pwc-archive/papers-with-abstracts huggingface dataset:
A Neurodynamic model of Saliency prediction in V1
Authors: David Berga, Xavier Otazu
Date: 2018-11-15 00:00:00
Tasks: model, Prediction, Saliency Prediction

Lateral connections in the primary visual cortex (V1) have long been hypothesized to be responsible of several visual processing mechanisms such as brightness induction, chromatic induction, visual discomfort and bottom-up visual attention (also named saliency). Many computational models have been developed to independently predict these and other visual processes, but no computational model has been able to reproduce all of them simultaneously. In this work we show that a biologically plausible computational model of lateral interactions of V1 is able to simultaneously predict saliency and all the aforementioned visual processes. Our model's (NSWAM) architecture is based on Pennachio's neurodynamic model of lateral connections of V1. It is defined as a network of firing rate neurons, sensitive to visual features such as brightness, color, orientation and scale. We tested NSWAM saliency predictions using images from several eye tracking datasets. We show that accuracy of predictions, using shuffled metrics, obtained by our architecture is similar to other state-of-the-art computational methods, particularly with synthetic images (CAT2000-Pattern & SID4VAM) which mainly contain low level features. Moreover, we outperform other biologically-inspired saliency models that are specifically designed to exclusively reproduce saliency. Hence, we show that our biologically plausible model of lateral connections can simultaneously explain different visual proceses present in V1 (without applying any type of training or optimization and keeping the same parametrization for all the visual processes). This can be useful for the definition of a unified architecture of the primary visual cortex.
End-to-End Learning for Answering Structured Queries Directly over Text
Authors: Paul Groth, Antony Scerri, Ron Daniel, Jr., Bradley P. Allen
Date: 2018-11-15 00:00:00
Tasks: Extractive Question-Answering, Knowledge Graphs, Question Answering

Structured queries expressed in languages (such as SQL, SPARQL, or XQuery)
offer a convenient and explicit way for users to express their information
needs for a number of tasks. In this work, we present an approach to answer
these directly over text data without storing results in a database. We
specifically look at the case of knowledge bases where queries are over
entities and the relations between them. Our approach combines distributed
query answering (e.g. Triple Pattern Fragments) with models built for
extractive question answering. Importantly, by applying distributed querying
answering we are able to simplify the model learning problem. We train models
for a large portion (572) of the relations within Wikidata and achieve an
average 0.70 F1 measure across all models. We also present a systematic method
to construct the necessary training data for this task from knowledge graphs
and describe a prototype implementation.
Selective Feature Connection Mechanism: Concatenating Multi-layer CNN Features with a Feature Selector
Authors: Chen Du, Chunheng Wang, Yanna Wang, Cunzhao Shi, Baihua Xiao
Date: 2018-11-15 00:00:00
Tasks: image-classification, Image Classification, Image-to-Image Translation, Scene Text Detection, Text Detection, Translation

Different layers of deep convolutional neural networks(CNNs) can encode
different-level information. High-layer features always contain more semantic
information, and low-layer features contain more detail information. However,
low-layer features suffer from the background clutter and semantic ambiguity.
During visual recognition, the feature combination of the low-layer and
high-level features plays an important role in context modulation. If directly
combining the high-layer and low-layer features, the background clutter and
semantic ambiguity may be caused due to the introduction of detailed
information. In this paper, we propose a general network architecture to
concatenate CNN features of different layers in a simple and effective way,
called Selective Feature Connection Mechanism (SFCM). Low-level features are
selectively linked to high-level features with a feature selector which is
generated by high-level features. The proposed connection mechanism can
effectively overcome the above-mentioned drawbacks. We demonstrate the
effectiveness, superiority, and universal applicability of this method on
multiple challenging computer vision tasks, including image classification,
scene text detection, and image-to-image translation.
Sketch based Reduced Memory Hough Transform
Authors: Levi Offen, Michael Werman
Date: 2018-11-15 00:00:00

This paper proposes using sketch algorithms to represent the votes in Hough
transforms. Replacing the accumulator array with a sketch (Sketch Hough
Transform - SHT) significantly reduces the memory needed to compute a Hough
transform. We also present a new sketch, Count Median Update, which works
better than known sketch methods for replacing the accumulator array in the
Hough Transform.
Guiding the One-to-one Mapping in CycleGAN via Optimal Transport
Authors: Guansong Lu, Zhiming Zhou, Yuxuan Song, Kan Ren, Yong Yu
Date: 2018-11-15 00:00:00
Tasks: Translation

CycleGAN is capable of learning a one-to-one mapping between two data
distributions without paired examples, achieving the task of unsupervised data
translation. However, there is no theoretical guarantee on the property of the
learned one-to-one mapping in CycleGAN. In this paper, we experimentally find
that, under some circumstances, the one-to-one mapping learned by CycleGAN is
just a random one within the large feasible solution space. Based on this
observation, we explore to add extra constraints such that the one-to-one
mapping is controllable and satisfies more properties related to specific
tasks. We propose to solve an optimal transport mapping restrained by a
task-specific cost function that reflects the desired properties, and use the
barycenters of optimal transport mapping to serve as references for CycleGAN.
Our experiments indicate that the proposed algorithm is capable of learning a
one-to-one mapping with the desired properties.
Survey of Computational Approaches to Lexical Semantic Change
Authors: Nina Tahmasebi, Lars Borin, Adam Jatowt
Date: 2018-11-15 00:00:00
Tasks: Change Detection, Information Retrieval, Optical Character Recognition (OCR), Retrieval, Survey

Our languages are in constant flux driven by external factors such as
cultural, societal and technological changes, as well as by only partially
understood internal motivations. Words acquire new meanings and lose old
senses, new words are coined or borrowed from other languages and obsolete
words slide into obscurity. Understanding the characteristics of shifts in the
meaning and in the use of words is useful for those who work with the content
of historical texts, the interested general public, but also in and of itself.
The findings from automatic lexical semantic change detection, and the models
of diachronic conceptual change are currently being incorporated in approaches
for measuring document across-time similarity, information retrieval from
long-term document archives, the design of OCR algorithms, and so on. In recent
years we have seen a surge in interest in the academic community in
computational methods and tools supporting inquiry into diachronic conceptual
change and lexical replacement. This article is an extract of a survey of
recent computational techniques to tackle lexical semantic change currently
under review. In this article we focus on diachronic conceptual change as an
extension of semantic change.
Image declipping with deep networks
Authors: Shachar Honig, Michael Werman
Date: 2018-11-15 00:00:00
Tasks: Image Declipping

We present a deep network to recover pixel values lost to clipping. The
clipped area of the image is typically a uniform area of minimum or maximum
brightness, losing image detail and color fidelity. The degree to which the
clipping is visually noticeable depends on the amount by which values were
clipped, and the extent of the clipped area. Clipping may occur in any (or all)
of the pixel's color channels. Although clipped pixels are common and occur to
some degree in almost every image we tested, current automatic solutions have
only partial success in repairing clipped pixels and work only in limited cases
such as only with overexposure (not under-exposure) and when some of the color
channels are not clipped. Using neural networks and their ability to model
natural images allows our neural network, DeclipNet, to reconstruct data in
clipped regions producing state of the art results.
Woulda, Coulda, Shoulda: Counterfactually-Guided Policy Search
Authors: Lars Buesing, Theophane Weber, Yori Zwols, Sebastien Racaniere, Arthur Guez, Jean-Baptiste Lespiau, Nicolas Heess
Date: 2018-11-15 00:00:00
Proceeding: ICLR 2019 5
Tasks: counterfactual, Reinforcement Learning

Learning policies on data synthesized by models can in principle quench the
thirst of reinforcement learning algorithms for large amounts of real
experience, which is often costly to acquire. However, simulating plausible
experience de novo is a hard problem for many complex environments, often
resulting in biases for model-based policy evaluation and search. Instead of de
novo synthesis of data, here we assume logged, real experience and model
alternative outcomes of this experience under counterfactual actions, actions
that were not actually taken. Based on this, we propose the
Counterfactually-Guided Policy Search (CF-GPS) algorithm for learning policies
in POMDPs from off-policy experience. It leverages structural causal models for
counterfactual evaluation of arbitrary policies on individual off-policy
episodes. CF-GPS can improve on vanilla model-based RL algorithms by making use
of available logged data to de-bias model predictions. In contrast to
off-policy algorithms based on Importance Sampling which re-weight data, CF-GPS
leverages a model to explicitly consider alternative outcomes, allowing the
algorithm to make better use of experience data. We find empirically that these
advantages translate into improved policy evaluation and search results on a
non-trivial grid-world task. Finally, we show that CF-GPS generalizes the
previously proposed Guided Policy Search and that reparameterization-based
algorithms such Stochastic Value Gradient can be interpreted as counterfactual
methods.
Effects of Lombard Reflex on the Performance of Deep-Learning-Based Audio-Visual Speech Enhancement Systems
Authors: Daniel Michelsanti, Zheng-Hua Tan, Sigurdur Sigurdsson, Jesper Jensen
Date: 2018-11-15 00:00:00
Tasks: Deep Learning, Speech Enhancement

Humans tend to change their way of speaking when they are immersed in a noisy
environment, a reflex known as Lombard effect. Current speech enhancement
systems based on deep learning do not usually take into account this change in
the speaking style, because they are trained with neutral (non-Lombard) speech
utterances recorded under quiet conditions to which noise is artificially
added. In this paper, we investigate the effects that the Lombard reflex has on
the performance of audio-visual speech enhancement systems based on deep
learning. The results show that a gap in the performance of as much as
approximately 5 dB between the systems trained on neutral speech and the ones
trained on Lombard speech exists. This indicates the benefit of taking into
account the mismatch between neutral and Lombard speech in the design of
audio-visual speech enhancement systems.
SGR: Self-Supervised Spectral Graph Representation Learning
Authors: Anton Tsitsulin, Davide Mottin, Panagiotis Karras, Alex Bronstein, Emmanuel Müller
Date: 2018-11-15 00:00:00
Tasks: Graph Representation Learning, Representation Learning

Representing a graph as a vector is a challenging task; ideally, the
representation should be easily computable and conducive to efficient
comparisons among graphs, tailored to the particular data and analytical task
at hand. Unfortunately, a "one-size-fits-all" solution is unattainable, as
different analytical tasks may require different attention to global or local
graph features. We develop SGR, the first, to our knowledge, method for
learning graph representations in a self-supervised manner. Grounded on
spectral graph analysis, SGR seamlessly combines all aforementioned desirable
properties. In extensive experiments, we show how our approach works on large
graph collections, facilitates self-supervised representation learning across a
variety of application domains, and performs competitively to state-of-the-art
methods without re-training.
On Training Targets and Objective Functions for Deep-Learning-Based Audio-Visual Speech Enhancement
Authors: Daniel Michelsanti, Zheng-Hua Tan, Sigurdur Sigurdsson, Jesper Jensen
Date: 2018-11-15 00:00:00
Tasks: Deep Learning, Speech Enhancement

Audio-visual speech enhancement (AV-SE) is the task of improving speech
quality and intelligibility in a noisy environment using audio and visual
information from a talker. Recently, deep learning techniques have been adopted
to solve the AV-SE task in a supervised manner. In this context, the choice of
the target, i.e. the quantity to be estimated, and the objective function,
which quantifies the quality of this estimate, to be used for training is
critical for the performance. This work is the first that presents an
experimental study of a range of different targets and objective functions used
to train a deep-learning-based AV-SE system. The results show that the
approaches that directly estimate a mask perform the best overall in terms of
estimated speech quality and intelligibility, although the model that directly
estimates the log magnitude spectrum performs as good in terms of estimated
speech quality.
Graph Convolutional Neural Networks for Polymers Property Prediction
Authors: Minggang Zeng, Jatin Nitin Kumar, Zeng Zeng, Ramasamy Savitha, Vijay Ramaseshan Chandrasekhar, Kedar Hippalgaonkar
Date: 2018-11-15 00:00:00
Tasks: BIG-bench Machine Learning, Dielectric Constant, Prediction, Property Prediction

A fast and accurate predictive tool for polymer properties is demanding and
will pave the way to iterative inverse design. In this work, we apply graph
convolutional neural networks (GCNN) to predict the dielectric constant and
energy bandgap of polymers. Using density functional theory (DFT) calculated
properties as the ground truth, GCNN can achieve remarkable agreement with DFT
results. Moreover, we show that GCNN outperforms other machine learning
algorithms. Our work proves that GCNN relies only on morphological data of
polymers and removes the requirement for complicated hand-crafted descriptors,
while still offering accuracy in fast predictions.
Reward-estimation variance elimination in sequential decision processes
Authors: Sergey Pankov
Date: 2018-11-15 00:00:00
Tasks: Policy Gradient Methods, Reinforcement Learning

Policy gradient methods are very attractive in reinforcement learning due to
their model-free nature and convergence guarantees. These methods, however,
suffer from high variance in gradient estimation, resulting in poor sample
efficiency. To mitigate this issue, a number of variance-reduction approaches
have been proposed. Unfortunately, in the challenging problems with delayed
rewards, these approaches either bring a relatively modest improvement or do
reduce variance at expense of introducing a bias and undermining convergence.
The unbiased methods of gradient estimation, in general, only partially reduce
variance, without eliminating it completely even in the limit of exact
knowledge of the value functions and problem dynamics, as one might have
wished. In this work we propose an unbiased method that does completely
eliminate variance under some, commonly encountered, conditions. Of practical
interest is the limit of deterministic dynamics and small policy stochasticity.
In the case of a quadratic value function, as in linear quadratic Gaussian
models, the policy randomness need not be small. We use such a model to analyze
performance of the proposed variance-elimination approach and compare it with
standard variance-reduction methods. The core idea behind the approach is to
use control variates at all future times down the trajectory. We present both a
model-based and model-free formulations.
Model-based Approximate Query Processing
Authors: Moritz Kulessa, Alejandro Molina, Carsten Binnig, Benjamin Hilprecht, Kristian Kersting
Date: 2018-11-15 00:00:00
Tasks: model

Interactive visualizations are arguably the most important tool to explore,
understand and convey facts about data. In the past years, the database
community has been working on different techniques for Approximate Query
Processing (AQP) that aim to deliver an approximate query result given a fixed
time bound to support interactive visualizations better. However, classical AQP
approaches suffer from various problems that limit the applicability to support
the ad-hoc exploration of a new data set: (1) Classical AQP approaches that
perform online sampling can support ad-hoc exploration queries but yield low
quality if executed over rare subpopulations. (2) Classical AQP approaches that
rely on offline sampling can use some form of biased sampling to mitigate these
problems but require a priori knowledge of the workload, which is often not
realistic if users want to explore a new database. In this paper, we present a
new approach to AQP called Model-based AQP that leverages generative models
learned over the complete database to answer SQL queries at interactive speeds.
Different from classical AQP approaches, generative models allow us to compute
responses to ad-hoc queries and deliver high-quality estimates also over rare
subpopulations at the same time. In our experiments with real and synthetic
data sets, we show that Model-based AQP can in many scenarios return more
accurate results in a shorter runtime. Furthermore, we think that our
techniques of using generative models presented in this paper can not only be
used for AQP in databases but also has applications for other database problems
including Query Optimization as well as Data Cleaning.
Predicting thermoelectric properties from crystal graphs and material descriptors - first application for functional materials
Authors: Leo Laugier, Daniil Bash, Jose Recatala, Hong Kuan Ng, Savitha Ramasamy, Chuan-Sheng Foo, Vijay R. Chandrasekhar, Kedar Hippalgaonkar
Date: 2018-11-15 00:00:00
Tasks: Attribute

We introduce the use of Crystal Graph Convolutional Neural Networks (CGCNN),
Fully Connected Neural Networks (FCNN) and XGBoost to predict thermoelectric
properties. The dataset for the CGCNN is independent of Density Functional
Theory (DFT) and only relies on the crystal and atomic information, while that
for the FCNN is based on a rich attribute list mined from Materialsproject.org.
The results show that the optimized FCNN is three layer deep and is able to
predict the scattering-time independent thermoelectric powerfactor much better
than the CGCNN (or XGBoost), suggesting that bonding and density of states
descriptors informed from materials science knowledge obtained partially from
DFT are vital to predict functional properties.
Short-Term Wind-Speed Forecasting Using Kernel Spectral Hidden Markov Models
Authors: Shunsuke Tsuzuki, Yu Nishiyama
Date: 2018-11-15 00:00:00
Tasks: BIG-bench Machine Learning, Time Series, Time Series Analysis

In machine learning, a nonparametric forecasting algorithm for time series
data has been proposed, called the kernel spectral hidden Markov model (KSHMM).
In this paper, we propose a technique for short-term wind-speed prediction
based on KSHMM. We numerically compared the performance of our KSHMM-based
forecasting technique to other techniques with machine learning, using
wind-speed data offered by the National Renewable Energy Laboratory. Our
results demonstrate that, compared to these methods, the proposed technique
offers comparable or better performance.
Combining Axiom Injection and Knowledge Base Completion for Efficient Natural Language Inference
Authors: Masashi Yoshikawa, Koji Mineshima, Hiroshi Noji, Daisuke Bekki
Date: 2018-11-15 00:00:00
Tasks: Knowledge Base Completion, Natural Language Inference, RTE

In logic-based approaches to reasoning tasks such as Recognizing Textual
Entailment (RTE), it is important for a system to have a large amount of
knowledge data. However, there is a tradeoff between adding more knowledge data
for improved RTE performance and maintaining an efficient RTE system, as such a
big database is problematic in terms of the memory usage and computational
complexity. In this work, we show the processing time of a state-of-the-art
logic-based RTE system can be significantly reduced by replacing its
search-based axiom injection (abduction) mechanism by that based on Knowledge
Base Completion (KBC). We integrate this mechanism in a Coq plugin that
provides a proof automation tactic for natural language inference.
Additionally, we show empirically that adding new knowledge data contributes to
better RTE performance while not harming the processing speed in this
framework.
On Deep Domain Adaptation: Some Theoretical Understandings
Authors: Trung Le, Khanh Nguyen, Nhat Ho, Hung Bui, Dinh Phung
Date: 2018-11-15 00:00:00
Tasks: Domain Adaptation, Transfer Learning

Compared with shallow domain adaptation, recent progress in deep domain adaptation has shown that it can achieve higher predictive performance and stronger capacity to tackle structural data (e.g., image and sequential data). The underlying idea of deep domain adaptation is to bridge the gap between source and target domains in a joint space so that a supervised classifier trained on labeled source data can be nicely transferred to the target domain. This idea is certainly intuitive and powerful, however, limited theoretical understandings have been developed to support its underpinning principle. In this paper, we have provided a rigorous framework to explain why it is possible to close the gap of the target and source domains in the joint space. More specifically, we first study the loss incurred when performing transfer learning from the source to the target domain. This provides a theory that explains and generalizes existing work in deep domain adaptation which was mainly empirical. This enables us to further explain why closing the gap in the joint space can directly minimize the loss incurred for transfer learning between the two domains. To our knowledge, this offers the first theoretical result that characterizes a direct bound on the joint space and the gain of transfer learning via deep domain adaptation
Face Verification and Forgery Detection for Ophthalmic Surgery Images
Authors: Kaushal Bhogale, Nishant Shankar, Adheesh Juvekar, Asutosh Padhi
Date: 2018-11-15 00:00:00
Tasks: Face Verification, One-Shot Learning

Although modern face verification systems are accessible and accurate, they
are not always robust to pose variance and occlusions. Moreover, accurate
models require a large amount of data to train. We structure our experiments to
operate on small amounts of data obtained from an NGO that funds ophthalmic
surgeries. We set up our face verification task as that of verifying
pre-operation and post-operation images of a patient that undergoes ophthalmic
surgery, and as such the post-operation images have occlusions like an eye
patch. In this paper, we present a system that performs the face verification
task using one-shot learning. To this end, our paper uses deep convolutional
networks and compares different model architectures and loss functions. Our
best model achieves 85% test accuracy. During inference time, we also attempt
to detect image forgeries in addition to performing face verification. To
achieve this, we use Error Level Analysis. Finally, we propose an inference
pipeline that demonstrates how these techniques can be used to implement an
automated face verification and forgery detection system.