pwc-archive/papers-with-abstracts huggingface dataset:
TextZoo, a New Benchmark for Reconsidering Text Classification
Authors: Benyou Wang, Li Wang, Qikang Wei, Lichun Liu
Date: 2018-02-10 00:00:00
Tasks: Classification, General Classification, text-classification, Text Classification

Text representation is a fundamental concern in Natural Language Processing,
especially in text classification. Recently, many neural network approaches
with delicate representation model (e.g. FASTTEXT, CNN, RNN and many hybrid
models with attention mechanisms) claimed that they achieved state-of-art in
specific text classification datasets. However, it lacks an unified benchmark
to compare these models and reveals the advantage of each sub-components for
various settings. We re-implement more than 20 popular text representation
models for classification in more than 10 datasets. In this paper, we
reconsider the text classification task in the perspective of neural network
and get serval effects with analysis of the above results.
Estimation of lactate threshold with machine learning techniques in recreational runners
Authors: Urtats Etxegarai, Eva Portillo, Jon Irazusta, Ander Arriandiaga, Itziar Cabanes
Date: 2018-03-15 00:00:00
Tasks: BIG-bench Machine Learning

Lactate threshold is considered an essential parameter when assessing
performance of elite and recreational runners and prescribing training
intensities in endurance sports. However, the measurement of blood lactate
concentration requires expensive equipment and the extraction of blood samples,
which are inconvenient for frequent monitoring. Furthermore, most recreational
runners do not have access to routine assessment of their physical fitness by
the aforementioned equipment so they are not able to calculate the lactate
threshold without resorting to an expensive and specialized centre. Therefore,
the main objective of this study is to create an intelligent system capable of
estimating the lactate threshold of recreational athletes participating in
endurance running sports. The solution here proposed is based on a machine
learning system which models the lactate evolution using recurrent neural
networks and includes the proposal of standardization of the temporal axis as
well as a modification of the stratified sampling method. The results show that
the proposed system accurately estimates the lactate threshold of 89.52% of the
athletes and its correlation with the experimentally measured lactate threshold
is very high (R=0,89). Moreover, its behaviour with the test dataset is as good
as with the training set, meaning that the generalization power of the model is
high. Therefore, in this study a machine learning based system is proposed as
alternative to the traditional invasive lactate threshold measurement tests for
recreational runners.
Multimodal Sentiment Analysis: Addressing Key Issues and Setting up the Baselines
Authors: Soujanya Poria, Navonil Majumder, Devamanyu Hazarika, Erik Cambria, Alexander Gelbukh, Amir Hussain
Date: 2018-03-19 00:00:00
Tasks: General Classification, Multimodal Sentiment Analysis, Sentiment Analysis, Sentiment Classification

We compile baselines, along with dataset split, for multimodal sentiment
analysis. In this paper, we explore three different deep-learning based
architectures for multimodal sentiment classification, each improving upon the
previous. Further, we evaluate these architectures with multiple datasets with
fixed train/test partition. We also discuss some major issues, frequently
ignored in multimodal sentiment analysis research, e.g., role of
speaker-exclusive models, importance of different modalities, and
generalizability. This framework illustrates the different facets of analysis
to be considered while performing multimodal sentiment analysis and, hence,
serves as a new benchmark for future research in this emerging field.
White matter hyperintensity segmentation from T1 and FLAIR images using fully convolutional neural networks enhanced with residual connections
Authors: Dakai Jin, Ziyue Xu, Adam P. Harrison, Daniel J. Mollura
Date: 2018-03-19 00:00:00
Tasks: Segmentation

Segmentation and quantification of white matter hyperintensities (WMHs) are
of great importance in studying and understanding various neurological and
geriatric disorders. Although automatic methods have been proposed for WMH
segmentation on magnetic resonance imaging (MRI), manual corrections are often
necessary to achieve clinically practical results. Major challenges for WMH
segmentation stem from their inhomogeneous MRI intensities, random location and
size distributions, and MRI noise. The presence of other brain anatomies or
diseases with enhanced intensities adds further difficulties. To cope with
these challenges, we present a specifically designed fully convolutional neural
network (FCN) with residual connections to segment WMHs by using combined T1
and fluid-attenuated inversion recovery (FLAIR) images. Our customized FCN is
designed to be straightforward and generalizable, providing efficient
end-to-end training due to its enhanced information propagation. We tested our
method on the open WMH Segmentation Challenge MICCAI2017 dataset, and, despite
our method's relative simplicity, results show that it performs amongst the
leading techniques across five metrics. More importantly, our method achieves
the best score for hausdorff distance and average volume difference in testing
datasets from two MRI scanners that were not included in training,
demonstrating better generalization ability of our proposed method over its
competitors.
Comparing and Integrating Constraint Programming and Temporal Planning for Quantum Circuit Compilation
Authors: Kyle E. C. Booth, Minh Do, J. Christopher Beck, Eleanor Rieffel, Davide Venturelli, Jeremy Frank
Date: 2018-03-19 00:00:00

Recently, the makespan-minimization problem of compiling a general class of
quantum algorithms into near-term quantum processors has been introduced to the
AI community. The research demonstrated that temporal planning is a strong
approach for a class of quantum circuit compilation (QCC) problems. In this
paper, we explore the use of constraint programming (CP) as an alternative and
complementary approach to temporal planning. We extend previous work by
introducing two new problem variations that incorporate important
characteristics identified by the quantum computing community. We apply
temporal planning and CP to the baseline and extended QCC problems as both
stand-alone and hybrid approaches. Our hybrid methods use solutions found by
temporal planning to warm start CP, leveraging the ability of the former to
find satisficing solutions to problems with a high degree of task optionality,
an area that CP typically struggles with. The CP model, benefiting from
inferred bounds on planning horizon length and task counts provided by the warm
start, is then used to find higher quality solutions. Our empirical evaluation
indicates that while stand-alone CP is only competitive for the smallest
problems, CP in our hybridization with temporal planning out-performs
stand-alone temporal planning in the majority of problem classes.
Composable Deep Reinforcement Learning for Robotic Manipulation
Authors: Tuomas Haarnoja, Vitchyr Pong, Aurick Zhou, Murtaza Dalal, Pieter Abbeel, Sergey Levine
Date: 2018-03-19 00:00:00
Tasks: Deep Reinforcement Learning, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Model-free deep reinforcement learning has been shown to exhibit good
performance in domains ranging from video games to simulated robotic
manipulation and locomotion. However, model-free methods are known to perform
poorly when the interaction time with the environment is limited, as is the
case for most real-world robotic tasks. In this paper, we study how maximum
entropy policies trained using soft Q-learning can be applied to real-world
robotic manipulation. The application of this method to real-world manipulation
is facilitated by two important features of soft Q-learning. First, soft
Q-learning can learn multimodal exploration strategies by learning policies
represented by expressive energy-based models. Second, we show that policies
learned with soft Q-learning can be composed to create new policies, and that
the optimality of the resulting policy can be bounded in terms of the
divergence between the composed policies. This compositionality provides an
especially valuable tool for real-world manipulation, where constructing new
policies by composing existing skills can provide a large gain in efficiency
over training from scratch. Our experimental evaluation demonstrates that soft
Q-learning is substantially more sample efficient than prior model-free deep
reinforcement learning methods, and that compositionality can be performed for
both simulated and real-world tasks.
De-Biased Machine Learning of Global and Local Parameters Using Regularized Riesz Representers
Authors: Victor Chernozhukov, Whitney Newey, Rahul Singh
Date: 2018-02-23 00:00:00
Tasks: BIG-bench Machine Learning

We provide adaptive inference methods, based on $\ell_1$ regularization, for regular (semi-parametric) and non-regular (nonparametric) linear functionals of the conditional expectation function. Examples of regular functionals include average treatment effects, policy effects, and derivatives. Examples of non-regular functionals include average treatment effects, policy effects, and derivatives conditional on a covariate subvector fixed at a point. We construct a Neyman orthogonal equation for the target parameter that is approximately invariant to small perturbations of the nuisance parameters. To achieve this property, we include the Riesz representer for the functional as an additional nuisance parameter. Our analysis yields weak ``double sparsity robustness'': either the approximation to the regression or the approximation to the representer can be ``completely dense'' as long as the other is sufficiently ``sparse''. Our main results are non-asymptotic and imply asymptotic uniform validity over large classes of models, translating into honest confidence bands for both global and local parameters.
RLlib: Abstractions for Distributed Reinforcement Learning
Authors: Eric Liang, Richard Liaw, Philipp Moritz, Robert Nishihara, Roy Fox, Ken Goldberg, Joseph E. Gonzalez, Michael. I. Jordan, Ion Stoica
Date: 2017-12-26 00:00:00
Proceeding: ICML 2018 7
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), rllib

Reinforcement learning (RL) algorithms involve the deep nesting of highly
irregular computation patterns, each of which typically exhibits opportunities
for distributed computation. We argue for distributing RL components in a
composable way by adapting algorithms for top-down hierarchical control,
thereby encapsulating parallelism and resource requirements within
short-running compute tasks. We demonstrate the benefits of this principle
through RLlib: a library that provides scalable software primitives for RL.
These primitives enable a broad range of algorithms to be implemented with high
performance, scalability, and substantial code reuse. RLlib is available at
https://rllib.io/.
Testing Deep Neural Networks
Authors: Youcheng Sun, Xiaowei Huang, Daniel Kroening, James Sharp, Matthew Hill, Rob Ashmore
Date: 2018-03-10 00:00:00
Tasks: Heuristic Search

Deep neural networks (DNNs) have a wide range of applications, and software
employing them must be thoroughly tested, especially in safety-critical
domains. However, traditional software test coverage metrics cannot be applied
directly to DNNs. In this paper, inspired by the MC/DC coverage criterion, we
propose a family of four novel test criteria that are tailored to structural
features of DNNs and their semantics. We validate the criteria by demonstrating
that the generated test inputs guided via our proposed coverage criteria are
able to capture undesired behaviours in a DNN. Test cases are generated using a
symbolic approach and a gradient-based heuristic search. By comparing them with
existing methods, we show that our criteria achieve a balance between their
ability to find bugs (proxied using adversarial examples) and the computational
cost of test case generation. Our experiments are conducted on state-of-the-art
DNNs obtained using popular open source datasets, including MNIST, CIFAR-10 and
ImageNet.
Temporal Human Action Segmentation via Dynamic Clustering
Authors: Yan Zhang, He Sun, Siyu Tang, Heiko Neumann
Date: 2018-03-15 00:00:00
Tasks: Action Segmentation, Clustering

We present an effective dynamic clustering algorithm for the task of temporal
human action segmentation, which has comprehensive applications such as
robotics, motion analysis, and patient monitoring. Our proposed algorithm is
unsupervised, fast, generic to process various types of features, and
applicable in both the online and offline settings. We perform extensive
experiments of processing data streams, and show that our algorithm achieves
the state-of-the-art results for both online and offline settings.
DeepSolarEye: Power Loss Prediction and Weakly Supervised Soiling Localization via Fully Convolutional Networks for Solar Panels
Authors: Sachin Mehta, Amar P. Azad, Saneem A. Chemmengath, Vikas Raykar, Shivkumar Kalyanaraman
Date: 2017-10-10 00:00:00
Tasks: GPU

The impact of soiling on solar panels is an important and well-studied
problem in renewable energy sector. In this paper, we present the first
convolutional neural network (CNN) based approach for solar panel soiling and
defect analysis. Our approach takes an RGB image of solar panel and
environmental factors as inputs to predict power loss, soiling localization,
and soiling type. In computer vision, localization is a complex task which
typically requires manually labeled training data such as bounding boxes or
segmentation masks. Our proposed approach consists of specialized four stages
which completely avoids localization ground truth and only needs panel images
with power loss labels for training. The region of impact area obtained from
the predicted localization masks are classified into soiling types using the
webly supervised learning. For improving localization capabilities of CNNs, we
introduce a novel bi-directional input-aware fusion (BiDIAF) block that
reinforces the input at different levels of CNN to learn input-specific feature
maps. Our empirical study shows that BiDIAF improves the power loss prediction
accuracy by about 3% and localization accuracy by about 4%. Our end-to-end
model yields further improvement of about 24% on localization when learned in a
weakly supervised manner. Our approach is generalizable and showed promising
results on web crawled solar panel images. Our system has a frame rate of 22
fps (including all steps) on a NVIDIA TitanX GPU. Additionally, we collected
first of it's kind dataset for solar panel image analysis consisting 45,000+
images.
Towards an Efficient Anomaly-Based Intrusion Detection for Software-Defined Networks
Authors: Majd Latah, Levent Toker
Date: 2018-03-18 00:00:00
Tasks: Intrusion Detection

Software-defined networking (SDN) is a new paradigm that allows developing
more flexible network applications. SDN controller, which represents a
centralized controlling point, is responsible for running various network
applications as well as maintaining different network services and
functionalities. Choosing an efficient intrusion detection system helps in
reducing the overhead of the running controller and creates a more secure
network. In this study, we investigate the performance of the well-known
anomaly-based intrusion detection approaches in terms of accuracy, false alarm
rate, precision, recall, f1-measure, area under ROC curve, execution time and
Mc Nemar's test. Precisely, we focus on supervised machine-learning approaches
where we use the following classifiers: Decision Trees (DT), Extreme Learning
Machine (ELM), Naive Bayes (NB), Linear Discriminant Analysis (LDA), Neural
Networks (NN), Support Vector Machines (SVM), Random Forest (RT), K
Nearest-Neighbour (KNN), AdaBoost, RUSBoost, LogitBoost and BaggingTrees where
we employ the well-known NSL-KDD benchmark dataset to compare the performance
of each one of these classifiers.
Sentiment Analysis of Code-Mixed Indian Languages: An Overview of SAIL_Code-Mixed Shared Task @ICON-2017
Authors: Braja Gopal Patra, Dipankar Das, Amitava Das
Date: 2018-03-18 00:00:00
Tasks: Sentiment Analysis, Stance Detection

Sentiment analysis is essential in many real-world applications such as
stance detection, review analysis, recommendation system, and so on. Sentiment
analysis becomes more difficult when the data is noisy and collected from
social media. India is a multilingual country; people use more than one
languages to communicate within themselves. The switching in between the
languages is called code-switching or code-mixing, depending upon the type of
mixing. This paper presents overview of the shared task on sentiment analysis
of code-mixed data pairs of Hindi-English and Bengali-English collected from
the different social media platform. The paper describes the task, dataset,
evaluation, baseline and participant's systems.
Learned Neural Iterative Decoding for Lossy Image Compression Systems
Authors: Alexander G. Ororbia, Ankur Mali, Jian Wu, Scott O'Connell, David Miller, C. Lee Giles
Date: 2018-03-15 00:00:00
Tasks: Decoder, Image Compression

For lossy image compression systems, we develop an algorithm, iterative
refinement, to improve the decoder's reconstruction compared to standard
decoding techniques. Specifically, we propose a recurrent neural network
approach for nonlinear, iterative decoding. Our decoder, which works with any
encoder, employs self-connected memory units that make use of causal and
non-causal spatial context information to progressively reduce reconstruction
error over a fixed number of steps. We experiment with variants of our
estimator and find that iterative refinement consistently creates lower
distortion images of higher perceptual quality compared to other approaches.
Specifically, on the Kodak Lossless True Color Image Suite, we observe as much
as a 0.871 decibel (dB) gain over JPEG, a 1.095 dB gain over JPEG 2000, and a
0.971 dB gain over a competitive neural model.
Discriminative Learning of Latent Features for Zero-Shot Recognition
Authors: Yan Li, Junge Zhang, Jian-Guo Zhang, Kaiqi Huang
Date: 2018-03-18 00:00:00
Proceeding: CVPR 2018 6
Tasks: Zero-Shot Learning

Zero-shot learning (ZSL) aims to recognize unseen image categories by
learning an embedding space between image and semantic representations. For
years, among existing works, it has been the center task to learn the proper
mapping matrices aligning the visual and semantic space, whilst the importance
to learn discriminative representations for ZSL is ignored. In this work, we
retrospect existing methods and demonstrate the necessity to learn
discriminative representations for both visual and semantic instances of ZSL.
We propose an end-to-end network that is capable of 1) automatically
discovering discriminative regions by a zoom network; and 2) learning
discriminative semantic representations in an augmented space introduced for
both user-defined and latent attributes. Our proposed method is tested
extensively on two challenging ZSL datasets, and the experiment results show
that the proposed method significantly outperforms state-of-the-art methods.
Aggregating Strategies for Long-term Forecasting
Authors: Alexander Korotin, Vladimir V'yugin, Evgeny Burnaev
Date: 2018-03-18 00:00:00

The article is devoted to investigating the application of aggregating
algorithms to the problem of the long-term forecasting. We examine the classic
aggregating algorithms based on the exponential reweighing. For the general
Vovk's aggregating algorithm we provide its generalization for the long-term
forecasting. For the special basic case of Vovk's algorithm we provide its two
modifications for the long-term forecasting. The first one is theoretically
close to an optimal algorithm and is based on replication of independent
copies. It provides the time-independent regret bound with respect to the best
expert in the pool. The second one is not optimal but is more practical and has
$O(\sqrt{T})$ regret bound, where $T$ is the length of the game.
Provable Estimation of the Number of Blocks in Block Models
Authors: Bowei Yan, Purnamrita Sarkar, Xiuyuan Cheng
Date: 2017-05-24 00:00:00
Tasks: Clustering, Community Detection

Community detection is a fundamental unsupervised learning problem for
unlabeled networks which has a broad range of applications. Many community
detection algorithms assume that the number of clusters $r$ is known apriori.
In this paper, we propose an approach based on semi-definite relaxations, which
does not require prior knowledge of model parameters like many existing convex
relaxation methods and recovers the number of clusters and the clustering
matrix exactly under a broad parameter regime, with probability tending to one.
On a variety of simulated and real data experiments, we show that the proposed
method often outperforms state-of-the-art techniques for estimating the number
of clusters.
High Dimensional Linear Regression using Lattice Basis Reduction
Authors: David Gamarnik, Ilias Zadik
Date: 2018-03-18 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: regression, Vocal Bursts Intensity Prediction

We consider a high dimensional linear regression problem where the goal is to
efficiently recover an unknown vector $\beta^*$ from $n$ noisy linear
observations $Y=X\beta^*+W \in \mathbb{R}^n$, for known $X \in \mathbb{R}^{n
\times p}$ and unknown $W \in \mathbb{R}^n$. Unlike most of the literature on
this model we make no sparsity assumption on $\beta^*$. Instead we adopt a
regularization based on assuming that the underlying vectors $\beta^*$ have
rational entries with the same denominator $Q \in \mathbb{Z}_{>0}$. We call
this $Q$-rationality assumption.
  We propose a new polynomial-time algorithm for this task which is based on
the seminal Lenstra-Lenstra-Lovasz (LLL) lattice basis reduction algorithm. We
establish that under the $Q$-rationality assumption, our algorithm recovers
exactly the vector $\beta^*$ for a large class of distributions for the iid
entries of $X$ and non-zero noise $W$. We prove that it is successful under
small noise, even when the learner has access to only one observation ($n=1$).
Furthermore, we prove that in the case of the Gaussian white noise for $W$,
$n=o\left(p/\log p\right)$ and $Q$ sufficiently large, our algorithm tolerates
a nearly optimal information-theoretic level of the noise.
Rare Feature Selection in High Dimensions
Authors: Xiaohan Yan, Jacob Bien
Date: 2018-03-18 00:00:00
Tasks: feature selection, Vocal Bursts Intensity Prediction

It is common in modern prediction problems for many predictor variables to be counts of rarely occurring events. This leads to design matrices in which many columns are highly sparse. The challenge posed by such "rare features" has received little attention despite its prevalence in diverse areas, ranging from natural language processing (e.g., rare words) to biology (e.g., rare species). We show, both theoretically and empirically, that not explicitly accounting for the rareness of features can greatly reduce the effectiveness of an analysis. We next propose a framework for aggregating rare features into denser features in a flexible manner that creates better predictors of the response. Our strategy leverages side information in the form of a tree that encodes feature similarity. We apply our method to data from TripAdvisor, in which we predict the numerical rating of a hotel based on the text of the associated review. Our method achieves high accuracy by making effective use of rare words; by contrast, the lasso is unable to identify highly predictive words if they are too rare. A companion R package, called rare, implements our new estimator, using the alternating direction method of multipliers.