pwc-archive/papers-with-abstracts huggingface dataset:
Malthusian Reinforcement Learning
Authors: Joel Z. Leibo, Julien Perolat, Edward Hughes, Steven Wheelwright, Adam H. Marblestone, Edgar Duéñez-Guzmán, Peter Sunehag, Iain Dunning, Thore Graepel
Date: 2018-12-17 00:00:00
Tasks: Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Here we explore a new algorithmic framework for multi-agent reinforcement
learning, called Malthusian reinforcement learning, which extends self-play to
include fitness-linked population size dynamics that drive ongoing innovation.
In Malthusian RL, increases in a subpopulation's average return drive
subsequent increases in its size, just as Thomas Malthus argued in 1798 was the
relationship between preindustrial income levels and population growth.
Malthusian reinforcement learning harnesses the competitive pressures arising
from growing and shrinking population size to drive agents to explore regions
of state and policy spaces that they could not otherwise reach. Furthermore, in
environments where there are potential gains from specialization and division
of labor, we show that Malthusian reinforcement learning is better positioned
to take advantage of such synergies than algorithms based on self-play.
Instrument-Independent Dastgah Recognition of Iranian Classical Music Using AzarNet
Authors: Shahla RezezadehAzar, Ali Ahmadi, Saber Malekzadeh, Maryam Samami
Date: 2018-12-17 00:00:00
Tasks: Classification, General Classification, Recognizing Seven Different Dastgahs Of Iranian Classical Music

In this paper, AzarNet, a deep neural network (DNN), is proposed to
recognizing seven different Dastgahs of Iranian classical music in Maryam
Iranian classical music (MICM) dataset. Over the last years, there has been
remarkable interest in employing feature learning and DNNs which lead to
decreasing the required engineering effort. DNNs have shown better performance
in many classification tasks such as audio signal classification compares to
shallow processing architectures. Despite image data, audio data need some
preprocessing steps to extract spectra and temporal features. Some
transformations like Short-Time Fourier Transform (STFT) have been used in the
state of art researches to transform audio signals from time-domain to
time-frequency domain to extract both temporal and spectra features. In this
research, the STFT output results which are extracted features are given to
AzarNet for learning and classification processes. It is worth noting that, the
mentioned dataset contains music tracks composed with two instruments (violin
and straw). The overall f1 score of AzarNet on test set, for average of all
seven classes was 86.21% which is the best result ever reported in Dastgah
classification according to our best knowledge.
Multi Instance Learning For Unbalanced Data
Authors: Mark Kozdoba, Edward Moroshko, Lior Shani, Takuya Takagi, Takashi Katoh, Shie Mannor, Koby Crammer
Date: 2018-12-17 00:00:00

In the context of Multi Instance Learning, we analyze the Single Instance
(SI) learning objective. We show that when the data is unbalanced and the
family of classifiers is sufficiently rich, the SI method is a useful learning
algorithm. In particular, we show that larger data imbalance, a quality that is
typically perceived as negative, in fact implies a better resilience of the
algorithm to the statistical dependencies of the objects in bags. In addition,
our results shed new light on some known issues with the SI method in the
setting of linear classifiers, and we show that these issues are significantly
less likely to occur in the setting of neural networks. We demonstrate our
results on a synthetic dataset, and on the COCO dataset for the problem of
patch classification with weak image level labels derived from captions.
3D-SIS: 3D Semantic Instance Segmentation of RGB-D Scans
Authors: Ji Hou, Angela Dai, Matthias Nießner
Date: 2018-12-17 00:00:00
Proceeding: CVPR 2019 6
Tasks: 3D Instance Segmentation, 3D Object Detection, 3D Reconstruction, 3D Semantic Instance Segmentation, Instance Segmentation, object-detection, Object Detection, Segmentation, Semantic Segmentation

We introduce 3D-SIS, a novel neural network architecture for 3D semantic
instance segmentation in commodity RGB-D scans. The core idea of our method is
to jointly learn from both geometric and color signal, thus enabling accurate
instance predictions. Rather than operate solely on 2D frames, we observe that
most computer vision applications have multi-view RGB-D input available, which
we leverage to construct an approach for 3D instance segmentation that
effectively fuses together these multi-modal inputs. Our network leverages
high-resolution RGB input by associating 2D images with the volumetric grid
based on the pose alignment of the 3D reconstruction. For each image, we first
extract 2D features for each pixel with a series of 2D convolutions; we then
backproject the resulting feature vector to the associated voxel in the 3D
grid. This combination of 2D and 3D feature learning allows significantly
higher accuracy object detection and instance segmentation than
state-of-the-art alternatives. We show results on both synthetic and real-world
public benchmarks, achieving an improvement in mAP of over 13 on real-world
data.
Proceedings of the Artificial Intelligence for Cyber Security (AICS) Workshop 2019
Authors: William W. Streilein, Brad Dillman
Date: 2018-12-17 00:00:00

This volume represents the proceedings of the Artificial Intelligence for
Cyber Security (AICS) Workshop 2019, held on January 27, 2019 in Honolulu,
Hawaii.
Analogy Search Engine: Finding Analogies in Cross-Domain Research Papers
Authors: Jieli Zhou, Yuntao Zhou, Yi Xu
Date: 2018-12-17 00:00:00

In recent years, with the rapid proliferation of research publications in the
field of Artificial Intelligence, it is becoming increasingly difficult for
researchers to effectively keep up with all the latest research in one's own
domains. However, history has shown that scientific breakthroughs often come
from collaborations of researchers from different domains. Traditional search
algorithms like Lexical search, which look for literal matches or synonyms and
variants of the query words, are not effective for discovering cross-domain
research papers and meeting the needs of researchers in this age of information
overflow. In this paper, we developed and tested an innovative semantic search
engine, Analogy Search Engine (ASE), for 2000 AI research paper abstracts
across domains like Language Technologies, Robotics, Machine Learning,
Computational Biology, Human Computer Interactions, etc. ASE combines recent
theories and methods from Computational Analogy and Natural Language Processing
to go beyond keyword-based lexical search and discover the deeper analogical
relationships among research paper abstracts. We experimentally show that ASE
is capable of finding more interesting and useful research papers than baseline
elasticsearch. Furthermore, we believe that the methods used in ASE go beyond
academic paper and will benefit many other document search tasks.
An Improved Deep Belief Network Model for Road Safety Analyses
Authors: Guangyuan Pan, Liping Fu, Lalita Thakali, Matthew Muresan, Ming Yu
Date: 2018-12-17 00:00:00
Tasks: Prediction, regression

Crash prediction is a critical component of road safety analyses. A widely
adopted approach to crash prediction is application of regression based
techniques. The underlying calibration process is often time-consuming,
requiring significant domain knowledge and expertise and cannot be easily
automated. This paper introduces a new machine learning (ML) based approach as
an alternative to the traditional techniques. The proposed ML model is called
regularized deep belief network, which is a deep neural network with two
training steps: it is first trained using an unsupervised learning algorithm
and then fine-tuned by initializing a Bayesian neural network with the trained
weights from the first step. The resulting model is expected to have improved
prediction power and reduced need for the time-consuming human intervention. In
this paper, we attempt to demonstrate the potential of this new model for crash
prediction through two case studies including a collision data set from 800 km
stretch of Highway 401 and other highways in Ontario, Canada. Our intention is
to show the performance of this ML approach in comparison to various
traditional models including negative binomial (NB) model, kernel regression
(KR), and Bayesian neural network (Bayesian NN). We also attempt to address
other related issues such as effect of training data size and training
parameters.
Decentralized Computation Offloading for Multi-User Mobile Edge Computing: A Deep Reinforcement Learning Approach
Authors: Zhao Chen, Xiaodong Wang
Date: 2018-12-16 00:00:00
Tasks: Deep Reinforcement Learning, Edge-computing, Reinforcement Learning, Reinforcement Learning (RL)

Mobile edge computing (MEC) emerges recently as a promising solution to
relieve resource-limited mobile devices from computation-intensive tasks, which
enables devices to offload workloads to nearby MEC servers and improve the
quality of computation experience. Nevertheless, by considering a MEC system
consisting of multiple mobile users with stochastic task arrivals and wireless
channels in this paper, the design of computation offloading policies is
challenging to minimize the long-term average computation cost in terms of
power consumption and buffering delay. A deep reinforcement learning (DRL)
based decentralized dynamic computation offloading strategy is investigated to
build a scalable MEC system with limited feedback. Specifically, a continuous
action space-based DRL approach named deep deterministic policy gradient (DDPG)
is adopted to learn efficient computation offloading policies independently at
each mobile user. Thus, powers of both local execution and task offloading can
be adaptively allocated by the learned policies from each user's local
observation of the MEC system. Numerical results are illustrated to demonstrate
that efficient policies can be learned at each user, and performance of the
proposed DDPG based decentralized strategy outperforms the conventional deep
Q-network (DQN) based discrete power control strategy and some other greedy
strategies with reduced computation cost. Besides, the power-delay tradeoff is
also analyzed for both the DDPG based and DQN based strategies.
Deep UL2DL: Channel Knowledge Transfer from Uplink to Downlink
Authors: Mohammad Sadegh Safari, Vahid Pourahmadi, Shabnam Sodagari
Date: 2018-12-16 00:00:00
Tasks: Transfer Learning

Knowledge of the channel state information (CSI) at the transmitter side is one of the primary sources of information that can be used for the efficient allocation of wireless resources. Obtaining downlink (DL) CSI in Frequency Division Duplexing (FDD) systems from uplink (UL) CSI is not as straightforward as in TDD systems. Therefore, users usually feed the DL-CSI back to the transmitter. To remove the need for feedback (and thus having less signaling overhead), we propose to use two recent deep neural network structures, i.e., convolutional neural networks and generative adversarial networks (GANs) to infer the DL-CSI by observing the UL-CSI. The core idea of our data-driven scheme is exploiting the fact that both DL and UL channels share the same propagation environment. As such, we extracted the environment information from the UL channel response to a latent domain and then transferred the derived environment information from the latent domain to predict the DL channel. To overcome incorrect latent domain and the problem of oversimplistic assumptions, in this work, we did not use any specific parametric model and instead used data-driven approaches to discover the underlying structure of data without any prior model assumptions. To overcome the challenge of capturing the UL-DL joint distribution, we used a mean square error-based variant of the GAN structure with improved convergence properties called boundary equilibrium GAN (BEGAN). For training and testing we used simulated data of Extended Vehicular-A (EVA) and Extended Typical Urban (ETU) models. Simulation results verified that our methods can accurately infer and predict the downlink CSI from the uplink CSI for different multipath environments in FDD communications.
Distill-Net: Application-Specific Distillation of Deep Convolutional Neural Networks for Resource-Constrained IoT Platforms
Authors: Mohammad Motamedi, Felix Portillo, Daniel Fong, Soheil Ghiasi
Date: 2018-12-16 00:00:00

Many Internet-of-Things (IoT) applications demand fast and accurate
understanding of a few key events in their surrounding environment. Deep
Convolutional Neural Networks (CNNs) have emerged as an effective approach to
understand speech, images, and similar high dimensional data types. Algorithmic
performance of modern CNNs, however, fundamentally relies on learning
class-agnostic hierarchical features that only exist in comprehensive training
datasets with many classes. As a result, fast inference using CNNs trained on
such datasets is prohibitive for most resource-constrained IoT platforms. To
bridge this gap, we present a principled and practical methodology for
distilling a complex modern CNN that is trained to effectively recognize many
different classes of input data into an application-dependent essential core
that not only recognizes the few classes of interest to the application
accurately, but also runs efficiently on platforms with limited resources.
Experimental results confirm that our approach strikes a favorable balance
between classification accuracy (application constraint), inference efficiency
(platform constraint), and productive development of new applications (business
constraint).
Direction Finding Based on Multi-Step Knowledge-Aided Iterative Conjugate Gradient Algorithms
Authors: S. Pinto, R. C. de Lamare
Date: 2018-12-16 00:00:00

In this work, we present direction-of-arrival (DoA) estimation algorithms
based on the Krylov subspace that effectively exploit prior knowledge of the
signals that impinge on a sensor array. The proposed multi-step knowledge-aided
iterative conjugate gradient (CG) (MS-KAI-CG) algorithms perform subtraction of
the unwanted terms found in the estimated covariance matrix of the sensor data.
Furthermore, we develop a version of MS-KAI-CG equipped with forward-backward
averaging, called MS-KAI-CG-FB, which is appropriate for scenarios with
correlated signals. Unlike current knowledge-aided methods, which take
advantage of known DoAs to enhance the estimation of the covariance matrix of
the input data, the MS-KAI-CG algorithms take advantage of the knowledge of the
structure of the forward-backward smoothed covariance matrix and its
disturbance terms. Simulations with both uncorrelated and correlated signals
show that the MS-KAI-CG algorithms outperform existing techniques.
Geometric Scattering on Manifolds
Authors: Michael Perlmutter, Guy Wolf, Matthew Hirn
Date: 2018-12-15 00:00:00
Tasks: Translation

The Euclidean scattering transform was introduced nearly a decade ago to improve the mathematical understanding of the success of convolutional neural networks (ConvNets) in image data analysis and other tasks. Inspired by recent interest in geometric deep learning, which aims to generalize ConvNets to manifold and graph-structured domains, we generalize the scattering transform to compact manifolds. Similar to the Euclidean scattering transform, our geometric scattering transform is based on a cascade of designed filters and pointwise nonlinearities, which enables rigorous analysis of the feature extraction provided by scattering layers. Our main focus here is on theoretical understanding of this geometric scattering network, while setting aside implementation aspects, although we remark that application of similar transforms to graph data analysis has been studied recently in related work. Our results establish conditions under which geometric scattering provides localized isometry invariant descriptions of manifold signals, which are also stable to families of diffeomorphisms formulated in intrinsic manifolds terms. These results not only generalize the deformation stability and local roto-translation invariance of Euclidean scattering, but also demonstrate the importance of linking the used filter structures (e.g., in geometric deep learning) to the underlying manifold geometry, or the data geometry it represents.
Perturbation Analysis of Learning Algorithms: A Unifying Perspective on Generation of Adversarial Examples
Authors: Emilio Rafael Balda, Arash Behboodi, Rudolf Mathar
Date: 2018-12-15 00:00:00
Tasks: Classification, Colorization, General Classification, Image Colorization, regression

Despite the tremendous success of deep neural networks in various learning
problems, it has been observed that adding an intentionally designed
adversarial perturbation to inputs of these architectures leads to erroneous
classification with high confidence in the prediction. In this work, we propose
a general framework based on the perturbation analysis of learning algorithms
which consists of convex programming and is able to recover many current
adversarial attacks as special cases. The framework can be used to propose
novel attacks against learning algorithms for classification and regression
tasks under various new constraints with closed form solutions in many
instances. In particular we derive new attacks against classification
algorithms which are shown to achieve comparable performances to notable
existing attacks. The framework is then used to generate adversarial
perturbations for regression tasks which include single pixel and single subset
attacks. By applying this method to autoencoding and image colorization tasks,
it is shown that adversarial perturbations can effectively perturb the output
of regression tasks as well.
MMALFM: Explainable Recommendation by Leveraging Reviews and Images
Authors: Zhiyong Cheng, Xiaojun Chang, Lei Zhu, Rose C. Kanjirathinkal, Mohan Kankanhalli
Date: 2018-11-12 00:00:00
Tasks: Explainable Recommendation

Although the latent factor model achieves good accuracy in rating prediction,
it suffers from many problems including cold-start, non-transparency, and
suboptimal results for individual user-item pairs. In this paper, we exploit
textual reviews and item images together with ratings to tackle these
limitations. Specifically, we first apply a proposed multi-modal aspect-aware
topic model (MATM) on text reviews and item images to model users' preferences
and items' features from different aspects, and also estimate the aspect
importance of a user towards an item. Then the aspect importance is integrated
into a novel aspect-aware latent factor model (ALFM), which learns user's and
item's latent factors based on ratings. In particular, ALFM introduces a weight
matrix to associate those latent factors with the same set of aspects in MATM,
such that the latent factors could be used to estimate aspect ratings. Finally,
the overall rating is computed via a linear combination of the aspect ratings,
which are weighted by the corresponding aspect importance. To this end, our
model could alleviate the data sparsity problem and gain good interpretability
for recommendation. Besides, every aspect rating is weighted by its aspect
importance, which is dependent on the targeted user's preferences and the
targeted item's features. Therefore, it is expected that the proposed method
can model a user's preferences on an item more accurately for each user-item
pair. Comprehensive experimental studies have been conducted on the Yelp 2017
Challenge dataset and Amazon product datasets to demonstrate the effectiveness
of our method.
Visually Explainable Recommendation
Authors: Xu Chen, Yongfeng Zhang, Hongteng Xu, Yixin Cao, Zheng Qin, Hongyuan Zha
Date: 2018-01-31 00:00:00
Tasks: Explainable Recommendation, Recommendation Systems

Images account for a significant part of user decisions in many application
scenarios, such as product images in e-commerce, or user image posts in social
networks. It is intuitive that user preferences on the visual patterns of image
(e.g., hue, texture, color, etc) can be highly personalized, and this provides
us with highly discriminative features to make personalized recommendations.
  Previous work that takes advantage of images for recommendation usually
transforms the images into latent representation vectors, which are adopted by
a recommendation component to assist personalized user/item profiling and
recommendation. However, such vectors are hardly useful in terms of providing
visual explanations to users about why a particular item is recommended, and
thus weakens the explainability of recommendation systems.
  As a step towards explainable recommendation models, we propose visually
explainable recommendation based on attentive neural networks to model the user
attention on images, under the supervision of both implicit feedback and
textual reviews. By this, we can not only provide recommendation results to the
users, but also tell the users why an item is recommended by providing
intuitive visual highlights in a personalized manner. Experimental results show
that our models are not only able to improve the recommendation performance,
but also can provide persuasive visual explanations for the users to take the
recommendations.
Personalized PageRank Estimation and Search: A Bidirectional Approach
Authors: Peter Lofgren, Siddhartha Banerjee, Ashish Goel
Date: 2015-07-21 00:00:00

We present new algorithms for Personalized PageRank estimation and
Personalized PageRank search. First, for the problem of estimating Personalized
PageRank (PPR) from a source distribution to a target node, we present a new
bidirectional estimator with simple yet strong guarantees on correctness and
performance, and 3x to 8x speedup over existing estimators in experiments on a
diverse set of networks. Moreover, it has a clean algebraic structure which
enables it to be used as a primitive for the Personalized PageRank Search
problem: Given a network like Facebook, a query like "people named John", and a
searching user, return the top nodes in the network ranked by PPR from the
perspective of the searching user. Previous solutions either score all nodes or
score candidate nodes one at a time, which is prohibitively slow for large
candidate sets. We develop a new algorithm based on our bidirectional PPR
estimator which identifies the most relevant results by sampling candidates
based on their PPR; this is the first solution to PPR search that can find the
best results without iterating through the set of all candidate results.
Finally, by combining PPR sampling with sequential PPR estimation and Monte
Carlo, we develop practical algorithms for PPR search, and we show via
experiments that our algorithms are efficient on networks with billions of
edges.
Deep Gated Recurrent and Convolutional Network Hybrid Model for Univariate Time Series Classification
Authors: Nelly Elsayed, Anthony S. Maida, Magdy Bayoumi
Date: 2018-12-18 00:00:00
Tasks: Classification, General Classification, Time Series, Time Series Analysis, Time Series Classification

Hybrid LSTM-fully convolutional networks (LSTM-FCN) for time series
classification have produced state-of-the-art classification results on
univariate time series. We show that replacing the LSTM with a gated recurrent
unit (GRU) to create a GRU-fully convolutional network hybrid model (GRU-FCN)
can offer even better performance on many time series datasets. The proposed
GRU-FCN model outperforms state-of-the-art classification performance in many
univariate and multivariate time series datasets. In addition, since the GRU
uses a simpler architecture than the LSTM, it has fewer training parameters,
less training time, and a simpler hardware implementation, compared to the
LSTM-based models.