pwc-archive/papers-with-abstracts huggingface dataset:
Speeding Up Neural Machine Translation Decoding by Cube Pruning
Authors: Wen Zhang, Liang Huang, Yang Feng, Lei Shen, Qun Liu
Date: 2018-09-09 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Machine Translation, Translation

Although neural machine translation has achieved promising results, it
suffers from slow translation speed. The direct consequence is that a trade-off
has to be made between translation quality and speed, thus its performance can
not come into full play. We apply cube pruning, a popular technique to speed up
dynamic programming, into neural machine translation to speed up the
translation. To construct the equivalence class, similar target hidden states
are combined, leading to less RNN expansion operations on the target side and
less \$\mathrm{softmax}\$ operations over the large target vocabulary. The
experiments show that, at the same or even better translation quality, our
method can translate faster compared with naive beam search by \$3.3\times\$ on
GPUs and \$3.5\times\$ on CPUs.
Dual Attention Network for Scene Segmentation
Authors: Jun Fu, Jing Liu, Haijie Tian, Yong Li, Yongjun Bao, Zhiwei Fang, Hanqing Lu
Date: 2018-09-09 00:00:00
Proceeding: CVPR 2019 6
Tasks: Position, Scene Segmentation, Segmentation, Semantic Segmentation, Thermal Image Segmentation

In this paper, we address the scene segmentation task by capturing rich
contextual dependencies based on the selfattention mechanism. Unlike previous
works that capture contexts by multi-scale features fusion, we propose a Dual
Attention Networks (DANet) to adaptively integrate local features with their
global dependencies. Specifically, we append two types of attention modules on
top of traditional dilated FCN, which model the semantic interdependencies in
spatial and channel dimensions respectively. The position attention module
selectively aggregates the features at each position by a weighted sum of the
features at all positions. Similar features would be related to each other
regardless of their distances. Meanwhile, the channel attention module
selectively emphasizes interdependent channel maps by integrating associated
features among all channel maps. We sum the outputs of the two attention
modules to further improve feature representation which contributes to more
precise segmentation results. We achieve new state-of-the-art segmentation
performance on three challenging scene segmentation datasets, i.e., Cityscapes,
PASCAL Context and COCO Stuff dataset. In particular, a Mean IoU score of 81.5%
on Cityscapes test set is achieved without using coarse data. We make the code
and trained model publicly available at https://github.com/junfu1115/DANet
Geometry-Aware Face Completion and Editing
Authors: Linsen Song, Jie Cao, Linxiao Song, Yibo Hu, Ran He
Date: 2018-09-09 00:00:00
Tasks: Decoder, Facial Inpainting

Face completion is a challenging generation task because it requires
generating visually pleasing new pixels that are semantically consistent with
the unmasked face region. This paper proposes a geometry-aware Face Completion
and Editing NETwork (FCENet) by systematically studying facial geometry from
the unmasked region. Firstly, a facial geometry estimator is learned to
estimate facial landmark heatmaps and parsing maps from the unmasked face
image. Then, an encoder-decoder structure generator serves to complete a face
image and disentangle its mask areas conditioned on both the masked face image
and the estimated facial geometry images. Besides, since low-rank property
exists in manually labeled masks, a low-rank regularization term is imposed on
the disentangled masks, enforcing our completion network to manage occlusion
area with various shape and size. Furthermore, our network can generate diverse
results from the same masked input by modifying estimated facial geometry,
which provides a flexible mean to edit the completed face appearance. Extensive
experimental results qualitatively and quantitatively demonstrate that our
network is able to generate visually pleasing face completion results and edit
face attributes as well.
LS-Net: Learning to Solve Nonlinear Least Squares for Monocular Stereo
Authors: Ronald Clark, Michael Bloesch, Jan Czarnowski, Stefan Leutenegger, Andrew J. Davison
Date: 2018-09-09 00:00:00
Proceeding: ECCV 2018

Sum-of-squares objective functions are very popular in computer vision
algorithms. However, these objective functions are not always easy to optimize.
The underlying assumptions made by solvers are often not satisfied and many
problems are inherently ill-posed. In this paper, we propose LS-Net, a neural
nonlinear least squares optimization algorithm which learns to effectively
optimize these cost functions even in the presence of adversities. Unlike
traditional approaches, the proposed solver requires no hand-crafted
regularizers or priors as these are implicitly learned from the data. We apply
our method to the problem of motion stereo ie. jointly estimating the motion
and scene geometry from pairs of images of a monocular sequence. We show that
our learned optimizer is able to efficiently and effectively solve this
challenging optimization problem.
Variational Approximation Error in Bayesian Non-negative Matrix Factorization
Authors: Naoki Hayashi
Date: 2018-09-09 00:00:00
Tasks: Variational Inference

Non-negative matrix factorization (NMF) is a knowledge discovery method that is used in many fields. Variational inference and Gibbs sampling methods for it are also wellknown. However, the variational approximation error has not been clarified yet, because NMF is not statistically regular and the prior distribution used in variational Bayesian NMF (VBNMF) has zero or divergence points. In this paper, using algebraic geometrical methods, we theoretically analyze the difference in negative log evidence (a.k.a. free energy) between VBNMF and Bayesian NMF, i.e., the Kullback-Leibler divergence between the variational posterior and the true posterior. We derive an upper bound for the learning coefficient (a.k.a. the real log canonical threshold) in Bayesian NMF. By using the upper bound, we find a lower bound for the approximation error, asymptotically. The result quantitatively shows how well the VBNMF algorithm can approximate Bayesian NMF; the lower bound depends on the hyperparameters and the true nonnegative rank. A numerical experiment demonstrates the theoretical result.
Visual Relationship Prediction via Label Clustering and Incorporation of Depth Information
Authors: Hsuan-Kung Yang, An-Chieh Cheng, Kuan-Wei Ho, Tsu-Jui Fu, Chun-Yi Lee
Date: 2018-09-09 00:00:00
Tasks: Clustering, Depth Estimation, Depth Prediction, General Classification, Instance Segmentation, Prediction, Segmentation, Semantic Segmentation

In this paper, we investigate the use of an unsupervised label clustering
technique and demonstrate that it enables substantial improvements in visual
relationship prediction accuracy on the Person in Context (PIC) dataset. We
propose to group object labels with similar patterns of relationship
distribution in the dataset into fewer categories. Label clustering not only
mitigates both the large classification space and class imbalance issues, but
also potentially increases data samples for each clustered category. We further
propose to incorporate depth information as an additional feature into the
instance segmentation model. The additional depth prediction path supplements
the relationship prediction model in a way that bounding boxes or segmentation
masks are unable to deliver. We have rigorously evaluated the proposed
techniques and performed various ablation analysis to validate the benefits of
them.
Cellular automata as convolutional neural networks
Authors: William Gilpin
Date: 2018-09-09 00:00:00
Tasks: Specificity

Deep learning techniques have recently demonstrated broad success in predicting complex dynamical systems ranging from turbulence to human speech, motivating broader questions about how neural networks encode and represent dynamical rules. We explore this problem in the context of cellular automata (CA), simple dynamical systems that are intrinsically discrete and thus difficult to analyze using standard tools from dynamical systems theory. We show that any CA may readily be represented using a convolutional neural network with a network-in-network architecture. This motivates our development of a general convolutional multilayer perceptron architecture, which we find can learn the dynamical rules for arbitrary CA when given videos of the CA as training data. In the limit of large network widths, we find that training dynamics are nearly identical across replicates, and that common patterns emerge in the structure of networks trained on different CA rulesets. We train ensembles of networks on randomly-sampled CA, and we probe how the trained networks internally represent the CA rules using an information-theoretic technique based on distributions of layer activation patterns. We find that CA with simpler rule tables produce trained networks with hierarchical structure and layer specialization, while more complex CA produce shallower representations---illustrating how the underlying complexity of the CA's rules influences the specificity of these internal representations. Our results suggest how the entropy of a physical process can affect its representation when learned by neural networks.
Automated Strabismus Detection for Telemedicine Applications
Authors: Jiewei Lu, Zhun Fan, Ce Zheng, Jingan Feng, Longtao Huang, Wenji Li, Erik D. Goodman
Date: 2018-09-09 00:00:00
Tasks: Prognosis

Strabismus is one of the most influential ophthalmologic diseases in human's
life. Timely detection of strabismus contributes to its prognosis and
treatment. Telemedicine, which has great potential to alleviate the growing
demand of the diagnosis of ophthalmologic diseases, is an effective method to
achieve timely strabismus detection. In this paper, a tele strabismus dataset
is established by the ophthalmologists. Then an end-to-end framework named as
RF-CNN is proposed to achieve automated strabismus detection on the established
tele strabismus dataset. RF-CNN first performs eye region segmentation on each
individual image, and further classifies the segmented eye regions with deep
neural networks. The experimental results on the established tele strabismus
dataset demonstrates that the proposed RF-CNN can have a good performance on
automated strabismus detection for telemedicine application. Code is made
publicly available at:
https://github.com/jieWeiLu/Strabismus-Detection-for-Telemedicine-Application.
Generic Probabilistic Interactive Situation Recognition and Prediction: From Virtual to Real
Authors: Jiachen Li, Hengbo Ma, Wei Zhan, Masayoshi Tomizuka
Date: 2018-09-09 00:00:00
Tasks: Autonomous Driving, Decision Making, Transfer Learning

Accurate and robust recognition and prediction of traffic situation plays an
important role in autonomous driving, which is a prerequisite for risk
assessment and effective decision making. Although there exist a lot of works
dealing with modeling driver behavior of a single object, it remains a
challenge to make predictions for multiple highly interactive agents that react
to each other simultaneously. In this work, we propose a generic probabilistic
hierarchical recognition and prediction framework which employs a two-layer
Hidden Markov Model (TLHMM) to obtain the distribution of potential situations
and a learning-based dynamic scene evolution model to sample a group of future
trajectories. Instead of predicting motions of a single entity, we propose to
get the joint distribution by modeling multiple interactive agents as a whole
system. Moreover, due to the decoupling property of the layered structure, our
model is suitable for knowledge transfer from simulation to real world
applications as well as among different traffic scenarios, which can reduce the
computational efforts of training and the demand for a large data amount. A
case study of highway ramp merging scenario is demonstrated to verify the
effectiveness and accuracy of the proposed framework.
Probabilistic Prediction of Interactive Driving Behavior via Hierarchical Inverse Reinforcement Learning
Authors: Liting Sun, Wei Zhan, Masayoshi Tomizuka
Date: 2018-09-09 00:00:00
Tasks: Autonomous Vehicles, Prediction, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Autonomous vehicles (AVs) are on the road. To safely and efficiently interact
with other road participants, AVs have to accurately predict the behavior of
surrounding vehicles and plan accordingly. Such prediction should be
probabilistic, to address the uncertainties in human behavior. Such prediction
should also be interactive, since the distribution over all possible
trajectories of the predicted vehicle depends not only on historical
information, but also on future plans of other vehicles that interact with it.
To achieve such interaction-aware predictions, we propose a probabilistic
prediction approach based on hierarchical inverse reinforcement learning (IRL).
First, we explicitly consider the hierarchical trajectory-generation process of
human drivers involving both discrete and continuous driving decisions. Based
on this, the distribution over all future trajectories of the predicted vehicle
is formulated as a mixture of distributions partitioned by the discrete
decisions. Then we apply IRL hierarchically to learn the distributions from
real human demonstrations. A case study for the ramp-merging driving scenario
is provided. The quantitative results show that the proposed approach can
accurately predict both the discrete driving decisions such as yield or pass as
well as the continuous trajectories.
Discriminator-Actor-Critic: Addressing Sample Inefficiency and Reward Bias in Adversarial Imitation Learning
Authors: Ilya Kostrikov, Kumar Krishna Agrawal, Debidatta Dwibedi, Sergey Levine, Jonathan Tompson
Date: 2018-09-09 00:00:00
Proceeding: ICLR 2019 5
Tasks: Imitation Learning, Reinforcement Learning

We identify two issues with the family of algorithms based on the Adversarial
Imitation Learning framework. The first problem is implicit bias present in the
reward functions used in these algorithms. While these biases might work well
for some environments, they can also lead to sub-optimal behavior in others.
Secondly, even though these algorithms can learn from few expert
demonstrations, they require a prohibitively large number of interactions with
the environment in order to imitate the expert for many real-world
applications. In order to address these issues, we propose a new algorithm
called Discriminator-Actor-Critic that uses off-policy Reinforcement Learning
to reduce policy-environment interaction sample complexity by an average factor
of 10. Furthermore, since our reward function is designed to be unbiased, we
can apply our algorithm to many problems without making any task-specific
adjustments.
Transforming Question Answering Datasets Into Natural Language Inference Datasets
Authors: Dorottya Demszky, Kelvin Guu, Percy Liang
Date: 2018-09-09 00:00:00
Tasks: Natural Language Inference, Question Answering, Sentence

Existing datasets for natural language inference (NLI) have propelled
research on language understanding. We propose a new method for automatically
deriving NLI datasets from the growing abundance of large-scale question
answering datasets. Our approach hinges on learning a sentence transformation
model which converts question-answer pairs into their declarative forms.
Despite being primarily trained on a single QA dataset, we show that it can be
successfully applied to a variety of other QA resources. Using this system, we
automatically derive a new freely available dataset of over 500k NLI examples
(QA-NLI), and show that it exhibits a wide range of inference phenomena rarely
seen in previous NLI datasets.
Towards Query Efficient Black-box Attacks: An Input-free Perspective
Authors: Yali Du, Meng Fang, Jin-Feng Yi, Jun Cheng, DaCheng Tao
Date: 2018-09-09 00:00:00

Recent studies have highlighted that deep neural networks (DNNs) are
vulnerable to adversarial attacks, even in a black-box scenario. However, most
of the existing black-box attack algorithms need to make a huge amount of
queries to perform attacks, which is not practical in the real world. We note
one of the main reasons for the massive queries is that the adversarial example
is required to be visually similar to the original image, but in many cases,
how adversarial examples look like does not matter much. It inspires us to
introduce a new attack called \emph{input-free} attack, under which an
adversary can choose an arbitrary image to start with and is allowed to add
perceptible perturbations on it. Following this approach, we propose two
techniques to significantly reduce the query complexity. First, we initialize
an adversarial example with a gray color image on which every pixel has roughly
the same importance for the target model. Then we shrink the dimension of the
attack space by perturbing a small region and tiling it to cover the input
image. To make our algorithm more effective, we stabilize a projected gradient
ascent algorithm with momentum, and also propose a heuristic approach for
region size selection. Through extensive experiments, we show that with only
1,701 queries on average, we can perturb a gray image to any target class of
ImageNet with a 100\% success rate on InceptionV3. Besides, our algorithm has
successfully defeated two real-world systems, the Clarifai food detection API
and the Baidu Animal Identification API.
Elliptical Distributions-Based Weights-Determining Method for OWA Operators
Authors: Xiuyan Sha, Zeshui Xu, Chuancun Yin
Date: 2018-09-09 00:00:00

The ordered weighted averaging (OWA) operators play a crucial role in
aggregating multiple criteria evaluations into an overall assessment supporting
the decision makers' choice. One key point steps is to determine the associated
weights. In this paper, we first briefly review some main methods for
determining the weights by using distribution functions. Then we propose a new
approach for determining OWA weights by using the RIM quantifier. Motivated by
the idea of normal distribution-based method to determine the OWA weights, we
develop a method based on elliptical distributions for determining the OWA
weights, and some of its desirable properties have been investigated.
End-to-end Language Identification using NetFV and NetVLAD
Authors: Jinkun Chen, Weicheng Cai, Danwei Cai, Zexin Cai, Haibin Zhong, Ming Li
Date: 2018-09-09 00:00:00
Tasks: Language Identification

In this paper, we apply the NetFV and NetVLAD layers for the end-to-end
language identification task. NetFV and NetVLAD layers are the differentiable
implementations of the standard Fisher Vector and Vector of Locally Aggregated
Descriptors (VLAD) methods, respectively. Both of them can encode a sequence of
feature vectors into a fixed dimensional vector which is very important to
process those variable-length utterances. We first present the relevances and
differences between the classical i-vector and the aforementioned encoding
schemes. Then, we construct a flexible end-to-end framework including a
convolutional neural network (CNN) architecture and an encoding layer (NetFV or
NetVLAD) for the language identification task. Experimental results on the NIST
LRE 2007 close-set task show that the proposed system achieves significant EER
reductions against the conventional i-vector baseline and the CNN temporal
average pooling system, respectively.
A Continuous Information Gain Measure to Find the Most Discriminatory Problems for AI Benchmarking
Authors: Matthew Stephenson, Damien Anderson, Ahmed Khalifa, John Levine, Jochen Renz, Julian Togelius, Christoph Salge
Date: 2018-09-09 00:00:00
Tasks: Benchmarking, Game Design

This paper introduces an information-theoretic method for selecting a subset of problems which gives the most information about a group of problem-solving algorithms. This method was tested on the games in the General Video Game AI (GVGAI) framework, allowing us to identify a smaller set of games that still gives a large amount of information about the abilities of different game-playing agents. This approach can be used to make agent testing more efficient. We can achieve almost as good discriminatory accuracy when testing on only a handful of games as when testing on more than a hundred games, something which is often computationally infeasible. Furthermore, this method can be extended to study the dimensions of the effective variance in game design between these games, allowing us to identify which games differentiate between agents in the most complementary ways.
Cost-Sensitive Active Learning for Intracranial Hemorrhage Detection
Authors: Wei-cheng Kuo, Christian Häne, Esther Yuh, Pratik Mukherjee, Jitendra Malik
Date: 2018-09-08 00:00:00
Tasks: Active Learning, Computed Tomography (CT)

Deep learning for clinical applications is subject to stringent performance
requirements, which raises a need for large labeled datasets. However, the
enormous cost of labeling medical data makes this challenging. In this paper,
we build a cost-sensitive active learning system for the problem of
intracranial hemorrhage detection and segmentation on head computed tomography
(CT). We show that our ensemble method compares favorably with the
state-of-the-art, while running faster and using less memory. Moreover, our
experiments are done using a substantially larger dataset than earlier papers
on this topic. Since the labeling time could vary tremendously across examples,
we model the labeling time and optimize the return on investment. We validate
this idea by core-set selection on our large labeled dataset and by growing it
with data from the wild.
PhaseLink: A Deep Learning Approach to Seismic Phase Association
Authors: Zachary E. Ross, Yisong Yue, Men-Andrin Meier, Egill Hauksson, Thomas H. Heaton
Date: 2018-09-08 00:00:00
Tasks: Deep Learning

Seismic phase association is a fundamental task in seismology that pertains
to linking together phase detections on different sensors that originate from a
common earthquake. It is widely employed to detect earthquakes on permanent and
temporary seismic networks, and underlies most seismicity catalogs produced
around the world. This task can be challenging because the number of sources is
unknown, events frequently overlap in time, or can occur simultaneously in
different parts of a network. We present PhaseLink, a framework based on recent
advances in deep learning for grid-free earthquake phase association. Our
approach learns to link phases together that share a common origin, and is
trained entirely on tens of millions of synthetic sequences of P- and S-wave
arrival times generated using a simple 1D velocity model. Our approach is
simple to implement for any tectonic regime, suitable for real-time processing,
and can naturally incorporate errors in arrival time picks. Rather than tuning
a set of ad hoc hyperparameters to improve performance, PhaseLink can be
improved by simply adding examples of problematic cases to the training
dataset. We demonstrate the state-of-the-art performance of PhaseLink on a
challenging recent sequence from southern California, and synthesized sequences
from Japan designed to test the point at which the method fails. For the
examined datasets, PhaseLink can precisely associate P- and S-picks to events
that are separated by ~12 seconds in origin time. This approach is expected to
improve the resolution of seismicity catalogs, add stability to real-time
seismic monitoring, and streamline automated processing of large seismic
datasets.