pwc-archive/papers-with-abstracts huggingface dataset:
Image Super-Resolution by Neural Texture Transfer
Authors: Zhifei Zhang, Zhaowen Wang, Zhe Lin, Hairong Qi
Date: 2019-03-03 00:00:00
Proceeding: CVPR 2019 6
Tasks: Image Stylization, Image Super-Resolution, Reference-based Super-Resolution, Super-Resolution

Due to the significant information loss in low-resolution (LR) images, it has
become extremely challenging to further advance the state-of-the-art of single
image super-resolution (SISR). Reference-based super-resolution (RefSR), on the
other hand, has proven to be promising in recovering high-resolution (HR)
details when a reference (Ref) image with similar content as that of the LR
input is given. However, the quality of RefSR can degrade severely when Ref is
less similar. This paper aims to unleash the potential of RefSR by leveraging
more texture details from Ref images with stronger robustness even when
irrelevant Ref images are provided. Inspired by the recent work on image
stylization, we formulate the RefSR problem as neural texture transfer. We
design an end-to-end deep model which enriches HR details by adaptively
transferring the texture from Ref images according to their textural
similarity. Instead of matching content in the raw pixel space as done by
previous methods, our key contribution is a multi-level matching conducted in
the neural space. This matching scheme facilitates multi-scale neural transfer
that allows the model to benefit more from those semantically related Ref
patches, and gracefully degrade to SISR performance on the least relevant Ref
inputs. We build a benchmark dataset for the general research of RefSR, which
contains Ref images paired with LR inputs with varying levels of similarity.
Both quantitative and qualitative evaluations demonstrate the superiority of
our method over state-of-the-art.
A Model-Driven Stack-Based Fully Convolutional Network for Pancreas Segmentation
Authors: Hao Li, Jun Li, Xiaozhu Lin, Xiaohua Qian
Date: 2019-03-03 00:00:00
Tasks: Pancreas Segmentation, Segmentation

The irregular geometry and high inter-slice variability in computerized tomography (CT) scans of the human pancreas make an accurate segmentation of this crucial organ a challenging task for existing data-driven deep learning methods. To address this problem, we present a novel model-driven stack-based fully convolutional network with a sliding window fusion algorithm for pancreas segmentation, termed MDS-Net. The MDS-Net's cost function includes a data approximation term and a prior knowledge regularization term combined with a stack scheme for capturing and fusing the two-dimensional (2D) and local three-dimensional (3D) context information. Specifically, 3D CT scans are divided into multiple stacks to capture the local spatial context feature. To highlight the importance of single slices, the inter-slice relationships in the stack data are also incorporated in the MDS-Net framework. For implementing this proposed model-driven method, we create a stack-based U-Net architecture and successfully derive its back-propagation procedure for end-to-end training. Furthermore, a sliding window fusion algorithm is utilized to improve the consistency of adjacent CT slices and intra-stack. Finally, extensive quantitative assessments on the NIH Pancreas-CT dataset demonstrated higher pancreatic segmentation accuracy and reliability of MDS-Net compared to other state-of-the-art methods.
Predicting Algorithm Classes for Programming Word Problems
Authors: Vinayak Athavale, Aayush Naik, Rajas Vanjape, Manish Shrivastava
Date: 2019-03-03 00:00:00
Proceeding: WS 2019 11
Tasks: Classification, General Classification, text-classification, Text Classification

We introduce the task of algorithm class prediction for programming word
problems. A programming word problem is a problem written in natural language,
which can be solved using an algorithm or a program. We define classes of
various programming word problems which correspond to the class of algorithms
required to solve the problem. We present four new datasets for this task, two
multiclass datasets with 550 and 1159 problems each and two multilabel datasets
having 3737 and 3960 problems each. We pose the problem as a text
classification problem and train neural network and non-neural network-based
models on this task. Our best performing classifier gets an accuracy of 62.7
percent for the multiclass case on the five class classification dataset,
Codeforces Multiclass-5 (CFMC5). We also do some human-level analysis and
compare human performance with that of our text classification models. Our best
classifier has an accuracy only 9 percent lower than that of a human on this
task. To the best of our knowledge, these are the first reported results on
such a task. We make our code and datasets publicly available.
Asynchronous Episodic Deep Deterministic Policy Gradient: Towards Continuous Control in Computationally Complex Environments
Authors: Zhizheng Zhang, Jiale Chen, Zhibo Chen, Weiping Li
Date: 2019-03-03 00:00:00
Tasks: continuous-control, Continuous Control, MuJoCo, Reinforcement Learning, Reinforcement Learning (RL)

Deep Deterministic Policy Gradient (DDPG) has been proved to be a successful
reinforcement learning (RL) algorithm for continuous control tasks. However,
DDPG still suffers from data insufficiency and training inefficiency,
especially in computationally complex environments. In this paper, we propose
Asynchronous Episodic DDPG (AE-DDPG), as an expansion of DDPG, which can
achieve more effective learning with less training time required. First, we
design a modified scheme for data collection in an asynchronous fashion.
Generally, for asynchronous RL algorithms, sample efficiency or/and training
stability diminish as the degree of parallelism increases. We consider this
problem from the perspectives of both data generation and data utilization. In
detail, we re-design experience replay by introducing the idea of episodic
control so that the agent can latch on good trajectories rapidly. In addition,
we also inject a new type of noise in action space to enrich the exploration
behaviors. Experiments demonstrate that our AE-DDPG achieves higher rewards and
requires less time consuming than most popular RL algorithms in Learning to Run
task which has a computationally complex environment. Not limited to the
control tasks in computationally complex environments, AE-DDPG also achieves
higher rewards and 2- to 4-fold improvement in sample efficiency on average
compared to other variants of DDPG in MuJoCo environments. Furthermore, we
verify the effectiveness of each proposed technique component through abundant
ablation study.
Visual-based Autonomous Driving Deployment from a Stochastic and Uncertainty-aware Perspective
Authors: Lei Tai, Peng Yun, Yuying Chen, Congcong Liu, Haoyang Ye, Ming Liu
Date: 2019-03-03 00:00:00
Tasks: Autonomous Driving, Domain Adaptation, Imitation Learning

End-to-end visual-based imitation learning has been widely applied in autonomous driving. When deploying the trained visual-based driving policy, a deterministic command is usually directly applied without considering the uncertainty of the input data. Such kind of policies may bring dramatical damage when applied in the real world. In this paper, we follow the recent real-to-sim pipeline by translating the testing world image back to the training domain when using the trained policy. In the translating process, a stochastic generator is used to generate various images stylized under the training domain randomly or directionally. Based on those translated images, the trained uncertainty-aware imitation learning policy would output both the predicted action and the data uncertainty motivated by the aleatoric loss function. Through the uncertainty-aware imitation learning policy, we can easily choose the safest one with the lowest uncertainty among the generated images. Experiments in the Carla navigation benchmark show that our strategy outperforms previous methods, especially in dynamic environments.
Stability of decision trees and logistic regression
Authors: Nino Arsov, Martin Pavlovski, Ljupco Kocarev
Date: 2019-03-03 00:00:00
Tasks: regression

Decision trees and logistic regression are one of the most popular and
well-known machine learning algorithms, frequently used to solve a variety of
real-world problems. Stability of learning algorithms is a powerful tool to
analyze their performance and sensitivity and subsequently allow researchers to
draw reliable conclusions. The stability of these two algorithms has remained
obscure. To that end, in this paper, we derive two stability notions for
decision trees and logistic regression: hypothesis and pointwise hypothesis
stability. Additionally, we derive these notions for L2-regularized logistic
regression and confirm existing findings that it is uniformly stable. We show
that the stability of decision trees depends on the number of leaves in the
tree, i.e., its depth, while for logistic regression, it depends on the
smallest eigenvalue of the Hessian matrix of the cross-entropy loss. We show
that logistic regression is not a stable learning algorithm. We construct the
upper bounds on the generalization error of all three algorithms. Moreover, we
present a novel stability measuring framework that allows one to measure the
aforementioned notions of stability. The measures are equivalent to estimates
of expected loss differences at an input example and then leverage bootstrap
sampling to yield statistically reliable estimates. Finally, we apply this
framework to the three algorithms analyzed in this paper to confirm our
theoretical findings and, in addition, we discuss the possibilities of
developing new training techniques to optimize the stability of logistic
regression, and hence decrease its generalization error.
Calibration of Encoder Decoder Models for Neural Machine Translation
Authors: Aviral Kumar, Sunita Sarawagi
Date: 2019-03-03 00:00:00
Tasks: Decoder, Machine Translation, NMT, Translation

We study the calibration of several state of the art neural machine
translation(NMT) systems built on attention-based encoder-decoder models. For
structured outputs like in NMT, calibration is important not just for reliable
confidence with predictions, but also for proper functioning of beam-search
inference. We show that most modern NMT models are surprisingly miscalibrated
even when conditioned on the true previous tokens. Our investigation leads to
two main reasons -- severe miscalibration of EOS (end of sequence marker) and
suppression of attention uncertainty. We design recalibration methods based on
these signals and demonstrate improved accuracy, better sequence-level
calibration, and more intuitive results from beam-search.
Keyframe-based Direct Thermal-Inertial Odometry
Authors: Shehryar Khattak, Christos Papachristos, Kostas Alexis
Date: 2019-03-03 00:00:00

This paper proposes an approach for fusing direct radiometric data from a
thermal camera with inertial measurements to extend the robotic capabilities of
aerial robots for navigation in GPS-denied and visually degraded environments
in the conditions of darkness and in the presence of airborne obscurants such
as dust, fog and smoke. An optimization based approach is developed that
jointly minimizes the re-projection error of 3D landmarks and inertial
measurement errors. The developed solution is extensively verified against both
ground-truth in an indoor laboratory setting, as well as inside an underground
mine under severely visually degraded conditions.
Let's Transfer Transformations of Shared Semantic Representations
Authors: Nam Vo, Lu Jiang, James Hays
Date: 2019-03-02 00:00:00
Tasks: Attribute, Image Retrieval, Image to 3D, Retrieval, Zero-Shot Learning

With a good image understanding capability, can we manipulate the images high
level semantic representation? Such transformation operation can be used to
generate or retrieve similar images but with a desired modification (for
example changing beach background to street background); similar ability has
been demonstrated in zero shot learning, attribute composition and attribute
manipulation image search. In this work we show how one can learn
transformations with no training examples by learning them on another domain
and then transfer to the target domain. This is feasible if: first,
transformation training data is more accessible in the other domain and second,
both domains share similar semantics such that one can learn transformations in
a shared embedding space. We demonstrate this on an image retrieval task where
search query is an image, plus an additional transformation specification (for
example: search for images similar to this one but background is a street
instead of a beach). In one experiment, we transfer transformation from
synthesized 2D blobs image to 3D rendered image, and in the other, we transfer
from text domain to natural image domain.
Neural MMO: A Massively Multiagent Game Environment for Training and Evaluating Intelligent Agents
Authors: Joseph Suarez, Yilun Du, Phillip Isola, Igor Mordatch
Date: 2019-03-02 00:00:00

The emergence of complex life on Earth is often attributed to the arms race
that ensued from a huge number of organisms all competing for finite resources.
We present an artificial intelligence research environment, inspired by the
human game genre of MMORPGs (Massively Multiplayer Online Role-Playing Games,
a.k.a. MMOs), that aims to simulate this setting in microcosm. As with MMORPGs
and the real world alike, our environment is persistent and supports a large
and variable number of agents. Our environment is well suited to the study of
large-scale multiagent interaction: it requires that agents learn robust combat
and navigation policies in the presence of large populations attempting to do
the same. Baseline experiments reveal that population size magnifies and
incentivizes the development of skillful behaviors and results in agents that
outcompete agents trained in smaller populations. We further show that the
policies of agents with unshared weights naturally diverge to fill different
niches in order to avoid competition.
Marker based Thermal-Inertial Localization for Aerial Robots in Obscurant Filled Environments
Authors: Shehryar Khattak, Christos Papachristos, Kostas Alexis
Date: 2019-03-02 00:00:00
Tasks: Navigate, Pose Estimation

For robotic inspection tasks in known environments fiducial markers provide a
reliable and low-cost solution for robot localization. However, detection of
such markers relies on the quality of RGB camera data, which degrades
significantly in the presence of visual obscurants such as fog and smoke. The
ability to navigate known environments in the presence of obscurants can be
critical for inspection tasks especially, in the aftermath of a disaster.
Addressing such a scenario, this work proposes a method for the design of
fiducial markers to be used with thermal cameras for the pose estimation of
aerial robots. Our low cost markers are designed to work in the long wave
infrared spectrum, which is not affected by the presence of obscurants, and can
be affixed to any object that has measurable temperature difference with
respect to its surroundings. Furthermore, the estimated pose from the fiducial
markers is fused with inertial measurements in an extended Kalman filter to
remove high frequency noise and error present in the fiducial pose estimates.
The proposed markers and the pose estimation method are experimentally
evaluated in an obscurant filled environment using an aerial robot carrying a
thermal camera.
Fairness in Recommendation Ranking through Pairwise Comparisons
Authors: Alex Beutel, Jilin Chen, Tulsee Doshi, Hai Qian, Li Wei, Yi Wu, Lukasz Heldt, Zhe Zhao, Lichan Hong, Ed H. Chi, Cristos Goodrow
Date: 2019-03-02 00:00:00
Tasks: Fairness, Recommendation Systems

Recommender systems are one of the most pervasive applications of machine
learning in industry, with many services using them to match users to products
or information. As such it is important to ask: what are the possible fairness
risks, how can we quantify them, and how should we address them?
  In this paper we offer a set of novel metrics for evaluating algorithmic
fairness concerns in recommender systems. In particular we show how measuring
fairness based on pairwise comparisons from randomized experiments provides a
tractable means to reason about fairness in rankings from recommender systems.
Building on this metric, we offer a new regularizer to encourage improving this
metric during model training and thus improve fairness in the resulting
rankings. We apply this pairwise regularization to a large-scale, production
recommender system and show that we are able to significantly improve the
system's pairwise fairness.
Spatio-Temporal Vegetation Pixel Classification By Using Convolutional Networks
Authors: Keiller Nogueira, Jefersson A. dos Santos, Nathalia Menini, Thiago S. F. Silva, Leonor Patricia C. Morellato, Ricardo da S. Torres
Date: 2019-03-02 00:00:00
Tasks: Classification, General Classification

Plant phenology studies rely on long-term monitoring of life cycles of
plants. High-resolution unmanned aerial vehicles (UAVs) and near-surface
technologies have been used for plant monitoring, demanding the creation of
methods capable of locating and identifying plant species through time and
space. However, this is a challenging task given the high volume of data, the
constant data missing from temporal dataset, the heterogeneity of temporal
profiles, the variety of plant visual patterns, and the unclear definition of
individuals' boundaries in plant communities. In this letter, we propose a
novel method, suitable for phenological monitoring, based on Convolutional
Networks (ConvNets) to perform spatio-temporal vegetation pixel-classification
on high resolution images. We conducted a systematic evaluation using
high-resolution vegetation image datasets associated with the Brazilian Cerrado
biome. Experimental results show that the proposed approach is effective,
overcoming other spatio-temporal pixel-classification strategies.
Extreme Channel Prior Embedded Network for Dynamic Scene Deblurring
Authors: Jianrui Cai, WangMeng Zuo, Lei Zhang
Date: 2019-03-02 00:00:00
Tasks: Deblurring, Image Deblurring

Recent years have witnessed the significant progress on convolutional neural
networks (CNNs) in dynamic scene deblurring. While CNN models are generally
learned by the reconstruction loss defined on training data, incorporating
suitable image priors as well as regularization terms into the network
architecture could boost the deblurring performance. In this work, we propose
an Extreme Channel Prior embedded Network (ECPeNet) to plug the extreme channel
priors (i.e., priors on dark and bright channels) into a network architecture
for effective dynamic scene deblurring. A novel trainable extreme channel prior
embedded layer (ECPeL) is developed to aggregate both extreme channel and
blurry image representations, and sparse regularization is introduced to
regularize the ECPeNet model learning. Furthermore, we present an effective
multi-scale network architecture that works in both coarse-to-fine and
fine-to-coarse manners for better exploiting information flow across scales.
Experimental results on GoPro and Kohler datasets show that our proposed
ECPeNet performs favorably against state-of-the-art deep image deblurring
methods in terms of both quantitative metrics and visual quality.
Time-Delay Momentum: A Regularization Perspective on the Convergence and Generalization of Stochastic Momentum for Deep Learning
Authors: Ziming Zhang, Wenju Xu, Alan Sullivan
Date: 2019-03-02 00:00:00
Tasks: Deep Learning

In this paper we study the problem of convergence and generalization error bound of stochastic momentum for deep learning from the perspective of regularization. To do so, we first interpret momentum as solving an $\ell_2$-regularized minimization problem to learn the offsets between arbitrary two successive model parameters. We call this {\em time-delay momentum} because the model parameter is updated after a few iterations towards finding the minimizer. We then propose our learning algorithm, \ie stochastic gradient descent (SGD) with time-delay momentum. We show that our algorithm can be interpreted as solving a sequence of strongly convex optimization problems using SGD. We prove that under mild conditions our algorithm can converge to a stationary point with rate of $O(\frac{1}{\sqrt{K}})$ and generalization error bound of $O(\frac{1}{\sqrt{n\delta}})$ with probability at least $1-\delta$, where $K,n$ are the numbers of model updates and training samples, respectively. We demonstrate the empirical superiority of our algorithm in deep learning in comparison with the state-of-the-art deep learning solvers.
GraphVite: A High-Performance CPU-GPU Hybrid System for Node Embedding
Authors: Zhaocheng Zhu, Shizhen Xu, Meng Qu, Jian Tang
Date: 2019-03-02 00:00:00
Tasks: CPU, Dimensionality Reduction, GPU, Knowledge Graph Embedding, Link Prediction, Network Embedding, Node Classification, Vocal Bursts Intensity Prediction

Learning continuous representations of nodes is attracting growing interest
in both academia and industry recently, due to their simplicity and
effectiveness in a variety of applications. Most of existing node embedding
algorithms and systems are capable of processing networks with hundreds of
thousands or a few millions of nodes. However, how to scale them to networks
that have tens of millions or even hundreds of millions of nodes remains a
challenging problem. In this paper, we propose GraphVite, a high-performance
CPU-GPU hybrid system for training node embeddings, by co-optimizing the
algorithm and the system. On the CPU end, augmented edge samples are parallelly
generated by random walks in an online fashion on the network, and serve as the
training data. On the GPU end, a novel parallel negative sampling is proposed
to leverage multiple GPUs to train node embeddings simultaneously, without much
data transfer and synchronization. Moreover, an efficient collaboration
strategy is proposed to further reduce the synchronization cost between CPUs
and GPUs. Experiments on multiple real-world networks show that GraphVite is
super efficient. It takes only about one minute for a network with 1 million
nodes and 5 million edges on a single machine with 4 GPUs, and takes around 20
hours for a network with 66 million nodes and 1.8 billion edges. Compared to
the current fastest system, GraphVite is about 50 times faster without any
sacrifice on performance.
Equilibrated Recurrent Neural Network: Neuronal Time-Delayed Self-Feedback Improves Accuracy and Stability
Authors: Ziming Zhang, Anil Kag, Alan Sullivan, Venkatesh Saligrama
Date: 2019-03-02 00:00:00

We propose a novel {\it Equilibrated Recurrent Neural Network} (ERNN) to
combat the issues of inaccuracy and instability in conventional RNNs. Drawing
upon the concept of autapse in neuroscience, we propose augmenting an RNN with
a time-delayed self-feedback loop. Our sole purpose is to modify the dynamics
of each internal RNN state and, at any time, enforce it to evolve close to the
equilibrium point associated with the input signal at that time. We show that
such self-feedback helps stabilize the hidden state transitions leading to fast
convergence during training while efficiently learning discriminative latent
features that result in state-of-the-art results on several benchmark datasets
at test-time. We propose a novel inexact Newton method to solve fixed-point
conditions given model parameters for generating the latent features at each
hidden state. We prove that our inexact Newton method converges locally with
linear rate (under mild conditions). We leverage this result for efficient
training of ERNNs based on backpropagation.