pwc-archive/papers-with-abstracts huggingface dataset:
Evaluating the Effectiveness of Automated Identity Masking (AIM) Methods with Human Perception and a Deep Convolutional Neural Network (CNN)
Authors: Kimberley D. Orsten-Hooge, Asal Baragchizadeh, Thomas P. Karnowski, David S. Bolme, Regina Ferrell, Parisa R. Jesudasen, Carlos D. Castillo, Alice J. O'Toole
Date: 2019-02-19 00:00:00
Tasks: De-identification, Edge Detection, Face Recognition, Temporal Action Localization

Face de-identification algorithms have been developed in response to the prevalent use of public video recordings and surveillance cameras. Here, we evaluated the success of identity masking in the context of monitoring drivers as they actively operate a motor vehicle. We studied the effectiveness of eight de-identification algorithms using human perceivers and a state-of-the-art deep convolutional neural network (CNN). We used a standard face recognition experiment in which human subjects studied high-resolution (studio-style) images to learn driver identities. Subjects were tested subsequently on their ability to recognize those identities in low-resolution videos depicting the drivers operating a motor vehicle. The videos were in either unmasked format, or were masked by one of the eight de-identification algorithms. All masking algorithms lowered identification accuracy substantially, relative to the unmasked video. In all cases, identifications were made with stringent decision criteria indicating the subjects had low confidence in their decisions. When matching the identities in high-resolution still images to those in the masked videos, the CNN performed at chance. Next, we examined CNN performance on the same task, but using the unmasked videos and their masked counterparts. In this case, the network scored surprisingly well on a subset of mask conditions. We conclude that carefully tested de-identification approaches, used alone or in combination, can be an effective tool for protecting the privacy of individuals captured in videos. We note that no approach is equally effective in masking all stimuli, and that future work should examine possible methods for determining the most effective mask per individual stimulus.
DEDPUL: Difference-of-Estimated-Densities-based Positive-Unlabeled Learning
Authors: Dmitry Ivanov
Date: 2019-02-19 00:00:00
Tasks: Binary Classification, Density Estimation, General Classification

Positive-Unlabeled (PU) learning is an analog to supervised binary classification for the case when only the positive sample is clean, while the negative sample is contaminated with latent instances of positive class and hence can be considered as an unlabeled mixture. The objectives are to classify the unlabeled sample and train an unbiased PN classifier, which generally requires to identify the mixing proportions of positives and negatives first. Recently, unbiased risk estimation framework has achieved state-of-the-art performance in PU learning. This approach, however, exhibits two major bottlenecks. First, the mixing proportions are assumed to be identified, i.e. known in the domain or estimated with additional methods. Second, the approach relies on the classifier being a neural network. In this paper, we propose DEDPUL, a method that solves PU Learning without the aforementioned issues. The mechanism behind DEDPUL is to apply a computationally cheap post-processing procedure to the predictions of any classifier trained to distinguish positive and unlabeled data. Instead of assuming the proportions to be identified, DEDPUL estimates them alongside with classifying unlabeled sample. Experiments show that DEDPUL outperforms the current state-of-the-art in both proportion estimation and PU Classification.
Geometry of Deep Generative Models for Disentangled Representations
Authors: Ankita Shukla, Shagun Uppal, Sarthak Bhagat, Saket Anand, Pavan Turaga
Date: 2019-02-19 00:00:00
Tasks: Representation Learning

Deep generative models like variational autoencoders approximate the
intrinsic geometry of high dimensional data manifolds by learning
low-dimensional latent-space variables and an embedding function. The geometric
properties of these latent spaces has been studied under the lens of Riemannian
geometry; via analysis of the non-linearity of the generator function. In new
developments, deep generative models have been used for learning semantically
meaningful `disentangled' representations; that capture task relevant
attributes while being invariant to other attributes. In this work, we explore
the geometry of popular generative models for disentangled representation
learning. We use several metrics to compare the properties of latent spaces of
disentangled representation models in terms of class separability and curvature
of the latent-space. The results we obtain establish that the class
distinguishable features in the disentangled latent space exhibits higher
curvature as opposed to a variational autoencoder. We evaluate and compare the
geometry of three such models with variational autoencoder on two different
datasets. Further, our results show that distances and interpolation in the
latent space are significantly improved with Riemannian metrics derived from
the curvature of the space. We expect these results will have implications on
understanding how deep-networks can be made more robust, generalizable, as well
as interpretable.
On the Analysis of EM for truncated mixtures of two Gaussians
Authors: Sai Ganesh Nagarajan, Ioannis Panageas
Date: 2019-02-19 00:00:00
Tasks: Vocal Bursts Valence Prediction

Motivated by a recent result of Daskalakis et al. 2018, we analyze the population version of Expectation-Maximization (EM) algorithm for the case of \textit{truncated} mixtures of two Gaussians. Truncated samples from a $d$-dimensional mixture of two Gaussians $\frac{1}{2} \mathcal{N}(\vec{\mu}, \vec{\Sigma})+ \frac{1}{2} \mathcal{N}(-\vec{\mu}, \vec{\Sigma})$ means that a sample is only revealed if it falls in some subset $S \subset \mathbb{R}^d$ of positive (Lebesgue) measure. We show that for $d=1$, EM converges almost surely (under random initialization) to the true mean (variance $\sigma^2$ is known) for any measurable set $S$. Moreover, for $d>1$ we show EM almost surely converges to the true mean for any measurable set $S$ when the map of EM has only three fixed points, namely $-\vec{\mu}, \vec{0}, \vec{\mu}$ (covariance matrix $\vec{\Sigma}$ is known), and prove local convergence if there are more than three fixed points. We also provide convergence rates of our findings. Our techniques deviate from those of Daskalakis et al. 2017, which heavily depend on symmetry that the untruncated problem exhibits. For example, for an arbitrary measurable set $S$, it is impossible to compute a closed form of the update rule of EM. Moreover, arbitrarily truncating the mixture, induces further correlations among the variables. We circumvent these challenges by using techniques from dynamical systems, probability and statistics; implicit function theorem, stability analysis around the fixed points of the update rule of EM and correlation inequalities (FKG).
Air Quality Measurement Based on Double-Channel Convolutional Neural Network Ensemble Learning
Authors: Zhenyu Wang, Wei Zheng, Chunfeng Song
Date: 2019-02-19 00:00:00
Tasks: Ensemble Learning, Self-Learning

Environmental air quality affects people's life, obtaining real-time and
accurate environmental air quality has a profound guiding significance for the
development of social activities. At present, environmental air quality
measurement mainly adopts the method that setting air quality detector at
specific monitoring points in cities and timing sampling analysis, which is
easy to be restricted by time and space factors. Some air quality measurement
algorithms related to deep learning mostly adopt a single convolutional neural
network to train the whole image, which will ignore the difference of different
parts of the image. In this paper, we propose a method for air quality
measurement based on double-channel convolutional neural network ensemble
learning to solve the problem of feature extraction for different parts of
environmental images. Our method mainly includes two aspects: ensemble learning
of double-channel convolutional neural network and self-learning weighted
feature fusion. We constructed a double-channel convolutional neural network,
used each channel to train different parts of the environment images for
feature extraction. We propose a feature weight self-learning method, which
weights and concatenates the extracted feature vectors, and uses the fused
feature vectors to measure air quality. Our method can be applied to the two
tasks of air quality grade measurement and air quality index (AQI) measurement.
Moreover, we build an environmental image dataset of random time and location
condition. The experiments show that our method can achieve nearly 82% accuracy
and a small mean absolute error (MAE) on our test dataset. At the same time,
through comparative experiment, we proved that our proposed method gained
considerable improvement in performance compared with single channel
convolutional neural network air quality measurements.
Towards Hardware Implementation of Neural Network-based Communication Algorithms
Authors: Fayçal Ait Aoudia, Jakob Hoydis
Date: 2019-02-19 00:00:00

There is a recent interest in neural network (NN)-based communication
algorithms which have shown to achieve (beyond) state-of-the-art performance
for a variety of problems or lead to reduced implementation complexity.
However, most work on this topic is simulation based and implementation on
specialized hardware for fast inference, such as field-programmable gate arrays
(FPGAs), is widely ignored. In particular for practical uses, NN weights should
be quantized and inference carried out by a fixed-point instead of
floating-point system, widely used in consumer class computers and graphics
processing units (GPUs). Moving to such representations enables higher
inference rates and complexity reductions, at the cost of precision loss. We
demonstrate that it is possible to implement NN-based algorithms in fixed-point
arithmetic with quantized weights at negligible performance loss and with
hardware complexity compatible with practical systems, such as FPGAs and
application-specific integrated circuits (ASICs).
Label-Removed Generative Adversarial Networks Incorporating with K-Means
Authors: Ce Wang, Zhangling Chen, Kun Shang
Date: 2019-02-19 00:00:00
Tasks: Metric Learning

Generative Adversarial Networks (GANs) have achieved great success in
generating realistic images. Most of these are conditional models, although
acquisition of class labels is expensive and time-consuming in practice. To
reduce the dependence on labeled data, we propose an un-conditional generative
adversarial model, called K-Means-GAN (KM-GAN), which incorporates the idea of
updating centers in K-Means into GANs. Specifically, we redesign the framework
of GANs by applying K-Means on the features extracted from the discriminator.
With obtained labels from K-Means, we propose new objective functions from the
perspective of deep metric learning (DML). Distinct from previous works, the
discriminator is treated as a feature extractor rather than a classifier in
KM-GAN, meanwhile utilization of K-Means makes features of the discriminator
more representative. Experiments are conducted on various datasets, such as
MNIST, Fashion-10, CIFAR-10 and CelebA, and show that the quality of samples
generated by KM-GAN is comparable to some conditional generative adversarial
models.
Multifidelity Bayesian Optimization for Binomial Output
Authors: Leonid Matyushin, Alexey Zaytsev, Oleg Alenkin, Andrey Ustuzhanin
Date: 2019-02-19 00:00:00
Tasks: Bayesian Optimization

The key idea of Bayesian optimization is replacing an expensive target
function with a cheap surrogate model. By selection of an acquisition function
for Bayesian optimization, we trade off between exploration and exploitation.
The acquisition function typically depends on the mean and the variance of the
surrogate model at a given point.
  The most common Gaussian process-based surrogate model assumes that the
target with fixed parameters is a realization of a Gaussian process. However,
often the target function doesn't satisfy this approximation. Here we consider
target functions that come from the binomial distribution with the parameter
that depends on inputs. Typically we can vary how many Bernoulli samples we
obtain during each evaluation.
  We propose a general Gaussian process model that takes into account Bernoulli
outputs. To make things work we consider a simple acquisition function based on
Expected Improvement and a heuristic strategy to choose the number of samples
at each point thus taking into account precision of the obtained output.
Predicting tongue motion in unlabeled ultrasound videos using convolutional LSTM neural network
Authors: Chaojie Zhao, Peng Zhang, Jian Zhu, Chengrui Wu, Huaimin Wang, Kele Xu
Date: 2019-02-19 00:00:00
Tasks: motion prediction

A challenge in speech production research is to predict future tongue
movements based on a short period of past tongue movements. This study tackles
speaker-dependent tongue motion prediction problem in unlabeled ultrasound
videos with convolutional long short-term memory (ConvLSTM) networks. The model
has been tested on two different ultrasound corpora. ConvLSTM outperforms
3-dimensional convolutional neural network (3DCNN) in predicting the
9\textsuperscript{th} frames based on 8 preceding frames, and also demonstrates
good capacity to predict only the tongue contours in future frames. Further
tests reveal that ConvLSTM can also learn to predict tongue movements in more
distant frames beyond the immediately following frames. Our codes are available
at: https://github.com/shuiliwanwu/ConvLstm-ultrasound-videos.
Anomaly Detection with Adversarial Dual Autoencoders
Authors: Ha Son Vu, Daisuke Ueta, Kiyoshi Hashimoto, Kazuki Maeno, Sugiri Pranata, Sheng Mei Shen
Date: 2019-02-19 00:00:00
Proceeding: arXiv.org 2019 2
Tasks: Anomaly Detection, Image Generation

Semi-supervised and unsupervised Generative Adversarial Networks (GAN)-based
methods have been gaining popularity in anomaly detection task recently.
However, GAN training is somewhat challenging and unstable. Inspired from
previous work in GAN-based image generation, we introduce a GAN-based anomaly
detection framework - Adversarial Dual Autoencoders (ADAE) - consists of two
autoencoders as generator and discriminator to increase training stability. We
also employ discriminator reconstruction error as anomaly score for better
detection performance. Experiments across different datasets of varying
complexity show strong evidence of a robust model that can be used in different
scenarios, one of which is brain tumor detection.
Using Conditional Generative Adversarial Networks to Generate Ground-Level Views From Overhead Imagery
Authors: Xueqing Deng, Yi Zhu, Shawn Newsam
Date: 2019-02-19 00:00:00
Tasks: Decoder, General Classification, Generative Adversarial Network, Land Cover Classification

This paper develops a deep-learning framework to synthesize a ground-level
view of a location given an overhead image. We propose a novel conditional
generative adversarial network (cGAN) in which the trained generator generates
realistic looking and representative ground-level images using overhead imagery
as auxiliary information. The generator is an encoder-decoder network which
allows us to compare low- and high-level features as well as their
concatenation for encoding the overhead imagery. We also demonstrate how our
framework can be used to perform land cover classification by modifying the
trained cGAN to extract features from overhead imagery. This is interesting
because, although we are using this modified cGAN as a feature extractor for
overhead imagery, it incorporates knowledge of how locations look from the
ground.
2D LiDAR Map Prediction via Estimating Motion Flow with GRU
Authors: Yafei Song, Yonghong Tian, Gang Wang, Mingyang Li
Date: 2019-02-19 00:00:00
Tasks: Optical Flow Estimation, Representation Learning

It is a significant problem to predict the 2D LiDAR map at next moment for
robotics navigation and path-planning. To tackle this problem, we resort to the
motion flow between adjacent maps, as motion flow is a powerful tool to process
and analyze the dynamic data, which is named optical flow in video processing.
However, unlike video, which contains abundant visual features in each frame, a
2D LiDAR map lacks distinctive local features. To alleviate this challenge, we
propose to estimate the motion flow based on deep neural networks inspired by
its powerful representation learning ability in estimating the optical flow of
the video. To this end, we design a recurrent neural network based on gated
recurrent unit, which is named LiDAR-FlowNet. As a recurrent neural network can
encode the temporal dynamic information, our LiDAR-FlowNet can estimate motion
flow between the current map and the unknown next map only from the current
frame and previous frames. A self-supervised strategy is further designed to
train the LiDAR-FlowNet model effectively, while no training data need to be
manually annotated. With the estimated motion flow, it is straightforward to
predict the 2D LiDAR map at the next moment. Experimental results verify the
effectiveness of our LiDAR-FlowNet as well as the proposed training strategy.
The results of the predicted LiDAR map also show the advantages of our motion
flow based method.
Explaining a black-box using Deep Variational Information Bottleneck Approach
Authors: Seojin Bang, Pengtao Xie, Heewook Lee, Wei Wu, Eric Xing
Date: 2019-02-19 00:00:00
Tasks: BIG-bench Machine Learning, Interpretable Machine Learning

Interpretable machine learning has gained much attention recently. Briefness and comprehensiveness are necessary in order to provide a large amount of information concisely when explaining a black-box decision system. However, existing interpretable machine learning methods fail to consider briefness and comprehensiveness simultaneously, leading to redundant explanations. We propose the variational information bottleneck for interpretation, VIBI, a system-agnostic interpretable method that provides a brief but comprehensive explanation. VIBI adopts an information theoretic principle, information bottleneck principle, as a criterion for finding such explanations. For each instance, VIBI selects key features that are maximally compressed about an input (briefness), and informative about a decision made by a black-box system on that input (comprehensive). We evaluate VIBI on three datasets and compare with state-of-the-art interpretable machine learning methods in terms of both interpretability and fidelity evaluated by human and quantitative metrics
Universality of Computational Lower Bounds for Submatrix Detection
Authors: Matthew Brennan, Guy Bresler, Wasim Huleihel
Date: 2019-02-19 00:00:00
Tasks: Community Detection, Two-sample testing

In the general submatrix detection problem, the task is to detect the presence of a small $k \times k$ submatrix with entries sampled from a distribution $\mathcal{P}$ in an $n \times n$ matrix of samples from $\mathcal{Q}$. This formulation includes a number of well-studied problems, such as biclustering when $\mathcal{P}$ and $\mathcal{Q}$ are Gaussians and the planted dense subgraph formulation of community detection when the submatrix is a principal minor and $\mathcal{P}$ and $\mathcal{Q}$ are Bernoulli random variables. These problems all seem to exhibit a universal phenomenon: there is a statistical-computational gap depending on $\mathcal{P}$ and $\mathcal{Q}$ between the minimum $k$ at which this task can be solved and the minimum $k$ at which it can be solved in polynomial time. Our main result is to tightly characterize this computational barrier as a tradeoff between $k$ and the KL divergences between $\mathcal{P}$ and $\mathcal{Q}$ through average-case reductions from the planted clique conjecture. These computational lower bounds hold given mild assumptions on $\mathcal{P}$ and $\mathcal{Q}$ arising naturally from classical binary hypothesis testing. Our results recover and generalize the planted clique lower bounds for Gaussian biclustering in Ma-Wu (2015) and Brennan et al. (2018) and for the sparse and general regimes of planted dense subgraph in Hajek et al. (2015) and Brennan et al. (2018). This yields the first universality principle for computational lower bounds obtained through average-case reductions.
Fast Compressive Sensing Recovery Using Generative Models with Structured Latent Variables
Authors: Shaojie Xu, Sihan Zeng, Justin Romberg
Date: 2019-02-19 00:00:00
Tasks: Compressive Sensing

Deep learning models have significantly improved the visual quality and accuracy on compressive sensing recovery. In this paper, we propose an algorithm for signal reconstruction from compressed measurements with image priors captured by a generative model. We search and constrain on latent variable space to make the method stable when the number of compressed measurements is extremely limited. We show that, by exploiting certain structures of the latent variables, the proposed method produces improved reconstruction accuracy and preserves realistic and non-smooth features in the image. Our algorithm achieves high computation speed by projecting between the original signal space and the latent variable space in an alternating fashion.
Winning an Election: On Emergent Strategic Communication in Multi-Agent Networks
Authors: Shubham Gupta, Ambedkar Dukkipati
Date: 2019-02-19 00:00:00

Humans use language to collectively execute abstract strategies besides using it as a referential tool for identifying physical entities. Recently, multiple attempts at replicating the process of emergence of language in artificial agents have been made. While existing approaches study emergent languages as referential tools, in this paper, we study their role in discovering and implementing strategies. We formulate the problem using a voting game where two candidate agents contest in an election with the goal of convincing population members (other agents), that are connected to each other via an underlying network, to vote for them. To achieve this goal, agents are only allowed to exchange messages in the form of sequences of discrete symbols to spread their propaganda. We use neural networks with Gumbel-Softmax relaxation for sampling categorical random variables to parameterize the policies followed by all agents. Using our proposed framework, we provide concrete answers to the following questions: (i) Do the agents learn to communicate in a meaningful way and does the emergent communication play a role in deciding the winner? (ii) Does the system evolve as expected under various reward structures? (iii) How is the emergent language affected by the community structure in the network? To the best of our knowledge, we are the first to explore emergence of communication for discovering and implementing strategies in a setting where agents communicate over a network.