pwc-archive/papers-with-abstracts huggingface dataset:
Beyond the Self: Using Grounded Affordances to Interpret and Describe Others' Actions
Authors: Giovanni Saponaro, Lorenzo Jamone, Alexandre Bernardino, Giampiero Salvi
Date: 2019-02-26 00:00:00
Tasks: Action Recognition, Temporal Action Localization

We propose a developmental approach that allows a robot to interpret and
describe the actions of human agents by reusing previous experience. The robot
first learns the association between words and object affordances by
manipulating the objects in its environment. It then uses this information to
learn a mapping between its own actions and those performed by a human in a
shared environment. It finally fuses the information from these two models to
interpret and describe human actions in light of its own experience. In our
experiments, we show that the model can be used flexibly to do inference on
different aspects of the scene. We can predict the effects of an action on the
basis of object properties. We can revise the belief that a certain action
occurred, given the observed effects of the human action. In an early action
recognition fashion, we can anticipate the effects when the action has only
been partially observed. By estimating the probability of words given the
evidence and feeding them into a pre-defined grammar, we can generate relevant
descriptions of the scene. We believe that this is a step towards providing
robots with the fundamental skills to engage in social collaboration with
humans.
Learning Implicitly Recurrent CNNs Through Parameter Sharing
Authors: Pedro Savarese, Michael Maire
Date: 2019-02-26 00:00:00
Proceeding: ICLR 2019 5
Tasks: image-classification, Image Classification, Neural Architecture Search

We introduce a parameter sharing scheme, in which different layers of a
convolutional neural network (CNN) are defined by a learned linear combination
of parameter tensors from a global bank of templates. Restricting the number of
templates yields a flexible hybridization of traditional CNNs and recurrent
networks. Compared to traditional CNNs, we demonstrate substantial parameter
savings on standard image classification tasks, while maintaining accuracy.
  Our simple parameter sharing scheme, though defined via soft weights, in
practice often yields trained networks with near strict recurrent structure;
with negligible side effects, they convert into networks with actual loops.
Training these networks thus implicitly involves discovery of suitable
recurrent architectures. Though considering only the design aspect of recurrent
links, our trained networks achieve accuracy competitive with those built using
state-of-the-art neural architecture search (NAS) procedures.
  Our hybridization of recurrent and convolutional networks may also represent
a beneficial architectural bias. Specifically, on synthetic tasks which are
algorithmic in nature, our hybrid networks both train faster and extrapolate
better to test examples outside the span of the training set.
Learning to Sample Hard Instances for Graph Algorithms
Authors: Ryoma Sato, Makoto Yamada, Hisashi Kashima
Date: 2019-02-26 00:00:00
Tasks: Evolutionary Algorithms

Hard instances, which require a long time for a specific algorithm to solve, help (1) analyze the algorithm for accelerating it and (2) build a good benchmark for evaluating the performance of algorithms. There exist several efforts for automatic generation of hard instances. For example, evolutionary algorithms have been utilized to generate hard instances. However, they generate only finite number of hard instances. The merit of such methods is limited because it is difficult to extract meaningful patterns from small number of instances. We seek for a probabilistic generator of hard instances. Once the generative distribution of hard instances is obtained, we can sample a variety of hard instances to build a benchmark, and we can extract meaningful patterns of hard instances from sampled instances. The existing methods for modeling the hard instance distribution rely on parameters or rules that are found by domain experts; however, they are specific to the problem. Hence, it is challenging to model the distribution for general cases. In this paper, we focus on graph problems. We propose HiSampler, the hard instance sampler, to model the hard instance distribution of graph algorithms. HiSampler makes it possible to obtain the distribution of hard instances without hand-engineered features. To the best of our knowledge, this is the first method to learn the distribution of hard instances using machine learning. Through experiments, we demonstrate that our proposed method can generate instances that are a few to several orders of magnitude harder than the random-based approach in many settings. In particular, our method outperforms rule-based algorithms in the 3-coloring problem.
GAN-based Projector for Faster Recovery with Convergence Guarantees in Linear Inverse Problems
Authors: Ankit Raj, Yuqi Li, Yoram Bresler
Date: 2019-02-26 00:00:00
Proceeding: ICCV 2019 10
Tasks: compressed sensing, Generative Adversarial Network, Super-Resolution

A Generative Adversarial Network (GAN) with generator $G$ trained to model the prior of images has been shown to perform better than sparsity-based regularizers in ill-posed inverse problems. Here, we propose a new method of deploying a GAN-based prior to solve linear inverse problems using projected gradient descent (PGD). Our method learns a network-based projector for use in the PGD algorithm, eliminating expensive computation of the Jacobian of $G$. Experiments show that our approach provides a speed-up of $60\text{-}80\times$ over earlier GAN-based recovery methods along with better accuracy. Our main theoretical result is that if the measurement matrix is moderately conditioned on the manifold range($G$) and the projector is $\delta$-approximate, then the algorithm is guaranteed to reach $O(\delta)$ reconstruction error in $O(log(1/\delta))$ steps in the low noise regime. Additionally, we propose a fast method to design such measurement matrices for a given $G$. Extensive experiments demonstrate the efficacy of this method by requiring $5\text{-}10\times$ fewer measurements than random Gaussian measurement matrices for comparable recovery performance. Because the learning of the GAN and projector is decoupled from the measurement operator, our GAN-based projector and recovery algorithm are applicable without retraining to all linear inverse problems, as confirmed by experiments on compressed sensing, super-resolution, and inpainting.
Polyglot Contextual Representations Improve Crosslingual Transfer
Authors: Phoebe Mulcaire, Jungo Kasai, Noah A. Smith
Date: 2019-02-26 00:00:00
Proceeding: NAACL 2019 6
Tasks: Dependency Parsing, Language Modeling, Language Modelling, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Representation Learning, Semantic Role Labeling

We introduce Rosita, a method to produce multilingual contextual word
representations by training a single language model on text from multiple
languages. Our method combines the advantages of contextual word
representations with those of multilingual representation learning. We produce
language models from dissimilar language pairs (English/Arabic and
English/Chinese) and use them in dependency parsing, semantic role labeling,
and named entity recognition, with comparisons to monolingual and
non-contextual variants. Our results provide further evidence for the benefits
of polyglot learning, in which representations are shared across multiple
languages.
Optimal and Fast Real-time Resources Slicing with Deep Dueling Neural Networks
Authors: Nguyen Van Huynh, Dinh Thai Hoang, Diep N. Nguyen, Eryk Dutkiewicz
Date: 2019-02-26 00:00:00
Tasks: Combinatorial Optimization, Q-Learning

Effective network slicing requires an infrastructure/network provider to deal
with the uncertain demand and real-time dynamics of network resource requests.
Another challenge is the combinatorial optimization of numerous resources,
e.g., radio, computing, and storage. This article develops an optimal and fast
real-time resource slicing framework that maximizes the long-term return of the
network provider while taking into account the uncertainty of resource demand
from tenants. Specifically, we first propose a novel system model which enables
the network provider to effectively slice various types of resources to
different classes of users under separate virtual slices. We then capture the
real-time arrival of slice requests by a semi-Markov decision process. To
obtain the optimal resource allocation policy under the dynamics of slicing
requests, e.g., uncertain service time and resource demands, a Q-learning
algorithm is often adopted in the literature. However, such an algorithm is
notorious for its slow convergence, especially for problems with large
state/action spaces. This makes Q-learning practically inapplicable to our case
in which multiple resources are simultaneously optimized. To tackle it, we
propose a novel network slicing approach with an advanced deep learning
architecture, called deep dueling that attains the optimal average reward much
faster than the conventional Q-learning algorithm. This property is especially
desirable to cope with real-time resource requests and the dynamic demands of
users. Extensive simulations show that the proposed framework yields up to 40%
higher long-term average return while being few thousand times faster, compared
with state of the art network slicing approaches.
Optimal Clustering with Missing Values
Authors: Shahin Boluki, Siamak Zamani Dadaneh, Xiaoning Qian, Edward R. Dougherty
Date: 2019-02-26 00:00:00
Tasks: Clustering, Imputation, Missing Values

Missing values frequently arise in modern biomedical studies due to various
reasons, including missing tests or complex profiling technologies for
different omics measurements. Missing values can complicate the application of
clustering algorithms, whose goals are to group points based on some similarity
criterion. A common practice for dealing with missing values in the context of
clustering is to first impute the missing values, and then apply the clustering
algorithm on the completed data.
  We consider missing values in the context of optimal clustering, which finds
an optimal clustering operator with reference to an underlying random labeled
point process (RLPP). We show how the missing-value problem fits neatly into
the overall framework of optimal clustering by incorporating the missing value
mechanism into the random labeled point process and then marginalizing out the
missing-value process. In particular, we demonstrate the proposed framework for
the Gaussian model with arbitrary covariance structures. Comprehensive
experimental studies on both synthetic and real-world RNA-seq data show the
superior performance of the proposed optimal clustering with missing values
when compared to various clustering approaches. Optimal clustering with missing
values obviates the need for imputation-based pre-processing of the data, while
at the same time possessing smaller clustering errors.
QLMC-HD: Quasi Large Margin Classifier based on Hyperdisk
Authors: Hassan Ataeian, Shahriar Esmaeili, Saeideh Roshanfekr, Neda Maleki Khas, Ali Amiri, Hossein Safari
Date: 2019-02-26 00:00:00

In the area of data classification, the different classifiers have been developed by their own strengths and weaknesses. Among these classifiers, we propose a method that is based on the maximum margin between two classes. One of the main challenges in this area is dealt with noisy data. In this paper, our aim is to optimize the method of large margin classifiers based on hyperdisk (LMC-HD) and combine it into a quasisupport vector data description (QSVDD) method. In the proposed method, the bounding hypersphere is calculated based on the QSVDD method. So our convex class model is more robust compared with the support vector machine (SVM) and less tight than LMC-HD. Large margin classifiers aim to maximize the margin and minimizing the risk. Since our proposed method ignores the effect of outliers and noises, so this method has the widest margin compared with other large margin classifiers. In the end, we compare our proposed method with other popular large margin classifiers by the experiments on a set of standard data which indicates our results are more efficient than the others
Self-Selective Correlation Ship Tracking Method for Smart Ocean System
Authors: Xu Kang, Bin Song, Jie Guo, Xiaojiang Du, Mohsen Guizani
Date: 2019-02-26 00:00:00
Tasks: Management, regression

In recent years, with the development of the marine industry, navigation
environment becomes more complicated. Some artificial intelligence
technologies, such as computer vision, can recognize, track and count the
sailing ships to ensure the maritime security and facilitates the management
for Smart Ocean System. Aiming at the scaling problem and boundary effect
problem of traditional correlation filtering methods, we propose a
self-selective correlation filtering method based on box regression (BRCF). The
proposed method mainly include: 1) A self-selective model with negative samples
mining method which effectively reduces the boundary effect in strengthening
the classification ability of classifier at the same time; 2) A bounding box
regression method combined with a key points matching method for the scale
prediction, leading to a fast and efficient calculation. The experimental
results show that the proposed method can effectively deal with the problem of
ship size changes and background interference. The success rates and precisions
were higher than Discriminative Scale Space Tracking (DSST) by over 8
percentage points on the marine traffic dataset of our laboratory. In terms of
processing speed, the proposed method is higher than DSST by nearly 22 Frames
Per Second (FPS).
AntisymmetricRNN: A Dynamical System View on Recurrent Neural Networks
Authors: Bo Chang, Minmin Chen, Eldad Haber, Ed H. Chi
Date: 2019-02-26 00:00:00
Proceeding: ICLR 2019 5
Tasks: Sequential Image Classification

Recurrent neural networks have gained widespread use in modeling sequential
data. Learning long-term dependencies using these models remains difficult
though, due to exploding or vanishing gradients. In this paper, we draw
connections between recurrent networks and ordinary differential equations. A
special form of recurrent networks called the AntisymmetricRNN is proposed
under this theoretical framework, which is able to capture long-term
dependencies thanks to the stability property of its underlying differential
equation. Existing approaches to improving RNN trainability often incur
significant computation overhead. In comparison, AntisymmetricRNN achieves the
same goal by design. We showcase the advantage of this new architecture through
extensive simulations and experiments. AntisymmetricRNN exhibits much more
predictable dynamics. It outperforms regular LSTM models on tasks requiring
long-term memory and matches the performance on tasks where short-term
dependencies dominate despite being much simpler.
Multiscale Gaussian Process Level Set Estimation
Authors: Shubhanshu Shekhar, Tara Javidi
Date: 2019-02-26 00:00:00

In this paper, the problem of estimating the level set of a black-box
function from noisy and expensive evaluation queries is considered. A new
algorithm for this problem in the Bayesian framework with a Gaussian Process
(GP) prior is proposed. The proposed algorithm employs a hierarchical sequence
of partitions to explore different regions of the search space at varying
levels of detail depending upon their proximity to the level set boundary. It
is shown that this approach results in the algorithm having a low complexity
implementation whose computational cost is significantly smaller than the
existing algorithms for higher dimensional search space $\X$. Furthermore, high
probability bounds on a measure of discrepancy between the estimated level set
and the true level set for the the proposed algorithm are obtained, which are
shown to be strictly better than the existing guarantees for a large class of
GPs. In the process, a tighter characterization of the information gain of the
proposed algorithm is obtained which takes into account the structured nature
of the evaluation points. This approach improves upon the existing technique of
bounding the information gain with maximum information gain.
Event-driven Video Frame Synthesis
Authors: Zihao W. Wang, Weixin Jiang, Kuan He, Boxin Shi, Aggelos Katsaggelos, Oliver Cossairt
Date: 2019-02-26 00:00:00
Tasks: Deblurring, Denoising, Video Frame Interpolation, Video Prediction

Temporal Video Frame Synthesis (TVFS) aims at synthesizing novel frames at timestamps different from existing frames, which has wide applications in video codec, editing and analysis. In this paper, we propose a high framerate TVFS framework which takes hybrid input data from a low-speed frame-based sensor and a high-speed event-based sensor. Compared to frame-based sensors, event-based sensors report brightness changes at very high speed, which may well provide useful spatio-temoral information for high framerate TVFS. In our framework, we first introduce a differentiable forward model to approximate the physical sensing process, fusing the two different modes of data as well as unifying a variety of TVFS tasks, i.e., interpolation, prediction and motion deblur. We leverage autodifferentiation which propagates the gradients of a loss defined on the measured data back to the latent high framerate video. We show results with better performance compared to state-of-the-art. Second, we develop a deep learning-based strategy to enhance the results from the first step, which we refer as a residual "denoising" process. Our trained "denoiser" is beyond Gaussian denoising and shows properties such as contrast enhancement and motion awareness. We show that our framework is capable of handling challenging scenes including both fast motion and strong occlusions.
Developing and Using Special-Purpose Lexicons for Cohort Selection from Clinical Notes
Authors: Samarth Rawal, Ashok Prakash, Soumya Adhya, Sidharth Kulkarni, Saadat Anwar, Chitta Baral, Murthy Devarakonda
Date: 2019-02-26 00:00:00
Tasks: BIG-bench Machine Learning

Background and Significance: Selecting cohorts for a clinical trial typically
requires costly and time-consuming manual chart reviews resulting in poor
participation. To help automate the process, National NLP Clinical Challenges
(N2C2) conducted a shared challenge by defining 13 criteria for clinical trial
cohort selection and by providing training and test datasets. This research was
motivated by the N2C2 challenge.
  Methods: We broke down the task into 13 independent subtasks corresponding to
each criterion and implemented subtasks using rules or a supervised machine
learning model. Each task critically depended on knowledge resources in the
form of task-specific lexicons, for which we developed a novel model-driven
approach. The approach allowed us to first expand the lexicon from a seed set
and then remove noise from the list, thus improving the accuracy.
  Results: Our system achieved an overall F measure of 0.9003 at the challenge,
and was statistically tied for the first place out of 45 participants. The
model-driven lexicon development and further debugging the rules/code on the
training set improved overall F measure to 0.9140, overtaking the best
numerical result at the challenge.
  Discussion: Cohort selection, like phenotype extraction and classification,
is amenable to rule-based or simple machine learning methods, however, the
lexicons involved, such as medication names or medical terms referring to a
medical problem, critically determine the overall accuracy. Automated lexicon
development has the potential for scalability and accuracy.
Bootstrapping Domain-Specific Content Discovery on the Web
Authors: Kien Pham, Aécio Santos, Juliana Freire
Date: 2019-02-25 00:00:00

The ability to continuously discover domain-specific content from the Web is
critical for many applications. While focused crawling strategies have been
shown to be effective for discovery, configuring a focused crawler is difficult
and time-consuming. Given a domain of interest $D$, subject-matter experts
(SMEs) must search for relevant websites and collect a set of representative
Web pages to serve as training examples for creating a classifier that
recognizes pages in $D$, as well as a set of pages to seed the crawl. In this
paper, we propose DISCO, an approach designed to bootstrap domain-specific
search. Given a small set of websites, DISCO aims to discover a large
collection of relevant websites. DISCO uses a ranking-based framework that
mimics the way users search for information on the Web: it iteratively
discovers new pages, distills, and ranks them. It also applies multiple
discovery strategies, including keyword-based and related queries issued to
search engines, backward and forward crawling. By systematically combining
these strategies, DISCO is able to attain high harvest rates and coverage for a
variety of domains. We perform extensive experiments in four social-good
domains, using data gathered by SMEs in the respective domains, and show that
our approach is effective and outperforms state-of-the-art methods.
Predicting the Type and Target of Offensive Posts in Social Media
Authors: Marcos Zampieri, Shervin Malmasi, Preslav Nakov, Sara Rosenthal, Noura Farra, Ritesh Kumar
Date: 2019-02-25 00:00:00
Proceeding: NAACL 2019 6
Tasks: Language Identification, Vocal Bursts Type Prediction

As offensive content has become pervasive in social media, there has been
much research in identifying potentially offensive messages. However, previous
work on this topic did not consider the problem as a whole, but rather focused
on detecting very specific types of offensive content, e.g., hate speech,
cyberbulling, or cyber-aggression. In contrast, here we target several
different kinds of offensive content. In particular, we model the task
hierarchically, identifying the type and the target of offensive messages in
social media. For this purpose, we complied the Offensive Language
Identification Dataset (OLID), a new dataset with tweets annotated for
offensive content using a fine-grained three-layer annotation scheme, which we
make publicly available. We discuss the main similarities and differences
between OLID and pre-existing datasets for hate speech identification,
aggression detection, and similar tasks. We further experiment with and we
compare the performance of different machine learning models on OLID.
Detecting Lesion Bounding Ellipses With Gaussian Proposal Networks
Authors: Yi Li
Date: 2019-02-25 00:00:00
Tasks: Computed Tomography (CT), Lesion Detection, Object Localization, Region Proposal

Lesions characterized by computed tomography (CT) scans, are arguably often
elliptical objects. However, current lesion detection systems are predominantly
adopted from the popular Region Proposal Networks (RPNs) that only propose
bounding boxes without fully leveraging the elliptical geometry of lesions. In
this paper, we present Gaussian Proposal Networks (GPNs), a novel extension to
RPNs, to detect lesion bounding ellipses. Instead of directly regressing the
rotation angle of the ellipse as the common practice, GPN represents bounding
ellipses as 2D Gaussian distributions on the image plain and minimizes the
Kullback-Leibler (KL) divergence between the proposed Gaussian and the ground
truth Gaussian for object localization. We show the KL divergence loss
approximately incarnates the regression loss in the RPN framework when the
rotation angle is 0. Experiments on the DeepLesion dataset show that GPN
significantly outperforms RPN for lesion bounding ellipse detection thanks to
lower localization error. GPN is open sourced at
https://github.com/baidu-research/GPN