00
Tasks: Dimensionality Reduction, regression

Sliced inverse regression (SIR) is a pioneer tool for supervised dimension
reduction. It identifies the effective dimension reduction space, the subspace
of significant factors with intrinsic lower dimensionality. In this paper, we
propose to refine the SIR algorithm through an overlapping slicing scheme. The
new algorithm, called overlapping sliced inverse regression (OSIR), is able to
estimate the effective dimension reduction space and determine the number of
effective factors more accurately. We show that such overlapping procedure has
the potential to identify the information contained in the derivatives of the
inverse regression curve, which helps to explain the superiority of OSIR. We
also prove that OSIR algorithm is $\sqrt n $-consistent and verify its
effectiveness by simulations and real applications.
Zeta Distribution and Transfer Learning Problem
Authors: Eray Özkural
Date: 2018-06-23 00:00:00
Tasks: Transfer Learning

We explore the relations between the zeta distribution and algorithmic
information theory via a new model of the transfer learning problem. The
program distribution is approximated by a zeta distribution with parameter near
$1$. We model the training sequence as a stochastic process. We analyze the
upper temporal bound for learning a training sequence and its entropy rates,
assuming an oracle for the transfer learning problem. We argue from empirical
evidence that power-law models are suitable for natural processes. Four
sequence models are proposed. Random typing model is like no-free lunch where
transfer learning does not work. Zeta process independently samples programs
from the zeta distribution. A model of common sub-programs inspired by genetics
uses a database of sub-programs. An evolutionary zeta process samples mutations
from Zeta distribution. The analysis of stochastic processes inspired by
evolution suggest that AI may be feasible in nature, countering no-free lunch
sort of arguments.
Privacy-Protective-GAN for Face De-identification
Authors: Yifan Wu, Fan Yang, Haibin Ling
Date: 2018-06-23 00:00:00
Tasks: De-identification, Face Recognition

Face de-identification has become increasingly important as the image sources
are explosively growing and easily accessible. The advance of new face
recognition techniques also arises people's concern regarding the privacy
leakage. The mainstream pipelines of face de-identification are mostly based on
the k-same framework, which bears critiques of low effectiveness and poor
visual quality. In this paper, we propose a new framework called
Privacy-Protective-GAN (PP-GAN) that adapts GAN with novel verificator and
regulator modules specially designed for the face de-identification problem to
ensure generating de-identified output with retained structure similarity
according to a single input. We evaluate the proposed approach in terms of
privacy protection, utility preservation, and structure similarity. Our
approach not only outperforms existing face de-identification techniques but
also provides a practical framework of adapting GAN with priors of domain
knowledge.
Towards Practical Visual Search Engine within Elasticsearch
Authors: Cun Mu, Jun Zhao, Guang Yang, Jing Zhang, Zheng Yan
Date: 2018-06-23 00:00:00
Tasks: Content-Based Image Retrieval, Image Retrieval, Retrieval

In this paper, we describe our end-to-end content-based image retrieval
system built upon Elasticsearch, a well-known and popular textual search
engine. As far as we know, this is the first time such a system has been
implemented in eCommerce, and our efforts have turned out to be highly
worthwhile. We end up with a novel and exciting visual search solution that is
extremely easy to be deployed, distributed, scaled and monitored in a
cost-friendly manner. Moreover, our platform is intrinsically flexible in
supporting multimodal searches, where visual and textual information can be
jointly leveraged in retrieval.
  The core idea is to encode image feature vectors into a collection of string
tokens in a way such that closer vectors will share more string tokens in
common. By doing that, we can utilize Elasticsearch to efficiently retrieve
similar images based on similarities within encoded sting tokens. As part of
the development, we propose a novel vector to string encoding method, which is
shown to substantially outperform the previous ones in terms of both precision
and latency.
  First-hand experiences in implementing this Elasticsearch-based platform are
extensively addressed, which should be valuable to practitioners also
interested in building visual search engine on top of Elasticsearch.
Deep Reinforcement Learning: An Overview
Authors: Seyed Sajad Mousavi, Michael Schukat, Enda Howley
Date: 2018-06-23 00:00:00
Tasks: BIG-bench Machine Learning, Deep Learning, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), speech-recognition, Speech Recognition

In recent years, a specific machine learning method called deep learning has
gained huge attraction, as it has obtained astonishing results in broad
applications such as pattern recognition, speech recognition, computer vision,
and natural language processing. Recent research has also been shown that deep
learning techniques can be combined with reinforcement learning methods to
learn useful representations for the problems with high dimensional raw data
input. This chapter reviews the recent advances in deep reinforcement learning
with a focus on the most used deep architectures such as autoencoders,
convolutional neural networks and recurrent neural networks which have
successfully been come together with the reinforcement learning framework.
Emotion Representation Mapping for Automatic Lexicon Construction (Mostly) Performs on Human Level
Authors: Sven Buechel, Udo Hahn
Date: 2018-06-23 00:00:00
Proceeding: COLING 2018 8

Emotion Representation Mapping (ERM) has the goal to convert existing emotion
ratings from one representation format into another one, e.g., mapping
Valence-Arousal-Dominance annotations for words or sentences into Ekman's Basic
Emotions and vice versa. ERM can thus not only be considered as an alternative
to Word Emotion Induction (WEI) techniques for automatic emotion lexicon
construction but may also help mitigate problems that come from the
proliferation of emotion representation formats in recent years. We propose a
new neural network approach to ERM that not only outperforms the previous
state-of-the-art. Equally important, we present a refined evaluation
methodology and gather strong evidence that our model yields results which are
(almost) as reliable as human annotations, even in cross-lingual settings.
Based on these results we generate new emotion ratings for 13 typologically
diverse languages and claim that they have near-gold quality, at least.
The Sparse Manifold Transform
Authors: Yubei Chen, Dylan M. Paiton, Bruno A. Olshausen
Date: 2018-06-23 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Self-Supervised Learning

We present a signal representation framework called the sparse manifold
transform that combines key ideas from sparse coding, manifold learning, and
slow feature analysis. It turns non-linear transformations in the primary
sensory signal space into linear interpolations in a representational embedding
space while maintaining approximate invertibility. The sparse manifold
transform is an unsupervised and generative framework that explicitly and
simultaneously models the sparse discreteness and low-dimensional manifold
structure found in natural scenes. When stacked, it also models hierarchical
composition. We provide a theoretical description of the transform and
demonstrate properties of the learned representation on both synthetic data and
natural videos.
The Foundations of Deep Learning with a Path Towards General Intelligence
Authors: Eray Özkural
Date: 2018-06-22 00:00:00
Tasks: Deep Learning

Like any field of empirical science, AI may be approached axiomatically. We
formulate requirements for a general-purpose, human-level AI system in terms of
postulates. We review the methodology of deep learning, examining the explicit
and tacit assumptions in deep learning research. Deep Learning methodology
seeks to overcome limitations in traditional machine learning research as it
combines facets of model richness, generality, and practical applicability. The
methodology so far has produced outstanding results due to a productive synergy
of function approximation, under plausible assumptions of irreducibility and
the efficiency of back-propagation family of algorithms. We examine these
winning traits of deep learning, and also observe the various known failure
modes of deep learning. We conclude by giving recommendations on how to extend
deep learning methodology to cover the postulates of general-purpose AI
including modularity, and cognitive architecture. We also relate deep learning
to advances in theoretical neuroscience research.
xGEMs: Generating Examplars to Explain Black-Box Models
Authors: Shalmali Joshi, Oluwasanmi Koyejo, Been Kim, Joydeep Ghosh
Date: 2018-06-22 00:00:00

This work proposes xGEMs or manifold guided exemplars, a framework to
understand black-box classifier behavior by exploring the landscape of the
underlying data manifold as data points cross decision boundaries. To do so, we
train an unsupervised implicit generative model -- treated as a proxy to the
data manifold. We summarize black-box model behavior quantitatively by
perturbing data samples along the manifold. We demonstrate xGEMs' ability to
detect and quantify bias in model learning and also for understanding the
changes in model behavior as training progresses.
VUNet: Dynamic Scene View Synthesis for Traversability Estimation using an RGB Camera
Authors: Noriaki Hirose, Amir Sadeghian, Fei Xia, Roberto Martin-Martin, Silvio Savarese
Date: 2018-06-22 00:00:00
Tasks: Autonomous Vehicles

We present VUNet, a novel view(VU) synthesis method for mobile robots in
dynamic environments, and its application to the estimation of future
traversability. Our method predicts future images for given virtual robot
velocity commands using only RGB images at previous and current time steps. The
future images result from applying two types of image changes to the previous
and current images: 1) changes caused by different camera pose, and 2) changes
due to the motion of the dynamic obstacles. We learn to predict these two types
of changes disjointly using two novel network architectures, SNet and DNet. We
combine SNet and DNet to synthesize future images that we pass to our
previously presented method GONet to estimate the traversable areas around the
robot. Our quantitative and qualitative evaluation indicate that our approach
for view synthesis predicts accurate future images in both static and dynamic
environments. We also show that these virtual images can be used to estimate
future traversability correctly. We apply our view synthesis-based
traversability estimation method to two applications for assisted
teleoperation.
A deep learning framework for segmentation of retinal layers from OCT images
Authors: Karthik Gopinath, Samrudhdhi B Rangrej, Jayanthi Sivaswamy
Date: 2018-06-22 00:00:00
Tasks: Denoising, Diagnostic, Edge Detection

Segmentation of retinal layers from Optical Coherence Tomography (OCT)
volumes is a fundamental problem for any computer aided diagnostic algorithm
development. This requires preprocessing steps such as denoising, region of
interest extraction, flattening and edge detection all of which involve
separate parameter tuning. In this paper, we explore deep learning techniques
to automate all these steps and handle the presence/absence of pathologies. A
model is proposed consisting of a combination of Convolutional Neural Network
(CNN) and Long Short Term Memory (LSTM). The CNN is used to extract layers of
interest image and extract the edges, while the LSTM is used to trace the layer
boundary. This model is trained on a mixture of normal and AMD cases using
minimal data. Validation results on three public datasets show that the
pixel-wise mean absolute error obtained with our system is 1.30 plus or minus
0.48 which is lower than the inter-marker error of 1.79 plus or minus 0.76. Our
model's performance is also on par with the existing methods.
RUC+CMU: System Report for Dense Captioning Events in Videos
Authors: Shizhe Chen, Yuqing Song, Yida Zhao, Jiarong Qiu, Qin Jin, Alexander Hauptmann
Date: 2018-06-22 00:00:00
Tasks: Caption Generation, Dense Captioning, Dense Video Captioning, Video Captioning

This notebook paper presents our system in the ActivityNet Dense Captioning
in Video task (task 3). Temporal proposal generation and caption generation are
both important to the dense captioning task. Therefore, we propose a proposal
ranking model to employ a set of effective feature representations for proposal
generation, and ensemble a series of caption models enhanced with context
information to generate captions robustly on predicted proposals. Our approach
achieves the state-of-the-art performance on the dense video captioning task
with 8.529 METEOR score on the challenge testing set.
Multi-Task Handwritten Document Layout Analysis
Authors: Lorenzo Quirós
Date: 2018-06-22 00:00:00
Tasks: Document Layout Analysis

Document Layout Analysis is a fundamental step in Handwritten Text Processing
systems, from the extraction of the text lines to the type of zone it belongs
to. We present a system based on artificial neural networks which is able to
determine not only the baselines of text lines present in the document, but
also performs geometric and logic layout analysis of the document. Experiments
in three different datasets demonstrate the potential of the method and show
competitive results with respect to state-of-the-art methods.
Deep SNP: An End-to-end Deep Neural Network with Attention-based Localization for Break-point Detection in SNP Array Genomic data
Authors: Hamid Eghbal-zadeh, Lukas Fischer, Niko Popitsch, Florian Kromp, Sabine Taschner-Mandl, Khaled Koutini, Teresa Gerber, Eva Bozsaky, Peter F. Ambros, Inge M. Ambros, Gerhard Widmer, Bernhard A. Moser
Date: 2018-06-22 00:00:00

Diagnosis and risk stratification of cancer and many other diseases require
the detection of genomic breakpoints as a prerequisite of calling copy number
alterations (CNA). This, however, is still challenging and requires
time-consuming manual curation. As deep-learning methods outperformed classical
state-of-the-art algorithms in various domains and have also been successfully
applied to life science problems including medicine and biology, we here
propose Deep SNP, a novel Deep Neural Network to learn from genomic data.
Specifically, we used a manually curated dataset from 12 genomic single
nucleotide polymorphism array (SNPa) profiles as truth-set and aimed at
predicting the presence or absence of genomic breakpoints, an indicator of
structural chromosomal variations, in windows of 40,000 probes. We compare our
results with well-known neural network models as well as Rawcopy though this
tool is designed to predict breakpoints and in addition genomic segments with
high sensitivity. We show, that Deep SNP is capable of successfully predicting
the presence or absence of a breakpoint in large genomic windows and
outperforms state-of-the-art neural network models. Qualitative examples
suggest that integration of a localization unit may enable breakpoint detection
and prediction of genomic segments, even if the breakpoint coordinates were not
provided for network training. These results warrant further evaluation of
DeepSNP for breakpoint localization and subsequent calling of genomic segments.
Bayesian Optimization of Combinatorial Structures
Authors: Ricardo Baptista, Matthias Poloczek
Date: 2018-06-22 00:00:00
Proceeding: ICML 2018 7
Tasks: Bayesian Optimization, BIG-bench Machine Learning

The optimization of expensive-to-evaluate black-box functions over
combinatorial structures is an ubiquitous task in machine learning, engineering
and the natural sciences. The combinatorial explosion of the search space and
costly evaluations pose challenges for current techniques in discrete
optimization and machine learning, and critically require new algorithmic
ideas. This article proposes, to the best of our knowledge, the first algorithm
to overcome these challenges, based on an adaptive, scalable model that
identifies useful combinatorial structure even when data is scarce. Our
acquisition function pioneers the use of semidefinite programming to achieve
efficiency and scalability. Experimental evaluations demonstrate that this
algorithm consistently outperforms other methods from combinatorial and
Bayesian optimization.
Smart Inverter Grid Probing for Learning Loads: Part II - Probing Injection Design
Authors: Siddharth Bhela, Vassilis Kekatos, Sriharsha Veeramachaneni
Date: 2018-06-22 00:00:00

This two-part work puts forth the idea of engaging power electronics to probe
an electric grid to infer non-metered loads. Probing can be accomplished by
commanding inverters to perturb their power injections and record the induced
voltage response. Once a probing setup is deemed topologically observable by
the tests of Part I, Part II provides a methodology for designing probing
injections abiding by inverter and network constraints to improve load
estimates. The task is challenging since system estimates depend on both
probing injections and unknown loads in an implicit nonlinear fashion. The
methodology first constructs a library of candidate probing vectors by sampling
over the feasible set of inverter injections. Leveraging a linearized grid
model and a robust approach, the candidate probing vectors violating voltage
constraints for any anticipated load value are subsequently rejected. Among the
qualified candidates, the design finally identifies the probing vectors
yielding the most diverse system states. The probing task under noisy phasor
and non-phasor data is tackled using a semidefinite-program (SDP) relaxation.
Numerical tests using synthetic and real-world data on a benchmark feeder
validate the conditions of Part I; the SDP-based solver; the importance of
probing design; and the effects of probing duration and noise.