pwc-archive/papers-with-abstracts huggingface dataset:
BoostGAN for Occlusive Profile Face Frontalization and Recognition
Authors: Qingyan Duan, Lei Zhang
Date: 2019-02-26 00:00:00
Tasks: Face Generation, Face Recognition, Generative Adversarial Network

There are many facts affecting human face recognition, such as pose,
occlusion, illumination, age, etc. First and foremost are large pose and
occlusion problems, which can even result in more than 10% performance
degradation. Pose-invariant feature representation and face frontalization with
generative adversarial networks (GAN) have been widely used to solve the pose
problem. However, the synthesis and recognition of occlusive but profile faces
is still an uninvestigated problem. To address this issue, in this paper, we
aim to contribute an effective solution on how to recognize occlusive but
profile faces, even with facial keypoint region (e.g. eyes, nose, etc.)
corrupted. Specifically, we propose a boosting Generative Adversarial Network
(BoostGAN) for de-occlusion, frontalization, and recognition of faces. Upon the
assumption that facial occlusion is partial and incomplete, multiple patch
occluded images are fed as inputs for knowledge boosting, such as identity and
texture information. A new aggregation structure composed of a deep GAN for
coarse face synthesis and a shallow boosting net for fine face generation is
further designed. Exhaustive experiments demonstrate that the proposed approach
not only presents clear perceptual photo-realistic results but also shows
state-of-the-art recognition performance for occlusive but profile faces.
Recursive Subtree Composition in LSTM-Based Dependency Parsing
Authors: Miryam de Lhoneux, Miguel Ballesteros, Joakim Nivre
Date: 2019-02-26 00:00:00
Proceeding: NAACL 2019 6
Tasks: Dependency Parsing

The need for tree structure modelling on top of sequence modelling is an open
issue in neural dependency parsing. We investigate the impact of adding a tree
layer on top of a sequential model by recursively composing subtree
representations (composition) in a transition-based parser that uses features
extracted by a BiLSTM. Composition seems superfluous with such a model,
suggesting that BiLSTMs capture information about subtrees. We perform model
ablations to tease out the conditions under which composition helps. When
ablating the backward LSTM, performance drops and composition does not recover
much of the gap. When ablating the forward LSTM, performance drops less
dramatically and composition recovers a substantial part of the gap, indicating
that a forward LSTM and composition capture similar information. We take the
backward LSTM to be related to lookahead features and the forward LSTM to the
rich history-based features both crucial for transition-based parsers. To
capture history-based information, composition is better than a forward LSTM on
its own, but it is even better to have a forward LSTM as part of a BiLSTM. We
correlate results with language properties, showing that the improved lookahead
of a backward LSTM is especially important for head-final languages.
Directional Embedding Based Semi-supervised Framework For Bird Vocalization Segmentation
Authors: Anshul Thakur, Padmanabhan Rajan
Date: 2019-02-26 00:00:00
Tasks: Binary Classification, General Classification

This paper proposes a data-efficient, semi-supervised, two-pass framework for
segmenting bird vocalizations. The framework utilizes a binary classification
model to categorize frames of an input audio recording into the background or
bird vocalization. The first pass of the framework automatically generates
training labels from the input recording itself, while model training and
classification is done during the second pass. The proposed framework utilizes
a reference directional model for obtaining a feature representation called
directional embeddings (DE). This reference directional model acts as an
acoustic model for bird vocalizations and is obtained using the mixtures of
Von-Mises Fisher distribution (moVMF). The proposed DE space only contains
information about bird vocalizations, while no information about the background
disturbances is reflected. The framework employs supervised information only
for obtaining the reference directional model and avoids the background
modeling. Hence, it can be regarded as semi-supervised in nature. The proposed
framework is tested on approximately 79000 vocalizations of seven different
bird species. The performance of the framework is also analyzed in the presence
of noise at different SNRs. Experimental results convey that the proposed
framework performs better than the existing bird vocalization segmentation
methods.
Interaction-aware Factorization Machines for Recommender Systems
Authors: Fuxing Hong, Dongbo Huang, Ge Chen
Date: 2019-02-26 00:00:00
Tasks: Recommendation Systems

Factorization Machine (FM) is a widely used supervised learning approach by
effectively modeling of feature interactions. Despite the successful
application of FM and its many deep learning variants, treating every feature
interaction fairly may degrade the performance. For example, the interactions
of a useless feature may introduce noises; the importance of a feature may also
differ when interacting with different features. In this work, we propose a
novel model named \emph{Interaction-aware Factorization Machine} (IFM) by
introducing Interaction-Aware Mechanism (IAM), which comprises the
\emph{feature aspect} and the \emph{field aspect}, to learn flexible
interactions on two levels. The feature aspect learns feature interaction
importance via an attention network while the field aspect learns the feature
interaction effect as a parametric similarity of the feature interaction vector
and the corresponding field interaction prototype. IFM introduces more
structured control and learns feature interaction importance in a stratified
manner, which allows for more leverage in tweaking the interactions on both
feature-wise and field-wise levels. Besides, we give a more generalized
architecture and propose Interaction-aware Neural Network (INN) and DeepIFM to
capture higher-order interactions. To further improve both the performance and
efficiency of IFM, a sampling scheme is developed to select interactions based
on the field aspect importance. The experimental results from two well-known
datasets show the superiority of the proposed models over the state-of-the-art
methods.
Function Space Particle Optimization for Bayesian Neural Networks
Authors: Ziyu Wang, Tongzheng Ren, Jun Zhu, Bo Zhang
Date: 2019-02-26 00:00:00
Proceeding: ICLR 2019 5
Tasks: Reinforcement Learning, Variational Inference

While Bayesian neural networks (BNNs) have drawn increasing attention, their posterior inference remains challenging, due to the high-dimensional and over-parameterized nature. To address this issue, several highly flexible and scalable variational inference procedures based on the idea of particle optimization have been proposed. These methods directly optimize a set of particles to approximate the target posterior. However, their application to BNNs often yields sub-optimal performance, as such methods have a particular failure mode on over-parameterized models. In this paper, we propose to solve this issue by performing particle optimization directly in the space of regression functions. We demonstrate through extensive experiments that our method successfully overcomes this issue, and outperforms strong baselines in a variety of tasks including prediction, defense against adversarial examples, and reinforcement learning.
Online Predictive Optimization Framework for Stochastic Demand-Responsive Transit Services
Authors: Inon Peled, Kelvin Lee, Yu Jiang, Justin Dauwels, Francisco C. Pereira
Date: 2019-02-26 00:00:00
Tasks: Autonomous Vehicles, quantile regression

This study develops an online predictive optimization framework for dynamically operating a transit service in an area of crowd movements. The proposed framework integrates demand prediction and supply optimization to periodically redesign the service routes based on recently observed demand. To predict demand for the service, we use Quantile Regression to estimate the marginal distribution of movement counts between each pair of serviced locations. The framework then combines these marginals into a joint demand distribution by constructing a Gaussian copula, which captures the structure of correlation between the marginals. For supply optimization, we devise a linear programming model, which simultaneously determines the route structure and the service frequency according to the predicted demand. Importantly, our framework both preserves the uncertainty structure of future demand and leverages this for robust route optimization, while keeping both components decoupled. We evaluate our framework using a real-world case study of autonomous mobility in a university campus in Denmark. The results show that our framework often obtains the ground truth optimal solution, and can outperform conventional methods for route optimization, which do not leverage full predictive distributions.
Deep Variational Koopman Models: Inferring Koopman Observations for Uncertainty-Aware Dynamics Modeling and Control
Authors: Jeremy Morton, Freddie D. Witherden, Mykel J. Kochenderfer
Date: 2019-02-26 00:00:00

Koopman theory asserts that a nonlinear dynamical system can be mapped to a linear system, where the Koopman operator advances observations of the state forward in time. However, the observable functions that map states to observations are generally unknown. We introduce the Deep Variational Koopman (DVK) model, a method for inferring distributions over observations that can be propagated linearly in time. By sampling from the inferred distributions, we obtain a distribution over dynamical models, which in turn provides a distribution over possible outcomes as a modeled system advances in time. Experiments show that the DVK model is effective at long-term prediction for a variety of dynamical systems. Furthermore, we describe how to incorporate the learned models into a control framework, and demonstrate that accounting for the uncertainty present in the distribution over dynamical models enables more effective control.
Functional Transparency for Structured Data: a Game-Theoretic Approach
Authors: Guang-He Lee, Wengong Jin, David Alvarez-Melis, Tommi S. Jaakkola
Date: 2019-02-26 00:00:00
Tasks: Property Prediction, Representation Learning

We provide a new approach to training neural models to exhibit transparency
in a well-defined, functional manner. Our approach naturally operates over
structured data and tailors the predictor, functionally, towards a chosen
family of (local) witnesses. The estimation problem is setup as a co-operative
game between an unrestricted predictor such as a neural network, and a set of
witnesses chosen from the desired transparent family. The goal of the witnesses
is to highlight, locally, how well the predictor conforms to the chosen family
of functions, while the predictor is trained to minimize the highlighted
discrepancy. We emphasize that the predictor remains globally powerful as it is
only encouraged to agree locally with locally adapted witnesses. We analyze the
effect of the proposed approach, provide example formulations in the context of
deep graph and sequence models, and empirically illustrate the idea in chemical
property prediction, temporal modeling, and molecule representation learning.
Harmonic Unpaired Image-to-image Translation
Authors: Rui Zhang, Tomas Pfister, Jia Li
Date: 2019-02-26 00:00:00
Proceeding: ICLR 2019 5
Tasks: Image-to-Image Translation, Translation

The recent direction of unpaired image-to-image translation is on one hand
very exciting as it alleviates the big burden in obtaining label-intensive
pixel-to-pixel supervision, but it is on the other hand not fully satisfactory
due to the presence of artifacts and degenerated transformations. In this
paper, we take a manifold view of the problem by introducing a smoothness term
over the sample graph to attain harmonic functions to enforce consistent
mappings during the translation. We develop HarmonicGAN to learn bi-directional
translations between the source and the target domains. With the help of
similarity-consistency, the inherent self-consistency property of samples can
be maintained. Distance metrics defined on two types of features including
histogram and CNN are exploited. Under an identical problem setting as
CycleGAN, without additional manual inputs and only at a small training-time
cost, HarmonicGAN demonstrates a significant qualitative and quantitative
improvement over the state of the art, as well as improved interpretability. We
show experimental results in a number of applications including medical
imaging, object transfiguration, and semantic labeling. We outperform the
competing methods in all tasks, and for a medical imaging task in particular
our method turns CycleGAN from a failure to a success, halving the mean-squared
error, and generating images that radiologists prefer over competing methods in
95% of cases.
Band-to-Band Tunneling based Ultra-Energy Efficient Silicon Neuron
Authors: Tanmay Chavan, Sangya Dutta, Nihar R. Mohapatra, Udayan Ganguly
Date: 2019-02-26 00:00:00

The human brain comprises about a hundred billion neurons connected through
quadrillion synapses. Spiking Neural Networks (SNNs) take inspiration from the
brain to model complex cognitive and learning tasks. Neuromorphic engineering
implements SNNs in hardware, aspiring to mimic the brain at scale (i.e., 100
billion neurons) with biological area and energy efficiency. The design of
ultra-energy efficient and compact neurons is essential for the large-scale
implementation of SNNs in hardware. In this work, we have experimentally
demonstrated a Partially Depleted (PD) Silicon-On-Insulator (SOI) MOSFET based
Leaky-Integrate & Fire (LIF) neuron where energy-and area-efficiency is enabled
by two elements of design - first tunneling based operation and second compact
sub-threshold SOI control circuit design. Band-to-Band Tunneling (BTBT) induced
hole storage in the body is used for the "Integrate" function of the neuron. A
compact control circuit "Fires" a spike when the body potential exceeds the
firing threshold. The neuron then "Resets" by removing the stored holes from
the body contact of the device. Additionally, the control circuit provides
"Leakiness" in the neuron which is an essential property of biological neurons.
The proposed neuron provides 10x higher area efficiency compared to CMOS design
with equivalent energy/spike. Alternatively, it has 10^4x higher energy
efficiency at area-equivalent neuron technologies. Biologically comparable
energy- and area-efficiency along with CMOS compatibility make the proposed
device attractive for large-scale hardware implementation of SNNs.
Conservative Agency via Attainable Utility Preservation
Authors: Alexander Matt Turner, Dylan Hadfield-Menell, Prasad Tadepalli
Date: 2019-02-26 00:00:00

Reward functions are easy to misspecify; although designers can make corrections after observing mistakes, an agent pursuing a misspecified reward function can irreversibly change the state of its environment. If that change precludes optimization of the correctly specified reward function, then correction is futile. For example, a robotic factory assistant could break expensive equipment due to a reward misspecification; even if the designers immediately correct the reward function, the damage is done. To mitigate this risk, we introduce an approach that balances optimization of the primary reward function with preservation of the ability to optimize auxiliary reward functions. Surprisingly, even when the auxiliary reward functions are randomly generated and therefore uninformative about the correctly specified reward function, this approach induces conservative, effective behavior.
Automated Model Selection with Bayesian Quadrature
Authors: Henry Chai, Jean-Francois Ton, Roman Garnett, Michael A. Osborne
Date: 2019-02-26 00:00:00
Tasks: model, Model Selection

We present a novel technique for tailoring Bayesian quadrature (BQ) to model
selection. The state-of-the-art for comparing the evidence of multiple models
relies on Monte Carlo methods, which converge slowly and are unreliable for
computationally expensive models. Previous research has shown that BQ offers
sample efficiency superior to Monte Carlo in computing the evidence of an
individual model. However, applying BQ directly to model comparison may waste
computation producing an overly-accurate estimate for the evidence of a clearly
poor model. We propose an automated and efficient algorithm for computing the
most-relevant quantity for model selection: the posterior probability of a
model. Our technique maximizes the mutual information between this quantity and
observations of the models' likelihoods, yielding efficient acquisition of
samples across disparate model spaces when likelihood observations are limited.
Our method produces more-accurate model posterior estimates using fewer model
likelihood evaluations than standard Bayesian quadrature and Monte Carlo
estimators, as we demonstrate on synthetic and real-world examples.
Syntactic Recurrent Neural Network for Authorship Attribution
Authors: Fereshteh Jafariakinabad, Sansiri Tarnpradab, Kien A. Hua
Date: 2019-02-26 00:00:00
Tasks: Authorship Attribution, text-classification, Text Classification

Writing style is a combination of consistent decisions at different levels of
language production including lexical, syntactic, and structural associated to
a specific author (or author groups). While lexical-based models have been
widely explored in style-based text classification, relying on content makes
the model less scalable when dealing with heterogeneous data comprised of
various topics. On the other hand, syntactic models which are
content-independent, are more robust against topic variance. In this paper, we
introduce a syntactic recurrent neural network to encode the syntactic patterns
of a document in a hierarchical structure. The model first learns the syntactic
representation of sentences from the sequence of part-of-speech tags. For this
purpose, we exploit both convolutional filters and long short-term memories to
investigate the short-term and long-term dependencies of part-of-speech tags in
the sentences. Subsequently, the syntactic representations of sentences are
aggregated into document representation using recurrent neural networks. Our
experimental results on PAN 2012 dataset for authorship attribution task shows
that syntactic recurrent neural network outperforms the lexical model with the
identical architecture by approximately 14% in terms of accuracy.
Topological Bayesian Optimization with Persistence Diagrams
Authors: Tatsuya Shiraishi, Tam Le, Hisashi Kashima, Makoto Yamada
Date: 2019-02-26 00:00:00
Tasks: Bayesian Optimization, Topological Data Analysis

Finding an optimal parameter of a black-box function is important for
searching stable material structures and finding optimal neural network
structures, and Bayesian optimization algorithms are widely used for the
purpose. However, most of existing Bayesian optimization algorithms can only
handle vector data and cannot handle complex structured data. In this paper, we
propose the topological Bayesian optimization, which can efficiently find an
optimal solution from structured data using \emph{topological information}.
More specifically, in order to apply Bayesian optimization to structured data,
we extract useful topological information from a structure and measure the
proper similarity between structures. To this end, we utilize persistent
homology, which is a topological data analysis method that was recently applied
in machine learning. Moreover, we propose the Bayesian optimization algorithm
that can handle multiple types of topological information by using a linear
combination of kernels for persistence diagrams. Through experiments, we show
that topological information extracted by persistent homology contributes to a
more efficient search for optimal structures compared to the random search
baseline and the graph Bayesian optimization algorithm.
Interpretable Structure-aware Document Encoders with Hierarchical Attention
Authors: Khalil Mrini, Claudiu Musat, Michael Baeriswyl, Martin Jaggi
Date: 2019-02-26 00:00:00
Tasks: Document Classification, Sentence, Word Embeddings

We propose a method to create document representations that reflect their internal structure. We modify Tree-LSTMs to hierarchically merge basic elements such as words and sentences into blocks of increasing complexity. Our Structure Tree-LSTM implements a hierarchical attention mechanism over individual components and combinations thereof. We thus emphasize the usefulness of Tree-LSTMs for texts larger than a sentence. We show that structure-aware encoders can be used to improve the performance of document classification. We demonstrate that our method is resilient to changes to the basic building blocks, as it performs well with both sentence and word embeddings. The Structure Tree-LSTM outperforms all the baselines on two datasets by leveraging structural clues. We show our model's interpretability by visualizing how our model distributes attention inside a document. On a third dataset from the medical domain, our model achieves competitive performance with the state of the art. This result shows the Structure Tree-LSTM can leverage dependency relations other than text structure, such as a set of reports on the same patient.
Community-based 3-SAT Formulas with a Predefined Solution
Authors: Yamin Hu, Wenjian Luo, Junteng Wang
Date: 2019-02-26 00:00:00

It is crucial to generate crafted SAT formulas with predefined solutions for
the testing and development of SAT solvers since many SAT formulas from
real-world applications have solutions. Although some generating algorithms
have been proposed to generate SAT formulas with predefined solutions,
community structures of SAT formulas are not considered. We propose a 3-SAT
formula generating algorithm that not only guarantees the existence of a
predefined solution, but also simultaneously considers community structures and
clause distributions. The proposed 3-SAT formula generating algorithm controls
the quality of community structures through controlling (1) the number of
clauses whose variables have a common community, which we call intra-community
clauses, and (2) the number of variables that only belong to one community,
which we call intra-community variables. To study the combined effect of
community structures and clause distributions on the hardness of SAT formulas,
we measure solving runtimes of two solvers, gluHack (a leading CDCL solver) and
CPSparrow (a leading SLS solver), on the generated SAT formulas under different
groups of parameter settings. Through extensive experiments, we obtain some
noteworthy observations on the SAT formulas generated by the proposed
algorithm: (1) The community structure has little or no effects on the hardness
of SAT formulas with regard to CPSparrow but a strong effect with regard to
gluHack. (2) Only when the proportion of true literals in a SAT formula in
terms of the predefined solution is 0.5, SAT formulas are hard-to-solve with
regard to gluHack; when this proportion is below 0.5, SAT formulas are
hard-to-solve with regard to CPSparrow. (3) When the ratio of the number of
clauses to that of variables is around 4.25, the SAT formulas are hard-to-solve
with regard to both gluHack and CPSparrow.