pwc-archive/papers-with-abstracts huggingface dataset:
Deep Reinforcement Learning for Time Scheduling in RF-Powered Backscatter Cognitive Radio Networks
Authors: Tran The Anh, Nguyen Cong Luong, Dusit Niyato, Ying-Chang Liang, Dong In Kim
Date: 2018-10-03 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Scheduling

In an RF-powered backscatter cognitive radio network, multiple secondary
users communicate with a secondary gateway by backscattering or harvesting
energy and actively transmitting their data depending on the primary channel
state. To coordinate the transmission of multiple secondary transmitters, the
secondary gateway needs to schedule the backscattering time, energy harvesting
time, and transmission time among them. However, under the dynamics of the
primary channel and the uncertainty of the energy state of the secondary
transmitters, it is challenging for the gateway to find a time scheduling
mechanism which maximizes the total throughput. In this paper, we propose to
use the deep reinforcement learning algorithm to derive an optimal time
scheduling policy for the gateway. Specifically, to deal with the problem with
large state and action spaces, we adopt a Double Deep-Q Network (DDQN) that
enables the gateway to learn the optimal policy. The simulation results clearly
show that the proposed deep reinforcement learning algorithm outperforms
non-learning schemes in terms of network throughput.
A Practical Approach to Proper Inference with Linked Data
Authors: Andee Kaplan, Brenda Betancourt, Rebecca C. Steorts
Date: 2018-10-02 00:00:00
Tasks: Entity Resolution

Entity resolution (ER), comprising record linkage and de-duplication, is the process of merging noisy databases in the absence of unique identifiers to remove duplicate entities. One major challenge of analysis with linked data is identifying a representative record among determined matches to pass to an inferential or predictive task, referred to as the \emph{downstream task}. Additionally, incorporating uncertainty from ER in the downstream task is critical to ensure proper inference. To bridge the gap between ER and the downstream task in an analysis pipeline, we propose five methods to choose a representative (or canonical) record from linked data, referred to as canonicalization. Our methods are scalable in the number of records, appropriate in general data scenarios, and provide natural error propagation via a Bayesian canonicalization stage. The proposed methodology is evaluated on three simulated data sets and one application -- determining the relationship between demographic information and party affiliation in voter registration data from the North Carolina State Board of Elections. We first perform Bayesian ER and evaluate our proposed methods for canonicalization before considering the downstream tasks of linear and logistic regression. Bayesian canonicalization methods are empirically shown to improve downstream inference in both settings through prediction and coverage.
Deep Decoder: Concise Image Representations from Untrained Non-convolutional Networks
Authors: Reinhard Heckel, Paul Hand
Date: 2018-10-02 00:00:00
Proceeding: ICLR 2019 5
Tasks: Decoder, Denoising

Deep neural networks, in particular convolutional neural networks, have
become highly effective tools for compressing images and solving inverse
problems including denoising, inpainting, and reconstruction from few and noisy
measurements. This success can be attributed in part to their ability to
represent and generate natural images well. Contrary to classical tools such as
wavelets, image-generating deep neural networks have a large number of
parameters---typically a multiple of their output dimension---and need to be
trained on large datasets. In this paper, we propose an untrained simple image
model, called the deep decoder, which is a deep neural network that can
generate natural images from very few weight parameters. The deep decoder has a
simple architecture with no convolutions and fewer weight parameters than the
output dimensionality. This underparameterization enables the deep decoder to
compress images into a concise set of network weights, which we show is on par
with wavelet-based thresholding. Further, underparameterization provides a
barrier to overfitting, allowing the deep decoder to have state-of-the-art
performance for denoising. The deep decoder is simple in the sense that each
layer has an identical structure that consists of only one upsampling unit,
pixel-wise linear combination of channels, ReLU activation, and channelwise
normalization. This simplicity makes the network amenable to theoretical
analysis, and it sheds light on the aspects of neural networks that enable them
to form effective signal representations.
An Evolutionary Game Theoretic Model of Rhino Horn Devaluation
Authors: Nikoleta E. Glynatsi, Vincent Knight, Tamsin E. Lee
Date: 2017-12-20 00:00:00

Rhino populations are at a critical level due to the demand for rhino horn
and the subsequent poaching. Wildlife managers attempt to secure rhinos with
approaches to devalue the horn, the most common of which is dehorning. Game
theory has been used to examine the interaction of poachers and wildlife
managers where a manager can either `dehorn' their rhinos or leave the horn
attached and poachers may behave `selectively' or `indiscriminately'. The
approach described in this paper builds on this previous work and investigates
the interactions between the poachers. We build an evolutionary game theoretic
model and determine which strategy is preferred by a poacher in various
different populations of poachers. The purpose of this work is to discover
whether conditions which encourage the poachers to behave selectively exist,
that is, they only kill those rhinos with full horns.
  The analytical results show that full devaluation of all rhinos will likely
lead to indiscriminate poaching. In turn it shows that devaluing of rhinos can
only be effective when implemented along with a strong disincentive framework.
This paper aims to contribute to the necessary research required for informed
discussion about the lively debate on legalising rhino horn trade.
Variational Bayesian Monte Carlo
Authors: Luigi Acerbi
Date: 2018-10-12 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Bayesian Inference, Model Selection, Variational Inference

Many probabilistic models of interest in scientific computing and machine
learning have expensive, black-box likelihoods that prevent the application of
standard techniques for Bayesian inference, such as MCMC, which would require
access to the gradient or a large number of likelihood evaluations. We
introduce here a novel sample-efficient inference framework, Variational
Bayesian Monte Carlo (VBMC). VBMC combines variational inference with
Gaussian-process based, active-sampling Bayesian quadrature, using the latter
to efficiently approximate the intractable integral in the variational
objective. Our method produces both a nonparametric approximation of the
posterior distribution and an approximate lower bound of the model evidence,
useful for model selection. We demonstrate VBMC both on several synthetic
likelihoods and on a neuronal model with data from real neurons. Across all
tested problems and dimensions (up to $D = 10$), VBMC performs consistently
well in reconstructing the posterior and the model evidence with a limited
budget of likelihood evaluations, unlike other methods that work only in very
low dimensions. Our framework shows great promise as a novel tool for posterior
and model inference with expensive, black-box likelihoods.
Tuning Fairness by Balancing Target Labels
Authors: Thomas Kehrenberg, Zexun Chen, Novi Quadrianto
Date: 2018-10-12 00:00:00
Tasks: BIG-bench Machine Learning, Fairness

The issue of fairness in machine learning models has recently attracted a lot of attention as ensuring it will ensure continued confidence of the general public in the deployment of machine learning systems. We focus on mitigating the harm incurred by a biased machine learning system that offers better outputs (e.g. loans, job interviews) for certain groups than for others. We show that bias in the output can naturally be controlled in probabilistic models by introducing a latent target output. This formulation has several advantages: first, it is a unified framework for several notions of group fairness such as Demographic Parity and Equality of Opportunity; second, it is expressed as a marginalisation instead of a constrained problem; and third, it allows the encoding of our knowledge of what unbiased outputs should be. Practically, the second allows us to avoid unstable constrained optimisation procedures and to reuse off-the-shelf toolboxes. The latter translates to the ability to control the level of fairness by directly varying fairness target rates. In contrast, existing approaches rely on intermediate, arguably unintuitive, control parameters such as covariance thresholds.
Learning Grid Cells as Vector Representation of Self-Position Coupled with Matrix Representation of Self-Motion
Authors: Ruiqi Gao, Jianwen Xie, Song-Chun Zhu, Ying Nian Wu
Date: 2018-10-12 00:00:00
Proceeding: ICLR 2019 5
Tasks: Position

This paper proposes a representational model for grid cells. In this model, the 2D self-position of the agent is represented by a high-dimensional vector, and the 2D self-motion or displacement of the agent is represented by a matrix that transforms the vector. Each component of the vector is a unit or a cell. The model consists of the following three sub-models. (1) Vector-matrix multiplication. The movement from the current position to the next position is modeled by matrix-vector multiplication, i.e., the vector of the next position is obtained by multiplying the matrix of the motion to the vector of the current position. (2) Magnified local isometry. The angle between two nearby vectors equals the Euclidean distance between the two corresponding positions multiplied by a magnifying factor. (3) Global adjacency kernel. The inner product between two vectors measures the adjacency between the two corresponding positions, which is defined by a kernel function of the Euclidean distance between the two positions. Our representational model has explicit algebra and geometry. It can learn hexagon patterns of grid cells, and it is capable of error correction, path integral and path planning.
Custom Dual Transportation Mode Detection by Smartphone Devices Exploiting Sensor Diversity
Authors: Claudia Carpineti, Vincenzo Lomonaco, Luca Bedogni, Marco Di Felice, Luciano Bononi
Date: 2018-10-12 00:00:00
Tasks: Activity Recognition, Benchmarking, Diversity, Transportation Mode Detection

Making applications aware of the mobility experienced by the user can open
the door to a wide range of novel services in different use-cases, from smart
parking to vehicular traffic monitoring. In the literature, there are many
different studies demonstrating the theoretical possibility of performing
Transportation Mode Detection (TMD) by mining smart-phones embedded sensors
data. However, very few of them provide details on the benchmarking process and
on how to implement the detection process in practice. In this study, we
provide guidelines and fundamental results that can be useful for both
researcher and practitioners aiming at implementing a working TMD system. These
guidelines consist of three main contributions. First, we detail the
construction of a training dataset, gathered by heterogeneous users and
including five different transportation modes; the dataset is made available to
the research community as reference benchmark. Second, we provide an in-depth
analysis of the sensor-relevance for the case of Dual TDM, which is required by
most of mobility-aware applications. Third, we investigate the possibility to
perform TMD of unknown users/instances not present in the training set and we
compare with state-of-the-art Android APIs for activity recognition.
Fast Construction of Correcting Ensembles for Legacy Artificial Intelligence Systems: Algorithms and a Case Study
Authors: Ivan Y. Tyukin, Alexander N. Gorban, Stephen Green, Danil Prokhorov
Date: 2018-10-12 00:00:00

This paper presents a technology for simple and computationally efficient
improvements of a generic Artificial Intelligence (AI) system, including
Multilayer and Deep Learning neural networks. The improvements are, in essence,
small network ensembles constructed on top of the existing AI architectures.
Theoretical foundations of the technology are based on Stochastic Separation
Theorems and the ideas of the concentration of measure. We show that, subject
to mild technical assumptions on statistical properties of internal signals in
the original AI system, the technology enables instantaneous and
computationally efficient removal of spurious and systematic errors with
probability close to one on the datasets which are exponentially large in
dimension. The method is illustrated with numerical examples and a case study
of ten digits recognition from American Sign Language.
PointGrow: Autoregressively Learned Point Cloud Generation with Self-Attention
Authors: Yongbin Sun, Yue Wang, Ziwei Liu, Joshua E. Siegel, Sanjay E. Sarma
Date: 2018-10-12 00:00:00
Tasks: Diversity, Generating 3D Point Clouds, Point Cloud Generation

Generating 3D point clouds is challenging yet highly desired. This work presents a novel autoregressive model, PointGrow, which can generate diverse and realistic point cloud samples from scratch or conditioned on semantic contexts. This model operates recurrently, with each point sampled according to a conditional distribution given its previously-generated points, allowing inter-point correlations to be well-exploited and 3D shape generative processes to be better interpreted. Since point cloud object shapes are typically encoded by long-range dependencies, we augment our model with dedicated self-attention modules to capture such relations. Extensive evaluations show that PointGrow achieves satisfying performance on both unconditional and conditional point cloud generation tasks, with respect to realism and diversity. Several important applications, such as unsupervised feature learning and shape arithmetic operations, are also demonstrated.
A Survey and Critique of Multiagent Deep Reinforcement Learning
Authors: Pablo Hernandez-Leal, Bilal Kartal, Matthew E. Taylor
Date: 2018-10-12 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Survey

Deep reinforcement learning (RL) has achieved outstanding results in recent years. This has led to a dramatic increase in the number of applications and methods. Recent works have explored learning beyond single-agent scenarios and have considered multiagent learning (MAL) scenarios. Initial results report successes in complex multiagent domains, although there are several challenges to be addressed. The primary goal of this article is to provide a clear overview of current multiagent deep reinforcement learning (MDRL) literature. Additionally, we complement the overview with a broader analysis: (i) we revisit previous key components, originally presented in MAL and RL, and highlight how they have been adapted to multiagent deep reinforcement learning settings. (ii) We provide general guidelines to new practitioners in the area: describing lessons learned from MDRL works, pointing to recent benchmarks, and outlining open avenues of research. (iii) We take a more critical tone raising practical challenges of MDRL (e.g., implementation and computational demands). We expect this article will help unify and motivate future research to take advantage of the abundant literature that exists (e.g., RL and MAL) in a joint effort to promote fruitful research in the multiagent community.
Facility Locations Utility for Uncovering Classifier Overconfidence
Authors: Karsten Maurer, Walter Bennette
Date: 2018-10-12 00:00:00

Assessing the predictive accuracy of black box classifiers is challenging in
the absence of labeled test datasets. In these scenarios we may need to rely on
a human oracle to evaluate individual predictions; presenting the challenge to
create query algorithms to guide the search for points that provide the most
information about the classifier's predictive characteristics. Previous works
have focused on developing utility models and query algorithms for discovering
unknown unknowns --- misclassifications with a predictive confidence above some
arbitrary threshold. However, if misclassifications occur at the rate reflected
by the confidence values, then these search methods reveal nothing more than a
proper assessment of predictive certainty. We are unable to properly mitigate
the risks associated with model deficiency when the model's confidence in
prediction exceeds the actual model accuracy. We propose a facility locations
utility model and corresponding greedy query algorithm that instead searches
for overconfident unknown unknowns. Through robust empirical experiments we
demonstrate that the greedy query algorithm with the facility locations utility
model consistently results in oracle queries with superior performance in
discovering overconfident unknown unknowns than previous methods.
Characterization and extraction of condensed representation of correlated patterns based on formal concept analysis
Authors: Souad Bouasker
Date: 2018-10-12 00:00:00

Correlated pattern mining has increasingly become an important task in data
mining since these patterns allow conveying knowledge about meaningful and
surprising relations among data. Frequent correlated patterns were thoroughly
studied in the literature. In this thesis, we propose to benefit from both
frequent correlated as well as rare correlated patterns according to the bond
correlation measure. We propose to extract a subset without information loss of
the sets of frequent correlated and of rare correlated patterns, this subset is
called ``Condensed Representation``. In this regard, we are based on the
notions derived from the Formal Concept Analysis FCA, specifically the
equivalence classes associated to a closure operator fbond dedicated to the
bond measure, to introduce new concise representations of both frequent
correlated and rare correlated patterns.
Grand Challenge: Real-time Destination and ETA Prediction for Maritime Traffic
Authors: Oleh Bodunov, Florian Schmidt, André Martin, Andrey Brito, Christof Fetzer
Date: 2018-10-12 00:00:00
Tasks: Ensemble Learning, General Classification, Prediction

In this paper, we present our approach for solving the DEBS Grand Challenge
2018. The challenge asks to provide a prediction for (i) a destination and the
(ii) arrival time of ships in a streaming-fashion using Geo-spatial data in the
maritime context. Novel aspects of our approach include the use of ensemble
learning based on Random Forest, Gradient Boosting Decision Trees (GBDT),
XGBoost Trees and Extremely Randomized Trees (ERT) in order to provide a
prediction for a destination while for the arrival time, we propose the use of
Feed-forward Neural Networks. In our evaluation, we were able to achieve an
accuracy of 97% for the port destination classification problem and 90% (in
mins) for the ETA prediction.
Bayesian Inference of Self-intention Attributed by Observer
Authors: Yosuke Fukuchi, Masahiko Osawa, Hiroshi Yamakawa, Tatsuji Takahashi, Michita Imai
Date: 2018-10-12 00:00:00
Tasks: Attribute, Bayesian Inference, Human Agent Collaboration, Reinforcement Learning, Reinforcement Learning (RL)

Most of agents that learn policy for tasks with reinforcement learning (RL)
lack the ability to communicate with people, which makes human-agent
collaboration challenging. We believe that, in order for RL agents to
comprehend utterances from human colleagues, RL agents must infer the mental
states that people attribute to them because people sometimes infer an
interlocutor's mental states and communicate on the basis of this mental
inference. This paper proposes PublicSelf model, which is a model of a person
who infers how the person's own behavior appears to their colleagues. We
implemented the PublicSelf model for an RL agent in a simulated environment and
examined the inference of the model by comparing it with people's judgment. The
results showed that the agent's intention that people attributed to the agent's
movement was correctly inferred by the model in scenes where people could find
certain intentionality from the agent's behavior.
Effects of Image Degradations to CNN-based Image Classification
Authors: Yanting Pei, Yaping Huang, Qi Zou, Hao Zang, Xingyuan Zhang, Song Wang
Date: 2018-10-12 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification

Just like many other topics in computer vision, image classification has
achieved significant progress recently by using deep-learning neural networks,
especially the Convolutional Neural Networks (CNN). Most of the existing works
are focused on classifying very clear natural images, evidenced by the widely
used image databases such as Caltech-256, PASCAL VOCs and ImageNet. However, in
many real applications, the acquired images may contain certain degradations
that lead to various kinds of blurring, noise, and distortions. One important
and interesting problem is the effect of such degradations to the performance
of CNN-based image classification. More specifically, we wonder whether
image-classification performance drops with each kind of degradation, whether
this drop can be avoided by including degraded images into training, and
whether existing computer vision algorithms that attempt to remove such
degradations can help improve the image-classification performance. In this
paper, we empirically study this problem for four kinds of degraded images --
hazy images, underwater images, motion-blurred images and fish-eye images. For
this study, we synthesize a large number of such degraded images by applying
respective physical models to the clear natural images and collect a new hazy
image dataset from the Internet. We expect this work can draw more interests
from the community to study the classification of degraded images.
Feature Learning for Fault Detection in High-Dimensional Condition-Monitoring Signals
Authors: Gabriel Michau, Yang Hu, Thomas Palmé, Olga Fink
Date: 2018-10-12 00:00:00
Tasks: Dimensionality Reduction, Fault Detection, One-Class Classification, One-class classifier, Vocal Bursts Intensity Prediction

Complex industrial systems are continuously monitored by a large number of heterogeneous sensors. The diversity of their operating conditions and the possible fault types make it impossible to collect enough data for learning all the possible fault patterns. The paper proposes an integrated automatic unsupervised feature learning and one-class classification for fault detection that uses data on healthy conditions only for its training. The approach is based on stacked Extreme Learning Machines (namely Hierarchical, or HELM) and comprises an autoencoder, performing unsupervised feature learning, stacked with a one-class classifier monitoring the distance of the test data to the training healthy class, thereby assessing the health of the system. This study provides a comprehensive evaluation of HELM fault detection capability compared to other machine learning approaches, such as stand-alone one-class classifiers (ELM and SVM), these same one-class classifiers combined with traditional dimensionality reduction methods (PCA) and a Deep Belief Network. The performance is first evaluated on a synthetic dataset that encompasses typical characteristics of condition monitoring data. Subsequently, the approach is evaluated on a real case study of a power plant fault. The proposed algorithm for fault detection, combining feature learning with the one-class classifier, demonstrates a better performance, particularly in cases where condition monitoring data contain several non-informative signals.