pwc-archive/papers-with-abstracts huggingface dataset:
Novel Deep Learning Model for Traffic Sign Detection Using Capsule Networks
Authors: Amara Dinesh Kumar
Date: 2018-05-11 00:00:00
Tasks: Deep Learning, Traffic Sign Detection, Traffic Sign Recognition

Convolutional neural networks are the most widely used deep learning
algorithms for traffic signal classification till date but they fail to capture
pose, view, orientation of the images because of the intrinsic inability of max
pooling layer.This paper proposes a novel method for Traffic sign detection
using deep learning architecture called capsule networks that achieves
outstanding performance on the German traffic sign dataset.Capsule network
consists of capsules which are a group of neurons representing the
instantiating parameters of an object like the pose and orientation by using
the dynamic routing and route by agreement algorithms.unlike the previous
approaches of manual feature extraction,multiple deep neural networks with many
parameters,our method eliminates the manual effort and provides resistance to
the spatial variances.CNNs can be fooled easily using various adversary attacks
and capsule networks can overcome such attacks from the intruders and can offer
more reliability in traffic sign detection for autonomous vehicles.Capsule
network have achieved the state-of-the-art accuracy of 97.6% on German Traffic
Sign Recognition Benchmark dataset (GTSRB).
Machine Learning for Public Administration Research, with Application to Organizational Reputation
Authors: L. Jason Anastasopoulos, Andrew B. Whitford
Date: 2018-05-11 00:00:00
Tasks: BIG-bench Machine Learning, Causal Inference

Machine learning methods have gained a great deal of popularity in recent
years among public administration scholars and practitioners. These techniques
open the door to the analysis of text, image and other types of data that allow
us to test foundational theories of public administration and to develop new
theories. Despite the excitement surrounding machine learning methods, clarity
regarding their proper use and potential pitfalls is lacking. This paper
attempts to fill this gap in the literature through providing a machine
learning "guide to practice" for public administration scholars and
practitioners. Here, we take a foundational view of machine learning and
describe how these methods can enrich public administration research and
practice through their ability develop new measures, tap into new sources of
data and conduct statistical inference and causal inference in a principled
manner. We then turn our attention to the pitfalls of using these methods such
as unvalidated measures and lack of interpretability. Finally, we demonstrate
how machine learning techniques can help us learn about organizational
reputation in federal agencies through an illustrated example using tweets from
13 executive federal agencies.
Deep Hierarchical Reinforcement Learning Algorithm in Partially Observable Markov Decision Processes
Authors: Le Pham Tuyen, Ngo Anh Vien, Abu Layek, TaeChoong Chung
Date: 2018-05-11 00:00:00
Tasks: Deep Reinforcement Learning, Hierarchical Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In recent years, reinforcement learning has achieved many remarkable
successes due to the growing adoption of deep learning techniques and the rapid
growth in computing power. Nevertheless, it is well-known that flat
reinforcement learning algorithms are often not able to learn well and
data-efficient in tasks having hierarchical structures, e.g. consisting of
multiple subtasks. Hierarchical reinforcement learning is a principled approach
that is able to tackle these challenging tasks. On the other hand, many
real-world tasks usually have only partial observability in which state
measurements are often imperfect and partially observable. The problems of RL
in such settings can be formulated as a partially observable Markov decision
process (POMDP). In this paper, we study hierarchical RL in POMDP in which the
tasks have only partial observability and possess hierarchical properties. We
propose a hierarchical deep reinforcement learning approach for learning in
hierarchical POMDP. The deep hierarchical RL algorithm is proposed to apply to
both MDP and POMDP learning. We evaluate the proposed algorithm on various
challenging hierarchical POMDP.
PAD-Net: Multi-Tasks Guided Prediction-and-Distillation Network for Simultaneous Depth Estimation and Scene Parsing
Authors: Dan Xu, Wanli Ouyang, Xiaogang Wang, Nicu Sebe
Date: 2018-05-11 00:00:00
Proceeding: CVPR 2018 6
Tasks: Depth Estimation, Multi-Task Learning, Scene Parsing, Scene Understanding, Semantic Segmentation

Depth estimation and scene parsing are two particularly important tasks in
visual scene understanding. In this paper we tackle the problem of simultaneous
depth estimation and scene parsing in a joint CNN. The task can be typically
treated as a deep multi-task learning problem [42]. Different from previous
methods directly optimizing multiple tasks given the input training data, this
paper proposes a novel multi-task guided prediction-and-distillation network
(PAD-Net), which first predicts a set of intermediate auxiliary tasks ranging
from low level to high level, and then the predictions from these intermediate
auxiliary tasks are utilized as multi-modal input via our proposed multi-modal
distillation modules for the final tasks. During the joint learning, the
intermediate tasks not only act as supervision for learning more robust deep
representations but also provide rich multi-modal information for improving the
final tasks. Extensive experiments are conducted on two challenging datasets
(i.e. NYUD-v2 and Cityscapes) for both the depth estimation and scene parsing
tasks, demonstrating the effectiveness of the proposed approach.
Decision problems for Clark-congruential languages
Authors: Makoto Kanazawa, Tobias Kappé
Date: 2018-05-11 00:00:00

A common question when studying a class of context-free grammars is whether
equivalence is decidable within this class. We answer this question positively
for the class of Clark-congruential grammars, which are of interest to
grammatical inference. We also consider the problem of checking whether a given
CFG is Clark-congruential, and show that it is decidable given that the CFG is
a DCFG.
Iteratively Trained Interactive Segmentation
Authors: Sabarinath Mahadevan, Paul Voigtlaender, Bastian Leibe
Date: 2018-05-11 00:00:00
Tasks: Interactive Segmentation, Object, Segmentation, Semantic Segmentation

Deep learning requires large amounts of training data to be effective. For
the task of object segmentation, manually labeling data is very expensive, and
hence interactive methods are needed. Following recent approaches, we develop
an interactive object segmentation system which uses user input in the form of
clicks as the input to a convolutional network. While previous methods use
heuristic click sampling strategies to emulate user clicks during training, we
propose a new iterative training strategy. During training, we iteratively add
clicks based on the errors of the currently predicted segmentation. We show
that our iterative training strategy together with additional improvements to
the network architecture results in improved results over the state-of-the-art.
A Sensorimotor Perspective on Grounding the Semantic of Simple Visual Features
Authors: Alban Laflaquière
Date: 2018-05-11 00:00:00

In Machine Learning and Robotics, the semantic content of visual features is
usually provided to the system by a human who interprets its content. On the
contrary, strictly unsupervised approaches have difficulties relating the
statistics of sensory inputs to their semantic content without also relying on
prior knowledge introduced in the system. We proposed in this paper to tackle
this problem from a sensorimotor perspective. In line with the Sensorimotor
Contingencies Theory, we make the fundamental assumption that the semantic
content of sensory inputs at least partially stems from the way an agent can
actively transform it. We illustrate our approach by formalizing how simple
visual features can induce invariants in a naive agent's sensorimotor
experience, and evaluate it on a simple simulated visual system. Without any a
priori knowledge about the way its sensorimotor information is encoded, we show
how an agent can characterize the uniformity and edge-ness of the visual
features it interacts with.
Weakly Supervised Domain-Specific Color Naming Based on Attention
Authors: Lu Yu, Yongmei Cheng, Joost Van de Weijer
Date: 2018-05-11 00:00:00
Tasks: General Classification

The majority of existing color naming methods focuses on the eleven basic
color terms of the English language. However, in many applications, different
sets of color names are used for the accurate description of objects. Labeling
data to learn these domain-specific color names is an expensive and laborious
task. Therefore, in this article we aim to learn color names from weakly
labeled data. For this purpose, we add an attention branch to the color naming
network. The attention branch is used to modulate the pixel-wise color naming
predictions of the network. In experiments, we illustrate that the attention
branch correctly identifies the relevant regions. Furthermore, we show that our
method obtains state-of-the-art results for pixel-wise and image-wise
classification on the EBAY dataset and is able to learn color names for various
domains.
Exploiting Images for Video Recognition with Hierarchical Generative Adversarial Networks
Authors: Feiwu Yu, Xinxiao wu, Yuchao Sun, Lixin Duan
Date: 2018-05-11 00:00:00
Tasks: Domain Adaptation, Video Recognition

Existing deep learning methods of video recognition usually require a large
number of labeled videos for training. But for a new task, videos are often
unlabeled and it is also time-consuming and labor-intensive to annotate them.
Instead of human annotation, we try to make use of existing fully labeled
images to help recognize those videos. However, due to the problem of domain
shifts and heterogeneous feature representations, the performance of
classifiers trained on images may be dramatically degraded for video
recognition tasks. In this paper, we propose a novel method, called
Hierarchical Generative Adversarial Networks (HiGAN), to enhance recognition in
videos (i.e., target domain) by transferring knowledge from images (i.e.,
source domain). The HiGAN model consists of a \emph{low-level} conditional GAN
and a \emph{high-level} conditional GAN. By taking advantage of these two-level
adversarial learning, our method is capable of learning a domain-invariant
feature representation of source images and target videos. Comprehensive
experiments on two challenging video recognition datasets (i.e. UCF101 and
HMDB51) demonstrate the effectiveness of the proposed method when compared with
the existing state-of-the-art domain adaptation methods.
Scalable and accurate deep learning for electronic health records
Authors: Alvin Rajkomar, Eyal Oren, Kai Chen, Andrew M. Dai, Nissan Hajaj, Peter J. Liu, Xiaobing Liu, Mimi Sun, Patrik Sundberg, Hector Yee, Kun Zhang, Gavin E. Duggan, Gerardo Flores, Michaela Hardt, Jamie Irvine, Quoc Le, Kurt Litsch, Jake Marcus, Alexander Mossin, Justin Tansuwan, De Wang, James Wexler, Jimbo Wilson, Dana Ludwig, Samuel L. Volchenboum, Katherine Chou, Michael Pearson, Srinivasan Madabushi, Nigam H. Shah, Atul J. Butte, Michael Howell, Claire Cui, Greg Corrado, Jeff Dean
Date: 2018-01-24 00:00:00
Tasks: Deep Learning

Predictive modeling with electronic health record (EHR) data is anticipated
to drive personalized medicine and improve healthcare quality. Constructing
predictive statistical models typically requires extraction of curated
predictor variables from normalized EHR data, a labor-intensive process that
discards the vast majority of information in each patient's record. We propose
a representation of patients' entire, raw EHR records based on the Fast
Healthcare Interoperability Resources (FHIR) format. We demonstrate that deep
learning methods using this representation are capable of accurately predicting
multiple medical events from multiple centers without site-specific data
harmonization. We validated our approach using de-identified EHR data from two
U.S. academic medical centers with 216,221 adult patients hospitalized for at
least 24 hours. In the sequential format we propose, this volume of EHR data
unrolled into a total of 46,864,534,945 data points, including clinical notes.
Deep learning models achieved high accuracy for tasks such as predicting
in-hospital mortality (AUROC across sites 0.93-0.94), 30-day unplanned
readmission (AUROC 0.75-0.76), prolonged length of stay (AUROC 0.85-0.86), and
all of a patient's final discharge diagnoses (frequency-weighted AUROC 0.90).
These models outperformed state-of-the-art traditional predictive models in all
cases. We also present a case-study of a neural-network attribution system,
which illustrates how clinicians can gain some transparency into the
predictions. We believe that this approach can be used to create accurate and
scalable predictions for a variety of clinical scenarios, complete with
explanations that directly highlight evidence in the patient's chart.
A Distributed Frank-Wolfe Framework for Learning Low-Rank Matrices with the Trace Norm
Authors: Wenjie Zheng, Aurélien Bellet, Patrick Gallinari
Date: 2017-12-20 00:00:00

We consider the problem of learning a high-dimensional but low-rank matrix
from a large-scale dataset distributed over several machines, where
low-rankness is enforced by a convex trace norm constraint. We propose
DFW-Trace, a distributed Frank-Wolfe algorithm which leverages the low-rank
structure of its updates to achieve efficiency in time, memory and
communication usage. The step at the heart of DFW-Trace is solved approximately
using a distributed version of the power method. We provide a theoretical
analysis of the convergence of DFW-Trace, showing that we can ensure sublinear
convergence in expectation to an optimal solution with few power iterations per
epoch. We implement DFW-Trace in the Apache Spark distributed programming
framework and validate the usefulness of our approach on synthetic and real
data, including the ImageNet dataset with high-dimensional features extracted
from a deep neural network.
Quantitative Projection Coverage for Testing ML-enabled Autonomous Systems
Authors: Chih-Hong Cheng, Chung-Hao Huang, Hirotoshi Yasuoka
Date: 2018-05-11 00:00:00
Tasks: Autonomous Vehicles

Systematically testing models learned from neural networks remains a crucial
unsolved barrier to successfully justify safety for autonomous vehicles
engineered using data-driven approach. We propose quantitative k-projection
coverage as a metric to mediate combinatorial explosion while guiding the data
sampling process. By assuming that domain experts propose largely independent
environment conditions and by associating elements in each condition with
weights, the product of these conditions forms scenarios, and one may interpret
weights associated with each equivalence class as relative importance.
Achieving full k-projection coverage requires that the data set, when being
projected to the hyperplane formed by arbitrarily selected k-conditions, covers
each class with number of data points no less than the associated weight. For
the general case where scenario composition is constrained by rules, precisely
computing k-projection coverage remains in NP. In terms of finding minimum test
cases to achieve full coverage, we present theoretical complexity for important
sub-cases and an encoding to 0-1 integer programming. We have implemented a
research prototype that generates test cases for a visual object defection unit
in automated driving, demonstrating the technological feasibility of our
proposed coverage criterion.
Federated Learning for Ultra-Reliable Low-Latency V2V Communications
Authors: Sumudu Samarakoon, Mehdi Bennis, Walid Saad, Merouane Debbah
Date: 2018-05-11 00:00:00
Tasks: Federated Learning

In this paper, a novel joint transmit power and resource allocation approach
for enabling ultra-reliable low-latency communication (URLLC) in vehicular
networks is proposed. The objective is to minimize the network-wide power
consumption of vehicular users (VUEs) while ensuring high reliability in terms
of probabilistic queuing delays. In particular, a reliability measure is
defined to characterize extreme events (i.e., when vehicles' queue lengths
exceed a predefined threshold with non-negligible probability) using extreme
value theory (EVT). Leveraging principles from federated learning (FL), the
distribution of these extreme events corresponding to the tail distribution of
queues is estimated by VUEs in a decentralized manner. Finally, Lyapunov
optimization is used to find the joint transmit power and resource allocation
policies for each VUE in a distributed manner. The proposed solution is
validated via extensive simulations using a Manhattan mobility model. It is
shown that FL enables the proposed distributed method to estimate the tail
distribution of queues with an accuracy that is very close to a centralized
solution with up to 79\% reductions in the amount of data that need to be
exchanged. Furthermore, the proposed method yields up to 60\% reductions of
VUEs with large queue lengths, without an additional power consumption,
compared to an average queue-based baseline. Compared to systems with fixed
power consumption and focusing on queue stability while minimizing average
power consumption, the reduction in extreme events of the proposed method is
about two orders of magnitude.
Are ResNets Provably Better than Linear Predictors?
Authors: Ohad Shamir
Date: 2018-04-18 00:00:00
Proceeding: NeurIPS 2018 12

A residual network (or ResNet) is a standard deep neural net architecture,
with state-of-the-art performance across numerous applications. The main
premise of ResNets is that they allow the training of each layer to focus on
fitting just the residual of the previous layer's output and the target output.
Thus, we should expect that the trained network is no worse than what we can
obtain if we remove the residual layers and train a shallower network instead.
However, due to the non-convexity of the optimization problem, it is not at all
clear that ResNets indeed achieve this behavior, rather than getting stuck at
some arbitrarily poor local minimum. In this paper, we rigorously prove that
arbitrarily deep, nonlinear residual units indeed exhibit this behavior, in the
sense that the optimization landscape contains no local minima with value above
what can be obtained with a linear predictor (namely a 1-layer network).
Notably, we show this under minimal or no assumptions on the precise network
architecture, data distribution, or loss function used. We also provide a
quantitative analysis of approximate stationary points for this problem.
Finally, we show that with a certain tweak to the architecture, training the
network with standard stochastic gradient descent achieves an objective value
close or better than any linear predictor.
Weakly and Semi Supervised Human Body Part Parsing via Pose-Guided Knowledge Transfer
Authors: Hao-Shu Fang, Guansong Lu, Xiaolin Fang, Jianwen Xie, Yu-Wing Tai, Cewu Lu
Date: 2018-05-11 00:00:00
Proceeding: CVPR 2018 6
Tasks: Human Parsing, Human Part Segmentation, Segmentation, Semantic Segmentation, Transfer Learning

Human body part parsing, or human semantic part segmentation, is fundamental
to many computer vision tasks. In conventional semantic segmentation methods,
the ground truth segmentations are provided, and fully convolutional networks
(FCN) are trained in an end-to-end scheme. Although these methods have
demonstrated impressive results, their performance highly depends on the
quantity and quality of training data. In this paper, we present a novel method
to generate synthetic human part segmentation data using easily-obtained human
keypoint annotations. Our key idea is to exploit the anatomical similarity
among human to transfer the parsing results of a person to another person with
similar pose. Using these estimated results as additional training data, our
semi-supervised model outperforms its strong-supervised counterpart by 6 mIOU
on the PASCAL-Person-Part dataset, and we achieve state-of-the-art human
parsing results. Our approach is general and can be readily extended to other
object/animal parsing task assuming that their anatomical similarity can be
annotated by keypoints. The proposed model and accompanying source code are
available at https://github.com/MVIG-SJTU/WSHP
iLCM - A Virtual Research Infrastructure for Large-Scale Qualitative Data
Authors: Andreas Niekler, Arnim Bleier, Christian Kahmann, Lisa Posch, Gregor Wiedemann, Kenan Erdogan, Gerhard Heyer, Markus Strohmaier
Date: 2018-05-11 00:00:00
Proceeding: LREC 2018 5

The iLCM project pursues the development of an integrated research
environment for the analysis of structured and unstructured data in a "Software
as a Service" architecture (SaaS). The research environment addresses
requirements for the quantitative evaluation of large amounts of qualitative
data with text mining methods as well as requirements for the reproducibility
of data-driven research designs in the social sciences. For this, the iLCM
research environment comprises two central components. First, the Leipzig
Corpus Miner (LCM), a decentralized SaaS application for the analysis of large
amounts of news texts developed in a previous Digital Humanities project.
Second, the text mining tools implemented in the LCM are extended by an "Open
Research Computing" (ORC) environment for executable script documents,
so-called "notebooks". This novel integration allows to combine generic,
high-performance methods to process large amounts of unstructured text data and
with individual program scripts to address specific research requirements in
computational social science and digital humanities.
Piecewise classifier mappings: Learning fine-grained learners for novel categories with few examples
Authors: Xiu-Shen Wei, Peng Wang, Lingqiao Liu, Chunhua Shen, Jianxin Wu
Date: 2018-05-11 00:00:00
Tasks: Few-Shot Learning, Fine-Grained Image Recognition, Meta-Learning

Humans are capable of learning a new fine-grained concept with very little supervision, \emph{e.g.}, few exemplary images for a species of bird, yet our best deep learning systems need hundreds or thousands of labeled examples. In this paper, we try to reduce this gap by studying the fine-grained image recognition problem in a challenging few-shot learning setting, termed few-shot fine-grained recognition (FSFG). The task of FSFG requires the learning systems to build classifiers for novel fine-grained categories from few examples (only one or less than five). To solve this problem, we propose an end-to-end trainable deep network which is inspired by the state-of-the-art fine-grained recognition model and is tailored for the FSFG task. Specifically, our network consists of a bilinear feature learning module and a classifier mapping module: while the former encodes the discriminative information of an exemplar image into a feature vector, the latter maps the intermediate feature into the decision boundary of the novel category. The key novelty of our model is a "piecewise mappings" function in the classifier mapping module, which generates the decision boundary via learning a set of more attainable sub-classifiers in a more parameter-economic way. We learn the exemplar-to-classifier mapping based on an auxiliary dataset in a meta-learning fashion, which is expected to be able to generalize to novel categories. By conducting comprehensive experiments on three fine-grained datasets, we demonstrate that the proposed method achieves superior performance over the competing baselines.