pwc-archive/papers-with-abstracts huggingface dataset:
Cross-Database Micro-Expression Recognition: A Benchmark
Authors: Yuan Zong, Tong Zhang, Wenming Zheng, Xiaopeng Hong, Chuangao Tang, Zhen Cui, Guoying Zhao
Date: 2018-12-19 00:00:00
Tasks: Domain Adaptation, Micro Expression Recognition, Micro-Expression Recognition

Cross-database micro-expression recognition (CDMER) is one of recently emerging and interesting problem in micro-expression analysis. CDMER is more challenging than the conventional micro-expression recognition (MER), because the training and testing samples in CDMER come from different micro-expression databases, resulting in the inconsistency of the feature distributions between the training and testing sets. In this paper, we contribute to this topic from three aspects. First, we establish a CDMER experimental evaluation protocol aiming to allow the researchers to conveniently work on this topic and provide a standard platform for evaluating their proposed methods. Second, we conduct benchmark experiments by using NINE state-of-the-art domain adaptation (DA) methods and SIX popular spatiotemporal descriptors for respectively investigating CDMER problem from two different perspectives. Third, we propose a novel DA method called region selective transfer regression (RSTR) to deal with the CDMER task. Our RSTR takes advantage of one important cue for recognizing micro-expressions, i.e., the different contributions of the facial local regions in MER. The overall superior performance of RSTR demonstrates that taking into consideration the important cues benefiting MER, e.g., the facial local region information, contributes to develop effective DA methods for dealing with CDMER problem.
Learning Symmetry Consistent Deep CNNs for Face Completion
Authors: Xiaoming Li, Ming Liu, Jieru Zhu, WangMeng Zuo, Meng Wang, Guosheng Hu, Lei Zhang
Date: 2018-12-19 00:00:00
Tasks: Face Recognition, Facial Inpainting

Deep convolutional networks (CNNs) have achieved great success in face
completion to generate plausible facial structures. These methods, however, are
limited in maintaining global consistency among face components and recovering
fine facial details. On the other hand, reflectional symmetry is a prominent
property of face image and benefits face recognition and consistency modeling,
yet remaining uninvestigated in deep face completion. In this work, we leverage
two kinds of symmetry-enforcing subnets to form a symmetry-consistent CNN model
(i.e., SymmFCNet) for effective face completion. For missing pixels on only one
of the half-faces, an illumination-reweighted warping subnet is developed to
guide the warping and illumination reweighting of the other half-face. As for
missing pixels on both of half-faces, we present a generative reconstruction
subnet together with a perceptual symmetry loss to enforce symmetry consistency
of recovered structures. The SymmFCNet is constructed by stacking generative
reconstruction subnet upon illumination-reweighted warping subnet, and can be
end-to-end learned from training set of unaligned face images. Experiments show
that SymmFCNet can generate high quality results on images with synthetic and
real occlusion, and performs favorably against state-of-the-arts.
Max-Diversity Distributed Learning: Theory and Algorithms
Authors: Yong Liu, Jian Li, Weiping Wang
Date: 2018-12-19 00:00:00
Tasks: Diversity, Learning Theory

We study the risk performance of distributed learning for the regularization
empirical risk minimization with fast convergence rate, substantially improving
the error analysis of the existing divide-and-conquer based distributed
learning. An interesting theoretical finding is that the larger the diversity
of each local estimate is, the tighter the risk bound is. This theoretical
analysis motivates us to devise an effective maxdiversity distributed learning
algorithm (MDD). Experimental results show that MDD can outperform the existing
divide-andconquer methods but with a bit more time. Theoretical analysis and
empirical results demonstrate that our proposed MDD is sound and effective.
Pathological Voice Classification Using Mel-Cepstrum Vectors and Support Vector Machine
Authors: Maryam Pishgar, Fazle Karim, Somshubra Majumdar, Houshang Darabi
Date: 2018-12-19 00:00:00
Tasks: General Classification

Vocal disorders have affected several patients all over the world. Due to the
inherent difficulty of diagnosing vocal disorders without sophisticated
equipment and trained personnel, a number of patients remain undiagnosed. To
alleviate the monetary cost of diagnosis, there has been a recent growth in the
use of data analysis to accurately detect and diagnose individuals for a
fraction of the cost. We propose a cheap, efficient and accurate model to
diagnose whether a patient suffers from one of three vocal disorders on the
FEMH 2018 challenge.
Active Learning and CSI Acquisition for mmWave Initial Alignment
Authors: Sung-En Chiu, Nancy Ronquillo, Tara Javidi
Date: 2018-12-19 00:00:00
Tasks: Active Learning

Millimeter wave (mmWave) communication with large antenna arrays is a promising technique to enable extremely high data rates due to the large available bandwidth in mmWave frequency bands. In addition, given the knowledge of an optimal directional beamforming vector, large antenna arrays have been shown to overcome both the severe signal attenuation in mmWave as well as the interference problem. However, fundamental limits on achievable learning rate of an optimal beamforming vector remain. This paper considers the problem of adaptive and sequential optimization of the beamforming vectors during the initial access phase of communication. With a single-path channel model, the problem is reduced to actively learning the Angle-of-Arrival (AoA) of the signal sent from the user to the Base Station (BS). Drawing on the recent results in the design of a hierarchical beamforming codebook [1], sequential measurement dependent noisy search strategies [2], and active learning from an imperfect labeler [3], an adaptive and sequential alignment algorithm is proposed. An upper bound on the expected search time of the proposed algorithm is derived via Extrinsic Jensen-Shannon Divergence. which demonstrates that the search time of the proposed algorithm asymptotically matches the performance of the noiseless bisection search up to a constant factor. Furthermore, the upper bound shows that the acquired AoA error probability decays exponentially fast with the search time with an exponent that is a decreasing function of the acquisition rate. Numerically, the proposed algorithm is compared with prior work where a significant improvement of the system communication rate is observed. Most notably, in the relevant regime of low (-10dB to 5dB) raw SNR, this establishes the first practically viable solution for initial access and, hence, the first demonstration of stand-alone mmWave communication
Unsupervised Video Object Segmentation with Distractor-Aware Online Adaptation
Authors: Ye Wang, Jongmoo Choi, Yueru Chen, Siyang Li, Qin Huang, Kaitai Zhang, Ming-Sui Lee, C. -C. Jay Kuo
Date: 2018-12-19 00:00:00
Tasks: Instance Segmentation, Object, Segmentation, Semantic Segmentation, Unsupervised Video Object Segmentation, Video Object Segmentation, Video Semantic Segmentation

Unsupervised video object segmentation is a crucial application in video
analysis without knowing any prior information about the objects. It becomes
tremendously challenging when multiple objects occur and interact in a given
video clip. In this paper, a novel unsupervised video object segmentation
approach via distractor-aware online adaptation (DOA) is proposed. DOA models
spatial-temporal consistency in video sequences by capturing background
dependencies from adjacent frames. Instance proposals are generated by the
instance segmentation network for each frame and then selected by motion
information as hard negatives if they exist and positives. To adopt
high-quality hard negatives, the block matching algorithm is then applied to
preceding frames to track the associated hard negatives. General negatives are
also introduced in case that there are no hard negatives in the sequence and
experiments demonstrate both kinds of negatives (distractors) are
complementary. Finally, we conduct DOA using the positive, negative, and hard
negative masks to update the foreground/background segmentation. The proposed
approach achieves state-of-the-art results on two benchmark datasets, DAVIS
2016 and FBMS-59 datasets.
Training on Art Composition Attributes to Influence CycleGAN Art Generation
Authors: Holly Grimm
Date: 2018-12-19 00:00:00
Tasks: Attribute, Image-to-Image Translation, Translation

I consider how to influence CycleGAN, image-to-image translation, by using
additional constraints from a neural network trained on art composition
attributes. I show how I trained the the Art Composition Attributes Network
(ACAN) by incorporating domain knowledge based on the rules of art evaluation
and the result of applying each art composition attribute to apple2orange image
translation.
A Comparison of LSTMs and Attention Mechanisms for Forecasting Financial Time Series
Authors: Thomas Hollis, Antoine Viscardi, Seung Eun Yi
Date: 2018-12-18 00:00:00
Tasks: Time Series, Time Series Analysis

While LSTMs show increasingly promising results for forecasting Financial
Time Series (FTS), this paper seeks to assess if attention mechanisms can
further improve performance. The hypothesis is that attention can help prevent
long-term dependencies experienced by LSTM models. To test this hypothesis, the
main contribution of this paper is the implementation of an LSTM with
attention. Both the benchmark LSTM and the LSTM with attention were compared
and both achieved reasonable performances of up to 60% on five stocks from
Kaggle's Two Sigma dataset. This comparative analysis demonstrates that an LSTM
with attention can indeed outperform standalone LSTMs but further investigation
is required as issues do arise with such model architectures.
Training on the test set? An analysis of Spampinato et al. [arXiv:1609.00344]
Authors: Ren Li, Jared S. Johansen, Hamad Ahmed, Thomas V. Ilyevsky, Ronnie B Wilbur, Hari M Bharadwaj, Jeffrey Mark Siskind
Date: 2018-12-18 00:00:00
Tasks: All, EEG, Electroencephalogram (EEG), General Classification, Object, Transfer Learning

A recent paper [arXiv:1609.00344] claims to classify brain processing evoked
in subjects watching ImageNet stimuli as measured with EEG and to use a
representation derived from this processing to create a novel object
classifier. That paper, together with a series of subsequent papers [8, 15, 17,
20, 21, 30, 35], claims to revolutionize the field by achieving extremely
successful results on several computer-vision tasks, including object
classification, transfer learning, and generation of images depicting human
perception and thought using brain-derived representations measured through
EEG. Our novel experiments and analyses demonstrate that their results
crucially depend on the block design that they use, where all stimuli of a
given class are presented together, and fail with a rapid-event design, where
stimuli of different classes are randomly intermixed. The block design leads to
classification of arbitrary brain states based on block-level temporal
correlations that tend to exist in all EEG data, rather than stimulus-related
activity. Because every trial in their test sets comes from the same block as
many trials in the corresponding training sets, their block design thus leads
to surreptitiously training on the test set. This invalidates all subsequent
analyses performed on this data in multiple published papers and calls into
question all of the purported results. We further show that a novel object
classifier constructed with a random codebook performs as well as or better
than a novel object classifier constructed with the representation extracted
from EEG data, suggesting that the performance of their classifier constructed
with a representation extracted from EEG data does not benefit at all from the
brain-derived representation. Our results calibrate the underlying difficulty
of the tasks involved and caution against sensational and overly optimistic,
but false, claims to the contrary.
Fast Exact Computation of Expected HyperVolume Improvement
Authors: Guang Zhao, Raymundo Arroyave, Xiaoning Qian
Date: 2018-12-18 00:00:00
Tasks: Bayesian Optimization, Evolutionary Algorithms

In multi-objective Bayesian optimization and surrogate-based evolutionary
algorithms, Expected HyperVolume Improvement (EHVI) is widely used as the
acquisition function to guide the search approaching the Pareto front. This
paper focuses on the exact calculation of EHVI given a nondominated set, for
which the existing exact algorithms are complex and can be inefficient for
problems with more than three objectives. Integrating with different
decomposition algorithms, we propose a new method for calculating the integral
in each decomposed high-dimensional box in constant time. We develop three new
exact EHVI calculation algorithms based on three region decomposition methods.
The first grid-based algorithm has a complexity of $O(m\cdot n^m)$ with $n$
denoting the size of the nondominated set and $m$ the number of objectives. The
Walking Fish Group (WFG)-based algorithm has a worst-case complexity of
$O(m\cdot 2^n)$ but has a better average performance. These two can be applied
for problems with any $m$. The third CLM-based algorithm is only for $m=3$ and
asymptotically optimal with complexity $\Theta(n\log{n})$. Performance
comparison results show that all our three algorithms are at least twice faster
than the state-of-the-art algorithms with the same decomposition methods. When
$m>3$, our WFG-based algorithm can be over $10^2$ faster than the corresponding
existing algorithms. Our algorithm is demonstrated in an example involving
efficient multi-objective material design with Bayesian optimization.
Molecular Dynamics with Neural-Network Potentials
Authors: Michael Gastegger, Philipp Marquetand
Date: 2018-12-18 00:00:00
Tasks: Active Learning, BIG-bench Machine Learning

Molecular dynamics simulations are an important tool for describing the
evolution of a chemical system with time. However, these simulations are
inherently held back either by the prohibitive cost of accurate electronic
structure theory computations or the limited accuracy of classical empirical
force fields. Machine learning techniques can help to overcome these
limitations by providing access to potential energies, forces and other
molecular properties modeled directly after an electronic structure reference
at only a fraction of the original computational cost. The present text
discusses several practical aspects of conducting machine learning driven
molecular dynamics simulations. First, we study the efficient selection of
reference data points on the basis of an active learning inspired adaptive
sampling scheme. This is followed by the analysis of a machine-learning based
model for simulating molecular dipole moments in the framework of predicting
infrared spectra via molecular dynamics simulations. Finally, we show that
machine learning models can offer valuable aid in understanding chemical
systems beyond a simple prediction of quantities.
Deep Online Learning via Meta-Learning: Continual Adaptation for Model-Based RL
Authors: Anusha Nagabandi, Chelsea Finn, Sergey Levine
Date: 2018-12-18 00:00:00
Proceeding: ICLR 2019 5
Tasks: Meta-Learning, Model-based Reinforcement Learning, Reinforcement Learning

Humans and animals can learn complex predictive models that allow them to
accurately and reliably reason about real-world phenomena, and they can adapt
such models extremely quickly in the face of unexpected changes. Deep neural
network models allow us to represent very complex functions, but lack this
capacity for rapid online adaptation. The goal in this paper is to develop a
method for continual online learning from an incoming stream of data, using
deep neural network models. We formulate an online learning procedure that uses
stochastic gradient descent to update model parameters, and an expectation
maximization algorithm with a Chinese restaurant process prior to develop and
maintain a mixture of models to handle non-stationary task distributions. This
allows for all models to be adapted as necessary, with new models instantiated
for task changes and old models recalled when previously seen tasks are
encountered again. Furthermore, we observe that meta-learning can be used to
meta-train a model such that this direct online adaptation with SGD is
effective, which is otherwise not the case for large function approximators. In
this work, we apply our meta-learning for online learning (MOLe) approach to
model-based reinforcement learning, where adapting the predictive model is
critical for control; we demonstrate that MOLe outperforms alternative prior
methods, and enables effective continuous adaptation in non-stationary task
distributions such as varying terrains, motor failures, and unexpected
disturbances.
Machine Learning for Molecular Dynamics on Long Timescales
Authors: Frank Noé
Date: 2018-12-18 00:00:00
Tasks: Active Learning, BIG-bench Machine Learning, Representation Learning

Molecular Dynamics (MD) simulation is widely used to analyze the properties
of molecules and materials. Most practical applications, such as comparison
with experimental measurements, designing drug molecules, or optimizing
materials, rely on statistical quantities, which may be prohibitively expensive
to compute from direct long-time MD simulations. Classical Machine Learning
(ML) techniques have already had a profound impact on the field, especially for
learning low-dimensional models of the long-time dynamics and for devising more
efficient sampling schemes for computing long-time statistics. Novel ML methods
have the potential to revolutionize long-timescale MD and to obtain
interpretable models. ML concepts such as statistical estimator theory,
end-to-end learning, representation learning and active learning are highly
interesting for the MD researcher and will help to develop new solutions to
hard MD problems. With the aim of better connecting the MD and ML research
areas and spawning new research on this interface, we define the learning
problems in long-timescale MD, present successful approaches and outline some
of the unsolved ML problems in this application field.
GD-GAN: Generative Adversarial Networks for Trajectory Prediction and Group Detection in Crowds
Authors: Tharindu Fernando, Simon Denman, Sridha Sridharan, Clinton Fookes
Date: 2018-12-18 00:00:00
Tasks: Group Detection In Crowds, Trajectory Prediction

This paper presents a novel deep learning framework for human trajectory
prediction and detecting social group membership in crowds. We introduce a
generative adversarial pipeline which preserves the spatio-temporal structure
of the pedestrian's neighbourhood, enabling us to extract relevant attributes
describing their social identity. We formulate the group detection task as an
unsupervised learning problem, obviating the need for supervised learning of
group memberships via hand labeled databases, allowing us to directly employ
the proposed framework in different surveillance settings. We evaluate the
proposed trajectory prediction and group detection frameworks on multiple
public benchmarks, and for both tasks the proposed method demonstrates its
capability to better anticipate human sociological behaviour compared to the
existing state-of-the-art methods.
Deep Variational Sufficient Dimensionality Reduction
Authors: Ershad Banijamali, Amir-Hossein Karimi, Ali Ghodsi
Date: 2018-12-18 00:00:00
Tasks: Dimensionality Reduction, General Classification

We consider the problem of sufficient dimensionality reduction (SDR), where
the high-dimensional observation is transformed to a low-dimensional sub-space
in which the information of the observations regarding the label variable is
preserved. We propose DVSDR, a deep variational approach for sufficient
dimensionality reduction. The deep structure in our model has a bottleneck that
represent the low-dimensional embedding of the data. We explain the SDR problem
using graphical models and use the framework of variational autoencoders to
maximize the lower bound of the log-likelihood of the joint distribution of the
observation and label. We show that such a maximization problem can be
interpreted as solving the SDR problem. DVSDR can be easily adopted to
semi-supervised learning setting. In our experiment we show that DVSDR performs
competitively on classification tasks while being able to generate novel data
samples.
Clustering-Oriented Representation Learning with Attractive-Repulsive Loss
Authors: Kian Kenyon-Dean, Andre Cianflone, Lucas Page-Caccia, Guillaume Rabusseau, Jackie Chi Kit Cheung, Doina Precup
Date: 2018-12-18 00:00:00
Tasks: Clustering, General Classification, Representation Learning

The standard loss function used to train neural network classifiers,
categorical cross-entropy (CCE), seeks to maximize accuracy on the training
data; building useful representations is not a necessary byproduct of this
objective. In this work, we propose clustering-oriented representation learning
(COREL) as an alternative to CCE in the context of a generalized
attractive-repulsive loss framework. COREL has the consequence of building
latent representations that collectively exhibit the quality of natural
clustering within the latent space of the final hidden layer, according to a
predefined similarity function. Despite being simple to implement, COREL
variants outperform or perform equivalently to CCE in a variety of scenarios,
including image and news article classification using both feed-forward and
convolutional neural networks. Analysis of the latent spaces created with
different similarity functions facilitates insights on the different use cases
COREL variants can satisfy, where the Cosine-COREL variant makes a consistently
clusterable latent space, while Gaussian-COREL consistently obtains better
classification accuracy than CCE.
Universal Successor Features Approximators
Authors: Diana Borsa, André Barreto, John Quan, Daniel Mankowitz, Rémi Munos, Hado van Hasselt, David Silver, Tom Schaul
Date: 2018-12-18 00:00:00
Proceeding: ICLR 2019 5
Tasks: Navigate, Reinforcement Learning, Reinforcement Learning (RL)

The ability of a reinforcement learning (RL) agent to learn about many reward
functions at the same time has many potential benefits, such as the
decomposition of complex tasks into simpler ones, the exchange of information
between tasks, and the reuse of skills. We focus on one aspect in particular,
namely the ability to generalise to unseen tasks. Parametric generalisation
relies on the interpolation power of a function approximator that is given the
task description as input; one of its most common form are universal value
function approximators (UVFAs). Another way to generalise to new tasks is to
exploit structure in the RL problem itself. Generalised policy improvement
(GPI) combines solutions of previous tasks into a policy for the unseen task;
this relies on instantaneous policy evaluation of old policies under the new
reward function, which is made possible through successor features (SFs). Our
proposed universal successor features approximators (USFAs) combine the
advantages of all of these, namely the scalability of UVFAs, the instant
inference of SFs, and the strong generalisation of GPI. We discuss the
challenges involved in training a USFA, its generalisation properties and
demonstrate its practical benefits and transfer abilities on a large-scale
domain in which the agent has to navigate in a first-person perspective
three-dimensional environment.