pwc-archive/papers-with-abstracts huggingface dataset:
Optimal Multi-view Correction of Local Affine Frames
Authors: Ivan Eichhardt, Daniel Barath
Date: 2019-05-01 00:00:00
Tasks: Homography Estimation, Pose Estimation

The technique requires the epipolar geometry to be pre-estimated between each
image pair. It exploits the constraints which the camera movement implies, in
order to apply a closed-form correction to the parameters of the input
affinities. Also, it is shown that the rotations and scales obtained by
partially affine-covariant detectors, e.g., AKAZE or SIFT, can be completed to
be full affine frames by the proposed algorithm. It is validated both in
synthetic experiments and on publicly available real-world datasets that the
method always improves the output of the evaluated affine-covariant feature
detectors. As a by-product, these detectors are compared and the ones obtaining
the most accurate affine frames are reported. For demonstrating the
applicability, we show that the proposed technique as a pre-processing step
improves the accuracy of pose estimation for a camera rig, surface normal and
homography estimation.
From Abstractions to Grounded Languages for Robust Coordination of Task Planning Robots
Authors: Yu Zhang
Date: 2019-05-01 00:00:00
Tasks: Multi-Agent Path Finding, Task Planning

In this paper, we consider a first step to bridge a gap in coordinating task planning robots. Specifically, we study the automatic construction of languages that are maximally flexible while being sufficiently explicative for coordination. To this end, we view language as a machinery for specifying temporal-state constraints of plans. Such a view enables us to reverse-engineer a language from the ground up by mapping these composable constraints to words. Our language expresses a plan for any given task as a "plan sketch" to convey just-enough details while maximizing the flexibility to realize it, leading to robust coordination with optimality guarantees among other benefits. We formulate and analyze the problem, provide an approximate solution, and validate the advantages of our approach under various scenarios to shed light on its applications.
Land Use and Land Cover Classification Using Deep Learning Techniques
Authors: Nagesh Kumar Uba
Date: 2019-05-01 00:00:00
Tasks: Deep Learning, General Classification, Land Cover Classification

Large datasets of sub-meter aerial imagery represented as orthophoto mosaics
are widely available today, and these data sets may hold a great deal of
untapped information. This imagery has a potential to locate several types of
features; for example, forests, parking lots, airports, residential areas, or
freeways in the imagery. However, the appearances of these things vary based on
many things including the time that the image is captured, the sensor settings,
processing done to rectify the image, and the geographical and cultural context
of the region captured by the image. This thesis explores the use of deep
convolutional neural networks to classify land use from very high spatial
resolution (VHR), orthorectified, visible band multispectral imagery. Recent
technological and commercial applications have driven the collection a massive
amount of VHR images in the visible red, green, blue (RGB) spectral bands, this
work explores the potential for deep learning algorithms to exploit this
imagery for automatic land use/ land cover (LULC) classification.
Learning higher-order sequential structure with cloned HMMs
Authors: Antoine Dedieu, Nishad Gothoskar, Scott Swingle, Wolfgang Lehrach, Miguel Lázaro-Gredilla, Dileep George
Date: 2019-05-01 00:00:00
Tasks: Community Detection, Language Modeling, Language Modelling

Variable order sequence modeling is an important problem in artificial and natural intelligence. While overcomplete Hidden Markov Models (HMMs), in theory, have the capacity to represent long-term temporal structure, they often fail to learn and converge to local minima. We show that by constraining HMMs with a simple sparsity structure inspired by biology, we can make it learn variable order sequences efficiently. We call this model cloned HMM (CHMM) because the sparsity structure enforces that many hidden states map deterministically to the same emission state. CHMMs with over 1 billion parameters can be efficiently trained on GPUs without being severely affected by the credit diffusion problem of standard HMMs. Unlike n-grams and sequence memoizers, CHMMs can model temporal dependencies at arbitrarily long distances and recognize contexts with 'holes' in them. Compared to Recurrent Neural Networks and their Long Short-Term Memory extensions (LSTMs), CHMMs are generative models that can natively deal with uncertainty. Moreover, CHMMs return a higher-order graph that represents the temporal structure of the data which can be useful for community detection, and for building hierarchical models. Our experiments show that CHMMs can beat n-grams, sequence memoizers, and LSTMs on character-level language modeling tasks. CHMMs can be a viable alternative to these methods in some tasks that require variable order sequence modeling and the handling of uncertainty.
Semi-Conditional Normalizing Flows for Semi-Supervised Learning
Authors: Andrei Atanov, Alexandra Volokhova, Arsenii Ashukha, Ivan Sosnovik, Dmitry Vetrov
Date: 2019-05-01 00:00:00
Tasks: General Classification

This paper proposes a semi-conditional normalizing flow model for semi-supervised learning. The model uses both labelled and unlabeled data to learn an explicit model of joint distribution over objects and labels. Semi-conditional architecture of the model allows us to efficiently compute a value and gradients of the marginal likelihood for unlabeled objects. The conditional part of the model is based on a proposed conditional coupling layer. We demonstrate performance of the model for semi-supervised classification problem on different datasets. The model outperforms the baseline approach based on variational auto-encoders on MNIST dataset.
Machine Learning meets Stochastic Geometry: Determinantal Subset Selection for Wireless Networks
Authors: Chiranjib Saha, Harpreet S. Dhillon
Date: 2019-05-01 00:00:00
Tasks: BIG-bench Machine Learning, Scheduling

In wireless networks, many problems can be formulated as subset selection
problems where the goal is to select a subset from the ground set with the
objective of maximizing some objective function. These problems are typically
NP-hard and hence solved through carefully constructed heuristics, which are
themselves mostly NP-complete and thus not easily applicable to large networks.
On the other hand, subset selection problems occur in slightly different
context in machine learning (ML) where the goal is to select a subset of high
quality yet diverse items from a ground set. In this paper, we introduce a
novel DPP-based learning (DPPL) framework for efficiently solving subset
selection problems in wireless networks. The DPPL is intended to replace the
traditional optimization algorithms for subset selection by learning the
quality-diversity trade-off in the optimal subsets selected by an optimization
routine. As a case study, we apply DPPL to the wireless link scheduling
problem, where the goal is to determine the subset of simultaneously active
links which maximizes the network-wide sum-rate. We demonstrate that the
proposed DPPL approaches the optimal solution with significantly lower
computational complexity than the popular optimization algorithms used for this
problem in the literature.
Task Planning with a Weighted Functional Object-Oriented Network
Authors: David Paulius, Kelvin Sheng Pei Dong, Yu Sun
Date: 2019-05-01 00:00:00
Tasks: Robot Task Planning, Task Planning

In reality, there is still much to be done for robots to be able to perform manipulation actions with full autonomy. Complicated manipulation tasks, such as cooking, may still require a person to perform some actions that are very risky for a robot to perform. On the other hand, some other actions may be very risky for a human with physical disabilities to perform. Therefore, it is necessary to balance the workload of a robot and a human based on their limitations while minimizing the effort needed from a human in a collaborative robot (cobot) set-up. This paper proposes a new version of our functional object-oriented network (FOON) that integrates weights in its functional units to reflect a robot's chance of successfully executing an action of that functional unit. The paper also presents a task planning algorithm for the weighted FOON to allocate manipulation action load to the robot and human to achieve optimal performance while minimizing human effort. Through a number of experiments, this paper shows several successful cases in which using the proposed weighted FOON and the task planning algorithm allow a robot and a human to successfully complete complicated tasks together with higher success rates than a robot doing them alone.
A Unified Deep Learning Formalism For Processing Graph Signals
Authors: Myriam Bontonou, Carlos Lassance, Jean-Charles Vialatte, Vincent Gripon
Date: 2019-05-01 00:00:00
Tasks: Deep Learning

Convolutional Neural Networks are very efficient at processing signals
defined on a discrete Euclidean space (such as images). However, as they can
not be used on signals defined on an arbitrary graph, other models have
emerged, aiming to extend its properties. We propose to review some of the
major deep learning models designed to exploit the underlying graph structure
of signals. We express them in a unified formalism, giving them a new and
comparative reading.
Efficient Model-free Reinforcement Learning in Metric Spaces
Authors: Zhao Song, Wen Sun
Date: 2019-05-01 00:00:00
Tasks: Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Model-free Reinforcement Learning (RL) algorithms such as Q-learning
[Watkins, Dayan 92] have been widely used in practice and can achieve human
level performance in applications such as video games [Mnih et al. 15].
Recently, equipped with the idea of optimism in the face of uncertainty,
Q-learning algorithms [Jin, Allen-Zhu, Bubeck, Jordan 18] can be proven to be
sample efficient for discrete tabular Markov Decision Processes (MDPs) which
have finite number of states and actions. In this work, we present an efficient
model-free Q-learning based algorithm in MDPs with a natural metric on the
state-action space--hence extending efficient model-free Q-learning algorithms
to continuous state-action space. Compared to previous model-based RL
algorithms for metric spaces [Kakade, Kearns, Langford 03], our algorithm does
not require access to a black-box planning oracle.
A system for the 2019 Sentiment, Emotion and Cognitive State Task of DARPAs LORELEI project
Authors: Victor R. Martinez, Anil Ramakrishna, Ming-Chang Chiu, Karan Singla, Shrikanth Narayanan
Date: 2019-05-01 00:00:00
Tasks: Disaster Response, Humanitarian, Management, Sentiment Analysis

During the course of a Humanitarian Assistance-Disaster Relief (HADR) crisis,
that can happen anywhere in the world, real-time information is often posted
online by the people in need of help which, in turn, can be used by different
stakeholders involved with management of the crisis. Automated processing of
such posts can considerably improve the effectiveness of such efforts; for
example, understanding the aggregated emotion from affected populations in
specific areas may help inform decision-makers on how to best allocate
resources for an effective disaster response. However, these efforts may be
severely limited by the availability of resources for the local language. The
ongoing DARPA project Low Resource Languages for Emergent Incidents (LORELEI)
aims to further language processing technologies for low resource languages in
the context of such a humanitarian crisis. In this work, we describe our
submission for the 2019 Sentiment, Emotion and Cognitive state (SEC) pilot task
of the LORELEI project. We describe a collection of sentiment analysis systems
included in our submission along with the features extracted. Our fielded
systems obtained the best results in both English and Spanish language
evaluations of the SEC pilot task.
Semi-automatic System for Title Construction
Authors: Swagata Duari, Vasudha Bhatnagar
Date: 2019-05-01 00:00:00
Tasks: Keyword Extraction

In this paper, we propose a semi-automatic system for title construction from
scientific abstracts. The system extracts and recommends impactful words from
the text, which the author can creatively use to construct an appropriate title
for the manuscript. The work is based on the hypothesis that keywords are good
candidates for title construction. We extract important words from the document
by inducing a supervised keyword extraction model. The model is trained on
novel features extracted from graph-of-text representation of the document. We
empirically show that these graph-based features are capable of discriminating
keywords from non-keywords. We further establish empirically that the proposed
approach can be applied to any text irrespective of the training domain and
corpus. We evaluate the proposed system by computing the overlap between
extracted keywords and the list of title-words for documents, and we observe a
macro-averaged precision of 82%.
Fully Automatic Brain Tumor Segmentation using a Normalized Gaussian Bayesian Classifier and 3D Fluid Vector Flow
Authors: Tao Wang, Irene Cheng, Anup Basu
Date: 2019-05-01 00:00:00
Tasks: Brain Tumor Segmentation, Segmentation, Tumor Segmentation

Brain tumor segmentation from Magnetic Resonance Images (MRIs) is an
important task to measure tumor responses to treatments. However, automatic
segmentation is very challenging. This paper presents an automatic brain tumor
segmentation method based on a Normalized Gaussian Bayesian classification and
a new 3D Fluid Vector Flow (FVF) algorithm. In our method, a Normalized
Gaussian Mixture Model (NGMM) is proposed and used to model the healthy brain
tissues. Gaussian Bayesian Classifier is exploited to acquire a Gaussian
Bayesian Brain Map (GBBM) from the test brain MR images. GBBM is further
processed to initialize the 3D FVF algorithm, which segments the brain tumor.
This algorithm has two major contributions. First, we present a NGMM to model
healthy brains. Second, we extend our 2D FVF algorithm to 3D space and use it
for brain tumor segmentation. The proposed method is validated on a publicly
available dataset.
Full-stack Optimization for Accelerating CNNs with FPGA Validation
Authors: Bradley McDanel, Sai Qian Zhang, H. T. Kung, Xin Dong
Date: 2019-05-01 00:00:00

We present a full-stack optimization framework for accelerating inference of
CNNs (Convolutional Neural Networks) and validate the approach with
field-programmable gate arrays (FPGA) implementations. By jointly optimizing
CNN models, computing architectures, and hardware implementations, our
full-stack approach achieves unprecedented performance in the trade-off space
characterized by inference latency, energy efficiency, hardware utilization and
inference accuracy. As a validation vehicle, we have implemented a 170MHz FPGA
inference chip achieving 2.28ms latency for the ImageNet benchmark. The
achieved latency is among the lowest reported in the literature while achieving
comparable accuracy. However, our chip shines in that it has 9x higher energy
efficiency compared to other implementations achieving comparable latency. A
highlight of our full-stack approach which attributes to the achieved high
energy efficiency is an efficient Selector-Accumulator (SAC) architecture for
implementing the multiplier-accumulator (MAC) operation present in any digital
CNN hardware. For instance, compared to a FPGA implementation for a traditional
8-bit MAC, SAC substantially reduces required hardware resources (4.85x fewer
Look-up Tables) and power consumption (2.48x).
Detection of Single Grapevine Berries in Images Using Fully Convolutional Neural Networks
Authors: Laura Zabawa, Anna Kicherer, Lasse Klingbeil, Andres Milioto, Reinhard Töpfer, Heiner Kuhlmann, Ribana Roscher
Date: 2019-05-01 00:00:00

Yield estimation and forecasting are of special interest in the field of
grapevine breeding and viticulture. The number of harvested berries per plant
is strongly correlated with the resulting quality. Therefore, early yield
forecasting can enable a focused thinning of berries to ensure a high quality
end product. Traditionally yield estimation is done by extrapolating from a
small sample size and by utilizing historic data. Moreover, it needs to be
carried out by skilled experts with much experience in this field. Berry
detection in images offers a cheap, fast and non-invasive alternative to the
otherwise time-consuming and subjective on-site analysis by experts. We apply
fully convolutional neural networks on images acquired with the Phenoliner, a
field phenotyping platform. We count single berries in images to avoid the
error-prone detection of grapevine clusters. Clusters are often overlapping and
can vary a lot in the size which makes the reliable detection of them
difficult. We address especially the detection of white grapes directly in the
vineyard. The detection of single berries is formulated as a classification
task with three classes, namely 'berry', 'edge' and 'background'. A connected
component algorithm is applied to determine the number of berries in one image.
We compare the automatically counted number of berries with the manually
detected berries in 60 images showing Riesling plants in vertical shoot
positioned trellis (VSP) and semi minimal pruned hedges (SMPH). We are able to
detect berries correctly within the VSP system with an accuracy of 94.0 \% and
for the SMPH system with 85.6 \%.
Machine Learning for Classification of Protein Helix Capping Motifs
Authors: Sean Mullane, Ruoyan Chen, Sri Vaishnavi Vemulapalli, Eli J. Draizen, Ke Wang, Cameron Mura, Philip E. Bourne
Date: 2019-05-01 00:00:00
Tasks: BIG-bench Machine Learning, Classification, General Classification

The biological function of a protein stems from its 3-dimensional structure,
which is thermodynamically determined by the energetics of interatomic forces
between its amino acid building blocks (the order of amino acids, known as the
sequence, defines a protein). Given the costs (time, money, human resources) of
determining protein structures via experimental means such as X-ray
crystallography, can we better describe and compare protein 3D structures in a
robust and efficient manner, so as to gain meaningful biological insights? We
begin by considering a relatively simple problem, limiting ourselves to just
protein secondary structural elements. Historically, many computational methods
have been devised to classify amino acid residues in a protein chain into one
of several discrete secondary structures, of which the most well-characterized
are the geometrically regular $\alpha$-helix and $\beta$-sheet; irregular
structural patterns, such as 'turns' and 'loops', are less understood. Here, we
present a study of Deep Learning techniques to classify the loop-like end cap
structures which delimit $\alpha$-helices. Previous work used highly empirical
and heuristic methods to manually classify helix capping motifs. Instead, we
use structural data directly--including (i) backbone torsion angles computed
from 3D structures, (ii) macromolecular feature sets (e.g., physicochemical
properties), and (iii) helix cap classification data (from CAPS-DB)--as the
ground truth to train a bidirectional long short-term memory (BiLSTM) model to
classify helix cap residues. We tried different network architectures and
scanned hyperparameters in order to train and assess several models; we also
trained a Support Vector Classifier (SVC) to use as a baseline. Ultimately, we
achieved 85% class-balanced accuracy with a deep BiLSTM model.
Signed Distance-based Deep Memory Recommender
Authors: Thanh Tran, Xinyue Liu, Kyumin Lee, Xiangnan Kong
Date: 2019-05-01 00:00:00
Tasks: Recommendation Systems

Personalized recommendation algorithms learn a user's preference for an item
by measuring a distance/similarity between them. However, some of the existing
recommendation models (e.g., matrix factorization) assume a linear relationship
between the user and item. This approach limits the capacity of recommender
systems, since the interactions between users and items in real-world
applications are much more complex than the linear relationship. To overcome
this limitation, in this paper, we design and propose a deep learning framework
called Signed Distance-based Deep Memory Recommender, which captures non-linear
relationships between users and items explicitly and implicitly, and work well
in both general recommendation task and shopping basket-based recommendation
task. Through an extensive empirical study on six real-world datasets in the
two recommendation tasks, our proposed approach achieved significant
improvement over ten state-of-the-art recommendation models.
On Expected Accuracy
Authors: Ozan İrsoy
Date: 2019-05-01 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification, regression

We empirically investigate the (negative) expected accuracy as an alternative
loss function to cross entropy (negative log likelihood) for classification
tasks. Coupled with softmax activation, it has small derivatives over most of
its domain, and is therefore hard to optimize. A modified, leaky version is
evaluated on a variety of classification tasks, including digit recognition,
image classification, sequence tagging and tree tagging, using a variety of
neural architectures such as logistic regression, multilayer perceptron, CNN,
LSTM and Tree-LSTM. We show that it yields comparable or better accuracy
compared to cross entropy. Furthermore, the proposed objective is shown to be
more robust to label noise.
NATTACK: Learning the Distributions of Adversarial Examples for an Improved Black-Box Attack on Deep Neural Networks
Authors: Yandong Li, Lijun Li, Liqiang Wang, Tong Zhang, Boqing Gong
Date: 2019-05-01 00:00:00
Tasks: Adversarial Attack

Powerful adversarial attack methods are vital for understanding how to construct robust deep neural networks (DNNs) and for thoroughly testing defense techniques. In this paper, we propose a black-box adversarial attack algorithm that can defeat both vanilla DNNs and those generated by various defense techniques developed recently. Instead of searching for an "optimal" adversarial example for a benign input to a targeted DNN, our algorithm finds a probability density distribution over a small region centered around the input, such that a sample drawn from this distribution is likely an adversarial example, without the need of accessing the DNN's internal layers or weights. Our approach is universal as it can successfully attack different neural networks by a single algorithm. It is also strong; according to the testing against 2 vanilla DNNs and 13 defended ones, it outperforms state-of-the-art black-box or white-box attack methods for most test cases. Additionally, our results reveal that adversarial training remains one of the best defense techniques, and the adversarial examples are not as transferable across defended DNNs as them across vanilla DNNs.