pwc-archive/papers-with-abstracts huggingface dataset:
Action Model Acquisition using LSTM
Authors: Ankuj Arora, Humbert Fiorino, Damien Pellier, Sylvie Pesty
Date: 2018-10-03 00:00:00
Tasks: model, Scheduling

In the field of Automated Planning and Scheduling (APS), intelligent agents
by virtue require an action model (blueprints of actions whose interleaved
executions effectuates transitions of the system state) in order to plan and
solve real world problems. It is, however, becoming increasingly cumbersome to
codify this model, and is more efficient to learn it from observed plan
execution sequences (training data). While the underlying objective is to
subsequently plan from this learnt model, most approaches fall short as
anything less than a flawless reconstruction of the underlying model renders it
unusable in certain domains. This work presents a novel approach using long
short-term memory (LSTM) techniques for the acquisition of the underlying
action model. We use the sequence labelling capabilities of LSTMs to isolate
from an exhaustive model set a model identical to the one responsible for
producing the training data. This isolation capability renders our approach as
an effective one.
Verification for Machine Learning, Autonomy, and Neural Networks Survey
Authors: Weiming Xiang, Patrick Musau, Ayana A. Wild, Diego Manzanas Lopez, Nathaniel Hamilton, Xiaodong Yang, Joel Rosenfeld, Taylor T. Johnson
Date: 2018-10-03 00:00:00
Tasks: BIG-bench Machine Learning, General Classification, Survey

This survey presents an overview of verification techniques for autonomous
systems, with a focus on safety-critical autonomous cyber-physical systems
(CPS) and subcomponents thereof. Autonomy in CPS is enabling by recent advances
in artificial intelligence (AI) and machine learning (ML) through approaches
such as deep neural networks (DNNs), embedded in so-called learning enabled
components (LECs) that accomplish tasks from classification to control.
Recently, the formal methods and formal verification community has developed
methods to characterize behaviors in these LECs with eventual goals of formally
verifying specifications for LECs, and this article presents a survey of many
of these recent approaches.
The Blackbird Dataset: A large-scale dataset for UAV perception in aggressive flight
Authors: Amado Antonini, Winter Guerra, Varun Murali, Thomas Sayre-McCord, Sertac Karaman
Date: 2018-10-03 00:00:00

The Blackbird unmanned aerial vehicle (UAV) dataset is a large-scale,
aggressive indoor flight dataset collected using a custom-built quadrotor
platform for use in evaluation of agile perception.Inspired by the potential of
future high-speed fully-autonomous drone racing, the Blackbird dataset contains
over 10 hours of flight data from 168 flights over 17 flight trajectories and 5
environments at velocities up to $7.0ms^-1$. Each flight includes sensor data
from 120Hz stereo and downward-facing photorealistic virtual cameras, 100Hz
IMU, $\sim190Hz$ motor speed sensors, and 360Hz millimeter-accurate motion
capture ground truth. Camera images for each flight were photorealistically
rendered using FlightGoggles across a variety of environments to facilitate
easy experimentation of high performance perception algorithms. The dataset is
available for download at http://blackbird-dataset. mit.edu/.
Discriminative Data-driven Self-adaptive Fraud Control Decision System with Incomplete Information
Authors: Junxuan Li, Yung-wen Liu, Yuting Jia, Jay Nanduri
Date: 2018-10-03 00:00:00

While E-commerce has been growing explosively and online shopping has become popular and even dominant in the present era, online transaction fraud control has drawn considerable attention in business practice and academic research. Conventional fraud control considers mainly the interactions of two major involved decision parties, i.e. merchants and fraudsters, to make fraud classification decision without paying much attention to dynamic looping effect arose from the decisions made by other profit-related parties. This paper proposes a novel fraud control framework that can quantify interactive effects of decisions made by different parties and can adjust fraud control strategies using data analytics, artificial intelligence, and dynamic optimization techniques. Three control models, Naive, Myopic and Prospective Controls, were developed based on the availability of data attributes and levels of label maturity. The proposed models are purely data-driven and self-adaptive in a real-time manner. The field test on Microsoft real online transaction data suggested that new systems could sizably improve the company's profit.
Sparse Winograd Convolutional neural networks on small-scale systolic arrays
Authors: Feng Shi, Haochen Li, Yuhe Gao, Benjamin Kuschner, Song-Chun Zhu
Date: 2018-10-03 00:00:00
Tasks: Layout Design

The reconfigurability, energy-efficiency, and massive parallelism on FPGAs
make them one of the best choices for implementing efficient deep learning
accelerators. However, state-of-art implementations seldom consider the balance
between high throughput of computation power and the ability of the memory
subsystem to support it. In this paper, we implement an accelerator on FPGA by
combining the sparse Winograd convolution, clusters of small-scale systolic
arrays, and a tailored memory layout design. We also provide an analytical
model analysis for the general Winograd convolution algorithm as a design
reference. Experimental results on VGG16 show that it achieves very high
computational resource utilization, 20x ~ 30x energy efficiency, and more than
5x speedup compared with the dense implementation.
CoverBLIP: accelerated and scalable iterative matched-filtering for Magnetic Resonance Fingerprint reconstruction
Authors: Mohammad Golbabaee, Zhouye Chen, Yves Wiaux, Mike Davies
Date: 2018-10-03 00:00:00
Tasks: Dimensionality Reduction, Quantitative MRI

Current popular methods for Magnetic Resonance Fingerprint (MRF) recovery are
bottlenecked by the heavy computations of a matched-filtering step due to the
growing size and complexity of the fingerprint dictionaries in multi-parametric
quantitative MRI applications. We address this shortcoming by arranging
dictionary atoms in the form of cover tree structures and adopt the
corresponding fast approximate nearest neighbour searches to accelerate
matched-filtering. For datasets belonging to smooth low-dimensional manifolds
cover trees offer search complexities logarithmic in terms of data population.
With this motivation we propose an iterative reconstruction algorithm, named
CoverBLIP, to address large-size MRF problems where the fingerprint dictionary
i.e. discrete manifold of Bloch responses, encodes several intrinsic NMR
parameters. We study different forms of convergence for this algorithm and we
show that provided with a notion of embedding, the inexact and non-convex
iterations of CoverBLIP linearly convergence toward a near-global solution with
the same order of accuracy as using exact brute-force searches. Our further
examinations on both synthetic and real-world datasets and using different
sampling strategies, indicates between 2 to 3 orders of magnitude reduction in
total search computations. Cover trees are robust against the
curse-of-dimensionality and therefore CoverBLIP provides a notion of
scalability -- a consistent gain in time-accuracy performance-- for searching
high-dimensional atoms which may not be easily preprocessed (i.e. for
dimensionality reduction) due to the increasing degrees of non-linearities
appearing in the emerging multi-parametric MRF dictionaries.
CRED: A Deep Residual Network of Convolutional and Recurrent Units for Earthquake Signal Detection
Authors: S. Mostafa Mousavi, Weiqiang Zhu, Yixiao Sheng, Gregory C. Beroza
Date: 2018-10-03 00:00:00
Tasks: Template Matching

Earthquake signal detection is at the core of observational seismology. A
good detection algorithm should be sensitive to small and weak events with a
variety of waveform shapes, robust to background noise and non-earthquake
signals, and efficient for processing large data volumes. Here, we introduce
the Cnn-Rnn Earthquake Detector (CRED), a detector based on deep neural
networks. The network uses a combination of convolutional layers and
bi-directional long-short-term memory units in a residual structure. It learns
the time-frequency characteristics of the dominant phases in an earthquake
signal from three component data recorded on a single station. We train the
network using 500,000 seismograms (250k associated with tectonic earthquakes
and 250k identified as noise) recorded in Northern California and tested it
with an F-score of 99.95. The robustness of the trained model with respect to
the noise level and non-earthquake signals is shown by applying it to a set of
semi-synthetic signals. The model is applied to one month of continuous data
recorded at Central Arkansas to demonstrate its efficiency, generalization, and
sensitivity. Our model is able to detect more than 700 microearthquakes as
small as -1.3 ML induced during hydraulic fracturing far away than the training
region. The performance of the model is compared with STA/LTA, template
matching, and FAST algorithms. Our results indicate an efficient and reliable
performance of CRED. This framework holds great promise in lowering the
detection threshold while minimizing false positive detection rates.
Learning Scheduling Algorithms for Data Processing Clusters
Authors: Hongzi Mao, Malte Schwarzkopf, Shaileshh Bojja Venkatakrishnan, Zili Meng, Mohammad Alizadeh
Date: 2018-10-03 00:00:00
Tasks: Reinforcement Learning, Reinforcement Learning (RL), Scheduling

Efficiently scheduling data processing jobs on distributed compute clusters requires complex algorithms. Current systems, however, use simple generalized heuristics and ignore workload characteristics, since developing and tuning a scheduling policy for each workload is infeasible. In this paper, we show that modern machine learning techniques can generate highly-efficient policies automatically. Decima uses reinforcement learning (RL) and neural networks to learn workload-specific scheduling algorithms without any human instruction beyond a high-level objective such as minimizing average job completion time. Off-the-shelf RL techniques, however, cannot handle the complexity and scale of the scheduling problem. To build Decima, we had to develop new representations for jobs' dependency graphs, design scalable RL models, and invent RL training methods for dealing with continuous stochastic job arrivals. Our prototype integration with Spark on a 25-node cluster shows that Decima improves the average job completion time over hand-tuned scheduling heuristics by at least 21%, achieving up to 2x improvement during periods of high cluster load.
AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias
Authors: Rachel K. E. Bellamy, Kuntal Dey, Michael Hind, Samuel C. Hoffman, Stephanie Houde, Kalapriya Kannan, Pranay Lohia, Jacquelyn Martino, Sameep Mehta, Aleksandra Mojsilovic, Seema Nagar, Karthikeyan Natesan Ramamurthy, John Richards, Diptikalyan Saha, Prasanna Sattigeri, Moninder Singh, Kush R. Varshney, Yunfeng Zhang
Date: 2018-10-03 00:00:00
Tasks: Benchmarking, Decision Making, Fairness

Fairness is an increasingly important concern as machine learning models are
used to support decision making in high-stakes applications such as mortgage
lending, hiring, and prison sentencing. This paper introduces a new open source
Python toolkit for algorithmic fairness, AI Fairness 360 (AIF360), released
under an Apache v2.0 license {https://github.com/ibm/aif360). The main
objectives of this toolkit are to help facilitate the transition of fairness
research algorithms to use in an industrial setting and to provide a common
framework for fairness researchers to share and evaluate algorithms.
  The package includes a comprehensive set of fairness metrics for datasets and
models, explanations for these metrics, and algorithms to mitigate bias in
datasets and models. It also includes an interactive Web experience
(https://aif360.mybluemix.net) that provides a gentle introduction to the
concepts and capabilities for line-of-business users, as well as extensive
documentation, usage guidance, and industry-specific tutorials to enable data
scientists and practitioners to incorporate the most appropriate tool for their
problem into their work products. The architecture of the package has been
engineered to conform to a standard paradigm used in data science, thereby
further improving usability for practitioners. Such architectural design and
abstractions enable researchers and developers to extend the toolkit with their
new algorithms and improvements, and to use it for performance benchmarking. A
built-in testing infrastructure maintains code quality.
Comparison of Reinforcement Learning algorithms applied to the Cart Pole problem
Authors: Savinay Nagendra, Nikhil Podila, Rashmi Ugarakhod, Koshy George
Date: 2018-10-03 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Designing optimal controllers continues to be challenging as systems are
becoming complex and are inherently nonlinear. The principal advantage of
reinforcement learning (RL) is its ability to learn from the interaction with
the environment and provide optimal control strategy. In this paper, RL is
explored in the context of control of the benchmark cartpole dynamical system
with no prior knowledge of the dynamics. RL algorithms such as
temporal-difference, policy gradient actor-critic, and value function
approximation are compared in this context with the standard LQR solution.
Further, we propose a novel approach to integrate RL and swing-up controllers.
PADDIT: Probabilistic Augmentation of Data using Diffeomorphic Image Transformation
Authors: Mauricio Orbes Arteaga, Lauge Sørensen, M. Jorge Cardoso, Marc Modat, Sebastien Ourselin, Stefan Sommer, Mads Nielsen, Christian Igel, Akshay Pai
Date: 2018-10-03 00:00:00
Tasks: Image Segmentation, Medical Image Segmentation, Semantic Segmentation

For proper generalization performance of convolutional neural networks (CNNs) in medical image segmentation, the learnt features should be invariant under particular non-linear shape variations of the input. To induce invariance in CNNs to such transformations, we propose Probabilistic Augmentation of Data using Diffeomorphic Image Transformation (PADDIT) -- a systematic framework for generating realistic transformations that can be used to augment data for training CNNs. We show that CNNs trained with PADDIT outperforms CNNs trained without augmentation and with generic augmentation in segmenting white matter hyperintensities from T1 and FLAIR brain MRI scans.
Improving Solvability for Procedurally Generated Challenges in Physical Solitaire Games Through Entangled Components
Authors: Mark Goadrich, James Droscha
Date: 2018-10-03 00:00:00
Tasks: Solitaire

Challenges for physical solitaire puzzle games are typically designed in advance by humans and limited in number. Alternatively, some games incorporate rules for stochastic setup, where the human solver randomly sets up the game board before solving the challenge. These setup rules greatly increase the number of possible challenges, but can often generate unsolvable or uninteresting challenges. To better understand the compromises involved in minimizing undesirable challenges, we examine three games where component design choices can influence the stochastic nature of the resulting challenge generation algorithms. We evaluate the effect of these components and algorithms on challenge solvability and challenge engagement. We find that algorithms which control randomness through entangling components based on sub-elements of the puzzle mechanics can generate interesting challenges with a high probability of being solvable.
Bandit learning in concave $N$-person games
Authors: Mario Bravo, David S. Leslie, Panayotis Mertikopoulos
Date: 2018-10-03 00:00:00
Tasks: Stochastic Optimization

This paper examines the long-run behavior of learning with bandit feedback in
non-cooperative concave games. The bandit framework accounts for extremely
low-information environments where the agents may not even know they are
playing a game; as such, the agents' most sensible choice in this setting would
be to employ a no-regret learning algorithm. In general, this does not mean
that the players' behavior stabilizes in the long run: no-regret learning may
lead to cycles, even with perfect gradient information. However, if a standard
monotonicity condition is satisfied, our analysis shows that no-regret learning
based on mirror descent with bandit feedback converges to Nash equilibrium with
probability $1$. We also derive an upper bound for the convergence rate of the
process that nearly matches the best attainable rate for single-agent bandit
stochastic optimization.
Generalized Inverse Optimization through Online Learning
Authors: Chaosheng Dong, Yiran Chen, Bo Zeng
Date: 2018-10-03 00:00:00
Proceeding: NeurIPS 2018 12

Inverse optimization is a powerful paradigm for learning preferences and
restrictions that explain the behavior of a decision maker, based on a set of
external signal and the corresponding decision pairs. However, most inverse
optimization algorithms are designed specifically in batch setting, where all
the data is available in advance. As a consequence, there has been rare use of
these methods in an online setting suitable for real-time applications. In this
paper, we propose a general framework for inverse optimization through online
learning. Specifically, we develop an online learning algorithm that uses an
implicit update rule which can handle noisy data. Moreover, under additional
regularity assumptions in terms of the data and the model, we prove that our
algorithm converges at a rate of $\mathcal{O}(1/\sqrt{T})$ and is statistically
consistent. In our experiments, we show the online learning approach can learn
the parameters with great accuracy and is very robust to noises, and achieves a
dramatic improvement in computational efficacy over the batch learning
approach.
Analysis of Diffractive Optical Neural Networks and Their Integration with Electronic Neural Networks
Authors: Deniz Mengu, Yi Luo, Yair Rivenson, Aydogan Ozcan
Date: 2018-10-03 00:00:00
Tasks: BIG-bench Machine Learning, General Classification

Optical machine learning offers advantages in terms of power efficiency, scalability and computation speed. Recently, an optical machine learning method based on Diffractive Deep Neural Networks (D2NNs) has been introduced to execute a function as the input light diffracts through passive layers, designed by deep learning using a computer. Here we introduce improvements to D2NNs by changing the training loss function and reducing the impact of vanishing gradients in the error back-propagation step. Using five phase-only diffractive layers, we numerically achieved a classification accuracy of 97.18% and 89.13% for optical recognition of handwritten digits and fashion products, respectively; using both phase and amplitude modulation (complex-valued) at each layer, our inference performance improved to 97.81% and 89.32%, respectively. Furthermore, we report the integration of D2NNs with electronic neural networks to create hybrid-classifiers that significantly reduce the number of input pixels into an electronic network using an ultra-compact front-end D2NN with a layer-to-layer distance of a few wavelengths, also reducing the complexity of the successive electronic network. Using a 5-layer phase-only D2NN jointly-optimized with a single fully-connected electronic layer, we achieved a classification accuracy of 98.71% and 90.04% for the recognition of handwritten digits and fashion products, respectively. Moreover, the input to the electronic network was compressed by >7.8 times down to 10x10 pixels. Beyond creating low-power and high-frame rate machine learning platforms, D2NN-based hybrid neural networks will find applications in smart optical imager and sensor design.
Fast Approach to Build an Automatic Sentiment Annotator for Legal Domain using Transfer Learning
Authors: Viraj Gamage, Menuka Warushavithana, Nisansa de Silva, Amal Shehan Perera, Gathika Ratnayaka, Thejan Rupasinghe
Date: 2018-10-03 00:00:00
Proceeding: WS 2018 10
Tasks: Domain Adaptation, Transfer Learning

This study proposes a novel way of identifying the sentiment of the phrases
used in the legal domain. The added complexity of the language used in law, and
the inability of the existing systems to accurately predict the sentiments of
words in law are the main motivations behind this study. This is a transfer
learning approach, which can be used for other domain adaptation tasks as well.
The proposed methodology achieves an improvement of over 6\% compared to the
source model's accuracy in the legal domain.
Determining Optimal Number of k-Clusters based on Predefined Level-of-Similarity
Authors: Rabindra Lamsal, Shubham Katiyar
Date: 2018-10-03 00:00:00
Tasks: Clustering

This paper proposes a centroid-based clustering algorithm which is capable of clustering data-points with n-features, without having to specify the number of clusters to be formed. The core logic behind the algorithm is a similarity measure, which collectively decides whether to assign an incoming data-point to a pre-existing cluster, or create a new cluster and assign the data-point to it. The proposed clustering algorithm is application-specific and is applicable when the need is to perform clustering analysis of a stream of data-points, where the similarity measure between an incoming data-point and the cluster to which the data-point is to be associated with, is greater than the predefined Level-of-Similarity.
Understanding Weight Normalized Deep Neural Networks with Rectified Linear Units
Authors: Yixi Xu, Xiao Wang
Date: 2018-10-03 00:00:00
Proceeding: NeurIPS 2018 12

This paper presents a general framework for norm-based capacity control for
$L_{p,q}$ weight normalized deep neural networks. We establish the upper bound
on the Rademacher complexities of this family. With an $L_{p,q}$ normalization
where $q\le p^*$, and $1/p+1/p^{*}=1$, we discuss properties of a
width-independent capacity control, which only depends on depth by a square
root term. We further analyze the approximation properties of $L_{p,q}$ weight
normalized deep neural networks. In particular, for an $L_{1,\infty}$ weight
normalized network, the approximation error can be controlled by the $L_1$ norm
of the output layer, and the corresponding generalization error only depends on
the architecture by the square root of the depth.
Spurious samples in deep generative models: bug or feature?
Authors: Balázs Kégl, Mehdi Cherti, Akın Kazakçı
Date: 2018-10-03 00:00:00

Traditional wisdom in generative modeling literature is that spurious samples
that a model can generate are errors and they should be avoided. Recent
research, however, has shown interest in studying or even exploiting such
samples instead of eliminating them. In this paper, we ask the question whether
such samples can be eliminated all together without sacrificing coverage of the
generating distribution. For the class of models we consider, we experimentally
demonstrate that this is not possible without losing the ability to model some
of the test samples. While our results need to be confirmed on a broader set of
model families, these initial findings provide partial evidence that spurious
samples share structural properties with the learned dataset, which, in turn,
suggests they are not simply errors but a feature of deep generative nets.