pwc-archive/papers-with-abstracts huggingface dataset:
Combining Model-based and Model-free RL via Multi-step Control Variates
Authors: Tong Che, Yuchen Lu, George Tucker, Surya Bhupatiraju, Shane Gu, Sergey Levine, Yoshua Bengio
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: continuous-control, Continuous Control, Deep Reinforcement Learning, model, MuJoCo, OpenAI Gym, Reinforcement Learning

Model-free deep reinforcement learning algorithms are able to successfully solve a wide range of continuous control tasks, but typically require many on-policy samples to achieve good performance. Model-based RL algorithms are sample-efficient on the other hand, while learning accurate global models of complex dynamic environments has turned out to be tricky in practice, which leads to the unsatisfactory performance of the learned policies. In this work, we combine the sample-efficiency of model-based algorithms and the accuracy of model-free algorithms. We leverage multi-step neural network based predictive models by embedding real trajectories into imaginary rollouts of the model, and use the imaginary cumulative rewards as control variates for model-free algorithms. In this way, we achieved the strengths of both sides and derived an estimator which is not only sample-efficient, but also unbiased and of very low variance. We present our evaluation on the MuJoCo and OpenAI Gym benchmarks.
Tree2Tree Learning with Memory Unit
Authors: Ning Miao, Hengliang Wang, Ran Le, Chongyang Tao, Mingyue Shang, Rui Yan, Dongyan Zhao
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: Decoder, Machine Translation, Translation

Traditional recurrent neural network (RNN) or convolutional neural net- work (CNN) based sequence-to-sequence model can not handle tree structural data well. To alleviate this problem, in this paper, we propose a tree-to-tree model with specially designed encoder unit and decoder unit, which recursively encodes tree inputs into highly folded tree embeddings and decodes the embeddings into tree outputs. Our model could represent the complex information of a tree while also restore a tree from embeddings.
We evaluate our model in random tree recovery task and neural machine translation task. Experiments show that our model outperforms the baseline model.
Revisiting Knowledge Base Embedding as Tensor Decomposition
Authors: Jiezhong Qiu, Hao Ma, Yuxiao Dong, Kuansan Wang, Jie Tang
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: Link Prediction, Tensor Decomposition

We study the problem of knowledge base (KB) embedding, which is usually addressed through two frameworks---neural KB embedding and tensor decomposition. In this work, we theoretically analyze the neural embedding framework and subsequently connect it with tensor based embedding. Specifically, we show that in neural KB embedding the two commonly adopted optimization solutions---margin-based and negative sampling losses---are closely related to each other. We also reach the closed-form tensor that is implicitly approximated by popular neural KB approaches, revealing the underlying connection between neural and tensor based KB embedding models. Grounded in the theoretical results, we further present a tensor decomposition based framework KBTD to directly approximate the derived closed form tensor. Under this framework, the neural KB embedding models, such as NTN, TransE, Bilinear, and DISTMULT, are unified into a general tensor optimization architecture. Finally, we conduct experiments on the link prediction task in WordNet and Freebase, empirically demonstrating the effectiveness of the KBTD framework.
Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond
Authors: Mikel Artetxe, Holger Schwenk
Date: 2018-12-26 00:00:00
Proceeding: TACL 2019 3
Tasks: Cross-Lingual Bitext Mining, Cross-Lingual Document Classification, Cross-Lingual Natural Language Inference, Cross-Lingual Transfer, Decoder, Document Classification, Joint Multilingual Sentence Representations, Natural Language Inference, Parallel Corpus Mining, Sentence, Sentence Embeddings, Zero-Shot Cross-Lingual Transfer

We introduce an architecture to learn joint multilingual sentence representations for 93 languages, belonging to more than 30 different families and written in 28 different scripts. Our system uses a single BiLSTM encoder with a shared BPE vocabulary for all languages, which is coupled with an auxiliary decoder and trained on publicly available parallel corpora. This enables us to learn a classifier on top of the resulting embeddings using English annotated data only, and transfer it to any of the 93 languages without any modification. Our experiments in cross-lingual natural language inference (XNLI dataset), cross-lingual document classification (MLDoc dataset) and parallel corpus mining (BUCC dataset) show the effectiveness of our approach. We also introduce a new test set of aligned sentences in 112 languages, and show that our sentence embeddings obtain strong results in multilingual similarity search even for low-resource languages. Our implementation, the pre-trained encoder and the multilingual test set are available at https://github.com/facebookresearch/LASER
Structure Learning of Sparse GGMs over Multiple Access Networks
Authors: Mostafa Tavassolipour, Armin Karamzade, Reza Mirzaeifard, Seyed Abolfazl Motahari, Mohammad-Taghi Manzuri Shalmani
Date: 2018-12-26 00:00:00

A central machine is interested in estimating the underlying structure of a
sparse Gaussian Graphical Model (GGM) from datasets distributed across multiple
local machines. The local machines can communicate with the central machine
through a wireless multiple access channel. In this paper, we are interested in
designing effective strategies where reliable learning is feasible under power
and bandwidth limitations. Two approaches are proposed: Signs and Uncoded
methods. In Signs method, the local machines quantize their data into binary
vectors and an optimal channel coding scheme is used to reliably send the
vectors to the central machine where the structure is learned from the received
data. In Uncoded method, data symbols are scaled and transmitted through the
channel. The central machine uses the received noisy symbols to recover the
structure. Theoretical results show that both methods can recover the structure
with high probability for large enough sample size. Experimental results
indicate the superiority of Signs method over Uncoded method under several
circumstances.
Large Multistream Data Analytics for Monitoring and Diagnostics in Manufacturing Systems
Authors: Samaneh Ebrahimi, Chitta Ranjan, Kamran Paynabar
Date: 2018-12-26 00:00:00
Tasks: compressed sensing, Diagnostic

The high-dimensionality and volume of large scale multistream data has
inhibited significant research progress in developing an integrated monitoring
and diagnostics (M&D) approach. This data, also categorized as big data, is
becoming common in manufacturing plants. In this paper, we propose an
integrated M\&D approach for large scale streaming data. We developed a novel
monitoring method named Adaptive Principal Component monitoring (APC) which
adaptively chooses PCs that are most likely to vary due to the change for early
detection. Importantly, we integrate a novel diagnostic approach, Principal
Component Signal Recovery (PCSR), to enable a streamlined SPC. This diagnostics
approach draws inspiration from Compressed Sensing and uses Adaptive Lasso for
identifying the sparse change in the process. We theoretically motivate our
approaches and do a performance evaluation of our integrated M&D method through
simulations and case studies.
Stochastic Trust Region Inexact Newton Method for Large-scale Machine Learning
Authors: Vinod Kumar Chauhan, Anuj Sharma, Kalpana Dahiya
Date: 2018-12-26 00:00:00
Tasks: BIG-bench Machine Learning, Second-order methods

Nowadays stochastic approximation methods are one of the major research direction to deal with the large-scale machine learning problems. From stochastic first order methods, now the focus is shifting to stochastic second order methods due to their faster convergence and availability of computing resources. In this paper, we have proposed a novel Stochastic Trust RegiOn Inexact Newton method, called as STRON, to solve large-scale learning problems which uses conjugate gradient (CG) to inexactly solve trust region subproblem. The method uses progressive subsampling in the calculation of gradient and Hessian values to take the advantage of both, stochastic and full-batch regimes. We have extended STRON using existing variance reduction techniques to deal with the noisy gradients and using preconditioned conjugate gradient (PCG) as subproblem solver, and empirically proved that they do not work as expected, for the large-scale learning problems. Finally, our empirical results prove efficacy of the proposed method against existing methods with bench marked datasets.
ECG Segmentation by Neural Networks: Errors and Correction
Authors: Iana Sereda, Sergey Alekseev, Aleksandra Koneva, Roman Kataev, Grigory Osipov
Date: 2018-12-26 00:00:00
Tasks: Electrocardiography (ECG)

In this study we examined the question of how error correction occurs in an
ensemble of deep convolutional networks, trained for an important applied
problem: segmentation of Electrocardiograms(ECG). We also explore the
possibility of using the information about ensemble errors to evaluate a
quality of data representation, built by the network. This possibility arises
from the effect of distillation of outliers, which was demonstarted for the
ensemble, described in this paper.
A Poisson-Gaussian Denoising Dataset with Real Fluorescence Microscopy Images
Authors: Yide Zhang, Yinhao Zhu, Evan Nichols, Qingfei Wang, Si-Yuan Zhang, Cody Smith, Scott Howard
Date: 2018-12-26 00:00:00
Proceeding: CVPR 2019 6
Tasks: Denoising

Fluorescence microscopy has enabled a dramatic development in modern biology.
Due to its inherently weak signal, fluorescence microscopy is not only much
noisier than photography, but also presented with Poisson-Gaussian noise where
Poisson noise, or shot noise, is the dominating noise source. To get clean
fluorescence microscopy images, it is highly desirable to have effective
denoising algorithms and datasets that are specifically designed to denoise
fluorescence microscopy images. While such algorithms exist, no such datasets
are available. In this paper, we fill this gap by constructing a dataset - the
Fluorescence Microscopy Denoising (FMD) dataset - that is dedicated to
Poisson-Gaussian denoising. The dataset consists of 12,000 real fluorescence
microscopy images obtained with commercial confocal, two-photon, and wide-field
microscopes and representative biological samples such as cells, zebrafish, and
mouse brain tissues. We use image averaging to effectively obtain ground truth
images and 60,000 noisy images with different noise levels. We use this dataset
to benchmark 10 representative denoising algorithms and find that deep learning
methods have the best performance. To our knowledge, this is the first real
microscopy image dataset for Poisson-Gaussian denoising purposes and it could
be an important tool for high-quality, real-time denoising applications in
biomedical research.
A Review on The Use of Deep Learning in Android Malware Detection
Authors: Abdelmonim Naway, Yuancheng LI
Date: 2018-12-26 00:00:00
Tasks: Android Malware Detection, Malware Detection

Android is the predominant mobile operating system for the past few years.
The prevalence of devices that can be powered by Android magnetized not merely
application developers but also malware developers with criminal intention to
design and spread malicious applications that can affect the normal work of
Android phones and tablets, steal personal information and credential data, or
even worse lock the phone and ask for ransom. Researchers persistently devise
countermeasures strategies to fight back malware. One of these strategies
applied in the past five years is the use of deep learning methods in Android
malware detection. This necessitates a review to inspect the accomplished work
in order to know where the endeavors have been established, identify unresolved
problems, and motivate future research directions. In this work, an extensive
survey of static analysis, dynamic analysis, and hybrid analysis that utilized
deep learning methods are reviewed with an elaborated discussion on their key
concepts, contributions, and limitations.
Informative Object Annotations: Tell Me Something I Don't Know
Authors: Lior Bracha, Gal Chechik
Date: 2018-12-26 00:00:00
Proceeding: CVPR 2019 6
Tasks: Informativeness, Object

Capturing the interesting components of an image is a key aspect of image
understanding. When a speaker annotates an image, selecting labels that are
informative greatly depends on the prior knowledge of a prospective listener.
Motivated by cognitive theories of categorization and communication, we present
a new unsupervised approach to model this prior knowledge and quantify the
informativeness of a description. Specifically, we compute how knowledge of a
label reduces uncertainty over the space of labels and utilize this to rank
candidate labels for describing an image. While the full estimation problem is
intractable, we describe an efficient algorithm to approximate entropy
reduction using a tree-structured graphical model. We evaluate our approach on
the open-images dataset using a new evaluation set of 10K ground-truth ratings
and find that it achieves ~65% agreement with human raters, largely
outperforming other unsupervised baseline approaches.
Quantized-Dialog Language Model for Goal-Oriented Conversational Systems
Authors: R. Chulaka Gunasekara, David Nahamoo, Lazaros C. Polymenakos, Jatin Ganhotra, Kshitij P. Fadnis
Date: 2018-12-26 00:00:00
Tasks: Dialog Learning, Goal-Oriented Dialog, Language Modeling, Language Modelling

We propose a novel methodology to address dialog learning in the context of
goal-oriented conversational systems. The key idea is to quantize the dialog
space into clusters and create a language model across the clusters, thus
allowing for an accurate choice of the next utterance in the conversation. The
language model relies on n-grams associated with clusters of utterances. This
quantized-dialog language model methodology has been applied to the end-to-end
goal-oriented track of the latest Dialog System Technology Challenges (DSTC6).
The objective is to find the correct system utterance from a pool of candidates
in order to complete a dialog between a user and an automated
restaurant-reservation system. Our results show that the technique proposed in
this paper achieves high accuracy regarding selection of the correct candidate
utterance, and outperforms other state-of-the-art approaches based on neural
networks.
Learning Not to Learn: Training Deep Neural Networks with Biased Data
Authors: Byungju Kim, Hyunwoo Kim, Kyung-Su Kim, Sungjin Kim, Junmo Kim
Date: 2018-12-26 00:00:00
Proceeding: CVPR 2019 6

We propose a novel regularization algorithm to train deep neural networks, in
which data at training time is severely biased. Since a neural network
efficiently learns data distribution, a network is likely to learn the bias
information to categorize input data. It leads to poor performance at test
time, if the bias is, in fact, irrelevant to the categorization. In this paper,
we formulate a regularization loss based on mutual information between feature
embedding and bias. Based on the idea of minimizing this mutual information, we
propose an iterative algorithm to unlearn the bias information. We employ an
additional network to predict the bias distribution and train the network
adversarially against the feature embedding network. At the end of learning,
the bias prediction network is not able to predict the bias not because it is
poorly trained, but because the feature embedding network successfully unlearns
the bias information. We also demonstrate quantitative and qualitative
experimental results which show that our algorithm effectively removes the bias
information from feature embedding.
Region Proposal Networks with Contextual Selective Attention for Real-Time Organ Detection
Authors: Awais Mansoor, Antonio R. Porras, Marius George Linguraru
Date: 2018-12-26 00:00:00
Tasks: object-detection, Object Detection, Organ Detection, Region Proposal

State-of-the-art methods for object detection use region proposal networks
(RPN) to hypothesize object location. These networks simultaneously predicts
object bounding boxes and \emph{objectness} scores at each location in the
image. Unlike natural images for which RPN algorithms were originally designed,
most medical images are acquired following standard protocols, thus organs in
the image are typically at a similar location and possess similar geometrical
characteristics (e.g. scale, aspect-ratio, etc.). Therefore, medical image
acquisition protocols hold critical localization and geometric information that
can be incorporated for faster and more accurate detection. This paper presents
a novel attention mechanism for the detection of organs by incorporating
imaging protocol information. Our novel selective attention approach (i)
effectively shrinks the search space inside the feature map, (ii) appends
useful localization information to the hypothesized proposal for the detection
architecture to learn where to look for each organ, and (iii) modifies the
pyramid of regression references in the RPN by incorporating organ- and
modality-specific information, which results in additional time reduction. We
evaluated the proposed framework on a dataset of 768 chest X-ray images
obtained from a diverse set of sources. Our results demonstrate superior
performance for the detection of the lung field compared to the
state-of-the-art, both in terms of detection accuracy, demonstrating an
improvement of $>7\%$ in Dice score, and reduced processing time by $27.53\%$
due to fewer hypotheses.
A Multi-Stream Convolutional Neural Network Framework for Group Activity Recognition
Authors: Sina Mokhtarzadeh Azar, Mina Ghadimi Atigh, Ahmad Nickabadi
Date: 2018-12-26 00:00:00
Tasks: Activity Recognition, Group Activity Recognition, Pose Estimation

In this work, we present a framework based on multi-stream convolutional
neural networks (CNNs) for group activity recognition. Streams of CNNs are
separately trained on different modalities and their predictions are fused at
the end. Each stream has two branches to predict the group activity based on
person and scene level representations. A new modality based on the human pose
estimation is presented to add extra information to the model. We evaluate our
method on the Volleyball and Collective Activity datasets. Experimental results
show that the proposed framework is able to achieve state-of-the-art results
when multiple or single frames are given as input to the model with 90.50% and
86.61% accuracy on Volleyball dataset, respectively, and 87.01% accuracy of
multiple frames group activity on Collective Activity dataset.
Structure-Aware 3D Hourglass Network for Hand Pose Estimation from Single Depth Image
Authors: Fuyang Huang, Ailing Zeng, Minhao Liu, Jing Qin, Qiang Xu
Date: 2018-12-26 00:00:00
Tasks: Hand Pose Estimation, Pose Estimation

In this paper, we propose a novel structure-aware 3D hourglass network for
hand pose estimation from a single depth image, which achieves state-of-the-art
results on MSRA and NYU datasets. Compared to existing works that perform
image-to-coordination regression, our network takes 3D voxel as input and
directly regresses 3D heatmap for each joint. To be specific, we use hourglass
network as our backbone network and modify it into 3D form. We explicitly model
tree-like finger bone into the network as well as in the loss function in an
end-to-end manner, in order to take the skeleton constraints into
consideration. Final estimation can then be easily obtained from voxel density
map with simple post-processing. Experimental results show that the proposed
structure-aware 3D hourglass network is able to achieve a mean joint error of
7.4 mm in MSRA and 8.9 mm in NYU datasets, respectively.
DBpedia NIF: Open, Large-Scale and Multilingual Knowledge Extraction Corpus
Authors: Milan Dojchinovski, Julio Hernandez, Markus Ackermann, Amit Kirschenbaum, Sebastian Hellmann
Date: 2018-12-26 00:00:00
Tasks: Articles

In the past decade, the DBpedia community has put significant amount of
effort on developing technical infrastructure and methods for efficient
extraction of structured information from Wikipedia. These efforts have been
primarily focused on harvesting, refinement and publishing semi-structured
information found in Wikipedia articles, such as information from infoboxes,
categorization information, images, wikilinks and citations. Nevertheless,
still vast amount of valuable information is contained in the unstructured
Wikipedia article texts. In this paper, we present DBpedia NIF - a large-scale
and multilingual knowledge extraction corpus. The aim of the dataset is
two-fold: to dramatically broaden and deepen the amount of structured
information in DBpedia, and to provide large-scale and multilingual language
resource for development of various NLP and IR task. The dataset provides the
content of all articles for 128 Wikipedia languages. We describe the dataset
creation process and the NLP Interchange Format (NIF) used to model the
content, links and the structure the information of the Wikipedia articles. The
dataset has been further enriched with about 25% more links and selected
partitions published as Linked Data. Finally, we describe the maintenance and
sustainability plans, and selected use cases of the dataset from the TextExt
knowledge extraction challenge.
Spotting Micro-Expressions on Long Videos Sequences
Authors: Jingting Li, Catherine Soladie, Renaud Sguier, Su-Jing Wang, Moi Hoon Yap
Date: 2018-12-26 00:00:00
Tasks: General Classification, Micro-Expression Spotting

This paper presents baseline results for the first Micro-Expression Spotting Challenge 2019 by evaluating local temporal pattern (LTP) on SAMM and CAS(ME)2. The proposed LTP patterns are extracted by applying PCA in a temporal window on several facial local regions. The micro-expression sequences are then spotted by a local classification of LTP and a global fusion. The performance is evaluated by Leave-One-Subject-Out cross validation. Furthermore, we define the criteria of determining true positives in one video by overlap rate and set the metric F1-score for spotting performance of the whole database. The F1-score of baseline results for SAMM and CAS(ME)2 are 0.0316 and 0.0179, respectively.