pwc-archive/papers-with-abstracts huggingface dataset:
Question-Guided Hybrid Convolution for Visual Question Answering
Authors: Peng Gao, Pan Lu, Hongsheng Li, Shuang Li, Yikang Li, Steven Hoi, Xiaogang Wang
Date: 2018-08-08 00:00:00
Proceeding: ECCV 2018 9
Tasks: Question Answering, Visual Question Answering, Visual Question Answering (VQA)

In this paper, we propose a novel Question-Guided Hybrid Convolution (QGHC)
network for Visual Question Answering (VQA). Most state-of-the-art VQA methods
fuse the high-level textual and visual features from the neural network and
abandon the visual spatial information when learning multi-modal features.To
address these problems, question-guided kernels generated from the input
question are designed to convolute with visual features for capturing the
textual and visual relationship in the early stage. The question-guided
convolution can tightly couple the textual and visual information but also
introduce more parameters when learning kernels. We apply the group
convolution, which consists of question-independent kernels and
question-dependent kernels, to reduce the parameter size and alleviate
over-fitting. The hybrid convolution can generate discriminative multi-modal
features with fewer parameters. The proposed approach is also complementary to
existing bilinear pooling fusion and attention based VQA methods. By
integrating with them, our method could further boost the performance.
Extensive experiments on public VQA datasets validate the effectiveness of
QGHC.
Training Compact Neural Networks with Binary Weights and Low Precision Activations
Authors: Bohan Zhuang, Chunhua Shen, Ian Reid
Date: 2018-08-08 00:00:00

In this paper, we propose to train a network with binary weights and
low-bitwidth activations, designed especially for mobile devices with limited
power consumption. Most previous works on quantizing CNNs uncritically assume
the same architecture, though with reduced precision. However, we take the view
that for best performance it is possible (and even likely) that a different
architecture may be better suited to dealing with low precision weights and
activations.
  Specifically, we propose a "network expansion" strategy in which we aggregate
a set of homogeneous low-precision branches to implicitly reconstruct the
full-precision intermediate feature maps. Moreover, we also propose a
group-wise feature approximation strategy which is very flexible and highly
accurate. Experiments on ImageNet classification tasks demonstrate the superior
performance of the proposed model, named Group-Net, over various popular
architectures. In particular, with binary weights and activations, we
outperform the previous best binary neural network in terms of accuracy as well
as saving more than 5 times computational complexity on ImageNet with ResNet-18
and ResNet-50.
Learning to Write Notes in Electronic Health Records
Authors: Peter J. Liu
Date: 2018-08-08 00:00:00
Tasks: Language Modeling, Language Modelling

Clinicians spend a significant amount of time inputting free-form textual
notes into Electronic Health Records (EHR) systems. Much of this documentation
work is seen as a burden, reducing time spent with patients and contributing to
clinician burnout. With the aspiration of AI-assisted note-writing, we propose
a new language modeling task predicting the content of notes conditioned on
past data from a patient's medical record, including patient demographics,
labs, medications, and past notes. We train generative models using the public,
de-identified MIMIC-III dataset and compare generated notes with those in the
dataset on multiple measures. We find that much of the content can be
predicted, and that many common templates found in notes can be learned. We
discuss how such models can be useful in supporting assistive note-writing
features such as error-detection and auto-complete.
Harmonic Adversarial Attack Method
Authors: Wen Heng, Shuchang Zhou, Tingting Jiang
Date: 2018-07-18 00:00:00
Tasks: Adversarial Attack

Adversarial attacks find perturbations that can fool models into
misclassifying images. Previous works had successes in generating
noisy/edge-rich adversarial perturbations, at the cost of degradation of image
quality. Such perturbations, even when they are small in scale, are usually
easily spottable by human vision. In contrast, we propose Harmonic Adversar-
ial Attack Methods (HAAM), that generates edge-free perturbations by using
harmonic functions. The property of edge-free guarantees that the generated
adversarial images can still preserve visual quality, even when perturbations
are of large magnitudes. Experiments also show that adversaries generated by
HAAM often have higher rates of success when transferring between models. In
addition, we find harmonic perturbations can simulate natural phenomena like
natural lighting and shadows. It would then be possible to help find corner
cases for given models, as a first step to improving them.
L-Shapley and C-Shapley: Efficient Model Interpretation for Structured Data
Authors: Jianbo Chen, Le Song, Martin J. Wainwright, Michael. I. Jordan
Date: 2018-08-08 00:00:00
Proceeding: ICLR 2019 5
Tasks: Feature Importance

We study instancewise feature importance scoring as a method for model
interpretation. Any such method yields, for each predicted instance, a vector
of importance scores associated with the feature vector. Methods based on the
Shapley score have been proposed as a fair way of computing feature
attributions of this kind, but incur an exponential complexity in the number of
features. This combinatorial explosion arises from the definition of the
Shapley value and prevents these methods from being scalable to large data sets
and complex models. We focus on settings in which the data have a graph
structure, and the contribution of features to the target variable is
well-approximated by a graph-structured factorization. In such settings, we
develop two algorithms with linear complexity for instancewise feature
importance scoring. We establish the relationship of our methods to the Shapley
value and another closely related concept known as the Myerson value from
cooperative game theory. We demonstrate on both language and image data that
our algorithms compare favorably with other methods for model interpretation.
End-to-end Speech Recognition with Word-based RNN Language Models
Authors: Takaaki Hori, Jaejin Cho, Shinji Watanabe
Date: 2018-08-08 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

This paper investigates the impact of word-based RNN language models
(RNN-LMs) on the performance of end-to-end automatic speech recognition (ASR).
In our prior work, we have proposed a multi-level LM, in which character-based
and word-based RNN-LMs are combined in hybrid CTC/attention-based ASR. Although
this multi-level approach achieves significant error reduction in the Wall
Street Journal (WSJ) task, two different LMs need to be trained and used for
decoding, which increase the computational cost and memory usage. In this
paper, we further propose a novel word-based RNN-LM, which allows us to decode
with only the word-based LM, where it provides look-ahead word probabilities to
predict next characters instead of the character-based LM, leading competitive
accuracy with less computation compared to the multi-level LM. We demonstrate
the efficacy of the word-based RNN-LMs using a larger corpus, LibriSpeech, in
addition to WSJ we used in the prior work. Furthermore, we show that the
proposed model achieves 5.1 %WER for WSJ Eval'92 test set when the vocabulary
size is increased, which is the best WER reported for end-to-end ASR systems on
this benchmark.
Unsupervised/Semi-supervised Deep Learning for Low-dose CT Enhancement
Authors: Mingrui Geng, Yun Deng, Qian Zhao, Qi Xie, Dong Zeng, WangMeng Zuo, Deyu Meng
Date: 2018-08-08 00:00:00
Tasks: Computational Efficiency, Deep Learning

Recently, deep learning(DL) methods have been proposed for the low-dose
computed tomography(LdCT) enhancement, and obtain good trade-off between
computational efficiency and image quality. Most of them need large number of
pre-collected ground-truth/high-dose sinograms with less noise, and train the
network in a supervised end-to-end manner. This may bring major limitations on
these methods because the number of such low-dose/high-dose training sinogram
pairs would affect the network's capability and sometimes the ground-truth
sinograms are hard to be obtained in large scale. Since large number of
low-dose ones are relatively easy to obtain, it should be critical to make
these sources play roles in network training in an unsupervised learning
manner. To address this issue, we propose an unsupervised DL method for LdCT
enhancement that incorporates unlabeled LdCT sinograms directly into the
network training. The proposed method effectively considers the structure
characteristics and noise distribution in the measured LdCT sinogram, and then
learns the proper gradient of the LdCT sinogram in a pure unsupervised manner.
Similar to the labeled ground-truth, the gradient information in an unlabeled
LdCT sinogram can be used for sufficient network training. The experiments on
the patient data show effectiveness of the proposed method.
PIVETed-Granite: Computational Phenotypes through Constrained Tensor Factorization
Authors: Jette Henderson, Bradley A. Malin, Joyce C. Ho, Joydeep Ghosh
Date: 2018-08-08 00:00:00
Tasks: Computational Phenotyping

It has been recently shown that sparse, nonnegative tensor factorization of
multi-modal electronic health record data is a promising approach to
high-throughput computational phenotyping. However, such approaches typically
do not leverage available domain knowledge while extracting the phenotypes;
hence, some of the suggested phenotypes may not map well to clinical concepts
or may be very similar to other suggested phenotypes. To address these issues,
we present a novel, automatic approach called PIVETed-Granite that mines
existing biomedical literature (PubMed) to obtain cannot-link constraints that
are then used as side-information during a tensor-factorization based
computational phenotyping process. The resulting improvements are clearly
observed in experiments using a large dataset from VUMC to identify phenotypes
for hypertensive patients.
A Semi-Supervised Data Augmentation Approach using 3D Graphical Engines
Authors: Shuangjun Liu, Sarah Ostadabbas
Date: 2018-08-08 00:00:00
Tasks: Data Augmentation, Deep Learning, Domain Adaptation, Pose Estimation, Small Data Image Classification, valid

Deep learning approaches have been rapidly adopted across a wide range of
fields because of their accuracy and flexibility, but require large labeled
training datasets. This presents a fundamental problem for applications with
limited, expensive, or private data (i.e. small data), such as human pose and
behavior estimation/tracking which could be highly personalized. In this paper,
we present a semi-supervised data augmentation approach that can synthesize
large scale labeled training datasets using 3D graphical engines based on a
physically-valid low dimensional pose descriptor. To evaluate the performance
of our synthesized datasets in training deep learning-based models, we
generated a large synthetic human pose dataset, called ScanAva using 3D scans
of only 7 individuals based on our proposed augmentation approach. A
state-of-the-art human pose estimation deep learning model then was trained
from scratch using our ScanAva dataset and could achieve the pose estimation
accuracy of 91.2% at PCK0.5 criteria after applying an efficient domain
adaptation on the synthetic images, in which its pose estimation accuracy was
comparable to the same model trained on large scale pose data from real humans
such as MPII dataset and much higher than the model trained on other synthetic
human dataset such as SURREAL.
Adversarial Domain Adaptation for Variational Neural Language Generation in Dialogue Systems
Authors: Van-Khanh Tran, Le-Minh Nguyen
Date: 2018-08-08 00:00:00
Proceeding: COLING 2018 8
Tasks: Decoder, Domain Adaptation, Spoken Dialogue Systems, Text Generation

Domain Adaptation arises when we aim at learning from source domain a model
that can per- form acceptably well on a different target domain. It is
especially crucial for Natural Language Generation (NLG) in Spoken Dialogue
Systems when there are sufficient annotated data in the source domain, but
there is a limited labeled data in the target domain. How to effectively
utilize as much of existing abilities from source domains is a crucial issue in
domain adaptation. In this paper, we propose an adversarial training procedure
to train a Variational encoder-decoder based language generator via multiple
adaptation steps. In this procedure, a model is first trained on a source
domain data and then fine-tuned on a small set of target domain utterances
under the guidance of two proposed critics. Experimental results show that the
proposed method can effec- tively leverage the existing knowledge in the source
domain to adapt to another related domain by using only a small amount of
in-domain data.
Image Anomalies: a Review and Synthesis of Detection Methods
Authors: Thibaud Ehret, Axel Davy, Jean-Michel Morel, Mauricio Delbracio
Date: 2018-08-07 00:00:00
Tasks: Anomaly Detection

We review the broad variety of methods that have been proposed for anomaly detection in images. Most methods found in the literature have in mind a particular application. Yet we show that the methods can be classified mainly by the structural assumption they make on the "normal" image. Five different structural assumptions emerge. Our analysis leads us to reformulate the best representative algorithms by attaching to them an a contrario detection that controls the number of false positives and thus derive universal detection thresholds. By combining the most general structural assumptions expressing the background's normality with the best proposed statistical detection tools, we end up proposing generic algorithms that seem to generalize or reconcile most methods. We compare the six best representatives of our proposed classes of algorithms on anomalous images taken from classic papers on the subject, and on a synthetic database. Our conclusion is that it is possible to perform automatic anomaly detection on a single image.
Design Challenges in Named Entity Transliteration
Authors: Yuval Merhav, Stephen Ash
Date: 2018-08-07 00:00:00
Proceeding: COLING 2018 8
Tasks: Decoder, Transliteration

We analyze some of the fundamental design challenges that impact the
development of a multilingual state-of-the-art named entity transliteration
system, including curating bilingual named entity datasets and evaluation of
multiple transliteration methods. We empirically evaluate the transliteration
task using traditional weighted finite state transducer (WFST) approach against
two neural approaches: the encoder-decoder recurrent neural network method and
the recent, non-sequential Transformer method. In order to improve availability
of bilingual named entity transliteration datasets, we release personal name
bilingual dictionaries minded from Wikidata for English to Russian, Hebrew,
Arabic and Japanese Katakana. Our code and dictionaries are publicly available.
Belief likelihood function for generalised logistic regression
Authors: Fabio Cuzzolin
Date: 2018-08-07 00:00:00
Tasks: regression

The notion of belief likelihood function of repeated trials is introduced,
whenever the uncertainty for individual trials is encoded by a belief measure
(a finite random set). This generalises the traditional likelihood function,
and provides a natural setting for belief inference from statistical data.
Factorisation results are proven for the case in which conjunctive or
disjunctive combination are employed, leading to analytical expressions for the
lower and upper likelihoods of `sharp' samples in the case of Bernoulli trials,
and to the formulation of a generalised logistic regression framework.
A Joint Sequence Fusion Model for Video Question Answering and Retrieval
Authors: Youngjae Yu, Jongseok Kim, Gunhee Kim
Date: 2018-08-07 00:00:00
Proceeding: ECCV 2018 9
Tasks: Decoder, Multiple-choice, Question Answering, Retrieval, Semantic Similarity, Semantic Textual Similarity, Sentence, Video Question Answering, Video Retrieval, Visual Question Answering (VQA)

We present an approach named JSFusion (Joint Sequence Fusion) that can
measure semantic similarity between any pairs of multimodal sequence data (e.g.
a video clip and a language sentence). Our multimodal matching network consists
of two key components. First, the Joint Semantic Tensor composes a dense
pairwise representation of two sequence data into a 3D tensor. Then, the
Convolutional Hierarchical Decoder computes their similarity score by
discovering hidden hierarchical matches between the two sequence modalities.
Both modules leverage hierarchical attention mechanisms that learn to promote
well-matched representation patterns while prune out misaligned ones in a
bottom-up manner. Although the JSFusion is a universal model to be applicable
to any multimodal sequence data, this work focuses on video-language tasks
including multimodal retrieval and video QA. We evaluate the JSFusion model in
three retrieval and VQA tasks in LSMDC, for which our model achieves the best
performance reported so far. We also perform multiple-choice and movie
retrieval tasks for the MSR-VTT dataset, on which our approach outperforms many
state-of-the-art methods.
Deep context: end-to-end contextual speech recognition
Authors: Golan Pundak, Tara N. Sainath, Rohit Prabhavalkar, Anjuli Kannan, Ding Zhao
Date: 2018-08-07 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

In automatic speech recognition (ASR) what a user says depends on the
particular context she is in. Typically, this context is represented as a set
of word n-grams. In this work, we present a novel, all-neural, end-to-end (E2E)
ASR sys- tem that utilizes such context. Our approach, which we re- fer to as
Contextual Listen, Attend and Spell (CLAS) jointly- optimizes the ASR
components along with embeddings of the context n-grams. During inference, the
CLAS system can be presented with context phrases which might contain out-of-
vocabulary (OOV) terms not seen during training. We com- pare our proposed
system to a more traditional contextualiza- tion approach, which performs
shallow-fusion between inde- pendently trained LAS and contextual n-gram models
during beam search. Across a number of tasks, we find that the pro- posed CLAS
system outperforms the baseline method by as much as 68% relative WER,
indicating the advantage of joint optimization over individually trained
components. Index Terms: speech recognition, sequence-to-sequence models,
listen attend and spell, LAS, attention, embedded speech recognition.
Multi-robot Dubins Coverage with Autonomous Surface Vehicles
Authors: Nare Karapetyan, Jason Moulton, Jeremy S. Lewis, Alberto Quattrini Li, Jason M. O'Kane, Ioannis Rekleitis
Date: 2018-08-07 00:00:00
Tasks: Clustering, Traveling Salesman Problem

In large scale coverage operations, such as marine exploration or aerial
monitoring, single robot approaches are not ideal, as they may take too long to
cover a large area. In such scenarios, multi-robot approaches are preferable.
Furthermore, several real world vehicles are non-holonomic, but can be modeled
using Dubins vehicle kinematics. This paper focuses on environmental monitoring
of aquatic environments using Autonomous Surface Vehicles (ASVs). In
particular, we propose a novel approach for solving the problem of complete
coverage of a known environment by a multi-robot team consisting of Dubins
vehicles. It is worth noting that both multi-robot coverage and Dubins vehicle
coverage are NP-complete problems. As such, we present two heuristics methods
based on a variant of the traveling salesman problem -- k-TSP -- formulation
and clustering algorithms that efficiently solve the problem. The proposed
methods are tested both in simulations to assess their scalability and with a
team of ASVs operating on a lake to ensure their applicability in real world.
Collaborative Planning for Mixed-Autonomy Lane Merging
Authors: Shray Bansal, Akansel Cosgun, Alireza Nakhaei, Kikuo Fujimura
Date: 2018-08-07 00:00:00
Tasks: Decision Making

Driving is a social activity: drivers often indicate their intent to change
lanes via motion cues. We consider mixed-autonomy traffic where a Human-driven
Vehicle (HV) and an Autonomous Vehicle (AV) drive together. We propose a
planning framework where the degree to which the AV considers the other agent's
reward is controlled by a selfishness factor. We test our approach on a
simulated two-lane highway where the AV and HV merge into each other's lanes.
In a user study with 21 subjects and 6 different selfishness factors, we found
that our planning approach was sound and that both agents had less merging
times when a factor that balances the rewards for the two agents was chosen.
Our results on double lane merging suggest it to be a non-zero-sum game and
encourage further investigation on collaborative decision making algorithms for
mixed-autonomy traffic.
Parallel and Streaming Algorithms for K-Core Decomposition
Authors: Hossein Esfandiari, Silvio Lattanzi, Vahab Mirrokni
Date: 2018-08-07 00:00:00
Proceeding: ICML 2018 7

The $k$-core decomposition is a fundamental primitive in many machine
learning and data mining applications. We present the first distributed and the
first streaming algorithms to compute and maintain an approximate $k$-core
decomposition with provable guarantees. Our algorithms achieve rigorous bounds
on space complexity while bounding the number of passes or number of rounds of
computation. We do so by presenting a new powerful sketching technique for
$k$-core decomposition, and then by showing it can be computed efficiently in
both streaming and MapReduce models. Finally, we confirm the effectiveness of
our sketching technique empirically on a number of publicly available graphs.
Efficient Multi-Robot Coverage of a Known Environment
Authors: Nare Karapetyan, Kelly Benson, Chris McKinney, Perouz Taslakian, Ioannis Rekleitis
Date: 2018-08-07 00:00:00

This paper addresses the complete area coverage problem of a known
environment by multiple-robots. Complete area coverage is the problem of moving
an end-effector over all available space while avoiding existing obstacles. In
such tasks, using multiple robots can increase the efficiency of the area
coverage in terms of minimizing the operational time and increase the
robustness in the face of robot attrition. Unfortunately, the problem of
finding an optimal solution for such an area coverage problem with multiple
robots is known to be NP-complete. In this paper we present two approximation
heuristics for solving the multi-robot coverage problem. The first solution
presented is a direct extension of an efficient single robot area coverage
algorithm, based on an exact cellular decomposition. The second algorithm is a
greedy approach that divides the area into equal regions and applies an
efficient single-robot coverage algorithm to each region. We present
experimental results for two algorithms. Results indicate that our approaches
provide good coverage distribution between robots and minimize the workload per
robot, meanwhile ensuring complete coverage of the area.