pwc-archive/papers-with-abstracts huggingface dataset:
CFOF: A Concentration Free Measure for Anomaly Detection
Authors: Fabrizio Angiulli
Date: 2019-01-14 00:00:00
Tasks: Anomaly Detection

We present a novel notion of outlier, called the Concentration Free Outlier Factor, or CFOF. As a main contribution, we formalize the notion of concentration of outlier scores and theoretically prove that CFOF does not concentrate in the Euclidean space for any arbitrary large dimensionality. To the best of our knowledge, there are no other proposals of data analysis measures related to the Euclidean distance for which it has been provided theoretical evidence that they are immune to the concentration effect. We determine the closed form of the distribution of CFOF scores in arbitrarily large dimensionalities and show that the CFOF score of a point depends on its squared norm standard score and on the kurtosis of the data distribution, thus providing a clear and statistically founded characterization of this notion. Moreover, we leverage this closed form to provide evidence that the definition does not suffer of the hubness problem affecting other measures. We prove that the number of CFOF outliers coming from each cluster is proportional to cluster size and kurtosis, a property that we call semi-locality. We determine that semi-locality characterizes existing reverse nearest neighbor-based outlier definitions, thus clarifying the exact nature of their observed local behavior. We also formally prove that classical distance-based and density-based outliers concentrate both for bounded and unbounded sample sizes and for fixed and variable values of the neighborhood parameter. We introduce the fast-CFOF algorithm for detecting outliers in large high-dimensional dataset. The algorithm has linear cost, supports multi-resolution analysis, and is embarrassingly parallel. Experiments highlight that the technique is able to efficiently process huge datasets and to deal even with large values of the neighborhood parameter, to avoid concentration, and to obtain excellent accuracy.
Seizure Detection using Least EEG Channels by Deep Convolutional Neural Network
Authors: Mustafa Talha Avcu, Zhuo Zhang, Derrick Wei Shih Chan
Date: 2019-01-14 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Onset Detection, Seizure Detection, Sensitivity

This work aims to develop an end-to-end solution for seizure onset detection.
We design the SeizNet, a Convolutional Neural Network for seizure detection. To
compare SeizNet with traditional machine learning approach, a baseline
classifier is implemented using spectrum band power features with Support
Vector Machines (BPsvm). We explore the possibility to use the least number of
channels for accurate seizure detection by evaluating SeizNet and BPsvm
approaches using all channels and two channels settings respectively. EEG Data
is acquired from 29 pediatric patients admitted to KK Woman's and Children's
Hospital who were diagnosed as typical absence seizures. We conduct
leave-one-out cross validation for all subjects. Using full channel data, BPsvm
yields a sensitivity of 86.6\% and 0.84 false alarm (per hour) while SeizNet
yields overall sensitivity of 95.8 \% with 0.17 false alarm. More
interestingly, two channels seizNet outperforms full channel BPsvm with a
sensitivity of 93.3\% and 0.58 false alarm. We further investigate
interpretability of SeizNet by decoding the filters learned along convolutional
layers. Seizure-like characteristics can be clearly observed in the filters
from third and forth convolutional layers.
Machine-learning a virus assembly fitness landscape
Authors: Pierre-Philippe Dechant, Yang-Hui He
Date: 2019-01-13 00:00:00
Tasks: BIG-bench Machine Learning

Realistic evolutionary fitness landscapes are notoriously difficult to
construct. A recent cutting-edge model of virus assembly consists of a
dodecahedral capsid with $12$ corresponding packaging signals in three affinity
bands. This whole genome/phenotype space consisting of $3^{12}$ genomes has
been explored via computationally expensive stochastic assembly models, giving
a fitness landscape in terms of the assembly efficiency. Using latest
machine-learning techniques by establishing a neural network, we show that the
intensive computation can be short-circuited in a matter of minutes to
astounding accuracy.
Deep Learning-Based Channel Estimation
Authors: Mehran Soltani, Vahid Pourahmadi, Ali Mirzaei, Hamid Sheikhzadeh
Date: 2018-10-13 00:00:00
Tasks: Deep Learning, Denoising, Image Restoration, Image Super-Resolution, Super-Resolution

In this paper, we present a deep learning (DL) algorithm for channel
estimation in communication systems. We consider the time-frequency response of
a fast fading communication channel as a two-dimensional image. The aim is to
find the unknown values of the channel response using some known values at the
pilot locations. To this end, a general pipeline using deep image processing
techniques, image super-resolution (SR) and image restoration (IR) is proposed.
This scheme considers the pilot values, altogether, as a low-resolution image
and uses an SR network cascaded with a denoising IR network to estimate the
channel. Moreover, an implementation of the proposed pipeline is presented. The
estimation error shows that the presented algorithm is comparable to the
minimum mean square error (MMSE) with full knowledge of the channel statistics
and it is better than ALMMSE (an approximation to linear MMSE). The results
confirm that this pipeline can be used efficiently in channel estimation.
Multi-Agent Pathfinding with Continuous Time
Authors: Anton Andreychuk, Konstantin Yakovlev, Dor Atzmon, Roni Stern
Date: 2019-01-16 00:00:00

Multi-Agent Pathfinding (MAPF) is the problem of finding paths for multiple agents such that every agent reaches its goal and the agents do not collide. Most prior work on MAPF was on grids, assumed agents' actions have uniform duration, and that time is discretized into timesteps. We propose a MAPF algorithm that does not rely on these assumptions, is complete, and provides provably optimal solutions. This algorithm is based on a novel adaptation of Safe interval path planning (SIPP), a continuous time single-agent planning algorithm, and a modified version of Conflict-based search (CBS), a state of the art multi-agent pathfinding algorithm. We analyze this algorithm, discuss its pros and cons, and evaluate it experimentally on several standard benchmarks.
Survey of Bayesian Networks Applications to Intelligent Autonomous Vehicles
Authors: Rocío Díaz de León Torres, Martín Molina, Pascual Campoy
Date: 2019-01-16 00:00:00
Tasks: Autonomous Vehicles, Decision Making, Survey

This article reviews the applications of Bayesian Networks to Intelligent
Autonomous Vehicles (IAV) from the decision making point of view, which
represents the final step for fully Autonomous Vehicles (currently under
discussion). Until now, when it comes making high level decisions for
Autonomous Vehicles (AVs), humans have the last word. Based on the works cited
in this article and analysis done here, the modules of a general decision
making framework and its variables are inferred. Many efforts have been made in
the labs showing Bayesian Networks as a promising computer model for decision
making. Further research should go into the direction of testing Bayesian
Network models in real situations. In addition to the applications, Bayesian
Network fundamentals are introduced as elements to consider when developing
IAVs with the potential of making high level judgement calls.
Distance-Guided GA-Based Approach to Distributed Data-Intensive Web Service Composition
Authors: Soheila Sadeghiram, Hui Ma, Gang Chen
Date: 2019-01-16 00:00:00
Tasks: Distributed Computing, Service Composition

Distributed computing which uses Web services as fundamental elements,
enables high-speed development of software applications through composing many
interoperating, distributed, re-usable, and autonomous services. As a
fundamental challenge for service developers, service composition must fulfil
functional requirements and optimise Quality of Service (QoS) attributes,
simultaneously. On the other hand, huge amounts of data have been created by
advances in technologies, which may be exchanged between services.
Data-intensive Web services are of great interest to implement data-intensive
processes. However, current approaches to Web service composition have omitted
either the effect of data, or the distribution of services. Evolutionary
Computing (EC) techniques allow for the creation of compositions that meet all
the above factors. In this paper, we will develop Genetic Algorithm (GA)-based
approach for solving the problem of distributed data-intensive Web service
composition (DWSC). In particular, we will introduce two new heuristics, i.e.
Longest Common Subsequence(LCS) distance of services, in designing crossover
operators. Additionally, a new local search technique incorporating distance of
services will be proposed.
Optimization-Based Algorithm for Evolutionarily Stable Strategies against Pure Mutations
Authors: Sam Ganzfried
Date: 2018-03-01 00:00:00

Evolutionarily stable strategy (ESS) is an important solution concept in game
theory which has been applied frequently to biological models. Informally an
ESS is a strategy that if followed by the population cannot be taken over by a
mutation strategy that is initially rare. Finding such a strategy has been
shown to be difficult from a theoretical complexity perspective. We present an
algorithm for the case where mutations are restricted to pure strategies, and
present experiments on several game classes including random and a
recently-proposed cancer model. Our algorithm is based on a mixed-integer
non-convex feasibility program formulation, which constitutes the first general
optimization formulation for this problem. It turns out that the vast majority
of the games included in the experiments contain ESS with small support, and
our algorithm is outperformed by a support-enumeration based approach. However
we suspect our algorithm may be useful in the future as games are studied that
have ESS with potentially larger and unknown support size.
Generating Realistic Sequences of Customer-level Transactions for Retail Datasets
Authors: Thang Doan, Neil Veira, Saibal Ray, Brian Keng
Date: 2019-01-17 00:00:00
Tasks: Generative Adversarial Network

In order to better engage with customers, retailers rely on extensive customer and product databases which allows them to better understand customer behaviour and purchasing patterns. This has long been a challenging task as customer modelling is a multi-faceted, noisy and time-dependent problem. The most common way to tackle this problem is indirectly through task-specific supervised learning prediction problems, with relatively little literature on modelling a customer by directly simulating their future transactions. In this paper we propose a method for generating realistic sequences of baskets that a given customer is likely to purchase over a period of time. Customer embedding representations are learned using a Recurrent Neural Network (RNN) which takes into account the entire sequence of transaction data. Given the customer state at a specific point in time, a Generative Adversarial Network (GAN) is trained to generate a plausible basket of products for the following week. The newly generated basket is then fed back into the RNN to update the customer's state. The GAN is thus used in tandem with the RNN module in a pipeline alternating between basket generation and customer state updating steps. This allows for sampling over a distribution of a customer's future sequence of baskets, which then can be used to gain insight into how to service the customer more effectively. The methodology is empirically shown to produce baskets that appear similar to real baskets and enjoy many common properties, including frequencies of different product types, brands, and prices. Furthermore, the generated data is able to replicate most of the strongest sequential patterns that exist between product types in the real data.
Interactive Plan Explicability in Human-Robot Teaming
Authors: Mehrdad Zakershahrak, Yu Zhang
Date: 2019-01-17 00:00:00

Human-robot teaming is one of the most important applications of artificial
intelligence in the fast-growing field of robotics. For effective teaming, a
robot must not only maintain a behavioral model of its human teammates to
project the team status, but also be aware that its human teammates'
expectation of itself. Being aware of the human teammates' expectation leads to
robot behaviors that better align with human expectation, thus facilitating
more efficient and potentially safer teams. Our work addresses the problem of
human-robot cooperation with the consideration of such teammate models in
sequential domains by leveraging the concept of plan explicability. In plan
explicability, however, the human is considered solely as an observer. In this
paper, we extend plan explicability to consider interactive settings where
human and robot behaviors can influence each other. We term this new measure as
Interactive Plan Explicability. We compare the joint plan generated with the
consideration of this measure using the fast forward planner (FF) with the plan
created by FF without such consideration, as well as the plan created with
actual human subjects. Results indicate that the explicability score of plans
generated by our algorithm is comparable to the human plan, and better than the
plan created by FF without considering the measure, implying that the plans
created by our algorithms align better with expected joint plans of the human
during execution. This can lead to more efficient collaboration in practice.
Learning Credal Sum-Product Networks
Authors: Amelie Levray, Vaishak Belle
Date: 2019-01-17 00:00:00
Proceeding: AKBC 2020 6

Probabilistic representations, such as Bayesian and Markov networks, are fundamental to much of statistical machine learning. Thus, learning probabilistic representations directly from data is a deep challenge, the main computational bottleneck being inference that is intractable. Tractable learning is a powerful new paradigm that attempts to learn distributions that support efficient probabilistic querying. By leveraging local structure, representations such as sum-product networks (SPNs) can capture high tree-width models with many hidden layers, essentially a deep architecture, while still admitting a range of probabilistic queries to be computable in time polynomial in the network size. While the progress is impressive, numerous data sources are incomplete, and in the presence of missing data, structure learning methods nonetheless revert to single distributions without characterizing the loss in confidence. In recent work, credal sum-product networks, an imprecise extension of sum-product networks, were proposed to capture this robustness angle. In this work, we are interested in how such representations can be learnt and thus study how the computational machinery underlying tractable learning and inference can be generalized for imprecise probabilities.
Fast Deep Learning for Automatic Modulation Classification
Authors: Sharan Ramjee, Shengtai Ju, Diyu Yang, Xiaoyu Liu, Aly El Gamal, Yonina C. Eldar
Date: 2019-01-16 00:00:00
Tasks: Classification, Deep Learning, General Classification

In this work, we investigate the feasibility and effectiveness of employing
deep learning algorithms for automatic recognition of the modulation type of
received wireless communication signals from subsampled data. Recent work
considered a GNU radio-based data set that mimics the imperfections in a real
wireless channel and uses 10 different modulation types. A Convolutional Neural
Network (CNN) architecture was then developed and shown to achieve performance
that exceeds that of expert-based approaches. Here, we continue this line of
work and investigate deep neural network architectures that deliver high
classification accuracy. We identify three architectures - namely, a
Convolutional Long Short-term Deep Neural Network (CLDNN), a Long Short-Term
Memory neural network (LSTM), and a deep Residual Network (ResNet) - that lead
to typical classification accuracy values around 90% at high SNR. We then study
algorithms to reduce the training time by minimizing the size of the training
data set, while incurring a minimal loss in classification accuracy. To this
end, we demonstrate the performance of Principal Component Analysis in
significantly reducing the training time, while maintaining good performance at
low SNR. We also investigate subsampling techniques that further reduce the
training time, and pave the way for online classification at high SNR. Finally,
we identify representative SNR values for training each of the candidate
architectures, and consequently, realize drastic reductions of the training
time, with negligible loss in classification accuracy.
Amplifying the Imitation Effect for Reinforcement Learning of UCAV's Mission Execution
Authors: Gyeong Taek Lee, Chang Ouk Kim
Date: 2019-01-17 00:00:00
Tasks: Imitation Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

This paper proposes a new reinforcement learning (RL) algorithm that enhances
exploration by amplifying the imitation effect (AIE). This algorithm consists
of self-imitation learning and random network distillation algorithms. We argue
that these two algorithms complement each other and that combining these two
algorithms can amplify the imitation effect for exploration. In addition, by
adding an intrinsic penalty reward to the state that the RL agent frequently
visits and using replay memory for learning the feature state when using an
exploration bonus, the proposed approach leads to deep exploration and deviates
from the current converged policy. We verified the exploration performance of
the algorithm through experiments in a two-dimensional grid environment. In
addition, we applied the algorithm to a simulated environment of unmanned
combat aerial vehicle (UCAV) mission execution, and the empirical results show
that AIE is very effective for finding the UCAV's shortest flight path to avoid
an enemy's missiles.
Deep learning-based electroencephalography analysis: a systematic review
Authors: Yannick Roy, Hubert Banville, Isabela Albuquerque, Alexandre Gramfort, Tiago H. Falk, Jocelyn Faubert
Date: 2019-01-16 00:00:00
Tasks: Brain Decoding, Deep Learning, EEG, Electroencephalogram (EEG), Open-Ended Question Answering, Time Series Analysis

Electroencephalography (EEG) is a complex signal and can require several
years of training to be correctly interpreted. Recently, deep learning (DL) has
shown great promise in helping make sense of EEG signals due to its capacity to
learn good feature representations from raw data. Whether DL truly presents
advantages as compared to more traditional EEG processing approaches, however,
remains an open question. In this work, we review 156 papers that apply DL to
EEG, published between January 2010 and July 2018, and spanning different
application domains such as epilepsy, sleep, brain-computer interfacing, and
cognitive and affective monitoring. We extract trends and highlight interesting
approaches in order to inform future research and formulate recommendations.
Various data items were extracted for each study pertaining to 1) the data, 2)
the preprocessing methodology, 3) the DL design choices, 4) the results, and 5)
the reproducibility of the experiments. Our analysis reveals that the amount of
EEG data used across studies varies from less than ten minutes to thousands of
hours. As for the model, 40% of the studies used convolutional neural networks
(CNNs), while 14% used recurrent neural networks (RNNs), most often with a
total of 3 to 10 layers. Moreover, almost one-half of the studies trained their
models on raw or preprocessed EEG time series. Finally, the median gain in
accuracy of DL approaches over traditional baselines was 5.4% across all
relevant studies. More importantly, however, we noticed studies often suffer
from poor reproducibility: a majority of papers would be hard or impossible to
reproduce given the unavailability of their data and code. To help the field
progress, we provide a list of recommendations for future studies and we make
our summary table of DL and EEG papers available and invite the community to
contribute.
The information-theoretic value of unlabeled data in semi-supervised learning
Authors: Alexander Golovnev, Dávid Pál, Balázs Szörényi
Date: 2019-01-16 00:00:00

We quantify the separation between the numbers of labeled examples required to learn in two settings: Settings with and without the knowledge of the distribution of the unlabeled data. More specifically, we prove a separation by $\Theta(\log n)$ multiplicative factor for the class of projections over the Boolean hypercube of dimension $n$. We prove that there is no separation for the class of all functions on domain of any size. Learning with the knowledge of the distribution (a.k.a. fixed-distribution learning) can be viewed as an idealized scenario of semi-supervised learning where the number of unlabeled data points is so great that the unlabeled distribution is known exactly. For this reason, we call the separation the value of unlabeled data.
Lagging Inference Networks and Posterior Collapse in Variational Autoencoders
Authors: Junxian He, Daniel Spokoyny, Graham Neubig, Taylor Berg-Kirkpatrick
Date: 2019-01-16 00:00:00
Proceeding: ICLR 2019 5
Tasks: Text Generation

The variational autoencoder (VAE) is a popular combination of deep latent
variable model and accompanying variational learning technique. By using a
neural inference network to approximate the model's posterior on latent
variables, VAEs efficiently parameterize a lower bound on marginal data
likelihood that can be optimized directly via gradient methods. In practice,
however, VAE training often results in a degenerate local optimum known as
"posterior collapse" where the model learns to ignore the latent variable and
the approximate posterior mimics the prior. In this paper, we investigate
posterior collapse from the perspective of training dynamics. We find that
during the initial stages of training the inference network fails to
approximate the model's true posterior, which is a moving target. As a result,
the model is encouraged to ignore the latent encoding and posterior collapse
occurs. Based on this observation, we propose an extremely simple modification
to VAE training to reduce inference lag: depending on the model's current
mutual information between latent variable and observation, we aggressively
optimize the inference network before performing each model update. Despite
introducing neither new model components nor significant complexity over basic
VAE, our approach is able to avoid the problem of collapse that has plagued a
large amount of previous work. Empirically, our approach outperforms strong
autoregressive baselines on text and image benchmarks in terms of held-out
likelihood, and is competitive with more complex techniques for avoiding
collapse while being substantially faster.