pwc-archive/papers-with-abstracts huggingface dataset:
Adversarial Personalized Ranking for Recommendation
Authors: Xiangnan He, Zhankui He, Xiaoyu Du, Tat-Seng Chua
Date: 2018-08-12 00:00:00
Tasks: Recommendation Systems

Item recommendation is a personalized ranking task. To this end, many
recommender systems optimize models with pairwise ranking objectives, such as
the Bayesian Personalized Ranking (BPR). Using matrix Factorization (MF) ---
the most widely used model in recommendation --- as a demonstration, we show
that optimizing it with BPR leads to a recommender model that is not robust. In
particular, we find that the resultant model is highly vulnerable to
adversarial perturbations on its model parameters, which implies the possibly
large error in generalization.
  To enhance the robustness of a recommender model and thus improve its
generalization performance, we propose a new optimization framework, namely
Adversarial Personalized Ranking (APR). In short, our APR enhances the pairwise
ranking method BPR by performing adversarial training. It can be interpreted as
playing a minimax game, where the minimization of the BPR objective function
meanwhile defends an adversary, which adds adversarial perturbations on model
parameters to maximize the BPR objective function. To illustrate how it works,
we implement APR on MF by adding adversarial perturbations on the embedding
vectors of users and items. Extensive experiments on three public real-world
datasets demonstrate the effectiveness of APR --- by optimizing MF with APR, it
outperforms BPR with a relative improvement of 11.2% on average and achieves
state-of-the-art performance for item recommendation. Our implementation is
available at: https://github.com/hexiangnan/adversarial_personalized_ranking.
Video Logo Retrieval based on local Features
Authors: Bochen Guan, Hanrong Ye, Hong Liu, William A. Sethares
Date: 2018-08-11 00:00:00
Tasks: Image Retrieval, Retrieval, Video Retrieval

Estimation of the frequency and duration of logos in videos is important and challenging in the advertisement industry as a way of estimating the impact of ad purchases. Since logos occupy only a small area in the videos, the popular methods of image retrieval could fail. This paper develops an algorithm called Video Logo Retrieval (VLR), which is an image-to-video retrieval algorithm based on the spatial distribution of local image descriptors that measure the distance between the query image (the logo) and a collection of video images. VLR uses local features to overcome the weakness of global feature-based models such as convolutional neural networks (CNN). Meanwhile, VLR is flexible and does not require training after setting some hyper-parameters. The performance of VLR is evaluated on two challenging open benchmark tasks (SoccerNet and Standford I2V), and compared with other state-of-the-art logo retrieval or detection algorithms. Overall, VLR shows significantly higher accuracy compared with the existing methods.
Transfer Learning Enhanced Common Spatial Pattern Filtering for Brain Computer Interfaces (BCIs): Overview and a New Approach
Authors: He He, Dongrui Wu
Date: 2018-08-08 00:00:00
Tasks: EEG, Electroencephalogram (EEG), General Classification, Motor Imagery, Transfer Learning

The electroencephalogram (EEG) is the most widely used input for brain
computer interfaces (BCIs), and common spatial pattern (CSP) is frequently used
to spatially filter it to increase its signal-to-noise ratio. However, CSP is a
supervised filter, which needs some subject-specific calibration data to
design. This is time-consuming and not user-friendly. A promising approach for
shortening or even completely eliminating this calibration session is transfer
learning, which leverages relevant data or knowledge from other subjects or
tasks. This paper reviews three existing approaches for incorporating transfer
learning into CSP, and also proposes a new transfer learning enhanced CSP
approach. Experiments on motor imagery classification demonstrate their
effectiveness. Particularly, our proposed approach achieves the best
performance when the number of target domain calibration samples is small.
Spatial Filtering for Brain Computer Interfaces: A Comparison between the Common Spatial Pattern and Its Variant
Authors: He He, Dongrui Wu
Date: 2018-08-08 00:00:00
Tasks: Binary Classification, Classification, EEG, Electroencephalogram (EEG), General Classification, Motor Imagery

The electroencephalogram (EEG) is the most popular form of input for brain
computer interfaces (BCIs). However, it can be easily contaminated by various
artifacts and noise, e.g., eye blink, muscle activities, powerline noise, etc.
Therefore, the EEG signals are often filtered both spatially and temporally to
increase the signal-to-noise ratio before they are fed into a machine learning
algorithm for recognition. This paper considers spatial filtering,
particularly, the common spatial pattern (CSP) filters for EEG classification.
In binary classification, CSP seeks a set of filters to maximize the variance
for one class while minimizing it for the other. We first introduce the
traditional solution, and then a new solution based on a slightly different
objective function. We performed comprehensive experiments on motor imagery to
compare the two approaches, and found that generally the traditional CSP
solution still gives better results. We also showed that adding regularization
to the covariance matrices can improve the final classification performance, no
matter which objective function is used.
Has Machine Translation Achieved Human Parity? A Case for Document-level Evaluation
Authors: Samuel Läubli, Rico Sennrich, Martin Volk
Date: 2018-08-21 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Machine Translation, Sentence, Translation

Recent research suggests that neural machine translation achieves parity with
professional human translation on the WMT Chinese--English news translation
task. We empirically test this claim with alternative evaluation protocols,
contrasting the evaluation of single sentences and entire documents. In a
pairwise ranking experiment, human raters assessing adequacy and fluency show a
stronger preference for human over machine translation when evaluating
documents as compared to isolated sentences. Our findings emphasise the need to
shift towards document-level evaluation as machine translation improves to the
degree that errors which are hard or impossible to spot at the sentence-level
become decisive in discriminating quality of different translation outputs.
ISNA-Set: A novel English Corpus of Iran NEWS
Authors: Mohammad Kamel, Hadi Sadoghi-Yazdi
Date: 2018-08-21 00:00:00
Tasks: Part-Of-Speech Tagging

News agencies publish news on their websites all over the world. Moreover,
creating novel corpuses is necessary to bring natural processing to new
domains. Textual processing of online news is challenging in terms of the
strategy of collecting data, the complex structure of news websites, and
selecting or designing suitable algorithms for processing these types of data.
Despite the previous works which focus on creating corpuses for Iran news in
Persian, in this paper, we introduce a new corpus for English news of a
national news agency. ISNA-Set is a new dataset of English news of Iranian
Students News Agency (ISNA), as one of the most famous news agencies in Iran.
We statistically analyze the data and the sentiments of news, and also extract
entities and part-of-speech tagging.
CoQA: A Conversational Question Answering Challenge
Authors: Siva Reddy, Danqi Chen, Christopher D. Manning
Date: 2018-08-21 00:00:00
Proceeding: TACL 2019 3
Tasks: 8k, Conversational Question Answering, Generative Question Answering, Question Answering, Reading Comprehension

Humans gather information by engaging in conversations involving a series of
interconnected questions and answers. For machines to assist in information
gathering, it is therefore essential to enable them to answer conversational
questions. We introduce CoQA, a novel dataset for building Conversational
Question Answering systems. Our dataset contains 127k questions with answers,
obtained from 8k conversations about text passages from seven diverse domains.
The questions are conversational, and the answers are free-form text with their
corresponding evidence highlighted in the passage. We analyze CoQA in depth and
show that conversational questions have challenging phenomena not present in
existing reading comprehension datasets, e.g., coreference and pragmatic
reasoning. We evaluate strong conversational and reading comprehension models
on CoQA. The best system obtains an F1 score of 65.4%, which is 23.4 points
behind human performance (88.8%), indicating there is ample room for
improvement. We launch CoQA as a challenge to the community at
http://stanfordnlp.github.io/coqa/
QuAC : Question Answering in Context
Authors: Eunsol Choi, He He, Mohit Iyyer, Mark Yatskar, Wen-tau Yih, Yejin Choi, Percy Liang, Luke Zettlemoyer
Date: 2018-08-21 00:00:00
Tasks: Question Answering, Reading Comprehension

We present QuAC, a dataset for Question Answering in Context that contains
14K information-seeking QA dialogs (100K questions in total). The dialogs
involve two crowd workers: (1) a student who poses a sequence of freeform
questions to learn as much as possible about a hidden Wikipedia text, and (2) a
teacher who answers the questions by providing short excerpts from the text.
QuAC introduces challenges not found in existing machine comprehension
datasets: its questions are often more open-ended, unanswerable, or only
meaningful within the dialog context, as we show in a detailed qualitative
evaluation. We also report results for a number of reference models, including
a recently state-of-the-art reading comprehension architecture extended to
model dialog context. Our best model underperforms humans by 20 F1, suggesting
that there is significant room for future work on this data. Dataset, baseline,
and leaderboard available at http://quac.ai.
Hypernetwork Knowledge Graph Embeddings
Authors: Ivana Balažević, Carl Allen, Timothy M. Hospedales
Date: 2018-08-21 00:00:00
Tasks: Knowledge Graph Embeddings, Knowledge Graphs, Link Prediction, Prediction, Relation

Knowledge graphs are graphical representations of large databases of facts, which typically suffer from incompleteness. Inferring missing relations (links) between entities (nodes) is the task of link prediction. A recent state-of-the-art approach to link prediction, ConvE, implements a convolutional neural network to extract features from concatenated subject and relation vectors. Whilst results are impressive, the method is unintuitive and poorly understood. We propose a hypernetwork architecture that generates simplified relation-specific convolutional filters that (i) outperforms ConvE and all previous approaches across standard datasets; and (ii) can be framed as tensor factorization and thus set within a well established family of factorization models for link prediction. We thus demonstrate that convolution simply offers a convenient computational means of introducing sparsity and parameter tying to find an effective trade-off between non-linear expressiveness and the number of parameters to learn.
Real Time Elbow Angle Estimation Using Single RGB Camera
Authors: Muhammad Yahya, Jawad Ali Shah, Arif Warsi, Kushsairy Kadir, Sheroz Khan, M Izani
Date: 2018-08-21 00:00:00

The use of motion capture has increased from last decade in a varied spectrum
of applications like film special effects, controlling games and robots,
rehabilitation system, animations etc. The current human motion capture
techniques use markers, structured environment, and high resolution cameras in
a dedicated environment. Because of rapid movement, elbow angle estimation is
observed as the most difficult problem in human motion capture system. In this
paper, we take elbow angle estimation as our research subject and propose a
novel, markerless and cost-effective solution that uses RGB camera for
estimating elbow angle in real time using part affinity field. We have
recruited five (5) participants to perform cup to mouth movement and at the
same time measured the angle by both RGB camera and Microsoft Kinect. The
experimental results illustrate that markerless and cost-effective RGB camera
has a median RMS errors of 3.06{\deg} and 0.95{\deg} in sagittal and coronal
plane respectively as compared to Microsoft Kinect.
Gaussian Word Embedding with a Wasserstein Distance Loss
Authors: Chi Sun, Hang Yan, Xipeng Qiu, Xuanjing Huang
Date: 2018-08-21 00:00:00
Tasks: Document Classification, General Classification, Word Similarity

Compared with word embedding based on point representation,
distribution-based word embedding shows more flexibility in expressing
uncertainty and therefore embeds richer semantic information when representing
words. The Wasserstein distance provides a natural notion of dissimilarity with
probability measures and has a closed-form solution when measuring the distance
between two Gaussian distributions. Therefore, with the aim of representing
words in a highly efficient way, we propose to operate a Gaussian word
embedding model with a loss function based on the Wasserstein distance. Also,
external information from ConceptNet will be used to semi-supervise the results
of the Gaussian word embedding. Thirteen datasets from the word similarity
task, together with one from the word entailment task, and six datasets from
the downstream document classification task will be evaluated in this paper to
test our hypothesis.
Curse of Heterogeneity: Computational Barriers in Sparse Mixture Models and Phase Retrieval
Authors: Jianqing Fan, Han Liu, Zhaoran Wang, Zhuoran Yang
Date: 2018-08-21 00:00:00
Tasks: Clustering, Retrieval

We study the fundamental tradeoffs between statistical accuracy and
computational tractability in the analysis of high dimensional heterogeneous
data. As examples, we study sparse Gaussian mixture model, mixture of sparse
linear regressions, and sparse phase retrieval model. For these models, we
exploit an oracle-based computational model to establish conjecture-free
computationally feasible minimax lower bounds, which quantify the minimum
signal strength required for the existence of any algorithm that is both
computationally tractable and statistically accurate. Our analysis shows that
there exist significant gaps between computationally feasible minimax risks and
classical ones. These gaps quantify the statistical price we must pay to
achieve computational tractability in the presence of data heterogeneity. Our
results cover the problems of detection, estimation, support recovery, and
clustering, and moreover, resolve several conjectures of Azizyan et al. (2013,
2015); Verzelen and Arias-Castro (2017); Cai et al. (2016). Interestingly, our
results reveal a new but counter-intuitive phenomenon in heterogeneous data
analysis that more data might lead to less computation complexity.
Optimizing the Union of Intersections LASSO ($UoI_{LASSO}$) and Vector Autoregressive ($UoI_{VAR}$) Algorithms for Improved Statistical Estimation at Scale
Authors: Mahesh Balasubramanian, Trevor Ruiz, Brandon Cook, Sharmodeep Bhattacharyya, Prabhat, Aviral Shrivastava, Kristofer Bouchard
Date: 2018-08-21 00:00:00
Tasks: feature selection, Model Selection, Time Series, Time Series Analysis

The analysis of scientific data of increasing size and complexity requires
statistical machine learning methods that are both interpretable and
predictive. Union of Intersections (UoI), a recently developed framework, is a
two-step approach that separates model selection and model estimation. A linear
regression algorithm based on UoI, $UoI_{LASSO}$, simultaneously achieves low
false positives and low false negative feature selection as well as low bias
and low variance estimates. Together, these qualities make the results both
predictive and interpretable. In this paper, we optimize the $UoI_{LASSO}$
algorithm for single-node execution on NERSC's Cori Knights Landing, a Xeon Phi
based supercomputer. We then scale $UoI_{LASSO}$ to execute on cores ranging
from 68-278,528 cores on a range of dataset sizes demonstrating the weak and
strong scaling of the implementation. We also implement a variant of
$UoI_{LASSO}$, $UoI_{VAR}$ for vector autoregressive models, to analyze high
dimensional time-series data. We perform single node optimization and
multi-node scaling experiments for $UoI_{VAR}$ to demonstrate the effectiveness
of the algorithm for weak and strong scaling. Our implementations enable to use
estimate the largest VAR model (1000 nodes) we are aware of, and apply it to
large neurophysiology data 192 nodes).
A Skeleton-Based Model for Promoting Coherence Among Sentences in Narrative Story Generation
Authors: Jingjing Xu, Xuancheng Ren, Yi Zhang, Qi Zeng, Xiaoyan Cai, Xu sun
Date: 2018-08-21 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Reinforcement Learning, Sentence, Story Generation

Narrative story generation is a challenging problem because it demands the
generated sentences with tight semantic connections, which has not been well
studied by most existing generative models. To address this problem, we propose
a skeleton-based model to promote the coherence of generated stories. Different
from traditional models that generate a complete sentence at a stroke, the
proposed model first generates the most critical phrases, called skeleton, and
then expands the skeleton to a complete and fluent sentence. The skeleton is
not manually defined, but learned by a reinforcement learning method. Compared
to the state-of-the-art models, our skeleton-based model can generate
significantly more coherent text according to human evaluation and automatic
evaluation. The G-score is improved by 20.1% in the human evaluation. The code
is available at https://github.com/lancopku/Skeleton-Based-Generation-Model
Smart energy models for atomistic simulations using a DFT-driven multifidelity approach
Authors: Luca Messina, Alessio Quaglino, Alexandra Goryaeva, Mihai-Cosmin Marinica, Christophe Domain, Nicolas Castin, Giovanni Bonny, Rolf Krause
Date: 2018-08-21 00:00:00

The reliability of atomistic simulations depends on the quality of the
underlying energy models providing the source of physical information, for
instance for the calculation of migration barriers in atomistic Kinetic Monte
Carlo simulations. Accurate (high-fidelity) methods are often available, but
since they are usually computationally expensive, they must be replaced by less
accurate (low-fidelity) models that introduce some degrees of approximation.
Machine-learning techniques such as artificial neural networks are usually
employed to work around this limitation and extract the needed parameters from
large databases of high-fidelity data, but the latter are often computationally
expensive to produce. This work introduces an alternative method based on the
multifidelity approach, where correlations between high-fidelity and
low-fidelity outputs are exploited to make an educated guess of the
high-fidelity outcome based only on quick low-fidelity estimations, hence
without the need of running full expensive high-fidelity calculations. With
respect to neural networks, this approach is expected to require less training
data because of the lower amount of fitting parameters involved. The method is
tested on the prediction of ab initio formation and migration energies of
vacancy diffusion in iron-copper alloys, and compared with the neural networks
trained on the same database.
Backpropagation and Biological Plausibility
Authors: Alessandro Betti, Marco Gori, Giuseppe Marra
Date: 2018-08-21 00:00:00

By and large, Backpropagation (BP) is regarded as one of the most important
neural computation algorithms at the basis of the progress in machine learning,
including the recent advances in deep learning. However, its computational
structure has been the source of many debates on its arguable biological
plausibility. In this paper, it is shown that when framing supervised learning
in the Lagrangian framework, while one can see a natural emergence of
Backpropagation, biologically plausible local algorithms can also be devised
that are based on the search for saddle points in the learning adjoint space
composed of weights, neural outputs, and Lagrangian multipliers. This might
open the doors to a truly novel class of learning algorithms where, because of
the introduction of the notion of support neurons, the optimization scheme also
plays a fundamental role in the construction of the architecture.
On a New Improvement-Based Acquisition Function for Bayesian Optimization
Authors: Umberto Noè, Dirk Husmeier
Date: 2018-08-21 00:00:00
Tasks: Bayesian Optimization, global-optimization

Bayesian optimization (BO) is a popular algorithm for solving challenging
optimization tasks. It is designed for problems where the objective function is
expensive to evaluate, perhaps not available in exact form, without gradient
information and possibly returning noisy values. Different versions of the
algorithm vary in the choice of the acquisition function, which recommends the
point to query the objective at next. Initially, researchers focused on
improvement-based acquisitions, while recently the attention has shifted to
more computationally expensive information-theoretical measures. In this paper
we present two major contributions to the literature. First, we propose a new
improvement-based acquisition function that recommends query points where the
improvement is expected to be high with high confidence. The proposed algorithm
is evaluated on a large set of benchmark functions from the global optimization
literature, where it turns out to perform at least as well as current
state-of-the-art acquisition functions, and often better. This suggests that it
is a powerful default choice for BO. The novel policy is then compared to
widely used global optimization solvers in order to confirm that BO methods
reduce the computational costs of the optimization by keeping the number of
function evaluations small. The second main contribution represents an
application to precision medicine, where the interest lies in the estimation of
parameters of a partial differential equations model of the human pulmonary
blood circulation system. Once inferred, these parameters can help clinicians
in diagnosing a patient with pulmonary hypertension without going through the
standard invasive procedure of right heart catheterization, which can lead to
side effects and complications (e.g. severe pain, internal bleeding,
thrombosis).