pwc-archive/papers-with-abstracts huggingface dataset:
Interpretable Deep Learning in Drug Discovery
Authors: Kristina Preuer, Günter Klambauer, Friedrich Rippmann, Sepp Hochreiter, Thomas Unterthiner
Date: 2019-03-07 00:00:00
Tasks: Deep Learning, Drug Discovery

Without any means of interpretation, neural networks that predict molecular
properties and bioactivities are merely black boxes. We will unravel these
black boxes and will demonstrate approaches to understand the learned
representations which are hidden inside these models. We show how single
neurons can be interpreted as classifiers which determine the presence or
absence of pharmacophore- or toxicophore-like structures, thereby generating
new insights and relevant knowledge for chemistry, pharmacology and
biochemistry. We further discuss how these novel pharmacophores/toxicophores
can be determined from the network by identifying the most relevant components
of a compound for the prediction of the network. Additionally, we propose a
method which can be used to extract new pharmacophores from a model and will
show that these extracted structures are consistent with literature findings.
We envision that having access to such interpretable knowledge is a crucial aid
in the development and design of new pharmaceutically active molecules, and
helps to investigate and understand failures and successes of current methods.
GRATIS: GeneRAting TIme Series with diverse and controllable characteristics
Authors: Yanfei Kang, Rob J. Hyndman, Feng Li
Date: 2019-03-07 00:00:00
Tasks: Benchmarking, Clustering, Diversity, General Classification, Time Series, Time Series Analysis, Time Series Forecasting, Time Series Generation

The explosion of time series data in recent years has brought a flourish of new time series analysis methods, for forecasting, clustering, classification and other tasks. The evaluation of these new methods requires either collecting or simulating a diverse set of time series benchmarking data to enable reliable comparisons against alternative approaches. We propose GeneRAting TIme Series with diverse and controllable characteristics, named GRATIS, with the use of mixture autoregressive (MAR) models. We simulate sets of time series using MAR models and investigate the diversity and coverage of the generated time series in a time series feature space. By tuning the parameters of the MAR models, GRATIS is also able to efficiently generate new time series with controllable features. In general, as a costless surrogate to the traditional data collection approach, GRATIS can be used as an evaluation tool for tasks such as time series forecasting and classification. We illustrate the usefulness of our time series generation process through a time series forecasting application.
Doubly Aligned Incomplete Multi-view Clustering
Authors: Menglei Hu, Songcan Chen
Date: 2019-03-07 00:00:00
Tasks: Clustering, Incomplete multi-view clustering, regression

Nowadays, multi-view clustering has attracted more and more attention. To
date, almost all the previous studies assume that views are complete. However,
in reality, it is often the case that each view may contain some missing
instances. Such incompleteness makes it impossible to directly use traditional
multi-view clustering methods. In this paper, we propose a Doubly Aligned
Incomplete Multi-view Clustering algorithm (DAIMC) based on weighted
semi-nonnegative matrix factorization (semi-NMF). Specifically, on the one
hand, DAIMC utilizes the given instance alignment information to learn a common
latent feature matrix for all the views. On the other hand, DAIMC establishes a
consensus basis matrix with the help of $L_{2,1}$-Norm regularized regression
for reducing the influence of missing instances. Consequently, compared with
existing methods, besides inheriting the strength of semi-NMF with ability to
handle negative entries, DAIMC has two unique advantages: 1) solving the
incomplete view problem by introducing a respective weight matrix for each
view, making it able to easily adapt to the case with more than two views; 2)
reducing the influence of view incompleteness on clustering by enforcing the
basis matrices of individual views being aligned with the help of regression.
Experiments on four real-world datasets demonstrate its advantages.
Arabic natural language processing: An overview
Authors: Imane Guellil, Houda Saâdane, Faical Azouaou, Billel Gueni, Damien Nouvel
Date: 2019-03-07 00:00:00
Tasks: Survey

Arabic is recognised as the 4th most used language of the Internet. Arabic
has three main varieties: (1) classical Arabic (CA), (2) Modern Standard Arabic
(MSA), (3) Arabic Dialect (AD). MSA and AD could be written either in Arabic or
in Roman script (Arabizi), which corresponds to Arabic written with Latin
letters, numerals and punctuation. Due to the complexity of this language and
the number of corresponding challenges for NLP, many surveys have been
conducted, in order to synthesise the work done on Arabic. However these
surveys principally focus on two varieties of Arabic (MSA and AD, written in
Arabic letters only), they are slightly old (no such survey since 2015) and
therefore do not cover recent resources and tools. To bridge the gap, we
propose a survey focusing on 90 recent research papers (74% of which were
published after 2015). Our study presents and classifies the work done on the
three varieties of Arabic, by concentrating on both Arabic and Arabizi, and
associates each work to its publicly available resources whenever available.
Hair Segmentation on Time-of-Flight RGBD Images
Authors: Yuanxi Ma, Cen Wang, Shiying Li, Jingyi Yu
Date: 2019-03-07 00:00:00

Robust segmentation of hair from portrait images remains challenging: hair does not conform to a uniform shape, style or even color; dark hair in particular lacks features. We present a novel computational imaging solution that tackles the problem from both input and processing fronts. We explore using Time-of-Flight (ToF) RGBD sensors on recent mobile devices. We first conduct a comprehensive analysis to show that scattering and inter-reflection cause different noise patterns on hair vs. non-hair regions on ToF images, by changing the light path and/or combining multiple paths. We then develop a deep network based approach that employs both ToF depth map and the RGB gradient maps to produce an initial hair segmentation with labeled hair components. We then refine the result by imposing ToF noise prior under the conditional random field. We collect the first ToF RGBD hair dataset with 20k+ head images captured on 30 human subjects with a variety of hairstyles at different view angles. Comprehensive experiments show that our approach outperforms the RGB based techniques in accuracy and robustness and can handle traditionally challenging cases such as dark hair, similar hair/background, similar hair/foreground, etc.
Using DP Towards A Shortest Path Problem-Related Application
Authors: Jianhao Jiao, Rui Fan, Han Ma, Ming Liu
Date: 2019-03-07 00:00:00
Tasks: Autonomous Driving, Lane Detection

The detection of curved lanes is still challenging for autonomous driving
systems. Although current cutting-edge approaches have performed well in real
applications, most of them are based on strict model assumptions. Similar to
other visual recognition tasks, lane detection can be formulated as a
two-dimensional graph searching problem, which can be solved by finding several
optimal paths along with line segments and boundaries. In this paper, we
present a directed graph model, in which dynamic programming is used to deal
with a specific shortest path problem. This model is particularly suitable to
represent objects with long continuous shape structure, e.g., lanes and roads.
We apply the designed model and proposed an algorithm for detecting lanes by
formulating it as the shortest path problem. To evaluate the performance of our
proposed algorithm, we tested five sequences (including 1573 frames) from the
KITTI database. The results showed that our method achieves an average
successful detection precision of 97.5%.
On Transformations in Stochastic Gradient MCMC
Authors: Soma Yokoi, Takuma Otsuka, Issei Sato
Date: 2019-03-07 00:00:00

Stochastic gradient Langevin dynamics (SGLD) is a computationally efficient sampler for Bayesian posterior inference given a large scale dataset. Although SGLD is designed for unbounded random variables, many practical models incorporate variables with boundaries such as non-negative ones or those in a finite interval. To bridge this gap, we consider mapping unbounded samples into the target interval. This paper reveals that several mapping approaches commonly used in the literature produces erroneous samples from theoretical and empirical perspectives. We show that the change of random variable using an invertible Lipschitz mapping function overcomes the pitfall as well as attains the weak convergence. Experiments demonstrate its efficacy for widely-used models with bounded latent variables including Bayesian non-negative matrix factorization and binary neural networks.
CE-Net: Context Encoder Network for 2D Medical Image Segmentation
Authors: Zaiwang Gu, Jun Cheng, Huazhu Fu, Kang Zhou, Huaying Hao, Yitian Zhao, Tianyang Zhang, Shenghua Gao, Jiang Liu
Date: 2019-03-07 00:00:00
Tasks: Cell Segmentation, Decoder, Image Segmentation, Medical Image Analysis, Medical Image Segmentation, Optic Disc Segmentation, Segmentation, Semantic Segmentation, Vessel Detection

Medical image segmentation is an important step in medical image analysis.
With the rapid development of convolutional neural network in image processing,
deep learning has been used for medical image segmentation, such as optic disc
segmentation, blood vessel detection, lung segmentation, cell segmentation,
etc. Previously, U-net based approaches have been proposed. However, the
consecutive pooling and strided convolutional operations lead to the loss of
some spatial information. In this paper, we propose a context encoder network
(referred to as CE-Net) to capture more high-level information and preserve
spatial information for 2D medical image segmentation. CE-Net mainly contains
three major components: a feature encoder module, a context extractor and a
feature decoder module. We use pretrained ResNet block as the fixed feature
extractor. The context extractor module is formed by a newly proposed dense
atrous convolution (DAC) block and residual multi-kernel pooling (RMP) block.
We applied the proposed CE-Net to different 2D medical image segmentation
tasks. Comprehensive results show that the proposed method outperforms the
original U-Net method and other state-of-the-art methods for optic disc
segmentation, vessel detection, lung segmentation, cell contour segmentation
and retinal optical coherence tomography layer segmentation.
Integrating neural networks into the blind deblurring framework to compete with the end-to-end learning-based methods
Authors: Junde Wu, Xiaoguang Di, Jiehao Huang, Yu Zhang
Date: 2019-03-07 00:00:00
Tasks: Deblurring

Recently, end-to-end learning-based methods based on deep neural network (DNN) have been proven effective for blind deblurring. Without human-made assumptions and numerical algorithms, they are able to restore images with fewer artifacts and better perceptual quality. However, in practice, we also find some of their drawbacks. Without the theoretical guidance, these methods can not perform well when the motion is complex and sometimes generate unreasonable results. In this paper, for overcoming these drawbacks, we integrate deep convolution neural networks into conventional deblurring framework. Specifically, we build Stacked Estimation Residual Net (SEN) to estimate the motion flow map and Recurrent Prior Generative and Adversarial Net (RP-GAN) to learn the implicit image prior in the optimization model. Comparing with state-of-the-art end-to-end learning-based methods, our method restores reasonable details and shows better generalization ability.
Graphical Contrastive Losses for Scene Graph Parsing
Authors: Ji Zhang, Kevin J. Shih, Ahmed Elgammal, Andrew Tao, Bryan Catanzaro
Date: 2019-03-07 00:00:00
Proceeding: CVPR 2019 6
Tasks: Relationship Detection, Scene Graph Generation, Visual Relationship Detection

Most scene graph parsers use a two-stage pipeline to detect visual relationships: the first stage detects entities, and the second predicts the predicate for each entity pair using a softmax distribution. We find that such pipelines, trained with only a cross entropy loss over predicate classes, suffer from two common errors. The first, Entity Instance Confusion, occurs when the model confuses multiple instances of the same type of entity (e.g. multiple cups). The second, Proximal Relationship Ambiguity, arises when multiple subject-predicate-object triplets appear in close proximity with the same predicate, and the model struggles to infer the correct subject-object pairings (e.g. mis-pairing musicians and their instruments). We propose a set of contrastive loss formulations that specifically target these types of errors within the scene graph parsing problem, collectively termed the Graphical Contrastive Losses. These losses explicitly force the model to disambiguate related and unrelated instances through margin constraints specific to each type of confusion. We further construct a relationship detector, called RelDN, using the aforementioned pipeline to demonstrate the efficacy of our proposed losses. Our model outperforms the winning method of the OpenImages Relationship Detection Challenge by 4.7\% (16.5\% relative) on the test set. We also show improved results over the best previous methods on the Visual Genome and Visual Relationship Detection datasets.
Can Sophisticated Dispatching Strategy Acquired by Reinforcement Learning? - A Case Study in Dynamic Courier Dispatching System
Authors: Yujie Chen, Yu Qian, Yichen Yao, Zili Wu, Rongqi Li, Yinzhi Zhou, Haoyuan Hu, Yinghui Xu
Date: 2019-03-07 00:00:00
Tasks: Multi-agent Reinforcement Learning, Reinforcement Learning

In this paper, we study a courier dispatching problem (CDP) raised from an
online pickup-service platform of Alibaba. The CDP aims to assign a set of
couriers to serve pickup requests with stochastic spatial and temporal arrival
rate among urban regions. The objective is to maximize the revenue of served
requests given a limited number of couriers over a period of time. Many online
algorithms such as dynamic matching and vehicle routing strategy from existing
literature could be applied to tackle this problem. However, these methods rely
on appropriately predefined optimization objectives at each decision point,
which is hard in dynamic situations. This paper formulates the CDP as a Markov
decision process (MDP) and proposes a data-driven approach to derive the
optimal dispatching rule-set under different scenarios. Our method stacks
multi-layer images of the spatial-and-temporal map and apply multi-agent
reinforcement learning (MARL) techniques to evolve dispatching models. This
method solves the learning inefficiency caused by traditional centralized MDP
modeling. Through comprehensive experiments on both artificial dataset and
real-world dataset, we show: 1) By utilizing historical data and considering
long-term revenue gains, MARL achieves better performance than myopic online
algorithms; 2) MARL is able to construct the mapping between complex scenarios
to sophisticated decisions such as the dispatching rule. 3) MARL has the
scalability to adopt in large-scale real-world scenarios.
Concurrent Meta Reinforcement Learning
Authors: Emilio Parisotto, Soham Ghosh, Sai Bhargav Yalamanchi, Varsha Chinnaobireddy, Yuhuai Wu, Ruslan Salakhutdinov
Date: 2019-03-07 00:00:00
Tasks: Efficient Exploration, Meta-Learning, Meta Reinforcement Learning, Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

State-of-the-art meta reinforcement learning algorithms typically assume the
setting of a single agent interacting with its environment in a sequential
manner. A negative side-effect of this sequential execution paradigm is that,
as the environment becomes more and more challenging, and thus requiring more
interaction episodes for the meta-learner, it needs the agent to reason over
longer and longer time-scales. To combat the difficulty of long time-scale
credit assignment, we propose an alternative parallel framework, which we name
"Concurrent Meta-Reinforcement Learning" (CMRL), that transforms the temporal
credit assignment problem into a multi-agent reinforcement learning one. In
this multi-agent setting, a set of parallel agents are executed in the same
environment and each of these "rollout" agents are given the means to
communicate with each other. The goal of the communication is to coordinate, in
a collaborative manner, the most efficient exploration of the shared task the
agents are currently assigned. This coordination therefore represents the
meta-learning aspect of the framework, as each agent can be assigned or assign
itself a particular section of the current task's state space. This framework
is in contrast to standard RL methods that assume that each parallel rollout
occurs independently, which can potentially waste computation if many of the
rollouts end up sampling the same part of the state space. Furthermore, the
parallel setting enables us to define several reward sharing functions and
auxiliary losses that are non-trivial to apply in the sequential setting. We
demonstrate the effectiveness of our proposed CMRL at improving over sequential
methods in a variety of challenging tasks.
On Adversarial Mixup Resynthesis
Authors: Christopher Beckham, Sina Honari, Vikas Verma, Alex Lamb, Farnoosh Ghadiri, R. Devon Hjelm, Yoshua Bengio, Christopher Pal
Date: 2019-03-07 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Resynthesis

In this paper, we explore new approaches to combining information encoded within the learned representations of auto-encoders. We explore models that are capable of combining the attributes of multiple inputs such that a resynthesised output is trained to fool an adversarial discriminator for real versus synthesised data. Furthermore, we explore the use of such an architecture in the context of semi-supervised learning, where we learn a mixing function whose objective is to produce interpolations of hidden states, or masked combinations of latent representations that are consistent with a conditioned class label. We show quantitative and qualitative evidence that such a formulation is an interesting avenue of research.
Alternating Phase Projected Gradient Descent with Generative Priors for Solving Compressive Phase Retrieval
Authors: Rakib Hyder, Viraj Shah, Chinmay Hegde, M. Salman Asif
Date: 2019-03-07 00:00:00
Tasks: Retrieval

The classical problem of phase retrieval arises in various signal acquisition
systems. Due to the ill-posed nature of the problem, the solution requires
assumptions on the structure of the signal. In the last several years, sparsity
and support-based priors have been leveraged successfully to solve this
problem. In this work, we propose replacing the sparsity/support priors with
generative priors and propose two algorithms to solve the phase retrieval
problem. Our proposed algorithms combine the ideas from AltMin approach for
non-convex sparse phase retrieval and projected gradient descent approach for
solving linear inverse problems using generative priors. We empirically show
that the performance of our method with projected gradient descent is superior
to the existing approach for solving phase retrieval under generative priors.
We support our method with an analysis of sample complexity with Gaussian
measurements.
Twitter Speaks: A Case of National Disaster Situational Awareness
Authors: Amir Karami, Vishal Shah, Reza Vaezi, Amit Bansal
Date: 2019-03-07 00:00:00
Tasks: Management, Sentiment Analysis

In recent years, we have been faced with a series of natural disasters
causing a tremendous amount of financial, environmental, and human losses. The
unpredictable nature of natural disasters' behavior makes it hard to have a
comprehensive situational awareness (SA) to support disaster management. Using
opinion surveys is a traditional approach to analyze public concerns during
natural disasters; however, this approach is limited, expensive, and
time-consuming. Luckily the advent of social media has provided scholars with
an alternative means of analyzing public concerns. Social media enable users
(people) to freely communicate their opinions and disperse information
regarding current events including natural disasters. This research emphasizes
the value of social media analysis and proposes an analytical framework:
Twitter Situational Awareness (TwiSA). This framework uses text mining methods
including sentiment analysis and topic modeling to create a better SA for
disaster preparedness, response, and recovery. TwiSA has also effectively
deployed on a large number of tweets and tracks the negative concerns of people
during the 2015 South Carolina flood.
Selling Multiple Items via Social Networks
Authors: Dengji Zhao, Bin Li, Junping Xu, Dong Hao, Nicholas R. Jennings
Date: 2019-03-07 00:00:00

We consider a market where a seller sells multiple units of a commodity in a
social network. Each node/buyer in the social network can only directly
communicate with her neighbours, i.e. the seller can only sell the commodity to
her neighbours if she could not find a way to inform other buyers. In this
paper, we design a novel promotion mechanism that incentivizes all buyers, who
are aware of the sale, to invite all their neighbours to join the sale, even
though there is no guarantee that their efforts will be paid. While traditional
sale promotions such as sponsored search auctions cannot guarantee a positive
return for the advertiser (the seller), our mechanism guarantees that the
seller's revenue is better than not using the advertising. More importantly,
the seller does not need to pay if the advertising is not beneficial to her.
Robust Semantic Segmentation By Dense Fusion Network On Blurred VHR Remote Sensing Images
Authors: Yi Peng, Shihao Sun, Zheng Wang, Yining Pan, Ruirui Li
Date: 2019-03-07 00:00:00
Tasks: Decoder, Earth Observation, Segmentation, Semantic Segmentation

Robust semantic segmentation of VHR remote sensing images from UAV sensors is critical for earth observation, land use, land cover or mapping applications. Several factors such as shadows, weather disruption and camera shakes making this problem highly challenging, especially only using RGB images. In this paper, we propose the use of multi-modality data including NIR, RGB and DSM to increase robustness of segmentation in blurred or partially damaged VHR remote sensing images. By proposing a cascaded dense encoder-decoder network and the SELayer based fusion and assembling techniques, the proposed RobustDenseNet achieves steady performance when the image quality is decreasing, compared with the state-of-the-art semantic segmentation model.