pwc-archive/papers-with-abstracts huggingface dataset:
Learning from Sets of Items in Recommender Systems
Authors: Mohit Sharma, F. Maxwell Harper, George Karypis
Date: 2019-04-22 00:00:00
Tasks: Collaborative Filtering, Recommendation Systems

Most of the existing recommender systems use the ratings provided by users on
individual items. An additional source of preference information is to use the
ratings that users provide on sets of items. The advantages of using
preferences on sets are two-fold. First, a rating provided on a set conveys
some preference information about each of the set's items, which allows us to
acquire a user's preferences for more items that the number of ratings that the
user provided. Second, due to privacy concerns, users may not be willing to
reveal their preferences on individual items explicitly but may be willing to
provide a single rating to a set of items, since it provides some level of
information hiding. This paper investigates two questions related to using
set-level ratings in recommender systems. First, how users' item-level ratings
relate to their set-level ratings. Second, how collaborative filtering-based
models for item-level rating prediction can take advantage of such set-level
ratings. We have collected set-level ratings from active users of Movielens on
sets of movies that they have rated in the past. Our analysis of these ratings
shows that though the majority of the users provide the average of the ratings
on a set's constituent items as the rating on the set, there exists a
significant number of users that tend to consistently either under- or
over-rate the sets. We have developed collaborative filtering-based methods to
explicitly model these user behaviors that can be used to recommend items to
users. Experiments on real data and on synthetic data that resembles the under-
or over-rating behavior in the real data, demonstrate that these models can
recover the overall characteristics of the underlying data and predict the
user's ratings on individual items.
Density-based Community Detection/Optimization
Authors: Rui Portocarrero Sarmento
Date: 2019-04-07 00:00:00
Tasks: Community Detection

Modularity-based algorithms used for community detection have been increasing
in recent years. Modularity and its application have been generating
controversy since some authors argue it is not a metric without disadvantages.
It has been shown that algorithms that use modularity to detect communities
suffer a resolution limit and, therefore, it is unable to identify small
communities in some situations. In this work, we try to apply a density
optimization of communities found by the label propagation algorithm and study
what happens regarding modularity of optimized results. We introduce a metric
we call ADC (Average Density per Community); we use this metric to prove our
optimization provides improvements to the community density obtained with
benchmark algorithms. Additionally, we provide evidence this optimization might
not alter modularity of resulting communities significantly. Additionally, by
also using the SSC (Strongly Connected Components) concept we developed a
community detection algorithm that we also compare with the label propagation
algorithm. These comparisons were executed with several test networks and with
different network sizes. The results of the optimization algorithm proved to be
interesting. Additionally, the results of the community detection algorithm
turned out to be similar to the benchmark algorithm we used.
tsmp: An R Package for Time Series with Matrix Profile
Authors: Francisco Bischoff, Pedro Pereira Rodrigues
Date: 2019-04-18 00:00:00
Tasks: Articles, Semantic Segmentation, Time Series, Time Series Analysis

This article describes tsmp, an R package that implements the matrix profile
concept for time series. The tsmp package is a toolkit that allows all-pairs
similarity joins, motif, discords and chains discovery, semantic segmentation,
etc. Here we describe how the tsmp package may be used by showing some of the
use-cases from the original articles and evaluate the algorithm speed in the R
environment. This package can be downloaded at
https://CRAN.R-project.org/package=tsmp.
Automatic Model Building in GEFCom 2017 Qualifying Match
Authors: Ján Dolinský, Mária Starovská, Robert Tóth
Date: 2019-04-12 00:00:00
Tasks: Time Series, Time Series Analysis

The Tangent Works team participated in GEFCom 2017 to test its automatic
model building strategy for time series known as Tangent Information Modeller
(TIM). Model building using TIM combined with historical temperature shuffling
resulted in winning the competition. This strategy involved one remaining
degree of freedom, a decision on using a trend variable. This paper describes
our modelling efforts in the competition, and furthermore outlines a fully
automated scenario where the decision on using the trend variable is handled by
TIM. The results show that such a setup would also win the competition.
ExplaiNE: An Approach for Explaining Network Embedding-based Link Predictions
Authors: Bo Kang, Jefrey Lijffijt, Tijl De Bie
Date: 2019-04-22 00:00:00
Tasks: BIG-bench Machine Learning, counterfactual, Link Prediction, Network Embedding

Networks are powerful data structures, but are challenging to work with for
conventional machine learning methods. Network Embedding (NE) methods attempt
to resolve this by learning vector representations for the nodes, for
subsequent use in downstream machine learning tasks.
  Link Prediction (LP) is one such downstream machine learning task that is an
important use case and popular benchmark for NE methods. Unfortunately, while
NE methods perform exceedingly well at this task, they are lacking in
transparency as compared to simpler LP approaches.
  We introduce ExplaiNE, an approach to offer counterfactual explanations for
NE-based LP methods, by identifying existing links in the network that explain
the predicted links. ExplaiNE is applicable to a broad class of NE algorithms.
An extensive empirical evaluation for the NE method `Conditional Network
Embedding' in particular demonstrates its accuracy and scalability.
Lazy Random Walks for Superpixel Segmentation
Authors: Jianbing Shen, Yunfan Du, Wenguan Wang, Xuelong. Li
Date: 2014-01-27 00:00:00
Proceeding: IEEE Trans. on Image Processing 2014 1
Tasks: Segmentation, Superpixels

We present a novel image superpixel segmentation approach using the proposed lazy random walk (LRW) algorithm in this paper. Our method begins with initializing the seed positions and runs the LRW algorithm on the input image to obtain the probabilities of each pixel. Then, the boundaries of initial superpixels are obtained according to the probabilities and the commute time. The initial superpixels are iteratively optimized by the new energy function, which is defined on the commute time and the texture measurement. Our LRW algorithm with self-loops has the merits of segmenting the weak boundaries and complicated texture regions very well by the new global probability maps and the commute time strategy. The performance of superpixel is improved by relocating the center positions of superpixels and dividing the large superpixels into small ones with the proposed optimization algorithm. The experimental results have demonstrated that our method achieves better performance than previous superpixel approaches.
AST-Based Deep Learning for Detecting Malicious PowerShell
Authors: Gili Rusak, Abdullah Al-Dujaili, Una-May O'Reilly
Date: 2018-10-03 00:00:00
Tasks: Deep Learning

With the celebrated success of deep learning, some attempts to develop
effective methods for detecting malicious PowerShell programs employ neural
nets in a traditional natural language processing setup while others employ
convolutional neural nets to detect obfuscated malicious commands at a
character level. While these representations may express salient PowerShell
properties, our hypothesis is that tools from static program analysis will be
more effective. We propose a hybrid approach combining traditional program
analysis (in the form of abstract syntax trees) and deep learning. This poster
presents preliminary results of a fundamental step in our approach: learning
embeddings for nodes of PowerShell ASTs. We classify malicious scripts by
family type and explore embedded program vector representations.
Parallel Experimentation and Competitive Interference on Online Advertising Platforms
Authors: Caio Waisman, Navdeep S. Sahni, Harikesh S. Nair, Xiliang Lin
Date: 2019-03-27 00:00:00
Tasks: Decision Making, Experimental Design

This paper studies the measurement of advertising effects on online platforms when parallel experimentation occurs, that is, when multiple advertisers experiment concurrently. It provides a framework that makes precise how parallel experimentation affects the experiment's value: while ignoring parallel experimentation yields an estimate of the average effect of advertising in-place, which has limited value in decision-making in an environment with variable advertising competition, accounting for parallel experimentation captures the actual uncertainty advertisers face due to competitive actions. It then implements an experimental design that enables the estimation of these effects on JD.com, a large e-commerce platform that is also a publisher of digital ads. Using traditional and kernel-based estimators, it shows that not accounting for competitive actions can result in the advertiser inaccurately estimating the advertising lift by a factor of two or higher, which can be consequential for decision-making.
Categorical Feature Compression via Submodular Optimization
Authors: Mohammadhossein Bateni, Lin Chen, Hossein Esfandiari, Thomas Fu, Vahab S. Mirrokni, Afshin Rostamizadeh
Date: 2019-04-30 00:00:00
Tasks: Feature Compression

In the era of big data, learning from categorical features with very large
vocabularies (e.g., 28 million for the Criteo click prediction dataset) has
become a practical challenge for machine learning researchers and
practitioners. We design a highly-scalable vocabulary compression algorithm
that seeks to maximize the mutual information between the compressed
categorical feature and the target binary labels and we furthermore show that
its solution is guaranteed to be within a $1-1/e \approx 63\%$ factor of the
global optimal solution. To achieve this, we introduce a novel
re-parametrization of the mutual information objective, which we prove is
submodular, and design a data structure to query the submodular function in
amortized $O(\log n )$ time (where $n$ is the input vocabulary size). Our
complete algorithm is shown to operate in $O(n \log n )$ time. Additionally, we
design a distributed implementation in which the query data structure is
decomposed across $O(k)$ machines such that each machine only requires $O(\frac
n k)$ space, while still preserving the approximation guarantee and using only
logarithmic rounds of computation. We also provide analysis of simple
alternative heuristic compression methods to demonstrate they cannot achieve
any approximation guarantee. Using the large-scale Criteo learning task, we
demonstrate better performance in retaining mutual information and also verify
competitive learning performance compared to other baseline methods.
Risk-Averse Explore-Then-Commit Algorithms for Finite-Time Bandits
Authors: Ali Yekkehkhany, Ebrahim Arian, Mohammad Hajiesmaili, Rakesh Nagi
Date: 2019-04-30 00:00:00

In this paper, we study multi-armed bandit problems in explore-then-commit setting. In our proposed explore-then-commit setting, the goal is to identify the best arm after a pure experimentation (exploration) phase and exploit it once or for a given finite number of times. We identify that although the arm with the highest expected reward is the most desirable objective for infinite exploitations, it is not necessarily the one that is most probable to have the highest reward in a single or finite-time exploitations. Alternatively, we advocate the idea of risk-aversion where the objective is to compete against the arm with the best risk-return trade-off. Then, we propose two algorithms whose objectives are to select the arm that is most probable to reward the most. Using a new notion of finite-time exploitation regret, we find an upper bound for the minimum number of experiments before commitment, to guarantee an upper bound for the regret. As compared to existing risk-averse bandit algorithms, our algorithms do not rely on hyper-parameters, resulting in a more robust behavior in practice, which is verified by the numerical evaluation.
Active Manifolds: A non-linear analogue to Active Subspaces
Authors: Robert A. Bridges, Anthony D. Gruber, Christopher Felder, Miki Verma, Chelsey Hoff
Date: 2019-04-30 00:00:00
Tasks: Sensitivity

We present an approach to analyze $C^1(\mathbb{R}^m)$ functions that addresses limitations present in the Active Subspaces (AS) method of Constantine et al.(2015; 2014). Under appropriate hypotheses, our Active Manifolds (AM) method identifies a 1-D curve in the domain (the active manifold) on which nearly all values of the unknown function are attained, and which can be exploited for approximation or analysis, especially when $m$ is large (high-dimensional input space). We provide theorems justifying our AM technique and an algorithm permitting functional approximation and sensitivity analysis. Using accessible, low-dimensional functions as initial examples, we show AM reduces approximation error by an order of magnitude compared to AS, at the expense of more computation. Following this, we revisit the sensitivity analysis by Glaws et al. (2017), who apply AS to analyze a magnetohydrodynamic power generator model, and compare the performance of AM on the same data. Our analysis provides detailed information not captured by AS, exhibiting the influence of each parameter individually along an active manifold. Overall, AM represents a novel technique for analyzing functional models with benefits including: reducing $m$-dimensional analysis to a 1-D analogue, permitting more accurate regression than AS (at more computational expense), enabling more informative sensitivity analysis, and granting accessible visualizations(2-D plots) of parameter sensitivity along the AM.
Comparative evaluation of 2D feature correspondence selection algorithms
Authors: Chen Zhao, Jiaqi Yang, Yang Xiao, Zhiguo Cao
Date: 2019-04-30 00:00:00
Tasks: Diversity

Correspondence selection aiming at seeking correct feature correspondences
from raw feature matches is pivotal for a number of feature-matching-based
tasks. Various 2D (image) correspondence selection algorithms have been
presented with decades of progress. Unfortunately, the lack of an in-depth
evaluation makes it difficult for developers to choose a proper algorithm given
a specific application. This paper fills this gap by evaluating eight 2D
correspondence selection algorithms ranging from classical methods to the most
recent ones on four standard datasets. The diversity of experimental datasets
brings various nuisances including zoom, rotation, blur, viewpoint change, JPEG
compression, light change, different rendering styles and multi-structures for
comprehensive test. To further create different distributions of initial
matches, a set of combinations of detector and descriptor is also taken into
consideration. We measure the quality of a correspondence selection algorithm
from four perspectives, i.e., precision, recall, F-measure and efficiency.
According to evaluation results, the current advantages and limitations of all
considered algorithms are aggregately summarized which could be treated as a
"user guide" for the following developers.
Very Deep Self-Attention Networks for End-to-End Speech Recognition
Authors: Ngoc-Quan Pham, Thai-Son Nguyen, Jan Niehues, Markus Müller, Sebastian Stüker, Alexander Waibel
Date: 2019-04-30 00:00:00
Tasks: speech-recognition, Speech Recognition

Recently, end-to-end sequence-to-sequence models for speech recognition have gained significant interest in the research community. While previous architecture choices revolve around time-delay neural networks (TDNN) and long short-term memory (LSTM) recurrent neural networks, we propose to use self-attention via the Transformer architecture as an alternative. Our analysis shows that deep Transformer networks with high learning capacity are able to exceed performance from previous end-to-end approaches and even match the conventional hybrid systems. Moreover, we trained very deep models with up to 48 Transformer layers for both encoder and decoders combined with stochastic residual connections, which greatly improve generalizability and training efficiency. The resulting models outperform all previous end-to-end ASR approaches on the Switchboard benchmark. An ensemble of these models achieve 9.9% and 17.7% WER on Switchboard and CallHome test sets respectively. This finding brings our end-to-end models to competitive levels with previous hybrid systems. Further, with model ensembling the Transformers can outperform certain hybrid systems, which are more complicated in terms of both structure and training procedure.
Gradient Coding Based on Block Designs for Mitigating Adversarial Stragglers
Authors: Swanand Kadhe, O. Ozan Koyluoglu, Kannan Ramchandran
Date: 2019-04-30 00:00:00

Distributed implementations of gradient-based methods, wherein a server
distributes gradient computations across worker machines, suffer from slow
running machines, called 'stragglers'. Gradient coding is a coding-theoretic
framework to mitigate stragglers by enabling the server to recover the gradient
sum in the presence of stragglers. 'Approximate gradient codes' are variants of
gradient codes that reduce computation and storage overhead per worker by
allowing the server to approximately reconstruct the gradient sum.
  In this work, our goal is to construct approximate gradient codes that are
resilient to stragglers selected by a computationally unbounded adversary. Our
motivation for constructing codes to mitigate adversarial stragglers stems from
the challenge of tackling stragglers in massive-scale elastic and serverless
systems, wherein it is difficult to statistically model stragglers. Towards
this end, we propose a class of approximate gradient codes based on balanced
incomplete block designs (BIBDs). We show that the approximation error for
these codes depends only on the number of stragglers, and thus, adversarial
straggler selection has no advantage over random selection. In addition, the
proposed codes admit computationally efficient decoding at the server. Next, to
characterize fundamental limits of adversarial straggling, we consider the
notion of 'adversarial threshold' -- the smallest number of workers that an
adversary must straggle to inflict certain approximation error. We compute a
lower bound on the adversarial threshold, and show that codes based on
symmetric BIBDs maximize this lower bound among a wide class of codes, making
them excellent candidates for mitigating adversarial stragglers.
Factor Analysis in Fault Diagnostics Using Random Forest
Authors: Nagdev Amruthnath, Tarun Gupta
Date: 2019-04-30 00:00:00
Tasks: Attribute, Clustering, Fault Diagnosis, General Classification, Video Classification

Factor analysis or sometimes referred to as variable analysis has been
extensively used in classification problems for identifying specific factors
that are significant to particular classes. This type of analysis has been
widely used in application such as customer segmentation, medical research,
network traffic, image, and video classification. Today, factor analysis is
prominently being used in fault diagnosis of machines to identify the
significant factors and to study the root cause of a specific machine fault.
The advantage of performing factor analysis in machine maintenance is to
perform prescriptive analysis (helps answer what actions to take?) and
preemptive analysis (helps answer how to eliminate the failure mode?). In this
paper, a real case of an industrial rotating machine was considered where
vibration and ambient temperature data was collected for monitoring the health
of the machine. Gaussian mixture model-based clustering was used to cluster the
data into significant groups, and spectrum analysis was used to diagnose each
cluster to a specific state of the machine. The significant features that
attribute to a particular mode of the machine were identified by using the
random forest classification model. The significant features for specific modes
of the machine were used to conclude that the clusters generated are distinct
and have a unique set of significant features.
The Level Weighted Structural Similarity Loss: A Step Away from the MSE
Authors: Yingjing Lu
Date: 2019-04-30 00:00:00
Tasks: SSIM

The Mean Square Error (MSE) has shown its strength when applied in deep
generative models such as Auto-Encoders to model reconstruction loss. However,
in image domain especially, the limitation of MSE is obvious: it assumes pixel
independence and ignores spatial relationships of samples. This contradicts
most architectures of Auto-Encoders which use convolutional layers to extract
spatial dependent features. We base on the structural similarity metric (SSIM)
and propose a novel level weighted structural similarity (LWSSIM) loss for
convolutional Auto-Encoders. Experiments on common datasets on various
Auto-Encoder variants show that our loss is able to outperform the MSE loss and
the Vanilla SSIM loss. We also provide reasons why our model is able to succeed
in cases where the standard SSIM loss fails.
Structured Prediction using cGANs with Fusion Discriminator
Authors: Faisal Mahmood, Wenhao Xu, Nicholas J. Durr, Jeremiah W. Johnson, Alan Yuille
Date: 2019-04-30 00:00:00
Proceeding: ICLR 2019 5
Tasks: Depth Estimation, Generative Adversarial Network, Image Generation, Prediction, Semantic Segmentation, Structured Prediction

We propose the fusion discriminator, a single unified framework for
incorporating conditional information into a generative adversarial network
(GAN) for a variety of distinct structured prediction tasks, including image
synthesis, semantic segmentation, and depth estimation. Much like commonly used
convolutional neural network -- conditional Markov random field (CNN-CRF)
models, the proposed method is able to enforce higher-order consistency in the
model, but without being limited to a very specific class of potentials. The
method is conceptually simple and flexible, and our experimental results
demonstrate improvement on several diverse structured prediction tasks.