pwc-archive/papers-with-abstracts huggingface dataset:
A Deep Cascade Model for Multi-Document Reading Comprehension
Authors: Ming Yan, Jiangnan Xia, Chen Wu, Bin Bi, Zhongzhou Zhao, Ji Zhang, Luo Si, Rui Wang, Wei Wang, Haiqing Chen
Date: 2018-11-28 00:00:00
Tasks: Machine Reading Comprehension, Question Answering, Reading Comprehension, Retrieval, TriviaQA

A fundamental trade-off between effectiveness and efficiency needs to be
balanced when designing an online question answering system. Effectiveness
comes from sophisticated functions such as extractive machine reading
comprehension (MRC), while efficiency is obtained from improvements in
preliminary retrieval components such as candidate document selection and
paragraph ranking. Given the complexity of the real-world multi-document MRC
scenario, it is difficult to jointly optimize both in an end-to-end system. To
address this problem, we develop a novel deep cascade learning model, which
progressively evolves from the document-level and paragraph-level ranking of
candidate texts to more precise answer extraction with machine reading
comprehension. Specifically, irrelevant documents and paragraphs are first
filtered out with simple functions for efficiency consideration. Then we
jointly train three modules on the remaining texts for better tracking the
answer: the document extraction, the paragraph extraction and the answer
extraction. Experiment results show that the proposed method outperforms the
previous state-of-the-art methods on two large-scale multi-document benchmark
datasets, i.e., TriviaQA and DuReader. In addition, our online system can
stably serve typical scenarios with millions of daily requests in less than
50ms.
Formal Verification of CNN-based Perception Systems
Authors: Panagiotis Kouvaros, Alessio Lomuscio
Date: 2018-11-28 00:00:00

We address the problem of verifying neural-based perception systems
implemented by convolutional neural networks. We define a notion of local
robustness based on affine and photometric transformations. We show the notion
cannot be captured by previously employed notions of robustness. The method
proposed is based on reachability analysis for feed-forward neural networks and
relies on MILP encodings of both the CNNs and transformations under question.
We present an implementation and discuss the experimental results obtained for
a CNN trained from the MNIST data set.
DeepDPM: Dynamic Population Mapping via Deep Neural Network
Authors: Zefang Zong, Jie Feng, Kechun Liu, Hongzhi Shi, Yong Li
Date: 2018-10-25 00:00:00
Tasks: Population Mapping, Super-Resolution, Time Series, Time Series Analysis

Dynamic high resolution data on human population distribution is of great
importance for a wide spectrum of activities and real-life applications, but is
too difficult and expensive to obtain directly. Therefore, generating
fine-scaled population distributions from coarse population data is of great
significance. However, there are three major challenges: 1) the complexity in
spatial relations between high and low resolution population; 2) the dependence
of population distributions on other external information; 3) the difficulty in
retrieving temporal distribution patterns. In this paper, we first propose the
idea to generate dynamic population distributions in full-time series, then we
design dynamic population mapping via deep neural network(DeepDPM), a model
that describes both spatial and temporal patterns using coarse data and point
of interest information. In DeepDPM, we utilize super-resolution convolutional
neural network(SRCNN) based model to directly map coarse data into higher
resolution data, and a time-embedded long short-term memory model to
effectively capture the periodicity nature to smooth the finer-scaled results
from the previous static SRCNN model. We perform extensive experiments on a
real-life mobile dataset collected from Shanghai. Our results demonstrate that
DeepDPM outperforms previous state-of-the-art methods and a suite of frequent
data-mining approaches. Moreover, DeepDPM breaks through the limitation from
previous works in time dimension so that dynamic predictions in all-day time
slots can be obtained.
First-order Newton-type Estimator for Distributed Estimation and Inference
Authors: Xi Chen, Weidong Liu, Yichen Zhang
Date: 2018-11-28 00:00:00
Tasks: Vocal Bursts Type Prediction

This paper studies distributed estimation and inference for a general statistical problem with a convex loss that could be non-differentiable. For the purpose of efficient computation, we restrict ourselves to stochastic first-order optimization, which enjoys low per-iteration complexity. To motivate the proposed method, we first investigate the theoretical properties of a straightforward Divide-and-Conquer Stochastic Gradient Descent (DC-SGD) approach. Our theory shows that there is a restriction on the number of machines and this restriction becomes more stringent when the dimension $p$ is large. To overcome this limitation, this paper proposes a new multi-round distributed estimation procedure that approximates the Newton step only using stochastic subgradient. The key component in our method is the proposal of a computationally efficient estimator of $\Sigma^{-1} w$, where $\Sigma$ is the population Hessian matrix and $w$ is any given vector. Instead of estimating $\Sigma$ (or $\Sigma^{-1}$) that usually requires the second-order differentiability of the loss, the proposed First-Order Newton-type Estimator (FONE) directly estimates the vector of interest $\Sigma^{-1} w$ as a whole and is applicable to non-differentiable losses. Our estimator also facilitates the inference for the empirical risk minimizer. It turns out that the key term in the limiting covariance has the form of $\Sigma^{-1} w$, which can be estimated by FONE.
Unsupervised Multi-modal Neural Machine Translation
Authors: Yuanhang Su, Kai Fan, Nguyen Bach, C. -C. Jay Kuo, Fei Huang
Date: 2018-11-28 00:00:00
Proceeding: CVPR 2019 6
Tasks: Machine Translation, Translation

Unsupervised neural machine translation (UNMT) has recently achieved remarkable results with only large monolingual corpora in each language. However, the uncertainty of associating target with source sentences makes UNMT theoretically an ill-posed problem. This work investigates the possibility of utilizing images for disambiguation to improve the performance of UNMT. Our assumption is intuitively based on the invariant property of image, i.e., the description of the same visual content by different languages should be approximately similar. We propose an unsupervised multi-modal machine translation (UMNMT) framework based on the language translation cycle consistency loss conditional on the image, targeting to learn the bidirectional multi-modal translation simultaneously. Through an alternate training between multi-modal and uni-modal, our inference model can translate with or without the image. On the widely used Multi30K dataset, the experimental results of our approach are significantly better than those of the text-only UNMT on the 2016 test dataset.
Unsupervised Control Through Non-Parametric Discriminative Rewards
Authors: David Warde-Farley, Tom Van de Wiele, tejas kulkarni, Catalin Ionescu, Steven Hansen, Volodymyr Mnih
Date: 2018-11-28 00:00:00
Proceeding: ICLR 2019 5
Tasks: Reinforcement Learning, Reinforcement Learning (RL)

Learning to control an environment without hand-crafted rewards or expert
data remains challenging and is at the frontier of reinforcement learning
research. We present an unsupervised learning algorithm to train agents to
achieve perceptually-specified goals using only a stream of observations and
actions. Our agent simultaneously learns a goal-conditioned policy and a goal
achievement reward function that measures how similar a state is to the goal
state. This dual optimization leads to a co-operative game, giving rise to a
learned reward function that reflects similarity in controllable aspects of the
environment instead of distance in the space of observations. We demonstrate
the efficacy of our agent to learn, in an unsupervised manner, to reach a
diverse set of goals on three domains -- Atari, the DeepMind Control Suite and
DeepMind Lab.
Future Segmentation Using 3D Structure
Authors: Suhani Vora, Reza Mahjourian, Soeren Pirk, Anelia Angelova
Date: 2018-11-28 00:00:00
Tasks: Attribute, Decision Making, Segmentation, Semantic Segmentation

Predicting the future to anticipate the outcome of events and actions is a
critical attribute of autonomous agents; particularly for agents which must
rely heavily on real time visual data for decision making. Working towards this
capability, we address the task of predicting future frame segmentation from a
stream of monocular video by leveraging the 3D structure of the scene. Our
framework is based on learnable sub-modules capable of predicting pixel-wise
scene semantic labels, depth, and camera ego-motion of adjacent frames. We
further propose a recurrent neural network based model capable of predicting
future ego-motion trajectory as a function of a series of past ego-motion
steps. Ultimately, we observe that leveraging 3D structure in the model
facilitates successful prediction, achieving state of the art accuracy in
future semantic segmentation.
Metropolis-Hastings Generative Adversarial Networks
Authors: Ryan Turner, Jane Hung, Eric Frank, Yunus Saatci, Jason Yosinski
Date: 2018-11-28 00:00:00
Tasks: Generative Adversarial Network

We introduce the Metropolis-Hastings generative adversarial network (MH-GAN), which combines aspects of Markov chain Monte Carlo and GANs. The MH-GAN draws samples from the distribution implicitly defined by a GAN's discriminator-generator pair, as opposed to standard GANs which draw samples from the distribution defined only by the generator. It uses the discriminator from GAN training to build a wrapper around the generator for improved sampling. With a perfect discriminator, this wrapped generator samples from the true distribution on the data exactly even when the generator is imperfect. We demonstrate the benefits of the improved generator on multiple benchmark datasets, including CIFAR-10 and CelebA, using the DCGAN, WGAN, and progressive GAN.
Robust neural circuit reconstruction from serial electron microscopy with convolutional recurrent networks
Authors: Drew Linsley, Junkyung Kim, David Berson, Thomas Serre
Date: 2018-11-28 00:00:00
Tasks: Cell Segmentation, Contour Detection, Segmentation, Semantic Segmentation

Recent successes in deep learning have started to impact neuroscience. Of particular significance are claims that current segmentation algorithms achieve "super-human" accuracy in an area known as connectomics. However, as we will show, these algorithms do not effectively generalize beyond the particular source and brain tissues used for training -- severely limiting their usability by the broader neuroscience community. To fill this gap, we describe a novel connectomics challenge for source- and tissue-agnostic reconstruction of neurons (STAR), which favors broad generalization over fitting specific datasets. We first demonstrate that current state-of-the-art approaches to neuron segmentation perform poorly on the challenge. We further describe a novel convolutional recurrent neural network module that combines short-range horizontal connections within a processing stage and long-range top-down connections between stages. The resulting architecture establishes the state of the art on the STAR challenge and represents a significant step towards widely usable and fully-automated connectomics analysis.
Multi-label classification search space in the MEKA software
Authors: Alex G. C. de Sá, Cristiano G. Pimenta, Gisele L. Pappa, Alex A. Freitas
Date: 2018-11-28 00:00:00
Tasks: Classification, General Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

This supplementary material aims to describe the proposed multi-label classification (MLC) search spaces based on the MEKA and WEKA softwares. First, we overview 26 MLC algorithms and meta-algorithms in MEKA, presenting their main characteristics, such as hyper-parameters, dependencies and constraints. Second, we review 28 single-label classification (SLC) algorithms, preprocessing algorithms and meta-algorithms in the WEKA software. These SLC algorithms were also studied because they are part of the proposed MLC search spaces. Fundamentally, this occurs due to the problem transformation nature of several MLC algorithms used in this work. These algorithms transform an MLC problem into one or several SLC problems in the first place and solve them with SLC model(s) in a next step. Therefore, understanding their main characteristics is crucial to this work. Finally, we present a formal description of the search spaces by proposing a context-free grammar that encompasses the 54 learning algorithms. This grammar basically comprehends the possible combinations, the constraints and dependencies among the learning algorithms.
Effective Ways to Build and Evaluate Individual Survival Distributions
Authors: Humza Haider, Bret Hoehn, Sarah Davis, Russell Greiner
Date: 2018-11-28 00:00:00

An accurate model of a patient's individual survival distribution can help
determine the appropriate treatment for terminal patients. Unfortunately, risk
scores (e.g., from Cox Proportional Hazard models) do not provide survival
probabilities, single-time probability models (e.g., the Gail model, predicting
5 year probability) only provide for a single time point, and standard
Kaplan-Meier survival curves provide only population averages for a large class
of patients meaning they are not specific to individual patients. This
motivates an alternative class of tools that can learn a model which provides
an individual survival distribution which gives survival probabilities across
all times - such as extensions to the Cox model, Accelerated Failure Time, an
extension to Random Survival Forests, and Multi-Task Logistic Regression. This
paper first motivates such "individual survival distribution" (ISD) models, and
explains how they differ from standard models. It then discusses ways to
evaluate such models - namely Concordance, 1-Calibration, Brier score, and
various versions of L1-loss - and then motivates and defines a novel approach
"D-Calibration", which determines whether a model's probability estimates are
meaningful. We also discuss how these measures differ, and use them to evaluate
several ISD prediction tools, over a range of survival datasets.
Statistical Robust Chinese Remainder Theorem for Multiple Numbers: Wrapped Gaussian Mixture Model
Authors: Nan Du, Zhikang Wang, Hanshen Xiao
Date: 2018-11-28 00:00:00
Tasks: Clustering, parameter estimation

Generalized Chinese Remainder Theorem (CRT) has been shown to be a powerful
approach to solve the ambiguity resolution problem. However, with its close
relationship to number theory, study in this area is mainly from a coding
theory perspective under deterministic conditions. Nevertheless, it can be
proved that even with the best deterministic condition known, the probability
of success in robust reconstruction degrades exponentially as the number of
estimand increases. In this paper, we present the first rigorous analysis on
the underlying statistical model of CRT-based multiple parameter estimation,
where a generalized Gaussian mixture with background knowledge on samplings is
proposed. To address the problem, two novel approaches are introduced. One is
to directly calculate the conditional maximal a posteriori probability (MAP)
estimation of residue clustering, and the other is to iteratively search for
MAP of both common residues and clustering. Moreover, remainder
error-correcting codes are introduced to improve the robustness further. It is
shown that this statistically based scheme achieves much stronger robustness
compared to state-of-the-art deterministic schemes, especially in low and
median Signal Noise Ratio (SNR) scenarios.
Deep Reinforcement Learning for Autonomous Driving
Authors: Sen Wang, Daoyuan Jia, Xinshuo Weng
Date: 2018-11-28 00:00:00
Tasks: Autonomous Driving, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Reinforcement learning has steadily improved and outperform human in lots of traditional games since the resurgence of deep neural network. However, these success is not easy to be copied to autonomous driving because the state spaces in real world are extreme complex and action spaces are continuous and fine control is required. Moreover, the autonomous driving vehicles must also keep functional safety under the complex environments. To deal with these challenges, we first adopt the deep deterministic policy gradient (DDPG) algorithm, which has the capacity to handle complex state and action spaces in continuous domain. We then choose The Open Racing Car Simulator (TORCS) as our environment to avoid physical damage. Meanwhile, we select a set of appropriate sensor information from TORCS and design our own rewarder. In order to fit DDPG algorithm to TORCS, we design our network architecture for both actor and critic inside DDPG paradigm. To demonstrate the effectiveness of our model, We evaluate on different modes in TORCS and show both quantitative and qualitative results.
CyLKs: Unsupervised Cycle Lucas-Kanade Network for Landmark Tracking
Authors: Xinshuo Weng, Wentao Han
Date: 2018-11-28 00:00:00
Tasks: Landmark Tracking

Across a majority of modern learning-based tracking systems, expensive annotations are needed to achieve state-of-the-art performance. In contrast, the Lucas-Kanade (LK) algorithm works well without any annotation. However, LK has a strong assumption of photometric (brightness) consistency on image intensity and is easy to drift because of large motion, occlusion, and aperture problem. To relax the assumption and alleviate the drift problem, we propose CyLKs, a data-driven way of training Lucas-Kanade in an unsupervised manner. CyLKs learns a feature transformation through CNNs, transforming the input images to a feature space which is especially favorable to LK tracking. During training, we perform differentiable Lucas-Kanade forward and backward on the convolutional feature maps, and then minimize the re-projection error. During testing, we perform the LK tracking on the learned features. We apply our model to the task of landmark tracking and perform experiments on datasets of THUMOS and 300VW.
Image Labeling with Markov Random Fields and Conditional Random Fields
Authors: Shangxuan Wu, Xinshuo Weng
Date: 2018-11-28 00:00:00
Tasks: Segmentation, Semantic Segmentation

Most existing methods for object segmentation in computer vision are formulated as a labeling task. This, in general, could be transferred to a pixel-wise label assignment task, which is quite similar to the structure of hidden Markov random field. In terms of Markov random field, each pixel can be regarded as a state and has a transition probability to its neighbor pixel, the label behind each pixel is a latent variable and has an emission probability from its corresponding state. In this paper, we reviewed several modern image labeling methods based on Markov random field and conditional random Field. And we compare the result of these methods with some classical image labeling methods. The experiment demonstrates that the introduction of Markov random field and conditional random field make a big difference in the segmentation result.
User-Guided Clustering in Heterogeneous Information Networks via Motif-Based Comprehensive Transcription
Authors: Yu Shi, Xinwei He, Naijing Zhang, Carl Yang, Jiawei Han
Date: 2018-11-28 00:00:00
Tasks: Clustering

Heterogeneous information networks (HINs) with rich semantics are ubiquitous in real-world applications. For a given HIN, many reasonable clustering results with distinct semantic meaning can simultaneously exist. User-guided clustering is hence of great practical value for HINs where users provide labels to a small portion of nodes. To cater to a broad spectrum of user guidance evidenced by different expected clustering results, carefully exploiting the signals residing in the data is potentially useful. Meanwhile, as one type of complex networks, HINs often encapsulate higher-order interactions that reflect the interlocked nature among nodes and edges. Network motifs, sometimes referred to as meta-graphs, have been used as tools to capture such higher-order interactions and reveal the many different semantics. We therefore approach the problem of user-guided clustering in HINs with network motifs. In this process, we identify the utility and importance of directly modeling higher-order interactions without collapsing them to pairwise interactions. To achieve this, we comprehensively transcribe the higher-order interaction signals to a series of tensors via motifs and propose the MoCHIN model based on joint non-negative tensor factorization. This approach applies to arbitrarily many, arbitrary forms of HIN motifs. An inference algorithm with speed-up methods is also proposed to tackle the challenge that tensor size grows exponentially as the number of nodes in a motif increases. We validate the effectiveness of the proposed method on two real-world datasets and three tasks, and MoCHIN outperforms all baselines in three evaluation tasks under three different metrics. Additional experiments demonstrated the utility of motifs and the benefit of directly modeling higher-order information especially when user guidance is limited.
Deep Regionlets: Blended Representation and Deep Learning for Generic Object Detection
Authors: Hongyu Xu, Xutao Lv, Xiaoyu Wang, Zhou Ren, Navaneeth Bodla, Rama Chellappa
Date: 2018-11-28 00:00:00
Tasks: feature selection, Novel Object Detection, Object, object-detection, Object Detection

In this paper, we propose a novel object detection algorithm named "Deep Regionlets" by integrating deep neural networks and a conventional detection schema for accurate generic object detection. Motivated by the effectiveness of regionlets for modeling object deformations and multiple aspect ratios, we incorporate regionlets into an end-to-end trainable deep learning framework. The deep regionlets framework consists of a region selection network and a deep regionlet learning module. Specifically, given a detection bounding box proposal, the region selection network provides guidance on where to select sub-regions from which features can be learned from. An object proposal typically contains 3-16 sub-regions. The regionlet learning module focuses on local feature selection and transformations to alleviate the effects of appearance variations. To this end, we first realize non-rectangular region selection within the detection framework to accommodate variations in object appearance. Moreover, we design a "gating network" within the regionlet leaning module to enable instance dependent soft feature selection and pooling. The Deep Regionlets framework is trained end-to-end without additional efforts. We present ablation studies and extensive experiments on the PASCAL VOC dataset and the Microsoft COCO dataset. The proposed method yields competitive performance over state-of-the-art algorithms, such as RetinaNet and Mask R-CNN, even without additional segmentation labels.
Skin lesion segmentation using U-Net and good training strategies
Authors: Fred Guth, Teofilo E. deCampos
Date: 2018-11-27 00:00:00
Tasks: Lesion Segmentation, Skin Lesion Segmentation

In this paper we approach the problem of skin lesion segmentation using a
convolutional neural network based on the U-Net architecture. We present a set
of training strategies that had a significant impact on the performance of this
model. We evaluated this method on the ISIC Challenge 2018 - Skin Lesion
Analysis Towards Melanoma Detection, obtaining threshold Jaccard index of
77.5%.