pwc-archive/papers-with-abstracts huggingface dataset:
EnKCF: Ensemble of Kernelized Correlation Filters for High-Speed Object Tracking
Authors: Burak Uzkent, Young-Woo Seo
Date: 2018-01-20 00:00:00
Tasks: Object Tracking, Vocal Bursts Intensity Prediction

Computer vision technologies are very attractive for practical applications
running on embedded systems. For such an application, it is desirable for the
deployed algorithms to run in high-speed and require no offline training. To
develop a single-target tracking algorithm with these properties, we propose an
ensemble of the kernelized correlation filters (KCF), we call it EnKCF. A
committee of KCFs is specifically designed to address the variations in scale
and translation of moving objects. To guarantee a high-speed run-time
performance, we deploy each of KCFs in turn, instead of applying multiple KCFs
to each frame. To minimize any potential drifts between individual KCFs
transition, we developed a particle filter. Experimental results showed that
the performance of ours is, on average, 70.10% for precision at 20 pixels,
53.00% for success rate for the OTB100 data, and 54.50% and 40.2% for the
UAV123 data. Experimental results showed that our method is better than other
high-speed trackers over 5% on precision on 20 pixels and 10-20% on AUC on
average. Moreover, our implementation ran at 340 fps for the OTB100 and at 416
fps for the UAV123 dataset that is faster than DCF (292 fps) for the OTB100 and
KCF (292 fps) for the UAV123. To increase flexibility of the proposed EnKCF
running on various platforms, we also explored different levels of deep
convolutional features.
A Second Order Cumulant Spectrum Test That a Stochastic Process is Strictly Stationary and a Step Toward a Test for Graph Signal Strict Stationarity
Authors: Denisa Roberts, Douglas Patterson
Date: 2018-01-20 00:00:00
Tasks: Denoising

This article develops a statistical test for the null hypothesis of strict stationarity of a discrete time stochastic process in the frequency domain. When the null hypothesis is true, the second order cumulant spectrum is zero at all the discrete Fourier frequency pairs in the principal domain. The test uses a window averaged sample estimate of the second order cumulant spectrum to build a test statistic with an asymptotic complex standard normal distribution. We derive the test statistic, study the properties of the test and demonstrate its application using 137Cs gamma ray decay data. Future areas of research include testing for strict stationarity of graph signals, with applications in learning convolutional neural networks on graphs, denoising, and inpainting.
DeepISP: Towards Learning an End-to-End Image Processing Pipeline
Authors: Eli Schwartz, Raja Giryes, Alex M. Bronstein
Date: 2018-01-20 00:00:00
Tasks: Demosaicking, Denoising

We present DeepISP, a full end-to-end deep neural model of the camera image
signal processing (ISP) pipeline. Our model learns a mapping from the raw
low-light mosaiced image to the final visually compelling image and encompasses
low-level tasks such as demosaicing and denoising as well as higher-level tasks
such as color correction and image adjustment. The training and evaluation of
the pipeline were performed on a dedicated dataset containing pairs of
low-light and well-lit images captured by a Samsung S7 smartphone camera in
both raw and processed JPEG formats. The proposed solution achieves
state-of-the-art performance in objective evaluation of PSNR on the subtask of
joint denoising and demosaicing. For the full end-to-end pipeline, it achieves
better visual quality compared to the manufacturer ISP, in both a subjective
human assessment and when rated by a deep model trained for assessing image
quality.
Active Expansion Sampling for Learning Feasible Domains in an Unbounded Input Space
Authors: Wei Chen, Mark Fuge
Date: 2017-08-25 00:00:00
Tasks: Active Learning

Many engineering problems require identifying feasible domains under implicit
constraints. One example is finding acceptable car body styling designs based
on constraints like aesthetics and functionality. Current active-learning based
methods learn feasible domains for bounded input spaces. However, we usually
lack prior knowledge about how to set those input variable bounds. Bounds that
are too small will fail to cover all feasible domains; while bounds that are
too large will waste query budget. To avoid this problem, we introduce Active
Expansion Sampling (AES), a method that identifies (possibly disconnected)
feasible domains over an unbounded input space. AES progressively expands our
knowledge of the input space, and uses successive exploitation and exploration
stages to switch between learning the decision boundary and searching for new
feasible domains. We show that AES has a misclassification loss guarantee
within the explored region, independent of the number of iterations or labeled
samples. Thus it can be used for real-time prediction of samples' feasibility
within the explored region. We evaluate AES on three test examples and compare
AES with two adaptive sampling methods -- the Neighborhood-Voronoi algorithm
and the straddle heuristic -- that operate over fixed input variable bounds.
A Deep Reinforcement Learning Chatbot (Short Version)
Authors: Iulian V. Serban, Chinnadhurai Sankar, Mathieu Germain, Saizheng Zhang, Zhouhan Lin, Sandeep Subramanian, Taesup Kim, Michael Pieper, Sarath Chandar, Nan Rosemary Ke, Sai Rajeswar, Alexandre de Brebisson, Jose M. R. Sotelo, Dendi Suhubdy, Vincent Michalski, Alexandre Nguyen, Joelle Pineau, Yoshua Bengio
Date: 2018-01-20 00:00:00
Tasks: Chatbot, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Retrieval, Text Generation

We present MILABOT: a deep reinforcement learning chatbot developed by the
Montreal Institute for Learning Algorithms (MILA) for the Amazon Alexa Prize
competition. MILABOT is capable of conversing with humans on popular small talk
topics through both speech and text. The system consists of an ensemble of
natural language generation and retrieval models, including neural network and
template-based models. By applying reinforcement learning to crowdsourced data
and real-world user interactions, the system has been trained to select an
appropriate response from the models in its ensemble. The system has been
evaluated through A/B testing with real-world users, where it performed
significantly better than other systems. The results highlight the potential of
coupling ensemble systems with deep reinforcement learning as a fruitful path
for developing real-world, open-domain conversational agents.
Determination of Digital Straight Segments Using the Slope
Authors: Alejandro Cartas, María Elena Algorri
Date: 2018-01-20 00:00:00

We present a new method for the recognition of digital straight lines based
on the slope. This method combines the Freeman's chain coding scheme and new
discovered properties of the digital slope introduced in this paper. We also
present the efficiency of our method from a testbed.
Learning model-based strategies in simple environments with hierarchical q-networks
Authors: Necati Alp Muyesser, Kyle Dunovan, Timothy Verstynen
Date: 2018-01-20 00:00:00
Tasks: Reinforcement Learning, Reinforcement Learning (RL)

Recent advances in deep learning have allowed artificial agents to rival
human-level performance on a wide range of complex tasks; however, the ability
of these networks to learn generalizable strategies remains a pressing
challenge. This critical limitation is due in part to two factors: the opaque
information representation in deep neural networks and the complexity of the
task environments in which they are typically deployed. Here we propose a novel
Hierarchical Q-Network (HQN) motivated by theories of the hierarchical
organization of the human prefrontal cortex, that attempts to identify lower
dimensional patterns in the value landscape that can be exploited to construct
an internal model of rules in simple environments. We draw on combinatorial
games, where there exists a single optimal strategy for winning that
generalizes across other features of the game, to probe the strategy
generalization of the HQN and other reinforcement learning (RL) agents using
variations of Wythoff's game. Traditional RL approaches failed to reach
satisfactory performance on variants of Wythoff's Game; however, the HQN
learned heuristic-like strategies that generalized across changes in board
configuration. More importantly, the HQN allowed for transparent inspection of
the agent's internal model of the game following training. Our results show how
a biologically inspired hierarchical learner can facilitate learning abstract
rules to promote robust and flexible action policies in simplified training
environments with clearly delineated optimal strategies.
A Directionally Selective Small Target Motion Detecting Visual Neural Network in Cluttered Backgrounds
Authors: Hongxin Wang, Jigen Peng, Shigang Yue
Date: 2018-01-20 00:00:00

Discriminating targets moving against a cluttered background is a huge
challenge, let alone detecting a target as small as one or a few pixels and
tracking it in flight. In the fly's visual system, a class of specific neurons,
called small target motion detectors (STMDs), have been identified as showing
exquisite selectivity for small target motion. Some of the STMDs have also
demonstrated directional selectivity which means these STMDs respond strongly
only to their preferred motion direction. Directional selectivity is an
important property of these STMD neurons which could contribute to tracking
small targets such as mates in flight. However, little has been done on
systematically modeling these directional selective STMD neurons. In this
paper, we propose a directional selective STMD-based neural network (DSTMD) for
small target detection in a cluttered background. In the proposed neural
network, a new correlation mechanism is introduced for direction selectivity
via correlating signals relayed from two pixels. Then, a lateral inhibition
mechanism is implemented on the spatial field for size selectivity of STMD
neurons. Extensive experiments showed that the proposed neural network not only
is in accord with current biological findings, i.e. showing directional
preferences, but also worked reliably in detecting small targets against
cluttered backgrounds.
Pairing an arbitrary regressor with an artificial neural network estimating aleatoric uncertainty
Authors: Pavel Gurevich, Hannes Stuke
Date: 2017-07-23 00:00:00
Tasks: Uncertainty Quantification

We suggest a general approach to quantification of different forms of
aleatoric uncertainty in regression tasks performed by artificial neural
networks. It is based on the simultaneous training of two neural networks with
a joint loss function and a specific hyperparameter $\lambda>0$ that allows for
automatically detecting noisy and clean regions in the input space and
controlling their {\em relative contribution} to the loss and its gradients.
After the model has been trained, one of the networks performs predictions and
the other quantifies the uncertainty of these predictions by estimating the
locally averaged loss of the first one. Unlike in many classical uncertainty
quantification methods, we do not assume any a priori knowledge of the ground
truth probability distribution, neither do we, in general, maximize the
likelihood of a chosen parametric family of distributions. We analyze the
learning process and the influence of clean and noisy regions of the input
space on the loss surface, depending on $\lambda$. In particular, we show that
small values of $\lambda$ increase the relative contribution of clean regions
to the loss and its gradients. This explains why choosing small $\lambda$
allows for better predictions compared with neural networks without uncertainty
counterparts and those based on classical likelihood maximization. Finally, we
demonstrate that one can naturally form ensembles of pairs of our networks and
thus capture both aleatoric and epistemic uncertainty and avoid overfitting.
Robustly representing uncertainty in deep neural networks through sampling
Authors: Patrick McClure, Nikolaus Kriegeskorte
Date: 2016-11-05 00:00:00
Tasks: Variational Inference

As deep neural networks (DNNs) are applied to increasingly challenging
problems, they will need to be able to represent their own uncertainty.
Modeling uncertainty is one of the key features of Bayesian methods. Using
Bernoulli dropout with sampling at prediction time has recently been proposed
as an efficient and well performing variational inference method for DNNs.
However, sampling from other multiplicative noise based variational
distributions has not been investigated in depth. We evaluated Bayesian DNNs
trained with Bernoulli or Gaussian multiplicative masking of either the units
(dropout) or the weights (dropconnect). We tested the calibration of the
probabilistic predictions of Bayesian convolutional neural networks (CNNs) on
MNIST and CIFAR-10. Sampling at prediction time increased the calibration of
the DNNs' probabalistic predictions. Sampling weights, whether Gaussian or
Bernoulli, led to more robust representation of uncertainty compared to
sampling of units. However, using either Gaussian or Bernoulli dropout led to
increased test set classification accuracy. Based on these findings we used
both Bernoulli dropout and Gaussian dropconnect concurrently, which we show
approximates the use of a spike-and-slab variational distribution without
increasing the number of learned parameters. We found that spike-and-slab
sampling had higher test set performance than Gaussian dropconnect and more
robustly represented its uncertainty compared to Bernoulli dropout.
Visual Data Augmentation through Learning
Authors: Grigorios G. Chrysos, Yannis Panagakis, Stefanos Zafeiriou
Date: 2018-01-20 00:00:00
Tasks: Data Augmentation

The rapid progress in machine learning methods has been empowered by i) huge
datasets that have been collected and annotated, ii) improved engineering (e.g.
data pre-processing/normalization). The existing datasets typically include
several million samples, which constitutes their extension a colossal task. In
addition, the state-of-the-art data-driven methods demand a vast amount of
data, hence a standard engineering trick employed is artificial data
augmentation for instance by adding into the data cropped and (affinely)
transformed images. However, this approach does not correspond to any change in
the natural 3D scene.
  We propose instead to perform data augmentation through learning realistic
local transformations. We learn a forward and an inverse transformation that
maps an image from the high-dimensional space of pixel intensities to a latent
space which varies (approximately) linearly with the latent space of a
realistically transformed version of the image. Such transformed images can be
considered two successive frames in a video. Next, we utilize these
transformations to learn a linear model that modifies the latent spaces and
then use the inverse transformation to synthesize a new image. We argue that
the this procedure produces powerful invariant representations. We perform both
qualitative and quantitative experiments that demonstrate our proposed method
creates new realistic images.
A Random Sample Partition Data Model for Big Data Analysis
Authors: Salman Salloum, Yulin He, Joshua Zhexue Huang, Xiaoliang Zhang, Tamer Z. Emara, Chenghao Wei, Heping He
Date: 2017-12-12 00:00:00

Big data sets must be carefully partitioned into statistically similar data
subsets that can be used as representative samples for big data analysis tasks.
In this paper, we propose the random sample partition (RSP) data model to
represent a big data set as a set of non-overlapping data subsets, called RSP
data blocks, where each RSP data block has a probability distribution similar
to the whole big data set. Under this data model, efficient block level
sampling is used to randomly select RSP data blocks, replacing expensive record
level sampling to select sample data from a big distributed data set on a
computing cluster. We show how RSP data blocks can be employed to estimate
statistics of a big data set and build models which are equivalent to those
built from the whole big data set. In this approach, analysis of a big data set
becomes analysis of few RSP data blocks which have been generated in advance on
the computing cluster. Therefore, the new method for data analysis based on RSP
data blocks is scalable to big data.
Structured Inhomogeneous Density Map Learning for Crowd Counting
Authors: Hanhui Li, Xiangjian He, Hefeng Wu, Saeed Amirgholipour Kasmani, Ruomei Wang, Xiaonan Luo, Liang Lin
Date: 2018-01-20 00:00:00
Tasks: Crowd Counting

In this paper, we aim at tackling the problem of crowd counting in extremely
high-density scenes, which contain hundreds, or even thousands of people. We
begin by a comprehensive analysis of the most widely used density map-based
methods, and demonstrate how easily existing methods are affected by the
inhomogeneous density distribution problem, e.g., causing them to be sensitive
to outliers, or be hard to optimized. We then present an extremely simple
solution to the inhomogeneous density distribution problem, which can be
intuitively summarized as extending the density map from 2D to 3D, with the
extra dimension implicitly indicating the density level. Such solution can be
implemented by a single Density-Aware Network, which is not only easy to train,
but also can achieve the state-of-art performance on various challenging
datasets.
Feature overwriting as a finite mixture process: Evidence from comprehension data
Authors: Shravan Vasishth, Lena A. Jäger, Bruno Nicenboim
Date: 2017-03-12 00:00:00
Tasks: Retrieval, Sentence

The ungrammatical sentence "The key to the cabinets are on the table" is
known to lead to an illusion of grammaticality. As discussed in the
meta-analysis by Jaeger et al., 2017, faster reading times are observed at the
verb are in the agreement-attraction sentence above compared to the equally
ungrammatical sentence "The key to the cabinet are on the table". One
explanation for this facilitation effect is the feature percolation account:
the plural feature on cabinets percolates up to the head noun key, leading to
the illusion. An alternative account is in terms of cue-based retrieval (Lewis
& Vasishth, 2005), which assumes that the non-subject noun cabinets is
misretrieved due to a partial feature-match when a dependency completion
process at the auxiliary initiates a memory access for a subject with plural
marking. We present evidence for yet another explanation for the observed
facilitation. Because the second sentence has two nouns with identical number,
it is possible that these are, in some proportion of trials, more difficult to
keep distinct, leading to slower reading times at the verb in the first
sentence above; this is the feature overwriting account of Nairne, 1990. We
show that the feature overwriting proposal can be implemented as a finite
mixture process. We reanalysed ten published data-sets, fitting hierarchical
Bayesian mixture models to these data assuming a two-mixture distribution. We
show that in nine out of the ten studies, a mixture distribution corresponding
to feature overwriting furnishes a superior fit over both the feature
percolation and the cue-based retrieval accounts.
Deep Hidden Physics Models: Deep Learning of Nonlinear Partial Differential Equations
Authors: Maziar Raissi
Date: 2018-01-20 00:00:00
Tasks: Deep Learning

A long-standing problem at the interface of artificial intelligence and
applied mathematics is to devise an algorithm capable of achieving human level
or even superhuman proficiency in transforming observed data into predictive
mathematical models of the physical world. In the current era of abundance of
data and advanced machine learning capabilities, the natural question arises:
How can we automatically uncover the underlying laws of physics from
high-dimensional data generated from experiments? In this work, we put forth a
deep learning approach for discovering nonlinear partial differential equations
from scattered and potentially noisy observations in space and time.
Specifically, we approximate the unknown solution as well as the nonlinear
dynamics by two deep neural networks. The first network acts as a prior on the
unknown solution and essentially enables us to avoid numerical differentiations
which are inherently ill-conditioned and unstable. The second network
represents the nonlinear dynamics and helps us distill the mechanisms that
govern the evolution of a given spatiotemporal data-set. We test the
effectiveness of our approach for several benchmark problems spanning a number
of scientific domains and demonstrate how the proposed framework can help us
accurately learn the underlying dynamics and forecast future states of the
system. In particular, we study the Burgers', Korteweg-de Vries (KdV),
Kuramoto-Sivashinsky, nonlinear Schr\"{o}dinger, and Navier-Stokes equations.
Visualization of Hyperspectral Images Using Moving Least Squares
Authors: Danping Liao, Siyu Chen, Yuntao Qian
Date: 2018-01-20 00:00:00

Displaying the large number of bands in a hyper spectral image on a
trichromatic monitor has been an active research topic. The visualized image
shall convey as much information as possible form the original data and
facilitate image interpretation. Most existing methods display HSIs in false
colors which contradict with human's experience and expectation. In this paper,
we propose a nonlinear approach to visualize an input HSI with natural colors
by taking advantage of a corresponding RGB image. Our approach is based on
Moving Least Squares, an interpolation scheme for reconstructing a surface from
a set of control points, which in our case is a set of matching pixels between
the HSI and the corresponding RGB image. Based on MLS, the proposed method
solves for each spectral signature a unique transformation so that the non
linear structure of the HSI can be preserved. The matching pixels between a
pair of HSI and RGB image can be reused to display other HSIs captured b the
same imaging sensor with natural colors. Experiments show that the output image
of the proposed method no only have natural colors but also maintain the visual
information necessary for human analysis.
Composite convex minimization involving self-concordant-like cost functions
Authors: Quoc Tran-Dinh, Yen-Huan Li, Volkan Cevher
Date: 2015-02-04 00:00:00

The self-concordant-like property of a smooth convex function is a new
analytical structure that generalizes the self-concordant notion. While a wide
variety of important applications feature the self-concordant-like property,
this concept has heretofore remained unexploited in convex optimization. To
this end, we develop a variable metric framework of minimizing the sum of a
"simple" convex function and a self-concordant-like function. We introduce a
new analytic step-size selection procedure and prove that the basic gradient
algorithm has improved convergence guarantees as compared to "fast" algorithms
that rely on the Lipschitz gradient property. Our numerical tests with
real-data sets shows that the practice indeed follows the theory.