pwc-archive/papers-with-abstracts huggingface dataset:
Learning Context-Sensitive Convolutional Filters for Text Processing
Authors: Dinghan Shen, Martin Renqiang Min, Yitong Li, Lawrence Carin
Date: 2017-09-25 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Paraphrase Identification, Sentence, Sentiment Analysis, Text Classification

Convolutional neural networks (CNNs) have recently emerged as a popular
building block for natural language processing (NLP). Despite their success,
most existing CNN models employed in NLP share the same learned (and static)
set of filters for all input sentences. In this paper, we consider an approach
of using a small meta network to learn context-sensitive convolutional filters
for text processing. The role of meta network is to abstract the contextual
information of a sentence or document into a set of input-aware filters. We
further generalize this framework to model sentence pairs, where a
bidirectional filter generation mechanism is introduced to encapsulate
co-dependent sentence representations. In our benchmarks on four different
tasks, including ontology classification, sentiment analysis, answer sentence
selection, and paraphrase identification, our proposed model, a modified CNN
with context-sensitive filters, consistently outperforms the standard CNN and
attention-based CNN baselines. By visualizing the learned context-sensitive
filters, we further validate and rationalize the effectiveness of proposed
framework.
Object-Part Attention Model for Fine-grained Image Classification
Authors: Yuxin Peng, Xiangteng He, Junjie Zhao
Date: 2017-04-06 00:00:00
Tasks: Classification, Fine-Grained Image Classification, General Classification, image-classification, Image Classification, Object

Fine-grained image classification is to recognize hundreds of subcategories
belonging to the same basic-level category, such as 200 subcategories belonging
to the bird, which is highly challenging due to large variance in the same
subcategory and small variance among different subcategories. Existing methods
generally first locate the objects or parts and then discriminate which
subcategory the image belongs to. However, they mainly have two limitations:
(1) Relying on object or part annotations which are heavily labor consuming.
(2) Ignoring the spatial relationships between the object and its parts as well
as among these parts, both of which are significantly helpful for finding
discriminative parts. Therefore, this paper proposes the object-part attention
model (OPAM) for weakly supervised fine-grained image classification, and the
main novelties are: (1) Object-part attention model integrates two level
attentions: object-level attention localizes objects of images, and part-level
attention selects discriminative parts of object. Both are jointly employed to
learn multi-view and multi-scale features to enhance their mutual promotions.
(2) Object-part spatial constraint model combines two spatial constraints:
object spatial constraint ensures selected parts highly representative, and
part spatial constraint eliminates redundancy and enhances discrimination of
selected parts. Both are jointly employed to exploit the subtle and local
differences for distinguishing the subcategories. Importantly, neither object
nor part annotations are used in our proposed approach, which avoids the heavy
labor consumption of labeling. Comparing with more than 10 state-of-the-art
methods on 4 widely-used datasets, our OPAM approach achieves the best
performance.
Underwater Multi-Robot Convoying using Visual Tracking by Detection
Authors: Florian Shkurti, Wei-Di Chang, Peter Henderson, Md Jahidul Islam, Juan Camilo Gamboa Higuera, Jimmy Li, Travis Manderson, Anqi Xu, Gregory Dudek, Junaed Sattar
Date: 2017-09-25 00:00:00
Tasks: object-detection, Object Detection, Visual Tracking

We present a robust multi-robot convoying approach that relies on visual
detection of the leading agent, thus enabling target following in unstructured
3-D environments. Our method is based on the idea of tracking-by-detection,
which interleaves efficient model-based object detection with temporal
filtering of image-based bounding box estimation. This approach has the
important advantage of mitigating tracking drift (i.e. drifting away from the
target object), which is a common symptom of model-free trackers and is
detrimental to sustained convoying in practice. To illustrate our solution, we
collected extensive footage of an underwater robot in ocean settings, and
hand-annotated its location in each frame. Based on this dataset, we present an
empirical comparison of multiple tracker variants, including the use of several
convolutional neural networks, both with and without recurrent connections, as
well as frequency-based model-free trackers. We also demonstrate the
practicality of this tracking-by-detection strategy in real-world scenarios by
successfully controlling a legged underwater robot in five degrees of freedom
to follow another robot's independent motion.
3D Camouflaging Object using RGB-D Sensors
Authors: Ahmed M. Siddek, Mohsen A. Rashwan, Islam A. Eshrah
Date: 2017-09-24 00:00:00
Tasks: Object

This paper proposes a new optical camouflage system that uses RGB-D cameras,
for acquiring point cloud of background scene, and tracking observers eyes.
This system enables a user to conceal an object located behind a display that
surrounded by 3D objects. If we considered here the tracked point of observer s
eyes is a light source, the system will work on estimating shadow shape of the
display device that falls on the objects in background. The system uses the 3d
observer s eyes and the locations of display corners to predict their shadow
points which have nearest neighbors in the constructed point cloud of
background scene.
Calligraphic Stylisation Learning with a Physiologically Plausible Model of Movement and Recurrent Neural Networks
Authors: Daniel Berio, Memo Akten, Frederic Fol Leymarie, Mick Grierson, Réjean Plamondon
Date: 2017-09-24 00:00:00

We propose a computational framework to learn stylisation patterns from
example drawings or writings, and then generate new trajectories that possess
similar stylistic qualities. We particularly focus on the generation and
stylisation of trajectories that are similar to the ones that can be seen in
calligraphy and graffiti art. Our system is able to extract and learn dynamic
and visual qualities from a small number of user defined examples which can be
recorded with a digitiser device, such as a tablet, mouse or motion capture
sensors. Our system is then able to transform new user drawn traces to be
kinematically and stylistically similar to the training examples. We implement
the system using a Recurrent Mixture Density Network (RMDN) combined with a
representation given by the parameters of the Sigma Lognormal model, a
physiologically plausible model of movement that has been shown to closely
reproduce the velocity and trace of human handwriting gestures.
Discovery Radiomics via Deep Multi-Column Radiomic Sequencers for Skin Cancer Detection
Authors: Mohammad Javad Shafiee, Alexander Wong
Date: 2017-09-24 00:00:00
Tasks: Diagnostic, Prognosis, Specificity

While skin cancer is the most diagnosed form of cancer in men and women, with
more cases diagnosed each year than all other cancers combined, sufficiently
early diagnosis results in very good prognosis and as such makes early
detection crucial. While radiomics have shown considerable promise as a
powerful diagnostic tool for significantly improving oncological diagnostic
accuracy and efficiency, current radiomics-driven methods have largely rely on
pre-defined, hand-crafted quantitative features, which can greatly limit the
ability to fully characterize unique cancer phenotype that distinguish it from
healthy tissue. Recently, the notion of discovery radiomics was introduced,
where a large amount of custom, quantitative radiomic features are directly
discovered from the wealth of readily available medical imaging data. In this
study, we present a novel discovery radiomics framework for skin cancer
detection, where we leverage novel deep multi-column radiomic sequencers for
high-throughput discovery and extraction of a large amount of custom radiomic
features tailored for characterizing unique skin cancer tissue phenotype. The
discovered radiomic sequencer was tested against 9,152 biopsy-proven clinical
images comprising of different skin cancers such as melanoma and basal cell
carcinoma, and demonstrated sensitivity and specificity of 91% and 75%,
respectively, thus achieving dermatologist-level performance and \break hence
can be a powerful tool for assisting general practitioners and dermatologists
alike in improving the efficiency, consistency, and accuracy of skin cancer
diagnosis.
Learning Unmanned Aerial Vehicle Control for Autonomous Target Following
Authors: Siyi Li, Tianbo Liu, Chi Zhang, Dit-yan Yeung, Shaojie Shen
Date: 2017-09-24 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

While deep reinforcement learning (RL) methods have achieved unprecedented
successes in a range of challenging problems, their applicability has been
mainly limited to simulation or game domains due to the high sample complexity
of the trial-and-error learning process. However, real-world robotic
applications often need a data-efficient learning process with safety-critical
constraints. In this paper, we consider the challenging problem of learning
unmanned aerial vehicle (UAV) control for tracking a moving target. To acquire
a strategy that combines perception and control, we represent the policy by a
convolutional neural network. We develop a hierarchical approach that combines
a model-free policy gradient method with a conventional feedback
proportional-integral-derivative (PID) controller to enable stable learning
without catastrophic failure. The neural network is trained by a combination of
supervised learning from raw images and reinforcement learning from games of
self-play. We show that the proposed approach can learn a target following
policy in a simulator efficiently and the learned behavior can be successfully
transferred to the DJI quadrotor platform for real-world UAV control.
Learning crystal plasticity using digital image correlation: Examples from discrete dislocation dynamics
Authors: Stefanos Papanikolaou, Michail Tzimas, Andrew C. E. Reid, Stephen A. Langer
Date: 2017-09-24 00:00:00
Tasks: BIG-bench Machine Learning

Digital image correlation (DIC) is a well-established, non-invasive technique
for tracking and quantifying the deformation of mechanical samples under
strain. While it provides an obvious way to observe incremental and aggregate
displacement information, it seems likely that DIC data sets, which after all
reflect the spatially-resolved response of a microstructure to loads, contain
much richer information than has generally been extracted from them. In this
paper, we demonstrate a machine-learning approach to quantifying the prior
deformation history of a crystalline sample based on its response to a
subsequent DIC test. This prior deformation history is encoded in the
microstructure through the inhomogeneity of the dislocation microstructure, and
in the spatial correlations of the dislocation patterns, which mediate the
system's response to the DIC test load. Our domain consists of deformed
crystalline thin films generated by a discrete dislocation plasticity
simulation. We explore the range of applicability of machine learning (ML) for
typical experimental protocols, and as a function of possible size effects and
stochasticity. Plasticity size effects may directly influence the data,
rendering unsupervised techniques unable to distinguish different plasticity
regimes.
Large Scale Image Segmentation with Structured Loss based Deep Learning for Connectome Reconstruction
Authors: Jan Funke, Fabian David Tschopp, William Grisaitis, Arlo Sheridan, Chandan Singh, Stephan Saalfeld, Srinivas C. Turaga
Date: 2017-09-09 00:00:00
Proceeding: IEEE Transactions on Pattern Analysis and Machine Intelligence 2018 5
Tasks: Image Segmentation, Semantic Segmentation

We present a method combining affinity prediction with region agglomeration, which improves significantly upon the state of the art of neuron segmentation from electron microscopy (EM) in accuracy and scalability. Our method consists of a 3D U-NET, trained to predict affinities between voxels, followed by iterative region agglomeration. We train using a structured loss based on MALIS, encouraging topologically correct segmentations obtained from affinity thresholding. Our extension consists of two parts: First, we present a quasi-linear method to compute the loss gradient, improving over the original quadratic algorithm. Second, we compute the gradient in two separate passes to avoid spurious gradient contributions in early training stages. Our predictions are accurate enough that simple learning-free percentile-based agglomeration outperforms more involved methods used earlier on inferior predictions. We present results on three diverse EM datasets, achieving relative improvements over previous results of 27%, 15%, and 250%. Our findings suggest that a single method can be applied to both nearly isotropic block-face EM data and anisotropic serial sectioned EM data. The runtime of our method scales linearly with the size of the volume and achieves a throughput of about 2.6 seconds per megavoxel, qualifying our method for the processing of very large datasets.
DeepXplore: Automated Whitebox Testing of Deep Learning Systems
Authors: Kexin Pei, Yinzhi Cao, Junfeng Yang, Suman Jana
Date: 2017-05-18 00:00:00
Tasks: Deep Learning, Malware Detection, Self-Driving Cars

Deep learning (DL) systems are increasingly deployed in safety- and
security-critical domains including self-driving cars and malware detection,
where the correctness and predictability of a system's behavior for corner case
inputs are of great importance. Existing DL testing depends heavily on manually
labeled data and therefore often fails to expose erroneous behaviors for rare
inputs.
  We design, implement, and evaluate DeepXplore, the first whitebox framework
for systematically testing real-world DL systems. First, we introduce neuron
coverage for systematically measuring the parts of a DL system exercised by
test inputs. Next, we leverage multiple DL systems with similar functionality
as cross-referencing oracles to avoid manual checking. Finally, we demonstrate
how finding inputs for DL systems that both trigger many differential behaviors
and achieve high neuron coverage can be represented as a joint optimization
problem and solved efficiently using gradient-based search techniques.
  DeepXplore efficiently finds thousands of incorrect corner case behaviors
(e.g., self-driving cars crashing into guard rails and malware masquerading as
benign software) in state-of-the-art DL models with thousands of neurons
trained on five popular datasets including ImageNet and Udacity self-driving
challenge data. For all tested DL models, on average, DeepXplore generated one
test input demonstrating incorrect behavior within one second while running
only on a commodity laptop. We further show that the test inputs generated by
DeepXplore can also be used to retrain the corresponding DL model to improve
the model's accuracy by up to 3%.
Survey of Recent Advances in Visual Question Answering
Authors: Supriya Pandhre, Shagun Sodhani
Date: 2017-09-24 00:00:00
Tasks: Question Answering, Survey, Visual Question Answering, Visual Question Answering (VQA)

Visual Question Answering (VQA) presents a unique challenge as it requires
the ability to understand and encode the multi-modal inputs - in terms of image
processing and natural language processing. The algorithm further needs to
learn how to perform reasoning over this multi-modal representation so it can
answer the questions correctly. This paper presents a survey of different
approaches proposed to solve the problem of Visual Question Answering. We also
describe the current state of the art model in later part of paper. In
particular, the paper describes the approaches taken by various algorithms to
extract image features, text features and the way these are employed to predict
answers. We also briefly discuss the experiments performed to evaluate the VQA
models and report their performances on diverse datasets including newly
released VQA2.0[8].
Combining Generative and Discriminative Approaches to Unsupervised Dependency Parsing via Dual Decomposition
Authors: Yong Jiang, Wenjuan Han, Kewei Tu
Date: 2017-08-02 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Clustering, Dependency Grammar Induction, Unsupervised Dependency Parsing

Unsupervised dependency parsing aims to learn a dependency parser from
unannotated sentences. Existing work focuses on either learning generative
models using the expectation-maximization algorithm and its variants, or
learning discriminative models using the discriminative clustering algorithm.
In this paper, we propose a new learning strategy that learns a generative
model and a discriminative model jointly based on the dual decomposition
method. Our method is simple and general, yet effective to capture the
advantages of both models and improve their learning results. We tested our
method on the UD treebank and achieved a state-of-the-art performance on thirty
languages.
An Optimal Online Method of Selecting Source Policies for Reinforcement Learning
Authors: Siyuan Li, Chongjie Zhang
Date: 2017-09-24 00:00:00
Tasks: Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Robot Navigation, Transfer Learning

Transfer learning significantly accelerates the reinforcement learning
process by exploiting relevant knowledge from previous experiences. The problem
of optimally selecting source policies during the learning process is of great
importance yet challenging. There has been little theoretical analysis of this
problem. In this paper, we develop an optimal online method to select source
policies for reinforcement learning. This method formulates online source
policy selection as a multi-armed bandit problem and augments Q-learning with
policy reuse. We provide theoretical guarantees of the optimal selection
process and convergence to the optimal policy. In addition, we conduct
experiments on a grid-based robot navigation domain to demonstrate its
efficiency and robustness by comparing to the state-of-the-art transfer
learning method.
Identifying Phrasemes via Interlingual Association Measures -- A Data-driven Approach on Dependency-parsed and Word-aligned Parallel Corpora
Authors: Johannes Graën
Date: 2017-09-24 00:00:00

This is a preprint of the article "Identifying Phrasemes via Interlingual
Association Measures" that was presented in February 2016 at the LeKo (Lexical
combinations and typified speech in a multilingual context) conference in
Innsbruck.
Deep Packet: A Novel Approach For Encrypted Traffic Classification Using Deep Learning
Authors: Mohammad Lotfollahi, Ramin Shirali Hossein Zade, Mahdi Jafari Siavoshani, Mohammdsadegh Saberian
Date: 2017-09-08 00:00:00
Tasks: General Classification, Traffic Classification

Internet traffic classification has become more important with rapid growth
of current Internet network and online applications. There have been numerous
studies on this topic which have led to many different approaches. Most of
these approaches use predefined features extracted by an expert in order to
classify network traffic. In contrast, in this study, we propose a \emph{deep
learning} based approach which integrates both feature extraction and
classification phases into one system. Our proposed scheme, called "Deep
Packet," can handle both \emph{traffic characterization} in which the network
traffic is categorized into major classes (\eg, FTP and P2P) and application
identification in which end-user applications (\eg, BitTorrent and Skype)
identification is desired. Contrary to most of the current methods, Deep Packet
can identify encrypted traffic and also distinguishes between VPN and non-VPN
network traffic. After an initial pre-processing phase on data, packets are fed
into Deep Packet framework that embeds stacked autoencoder and convolution
neural network in order to classify network traffic. Deep packet with CNN as
its classification model achieved recall of $0.98$ in application
identification task and $0.94$ in traffic categorization task. To the best of
our knowledge, Deep Packet outperforms all of the proposed classification
methods on UNB ISCX VPN-nonVPN dataset.
Function approximation with zonal function networks with activation functions analogous to the rectified linear unit functions
Authors: Hrushikesh N. Mhaskar
Date: 2017-09-24 00:00:00

A zonal function (ZF) network on the $q$ dimensional sphere $\mathbb{S}^q$ is
a network of the form $\mathbf{x}\mapsto \sum_{k=1}^n
a_k\phi(\mathbf{x}\cdot\mathbf{x}_k)$ where $\phi :[-1,1]\to\mathbf{R}$ is the
activation function, $\mathbf{x}_k\in\mathbb{S}^q$ are the centers, and
$a_k\in\mathbb{R}$. While the approximation properties of such networks are
well studied in the context of positive definite activation functions, recent
interest in deep and shallow networks motivate the study of activation
functions of the form $\phi(t)=|t|$, which are not positive definite. In this
paper, we define an appropriate smoothess class and establish approximation
properties of such networks for functions in this class. The centers can be
chosen independently of the target function, and the coefficients are linear
combinations of the training data. The constructions preserve rotational
symmetries.
Can Image Retrieval help Visual Saliency Detection?
Authors: Shuang Li, Peter Mathews
Date: 2017-09-24 00:00:00
Tasks: Image Retrieval, Retrieval, Saliency Detection

We propose a novel image retrieval framework for visual saliency detection
using information about salient objects contained within bounding box
annotations for similar images. For each test image, we train a customized SVM
from similar example images to predict the saliency values of its object
proposals and generate an external saliency map (ES) by aggregating the
regional scores. To overcome limitations caused by the size of the training
dataset, we also propose an internal optimization module which computes an
internal saliency map (IS) by measuring the low-level contrast information of
the test image. The two maps, ES and IS, have complementary properties so we
take a weighted combination to further improve the detection performance.
Experimental results on several challenging datasets demonstrate that the
proposed algorithm performs favorably against the state-of-the-art methods.