pwc-archive/papers-with-abstracts huggingface dataset:
sharpDARTS: Faster and More Accurate Differentiable Architecture Search
Authors: Andrew Hundt, Varun Jain, Gregory D. Hager
Date: 2019-03-23 00:00:00
Tasks: Hyperparameter Optimization, Image Classification, Neural Architecture Search

Neural Architecture Search (NAS) has been a source of dramatic improvements
in neural network design, with recent results meeting or exceeding the
performance of hand-tuned architectures. However, our understanding of how to
represent the search space for neural net architectures and how to search that
space efficiently are both still in their infancy.
  We have performed an in-depth analysis to identify limitations in a widely
used search space and a recent architecture search method, Differentiable
Architecture Search (DARTS). These findings led us to introduce novel network
blocks with a more general, balanced, and consistent design; a better-optimized
Cosine Power Annealing learning rate schedule; and other improvements. Our
resulting sharpDARTS search is 50% faster with a 20-30% relative improvement in
final model error on CIFAR-10 when compared to DARTS. Our best single model run
has 1.93% (1.98+/-0.07) validation error on CIFAR-10 and 5.5% error (5.8+/-0.3)
on the recently released CIFAR-10.1 test set. To our knowledge, both are state
of the art for models of similar size. This model also generalizes
competitively to ImageNet at 25.1% top-1 (7.8% top-5) error.
  We found improvements for existing search spaces but does DARTS generalize to
new domains? We propose Differentiable Hyperparameter Grid Search and the
HyperCuboid search space, which are representations designed to leverage DARTS
for more general parameter optimization. Here we find that DARTS fails to
generalize when compared against a human's one shot choice of models. We look
back to the DARTS and sharpDARTS search spaces to understand why, and an
ablation study reveals an unusual generalization gap. We finally propose Max-W
regularization to solve this problem, which proves significantly better than
the handmade design. Code will be made available.
DRASIC: Distributed Recurrent Autoencoder for Scalable Image Compression
Authors: Enmao Diao, Jie Ding, Vahid Tarokh
Date: 2019-03-23 00:00:00
Tasks: Decoder, Image Compression

We propose a new architecture for distributed image compression from a group of distributed data sources. The work is motivated by practical needs of data-driven codec design, low power consumption, robustness, and data privacy. The proposed architecture, which we refer to as Distributed Recurrent Autoencoder for Scalable Image Compression (DRASIC), is able to train distributed encoders and one joint decoder on correlated data sources. Its compression capability is much better than the method of training codecs separately. Meanwhile, the performance of our distributed system with 10 distributed sources is only within 2 dB peak signal-to-noise ratio (PSNR) of the performance of a single codec trained with all data sources. We experiment distributed sources with different correlations and show how our data-driven methodology well matches the Slepian-Wolf Theorem in Distributed Source Coding (DSC). To the best of our knowledge, this is the first data-driven DSC framework for general distributed code design with deep learning.
Temporal Logic Guided Safe Reinforcement Learning Using Control Barrier Functions
Authors: Xiao Li, Calin Belta
Date: 2019-03-23 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Safe Exploration, Safe Reinforcement Learning

Using reinforcement learning to learn control policies is a challenge when
the task is complex with potentially long horizons. Ensuring adequate but safe
exploration is also crucial for controlling physical systems. In this paper, we
use temporal logic to facilitate specification and learning of complex tasks.
We combine temporal logic with control Lyapunov functions to improve
exploration. We incorporate control barrier functions to safeguard the
exploration and deployment process. We develop a flexible and learnable system
that allows users to specify task objectives and constraints in different forms
and at various levels. The framework is also able to take advantage of known
system dynamics and handle unknown environmental dynamics by integrating
model-free learning with model-based planning.
An End-to-end Framework For Integrated Pulmonary Nodule Detection and False Positive Reduction
Authors: Hao Tang, Xingwei Liu, Xiaohui Xie
Date: 2019-03-23 00:00:00
Tasks: Computed Tomography (CT)

Pulmonary nodule detection using low-dose Computed Tomography (CT) is often
the first step in lung disease screening and diagnosis. Recently, algorithms
based on deep convolutional neural nets have shown great promise for automated
nodule detection. Most of the existing deep learning nodule detection systems
are constructed in two steps: a) nodule candidates screening and b) false
positive reduction, using two different models trained separately. Although it
is commonly adopted, the two-step approach not only imposes significant
resource overhead on training two independent deep learning models, but also is
sub-optimal because it prevents cross-talk between the two. In this work, we
present an end-to-end framework for nodule detection, integrating nodule
candidate screening and false positive reduction into one model, trained
jointly. We demonstrate that the end-to-end system improves the performance by
3.88\% over the two-step approach, while at the same time reducing model
complexity by one third and cutting inference time by 3.6 fold. Code will be
made publicly available.
Automatic Pulmonary Lobe Segmentation Using Deep Learning
Authors: Hao Tang, Chupeng Zhang, Xiaohui Xie
Date: 2019-03-23 00:00:00
Tasks: Computed Tomography (CT), Deep Learning

Pulmonary lobe segmentation is an important task for pulmonary disease
related Computer Aided Diagnosis systems (CADs). Classical methods for lobe
segmentation rely on successful detection of fissures and other anatomical
information such as the location of blood vessels and airways. With the success
of deep learning in recent years, Deep Convolutional Neural Network (DCNN) has
been widely applied to analyze medical images like Computed Tomography (CT) and
Magnetic Resonance Imaging (MRI), which, however, requires a large number of
ground truth annotations. In this work, we release our manually labeled 50 CT
scans which are randomly chosen from the LUNA16 dataset and explore the use of
deep learning on this task. We propose pre-processing CT image by cropping
region that is covered by the convex hull of the lungs in order to mitigate the
influence of noise from outside the lungs. Moreover, we design a hybrid loss
function with dice loss to tackle extreme class imbalance issue and focal loss
to force model to focus on voxels that are hard to be discriminated. To
validate the robustness and performance of our proposed framework trained with
a small number of training examples, we further tested our model on CT scans
from an independent dataset. Experimental results show the robustness of the
proposed approach, which consistently improves performance across different
datasets by a maximum of $5.87\%$ as compared to a baseline model.
Expanding the Text Classification Toolbox with Cross-Lingual Embeddings
Authors: Meryem M'hamdi, Robert West, Andreea Hossmann, Michael Baeriswyl, Claudiu Musat
Date: 2019-03-23 00:00:00
Tasks: Classification, General Classification, Intent Detection, Multilingual Word Embeddings, text-classification, Text Classification, Topic Classification, Word Embeddings

Most work in text classification and Natural Language Processing (NLP)
focuses on English or a handful of other languages that have text corpora of
hundreds of millions of words. This is creating a new version of the digital
divide: the artificial intelligence (AI) divide. Transfer-based approaches,
such as Cross-Lingual Text Classification (CLTC) - the task of categorizing
texts written in different languages into a common taxonomy, are a promising
solution to the emerging AI divide. Recent work on CLTC has focused on
demonstrating the benefits of using bilingual word embeddings as features,
relegating the CLTC problem to a mere benchmark based on a simple averaged
perceptron.
  In this paper, we explore more extensively and systematically two flavors of
the CLTC problem: news topic classification and textual churn intent detection
(TCID) in social media. In particular, we test the hypothesis that embeddings
with context are more effective, by multi-tasking the learning of multilingual
word embeddings and text classification; we explore neural architectures for
CLTC; and we move from bi- to multi-lingual word embeddings. For all
architectures, types of word embeddings and datasets, we notice a consistent
gain trend in favor of multilingual joint training, especially for
low-resourced languages.
Automated pulmonary nodule detection using 3D deep convolutional neural networks
Authors: Hao Tang, Daniel R. Kim, Xiaohui Xie
Date: 2019-03-23 00:00:00
Tasks: Computed Tomography (CT), object-detection, Object Detection

Early detection of pulmonary nodules in computed tomography (CT) images is
essential for successful outcomes among lung cancer patients. Much attention
has been given to deep convolutional neural network (DCNN)-based approaches to
this task, but models have relied at least partly on 2D or 2.5D components for
inherently 3D data. In this paper, we introduce a novel DCNN approach,
consisting of two stages, that is fully three-dimensional end-to-end and
utilizes the state-of-the-art in object detection. First, nodule candidates are
identified with a U-Net-inspired 3D Faster R-CNN trained using online hard
negative mining. Second, false positive reduction is performed by 3D DCNN
classifiers trained on difficult examples produced during candidate screening.
Finally, we introduce a method to ensemble models from both stages via
consensus to give the final predictions. By using this framework, we ranked
first of 2887 teams in Season One of Alibaba's 2017 TianChi AI Competition for
Healthcare.
StartNet: Online Detection of Action Start in Untrimmed Videos
Authors: Mingfei Gao, Mingze Xu, Larry S. Davis, Richard Socher, Caiming Xiong
Date: 2019-03-23 00:00:00
Proceeding: ICCV 2019 10
Tasks: Action Classification, Policy Gradient Methods

We propose StartNet to address Online Detection of Action Start (ODAS) where
action starts and their associated categories are detected in untrimmed,
streaming videos. Previous methods aim to localize action starts by learning
feature representations that can directly separate the start point from its
preceding background. It is challenging due to the subtle appearance difference
near the action starts and the lack of training data. Instead, StartNet
decomposes ODAS into two stages: action classification (using ClsNet) and start
point localization (using LocNet). ClsNet focuses on per-frame labeling and
predicts action score distributions online. Based on the predicted action
scores of the past and current frames, LocNet conducts class-agnostic start
detection by optimizing long-term localization rewards using policy gradient
methods. The proposed framework is validated on two large-scale datasets,
THUMOS'14 and ActivityNet. The experimental results show that StartNet
significantly outperforms the state-of-the-art by 15%-30% p-mAP under the
offset tolerance of 1-10 seconds on THUMOS'14, and achieves comparable
performance on ActivityNet with 10 times smaller time offset.
Data Poisoning against Differentially-Private Learners: Attacks and Defenses
Authors: Yuzhe Ma, Xiaojin Zhu, Justin Hsu
Date: 2019-03-23 00:00:00
Tasks: Data Poisoning

Data poisoning attacks aim to manipulate the model produced by a learning algorithm by adversarially modifying the training set. We consider differential privacy as a defensive measure against this type of attack. We show that such learners are resistant to data poisoning attacks when the adversary is only able to poison a small number of items. However, this protection degrades as the adversary poisons more data. To illustrate, we design attack algorithms targeting objective and output perturbation learners, two standard approaches to differentially-private machine learning. Experiments show that our methods are effective when the attacker is allowed to poison sufficiently many training items.
Action-Centered Information Retrieval
Authors: Marcello Balduccini, Emily LeBlanc
Date: 2019-03-23 00:00:00
Tasks: Information Retrieval, Retrieval

Information Retrieval (IR) aims at retrieving documents that are most
relevant to a query provided by a user. Traditional techniques rely mostly on
syntactic methods. In some cases, however, links at a deeper semantic level
must be considered. In this paper, we explore a type of IR task in which
documents describe sequences of events, and queries are about the state of the
world after such events. In this context, successfully matching documents and
query requires considering the events' possibly implicit, uncertain effects and
side-effects. We begin by analyzing the problem, then propose an action
language based formalization, and finally automate the corresponding IR task
using Answer Set Programming.
Competence-based Curriculum Learning for Neural Machine Translation
Authors: Emmanouil Antonios Platanios, Otilia Stretcu, Graham Neubig, Barnabas Poczos, Tom M. Mitchell
Date: 2019-03-23 00:00:00
Proceeding: NAACL 2019 6
Tasks: Machine Translation, NMT, Translation

Current state-of-the-art NMT systems use large neural networks that are not
only slow to train, but also often require many heuristics and optimization
tricks, such as specialized learning rate schedules and large batch sizes. This
is undesirable as it requires extensive hyperparameter tuning. In this paper,
we propose a curriculum learning framework for NMT that reduces training time,
reduces the need for specialized heuristics or large batch sizes, and results
in overall better performance. Our framework consists of a principled way of
deciding which training samples are shown to the model at different times
during training, based on the estimated difficulty of a sample and the current
competence of the model. Filtering training samples in this manner prevents the
model from getting stuck in bad local optima, making it converge faster and
reach a better solution than the common approach of uniformly sampling training
examples. Furthermore, the proposed method can be easily applied to existing
NMT models by simply modifying their input data pipelines. We show that our
framework can help improve the training time and the performance of both
recurrent neural network models and Transformers, achieving up to a 70%
decrease in training time, while at the same time obtaining accuracy
improvements of up to 2.2 BLEU.
Monocular 3D Object Detection with Pseudo-LiDAR Point Cloud
Authors: Xinshuo Weng, Kris Kitani
Date: 2019-03-23 00:00:00
Tasks: 3D Object Detection, Depth Estimation, Monocular 3D Object Detection, Monocular Depth Estimation, Object, object-detection, Object Detection, Scene Understanding

Monocular 3D scene understanding tasks, such as object size estimation, heading angle estimation and 3D localization, is challenging. Successful modern day methods for 3D scene understanding require the use of a 3D sensor. On the other hand, single image based methods have significantly worse performance. In this work, we aim at bridging the performance gap between 3D sensing and 2D sensing for 3D object detection by enhancing LiDAR-based algorithms to work with single image input. Specifically, we perform monocular depth estimation and lift the input image to a point cloud representation, which we call pseudo-LiDAR point cloud. Then we can train a LiDAR-based 3D detection network with our pseudo-LiDAR end-to-end. Following the pipeline of two-stage 3D detection algorithms, we detect 2D object proposals in the input image and extract a point cloud frustum from the pseudo-LiDAR for each proposal. Then an oriented 3D bounding box is detected for each frustum. To handle the large amount of noise in the pseudo-LiDAR, we propose two innovations: (1) use a 2D-3D bounding box consistency constraint, adjusting the predicted 3D bounding box to have a high overlap with its corresponding 2D proposal after projecting onto the image; (2) use the instance mask instead of the bounding box as the representation of 2D proposals, in order to reduce the number of points not belonging to the object in the point cloud frustum. Through our evaluation on the KITTI benchmark, we achieve the top-ranked performance on both bird's eye view and 3D object detection among all monocular methods, effectively quadrupling the performance over previous state-of-the-art. Our code is available at https://github.com/xinshuoweng/Mono3D_PLiDAR.
Toward the Evaluation of Written Proficiency on a Collaborative Social Network for Learning Languages: Yask
Authors: Fabio N. Silva, Sergio Jimenez, George Dueñas
Date: 2019-03-23 00:00:00

Yask is an online social collaborative network for practicing languages in a
framework that includes requests, answers, and votes. Since measuring
linguistic competence using current approaches is difficult, expensive and in
many cases imprecise, we present a new alternative approach based on social
networks. Our method, called Proficiency Rank, extends the well-known Page Rank
algorithm to measure the reputation of users in a collaborative social graph.
First, we extended Page Rank so that it not only considers positive links
(votes) but also negative links. Second, in addition to using explicit links,
we also incorporate other 4 types of signals implicit in the social graph.
These extensions allow Proficiency Rank to produce proficiency rankings for
almost all users in the data set used, where only a minority contributes by
answering, while the majority contributes only by voting. This overcomes the
intrinsic limitation of Page Rank of only being able to rank the nodes that
have incoming links. Our experimental validation showed that the
reputation/importance of the users in Yask is significantly correlated with
their language proficiency. In contrast, their written production was poorly
correlated with the vocabulary profiles of the Common European Framework of
Reference. In addition, we found that negative signals (votes) are considerably
more informative than positive ones. We concluded that the use of this
technology is a promising tool for measuring second language proficiency, even
for relatively small groups of people.
Rotated Feature Network for multi-orientation object detection
Authors: Zhixin Zhang, Xudong Chen, Jie Liu, Kaibo Zhou
Date: 2019-03-23 00:00:00
Tasks: Classification, Decoder, General Classification, Object, object-detection, Object Detection, regression, Scene Classification

General detectors follow the pipeline that feature maps extracted from
ConvNets are shared between classification and regression tasks. However, there
exists obvious conflicting requirements in multi-orientation object detection
that classification is insensitive to orientations, while regression is quite
sensitive. To address this issue, we provide an Encoder-Decoder architecture,
called Rotated Feature Network (RFN), which produces rotation-sensitive feature
maps (RS) for regression and rotation-invariant feature maps (RI) for
classification. Specifically, the Encoder unit assigns weights for rotated
feature maps. The Decoder unit extracts RS and RI by performing resuming
operator on rotated and reweighed feature maps, respectively. To make the
rotation-invariant characteristics more reliable, we adopt a metric to
quantitatively evaluate the rotation-invariance by adding a constrain item in
the loss, yielding a promising detection performance. Compared with the
state-of-the-art methods, our method can achieve significant improvement on
NWPU VHR-10 and RSOD datasets. We further evaluate the RFN on the scene
classification in remote sensing images and object detection in natural images,
demonstrating its good generalization ability. The proposed RFN can be
integrated into an existing framework, leading to great performance with only a
slight increase in model complexity.
Detecting Curve Text with Local Segmentation Network and Curve Connection
Authors: Zhao Zhou, Hao Ye, Luhui Chen, Yingbin Zheng
Date: 2019-03-23 00:00:00
Tasks: Segmentation, Text Detection, Text Segmentation

Curve text or arbitrary shape text is very common in real-world scenarios. In this paper, we propose a novel framework with the local segmentation network (LSN) followed by the curve connection to detect text in horizontal, oriented and curved forms. The LSN is composed of two elements, i.e., proposal generation to get the horizontal rectangle proposals with high overlap with text and text segmentation to find the arbitrary shape text region within proposals. The curve connection is then designed to connect the local mask to the detection results. We conduct experiments using the proposed framework on two real-world curve text detection datasets and demonstrate the effectiveness over previous approaches.
1D-Convolutional Capsule Network for Hyperspectral Image Classification
Authors: Hai-Tao Zhang, Lingguo Meng, Xian Wei, Xiaoliang Tang, Xuan Tang, Xingping Wang, Bo Jin, Wei Yao
Date: 2019-03-23 00:00:00
Tasks: Classification, General Classification, Hyperspectral Image Classification, image-classification, Image Classification

Recently, convolutional neural networks (CNNs) have achieved excellent
performances in many computer vision tasks. Specifically, for hyperspectral
images (HSIs) classification, CNNs often require very complex structure due to
the high dimension of HSIs. The complex structure of CNNs results in
prohibitive training efforts. Moreover, the common situation in HSIs
classification task is the lack of labeled samples, which results in accuracy
deterioration of CNNs. In this work, we develop an easy-to-implement capsule
network to alleviate the aforementioned problems, i.e., 1D-convolution capsule
network (1D-ConvCapsNet). Firstly, 1D-ConvCapsNet separately extracts spatial
and spectral information on spatial and spectral domains, which is more
lightweight than 3D-convolution due to fewer parameters. Secondly,
1D-ConvCapsNet utilizes the capsule-wise constraint window method to reduce
parameter amount and computational complexity of conventional capsule network.
Finally, 1D-ConvCapsNet obtains accurate predictions with respect to input
samples via dynamic routing. The effectiveness of the 1D-ConvCapsNet is
verified by three representative HSI datasets. Experimental results demonstrate
that 1D-ConvCapsNet is superior to state-of-the-art methods in both the
accuracy and training effort.
Color Filter Arrays for Quanta Image Sensors
Authors: Omar A. Elgendy, Stanley H. Chan
Date: 2019-03-23 00:00:00
Tasks: Image Reconstruction, Sensitivity

Quanta image sensor (QIS) is envisioned to be the next generation image sensor after CCD and CMOS. In this paper, we discuss how to design color filter arrays for QIS and other small pixels. Designing color filter arrays for small pixels is challenging because maximizing the light efficiency while suppressing aliasing and crosstalk are conflicting tasks. We present an optimization-based framework which unifies several mainstream color filter array design methodologies. Our method offers greater generality and flexibility. Compared to existing methods, the new framework can simultaneously handle luminance sensitivity, chrominance sensitivity, cross-talk, anti-aliasing, manufacturability and orthogonality. Extensive experimental comparisons demonstrate the effectiveness of the framework.