pwc-archive/papers-with-abstracts huggingface dataset:
Strong Baselines for Simple Question Answering over Knowledge Graphs with and without Neural Networks
Authors: Salman Mohammed, Peng Shi, Jimmy Lin
Date: 2017-12-05 00:00:00
Proceeding: NAACL 2018 6
Tasks: Entity Linking, Knowledge Graphs, Question Answering, Relation Prediction

We examine the problem of question answering over knowledge graphs, focusing
on simple questions that can be answered by the lookup of a single fact.
Adopting a straightforward decomposition of the problem into entity detection,
entity linking, relation prediction, and evidence combination, we explore
simple yet strong baselines. On the popular SimpleQuestions dataset, we find
that basic LSTMs and GRUs plus a few heuristics yield accuracies that approach
the state of the art, and techniques that do not use neural networks also
perform reasonably well. These results show that gains from sophisticated deep
learning techniques proposed in the literature are quite modest and that some
previous models exhibit unnecessary complexity.
Y-Net: A deep Convolutional Neural Network for Polyp Detection
Authors: Ahmed Mohammed, Sule Yildirim, Ivar Farup, Marius Pedersen, Øistein Hovde
Date: 2018-06-05 00:00:00
Tasks: Decoder

Colorectal polyps are important precursors to colon cancer, the third most
common cause of cancer mortality for both men and women. It is a disease where
early detection is of crucial importance. Colonoscopy is commonly used for
early detection of cancer and precancerous pathology. It is a demanding
procedure requiring significant amount of time from specialized physicians and
nurses, in addition to a significant miss-rates of polyps by specialists.
Automated polyp detection in colonoscopy videos has been demonstrated to be a
promising way to handle this problem. {However, polyps detection is a
challenging problem due to the availability of limited amount of training data
and large appearance variations of polyps. To handle this problem, we propose a
novel deep learning method Y-Net that consists of two encoder networks with a
decoder network. Our proposed Y-Net method} relies on efficient use of
pre-trained and un-trained models with novel sum-skip-concatenation operations.
Each of the encoders are trained with encoder specific learning rate along the
decoder. Compared with the previous methods employing hand-crafted features or
2-D/3-D convolutional neural network, our approach outperforms state-of-the-art
methods for polyp detection with 7.3% F1-score and 13% recall improvement.
MRPC: An R package for accurate inference of causal graphs
Authors: Md. Bahadur Badsha, Evan A Martin, Audrey Qiuyan Fu
Date: 2018-06-05 00:00:00
Tasks: MRPC

We present MRPC, an R package that learns causal graphs with improved
accuracy over existing packages, such as pcalg and bnlearn. Our algorithm
builds on the powerful PC algorithm, the canonical algorithm in computer
science for learning directed acyclic graphs. The improvement in accuracy
results from online control of the false discovery rate (FDR) that reduces
false positive edges, a more accurate approach to identifying v-structures
(i.e., $T_1 \rightarrow T_2 \leftarrow T_3$), and robust estimation of the
correlation matrix among nodes. For genomic data that contain genotypes and
gene expression for each sample, MRPC incorporates the principle of Mendelian
randomization to orient the edges. Our package can be applied to continuous and
discrete data.
Digging Into Self-Supervised Monocular Depth Estimation
Authors: Clément Godard, Oisin Mac Aodha, Michael Firman, Gabriel Brostow
Date: 2018-06-04 00:00:00
Tasks: Camera Pose Estimation, Depth Estimation, Image Reconstruction, Monocular Depth Estimation, Motion Estimation, Scene Understanding, Self-Supervised Learning, Unsupervised Monocular Depth Estimation

Per-pixel ground-truth depth data is challenging to acquire at scale. To overcome this limitation, self-supervised learning has emerged as a promising alternative for training models to perform monocular depth estimation. In this paper, we propose a set of improvements, which together result in both quantitatively and qualitatively improved depth maps compared to competing self-supervised methods. Research on self-supervised monocular training usually explores increasingly complex architectures, loss functions, and image formation models, all of which have recently helped to close the gap with fully-supervised methods. We show that a surprisingly simple model, and associated design choices, lead to superior predictions. In particular, we propose (i) a minimum reprojection loss, designed to robustly handle occlusions, (ii) a full-resolution multi-scale sampling method that reduces visual artifacts, and (iii) an auto-masking loss to ignore training pixels that violate camera motion assumptions. We demonstrate the effectiveness of each component in isolation, and show high quality, state-of-the-art results on the KITTI benchmark.
Performance Evaluation of Deep Learning Networks for Semantic Segmentation of Traffic Stereo-Pair Images
Authors: Vlad Taran, Nikita Gordienko, Yuriy Kochura, Yuri Gordienko, Alexandr Rokovyi, Oleg Alienin, Sergii Stirenko
Date: 2018-06-05 00:00:00
Tasks: Image Segmentation, Segmentation, Self-Driving Cars, Semantic Segmentation

Semantic image segmentation is one the most demanding task, especially for
analysis of traffic conditions for self-driving cars. Here the results of
application of several deep learning architectures (PSPNet and ICNet) for
semantic image segmentation of traffic stereo-pair images are presented. The
images from Cityscapes dataset and custom urban images were analyzed as to the
segmentation accuracy and image inference time. For the models pre-trained on
Cityscapes dataset, the inference time was equal in the limits of standard
deviation, but the segmentation accuracy was different for various cities and
stereo channels even. The distributions of accuracy (mean intersection over
union - mIoU) values for each city and channel are asymmetric, long-tailed, and
have many extreme outliers, especially for PSPNet network in comparison to
ICNet network. Some statistical properties of these distributions (skewness,
kurtosis) allow us to distinguish these two networks and open the question
about relations between architecture of deep learning networks and statistical
distribution of the predicted results (mIoU here). The results obtained
demonstrated the different sensitivity of these networks to: (1) the local
street view peculiarities in different cities that should be taken into account
during the targeted fine tuning the models before their practical applications,
(2) the right and left data channels in stereo-pairs. For both networks, the
difference in the predicted results (mIoU here) for the right and left data
channels in stereo-pairs is out of the limits of statistical error in relation
to mIoU values. It means that the traffic stereo pairs can be effectively used
not only for depth calculations (as it is usually used), but also as an
additional data channel that can provide much more information about scene
objects than simple duplication of the same street view images.
A theory of the phenomenology of multipopulation genetic algorithm with an application to the Ising model
Authors: Bruno Messias, Bruno W. D. Morais
Date: 2018-03-25 00:00:00

Genetic algorithm (GA) is a stochastic metaheuristic process consisting on
the evolution of a population of candidate solutions for a given optimization
problem. By extension, multipopulation genetic algorithm (MPGA) aims for
efficiency by evolving many populations, or islands, in parallel and performing
migrations between them periodically. The connectivity between islands
constrains the directions of migration and characterizes MPGA as a dynamic
process over a network. As such, predicting the evolution of the quality of the
solutions is a difficult challenge, implying in the waste of computer resources
and energy when the parameters are inadequate. By using models derived from
statistical mechanics, this work aims to estimate equations for the study of
dynamics in relation to the connectivity in MPGA. To illustrate the importance
of understanding MPGA, we show its application as an efficient alternative to
the thermalization phase of Metropolis-Hastings algorithm applied to the Ising
model.
SBEED: Convergent Reinforcement Learning with Nonlinear Function Approximation
Authors: Bo Dai, Albert Shaw, Lihong Li, Lin Xiao, Niao He, Zhen Liu, Jianshu Chen, Le Song
Date: 2017-12-29 00:00:00
Proceeding: ICML 2018 7
Tasks: Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

When function approximation is used, solving the Bellman optimality equation
with stability guarantees has remained a major open problem in reinforcement
learning for decades. The fundamental difficulty is that the Bellman operator
may become an expansion in general, resulting in oscillating and even divergent
behavior of popular algorithms like Q-learning. In this paper, we revisit the
Bellman equation, and reformulate it into a novel primal-dual optimization
problem using Nesterov's smoothing technique and the Legendre-Fenchel
transformation. We then develop a new algorithm, called Smoothed Bellman Error
Embedding, to solve this optimization problem where any differentiable function
class may be used. We provide what we believe to be the first convergence
guarantee for general nonlinear function approximation, and analyze the
algorithm's sample complexity. Empirically, our algorithm compares favorably to
state-of-the-art baselines in several benchmark control problems.
State Classification with CNN
Authors: Astha Sharma
Date: 2018-06-05 00:00:00
Tasks: Classification, General Classification, Object, Object Recognition, Transfer Learning

There is a plenty of research going on in field of object recognition, but
object state recognition has not been addressed as much. There are many
important applications which can utilize object state recognition, such as, in
robotics, to decide for how to grab an object. A convolution neural network was
designed to classify an image to one of its states. The approach used for
training is transfer learning with Inception v3 module of GoogLeNet used as the
pre-trained model. The model was trained on images of 18 cooking objects and
tested on another set of cooking objects. The model was able to classify those
images with 76% accuracy.
Evolutionary Innovations and Where to Find Them: Routes to Open-Ended Evolution in Natural and Artificial Systems
Authors: Tim Taylor
Date: 2018-06-05 00:00:00

This paper presents a high-level conceptual framework to help orient the
discussion and implementation of open-endedness in evolutionary systems.
Drawing upon earlier work by Banzhaf et al., three different kinds of
open-endedness are identified: exploratory, expansive, and transformational.
These are characterised in terms of their relationship to the search space of
phenotypic behaviours. A formalism is introduced to describe three key
processes required for an evolutionary process: the generation of a phenotype
from a genetic description, the evaluation of that phenotype, and the
reproduction with variation of individuals according to their evaluation. The
distinction is made between intrinsic and extrinsic implementations of these
processes. A discussion then investigates how various interactions between
these processes, and their modes of implementation, can lead to open-endedness.
However, an important contribution of the paper is the demonstration that these
considerations relate to exploratory open-endedness only. Conditions for the
implementation of the more interesting kinds of open-endedness - expansive and
transformational - are also discussed, emphasizing factors such as multiple
domains of behaviour, transdomain bridges, and non-additive compositional
systems. These factors relate not to the generic evolutionary properties of
individuals and populations, but rather to the nature of the building blocks
out of which individual organisms are constructed, and the laws and properties
of the environment in which they exist. The paper ends with suggestions of how
the framework can be used to categorise and compare the open-ended evolutionary
potential of different systems, how it might guide the design of systems with
greater capacity for open-ended evolution, and how it might be further
improved.
Staff line Removal using Generative Adversarial Networks
Authors: Aishik Konwer, Ayan Kumar Bhunia, Abir Bhowmick, Ankan Kumar Bhunia, Prithaj Banerjee, Partha Pratim Roy, Umapada Pal
Date: 2018-01-22 00:00:00
Tasks: Binary Classification

Staff line removal is a crucial pre-processing step in Optical Music
Recognition. It is a challenging task to simultaneously reduce the noise and
also retain the quality of music symbol context in ancient degraded music score
images. In this paper we propose a novel approach for staff line removal, based
on Generative Adversarial Networks. We convert staff line images into patches
and feed them into a U-Net, used as Generator. The Generator intends to produce
staff-less images at the output. Then the Discriminator does binary
classification and differentiates between the generated fake staff-less image
and real ground truth staff less image. For training, we use a Loss function
which is a weighted combination of L2 loss and Adversarial loss. L2 loss
minimizes the difference between real and fake staff-less image. Adversarial
loss helps to retrieve more high quality textures in generated images. Thus our
architecture supports solutions which are closer to ground truth and it
reflects in our results. For evaluation we consider the ICDAR/GREC 2013 staff
removal database. Our method achieves superior performance in comparison to
other conventional approaches.
An explicit analysis of the entropic penalty in linear programming
Authors: Jonathan Weed
Date: 2018-06-05 00:00:00
Tasks: valid

Solving linear programs by using entropic penalization has recently attracted
new interest in the optimization community, since this strategy forms the basis
for the fastest-known algorithms for the optimal transport problem, with many
applications in modern large-scale machine learning. Crucial to these
applications has been an analysis of how quickly solutions to the penalized
program approach true optima to the original linear program. More than 20 years
ago, Cominetti and San Mart\'in showed that this convergence is exponentially
fast; however, their proof is asymptotic and does not give any indication of
how accurately the entropic program approximates the original program for any
particular choice of the penalization parameter. We close this long-standing
gap in the literature regarding entropic penalization by giving a new proof of
the exponential convergence, valid for any linear program. Our proof is
non-asymptotic, yields explicit constants, and has the virtue of being
extremely simple. We provide matching lower bounds and show that the entropic
approach does not lead to a near-linear time approximation scheme for the
linear assignment problem.
EEG-GAN: Generative adversarial networks for electroencephalograhic (EEG) brain signals
Authors: Kay Gregor Hartmann, Robin Tibor Schirrmeister, Tonio Ball
Date: 2018-06-05 00:00:00
Tasks: Data Augmentation, EEG, Electroencephalogram (EEG), Time Series, Time Series Analysis, Time Series Generation

Generative adversarial networks (GANs) are recently highly successful in
generative applications involving images and start being applied to time series
data. Here we describe EEG-GAN as a framework to generate
electroencephalographic (EEG) brain signals. We introduce a modification to the
improved training of Wasserstein GANs to stabilize training and investigate a
range of architectural choices critical for time series generation (most
notably up- and down-sampling). For evaluation we consider and compare
different metrics such as Inception score, Frechet inception distance and
sliced Wasserstein distance, together showing that our EEG-GAN framework
generated naturalistic EEG examples. It thus opens up a range of new generative
application scenarios in the neuroscientific and neurological context, such as
data augmentation in brain-computer interfacing tasks, EEG super-sampling, or
restoration of corrupted data segments. The possibility to generate signals of
a certain class and/or with specific properties may also open a new avenue for
research into the underlying structure of brain signals.
Focal Visual-Text Attention for Visual Question Answering
Authors: Junwei Liang, Lu Jiang, Liangliang Cao, Li-Jia Li, Alexander Hauptmann
Date: 2018-06-05 00:00:00
Proceeding: CVPR 2018 6
Tasks: Memex Question Answering, Question Answering, Visual Question Answering, Visual Question Answering (VQA)

Recent insights on language and vision with neural networks have been successfully applied to simple single-image visual question answering. However, to tackle real-life question answering problems on multimedia collections such as personal photos, we have to look at whole collections with sequences of photos or videos. When answering questions from a large collection, a natural problem is to identify snippets to support the answer. In this paper, we describe a novel neural network called Focal Visual-Text Attention network (FVTA) for collective reasoning in visual question answering, where both visual and text sequence information such as images and text metadata are presented. FVTA introduces an end-to-end approach that makes use of a hierarchical process to dynamically determine what media and what time to focus on in the sequential data to answer the question. FVTA can not only answer the questions well but also provides the justifications which the system results are based upon to get the answers. FVTA achieves state-of-the-art performance on the MemexQA dataset and competitive results on the MovieQA dataset.
Pathwise Derivatives for Multivariate Distributions
Authors: Martin Jankowiak, Theofanis Karaletsos
Date: 2018-06-05 00:00:00
Tasks: Variational Inference

We exploit the link between the transport equation and derivatives of
expectations to construct efficient pathwise gradient estimators for
multivariate distributions. We focus on two main threads. First, we use null
solutions of the transport equation to construct adaptive control variates that
can be used to construct gradient estimators with reduced variance. Second, we
consider the case of multivariate mixture distributions. In particular we show
how to compute pathwise derivatives for mixtures of multivariate Normal
distributions with arbitrary means and diagonal covariances. We demonstrate in
a variety of experiments in the context of variational inference that our
gradient estimators can outperform other methods, especially in high
dimensions.
Pathwise Derivatives Beyond the Reparameterization Trick
Authors: Martin Jankowiak, Fritz Obermeyer
Date: 2018-06-05 00:00:00
Proceeding: ICML 2018 7
Tasks: regression, Variational Inference

We observe that gradients computed via the reparameterization trick are in
direct correspondence with solutions of the transport equation in the formalism
of optimal transport. We use this perspective to compute (approximate) pathwise
gradients for probability distributions not directly amenable to the
reparameterization trick: Gamma, Beta, and Dirichlet. We further observe that
when the reparameterization trick is applied to the Cholesky-factorized
multivariate Normal distribution, the resulting gradients are suboptimal in the
sense of optimal transport. We derive the optimal gradients and show that they
have reduced variance in a Gaussian Process regression task. We demonstrate
with a variety of synthetic experiments and stochastic variational inference
tasks that our pathwise gradients are competitive with other methods.
Relational Deep Reinforcement Learning
Authors: Vinicius Zambaldi, David Raposo, Adam Santoro, Victor Bapst, Yujia Li, Igor Babuschkin, Karl Tuyls, David Reichert, Timothy Lillicrap, Edward Lockhart, Murray Shanahan, Victoria Langston, Razvan Pascanu, Matthew Botvinick, Oriol Vinyals, Peter Battaglia
Date: 2018-06-05 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Relational Reasoning, Starcraft, Starcraft II

We introduce an approach for deep reinforcement learning (RL) that improves
upon the efficiency, generalization capacity, and interpretability of
conventional approaches through structured perception and relational reasoning.
It uses self-attention to iteratively reason about the relations between
entities in a scene and to guide a model-free policy. Our results show that in
a novel navigation and planning task called Box-World, our agent finds
interpretable solutions that improve upon baselines in terms of sample
complexity, ability to generalize to more complex scenes than experienced
during training, and overall performance. In the StarCraft II Learning
Environment, our agent achieves state-of-the-art performance on six mini-games
-- surpassing human grandmaster performance on four. By considering
architectural inductive biases, our work opens new directions for overcoming
important, but stubborn, challenges in deep RL.
Performance Metric Elicitation from Pairwise Classifier Comparisons
Authors: Gaurush Hiranandani, Shant Boodaghians, Ruta Mehta, Oluwasanmi Koyejo
Date: 2018-06-05 00:00:00
Tasks: Binary Classification, Classification, General Classification

Given a binary prediction problem, which performance metric should the
classifier optimize? We address this question by formalizing the problem of
Metric Elicitation. The goal of metric elicitation is to discover the
performance metric of a practitioner, which reflects her innate rewards (costs)
for correct (incorrect) classification. In particular, we focus on eliciting
binary classification performance metrics from pairwise feedback, where a
practitioner is queried to provide relative preference between two classifiers.
By exploiting key geometric properties of the space of confusion matrices, we
obtain provably query efficient algorithms for eliciting linear and
linear-fractional performance metrics. We further show that our method is
robust to feedback and finite sample noise.
Integrating Flexible Normalization into Mid-Level Representations of Deep Convolutional Neural Networks
Authors: Luis Gonzalo Sanchez Giraldo, Odelia Schwartz
Date: 2018-06-05 00:00:00

Deep convolutional neural networks (CNNs) are becoming increasingly popular
models to predict neural responses in visual cortex. However, contextual
effects, which are prevalent in neural processing and in perception, are not
explicitly handled by current CNNs, including those used for neural prediction.
In primary visual cortex, neural responses are modulated by stimuli spatially
surrounding the classical receptive field in rich ways. These effects have been
modeled with divisive normalization approaches, including flexible models,
where spatial normalization is recruited only to the degree responses from
center and surround locations are deemed statistically dependent. We propose a
flexible normalization model applied to mid-level representations of deep CNNs
as a tractable way to study contextual normalization mechanisms in mid-level
cortical areas. This approach captures non-trivial spatial dependencies among
mid-level features in CNNs, such as those present in textures and other visual
stimuli, that arise from tiling high order features, geometrically. We expect
that the proposed approach can make predictions about when spatial
normalization might be recruited in mid-level cortical areas. We also expect
this approach to be useful as part of the CNN toolkit, therefore going beyond
more restrictive fixed forms of normalization.