pwc-archive/papers-with-abstracts huggingface dataset:
Efficient computational strategies to learn the structure of probabilistic graphical models of cumulative phenomena
Authors: Daniele Ramazzotti, Marco S. Nobile, Marco Antoniotti, Alex Graudenzi
Date: 2017-03-08 00:00:00

Structural learning of Bayesian Networks (BNs) is a NP-hard problem, which is
further complicated by many theoretical issues, such as the I-equivalence among
different structures. In this work, we focus on a specific subclass of BNs,
named Suppes-Bayes Causal Networks (SBCNs), which include specific structural
constraints based on Suppes' probabilistic causation to efficiently model
cumulative phenomena. Here we compare the performance, via extensive
simulations, of various state-of-the-art search strategies, such as local
search techniques and Genetic Algorithms, as well as of distinct regularization
methods. The assessment is performed on a large number of simulated datasets
from topologies with distinct levels of complexity, various sample size and
different rates of errors in the data. Among the main results, we show that the
introduction of Suppes' constraints dramatically improve the inference
accuracy, by reducing the solution space and providing a temporal ordering on
the variables. We also report on trade-offs among different search techniques
that can be efficiently employed in distinct experimental settings. This
manuscript is an extended version of the paper "Structural Learning of
Probabilistic Graphical Models of Cumulative Phenomena" presented at the 2018
International Conference on Computational Science.
Causal Data Science for Financial Stress Testing
Authors: Gelin Gao, Bud Mishra, Daniele Ramazzotti
Date: 2017-03-08 00:00:00
Tasks: Management

The most recent financial upheavals have cast doubt on the adequacy of some
of the conventional quantitative risk management strategies, such as VaR (Value
at Risk), in many common situations. Consequently, there has been an increasing
need for verisimilar financial stress testings, namely simulating and analyzing
financial portfolios in extreme, albeit rare scenarios. Unlike conventional
risk management which exploits statistical correlations among financial
instruments, here we focus our analysis on the notion of probabilistic
causation, which is embodied by Suppes-Bayes Causal Networks (SBCNs); SBCNs are
probabilistic graphical models that have many attractive features in terms of
more accurate causal analysis for generating financial stress scenarios. In
this paper, we present a novel approach for conducting stress testing of
financial portfolios based on SBCNs in combination with classical machine
learning classification tools. The resulting method is shown to be capable of
correctly discovering the causal relationships among financial factors that
affect the portfolios and thus, simulating stress testing scenarios with a
higher accuracy and lower computational complexity than conventional Monte
Carlo Simulations.
Forecasting using incomplete models
Authors: Vanessa Kosoy
Date: 2017-05-12 00:00:00
Tasks: Bayesian Inference

We consider the task of forecasting an infinite sequence of future observations based on some number of past observations, where the probability measure generating the observations is "suspected" to satisfy one or more of a set of incomplete models, i.e. convex sets in the space of probability measures. This setting is in some sense intermediate between the realizable setting where the probability measure comes from some known set of probability measures (which can be addressed using e.g. Bayesian inference) and the unrealizable setting where the probability measure is completely arbitrary. We demonstrate a method of forecasting which guarantees that, whenever the true probability measure satisfies an incomplete model in a given countable set, the forecast converges to the same incomplete model in the (appropriately normalized) Kantorovich-Rubinstein metric. This is analogous to merging of opinions for Bayesian inference, except that convergence in the Kantorovich-Rubinstein metric is weaker than convergence in total variation.
Imposing higher-level Structure in Polyphonic Music Generation using Convolutional Restricted Boltzmann Machines and Constraints
Authors: Stefan Lattner, Maarten Grachten, Gerhard Widmer
Date: 2016-12-14 00:00:00
Tasks: Music Generation

We introduce a method for imposing higher-level structure on generated,
polyphonic music. A Convolutional Restricted Boltzmann Machine (C-RBM) as a
generative model is combined with gradient descent constraint optimisation to
provide further control over the generation process. Among other things, this
allows for the use of a "template" piece, from which some structural properties
can be extracted, and transferred as constraints to the newly generated
material. The sampling process is guided with Simulated Annealing to avoid
local optima, and to find solutions that both satisfy the constraints, and are
relatively stable with respect to the C-RBM. Results show that with this
approach it is possible to control the higher-level self-similarity structure,
the meter, and the tonal properties of the resulting musical piece, while
preserving its local musical coherence.
Fast Optimal Bandwidth Selection for RBF Kernel using Reproducing Kernel Hilbert Space Operators for Kernel Based Classifiers
Authors: Bharath Bhushan Damodaran
Date: 2018-04-14 00:00:00
Tasks: Classification, General Classification

Kernel based methods have shown effective performance in many remote sensing
classification tasks. However their performance significantly depend on its
hyper-parameters. The conventional technique to estimate the parameter comes
with high computational complexity. Thus, the objective of this letter is to
propose an fast and efficient method to select the bandwidth parameter of the
Gaussian kernel in the kernel based classification methods. The proposed method
is developed based on the operators in the reproducing kernel Hilbert space and
it is evaluated on Support vector machines and PerTurbo classification method.
Experiments conducted with hyperspectral datasets show that our proposed method
outperforms the state-of-art method in terms in computational time and
classification performance.
Combining Difficulty Ranking with Multi-Armed Bandits to Sequence Educational Content
Authors: Avi Segal, Yossi Ben David, Joseph Jay Williams, Kobi Gal, Yaar Shalom
Date: 2018-04-14 00:00:00
Tasks: Multi-Armed Bandits

As e-learning systems become more prevalent, there is a growing need for them
to accommodate individual differences between students. This paper addresses
the problem of how to personalize educational content to students in order to
maximize their learning gains over time. We present a new computational
approach to this problem called MAPLE (Multi-Armed Bandits based
Personalization for Learning Environments) that combines difficulty ranking
with multi-armed bandits. Given a set of target questions MAPLE estimates the
expected learning gains for each question and uses an exploration-exploitation
strategy to choose the next question to pose to the student. It maintains a
personalized ranking over the difficulties of question in the target set which
is used in two ways: First, to obtain initial estimates over the learning gains
for the set of questions. Second, to update the estimates over time based on
the students responses. We show in simulations that MAPLE was able to improve
students' learning gains compared to approaches that sequence questions in
increasing level of difficulty, or rely on content experts. When implemented in
a live e-learning system in the wild, MAPLE showed promising results. This work
demonstrates the efficacy of using stochastic approaches to the sequencing
problem when augmented with information about question difficulty.
On Asynchronous Non-Dominated Sorting for Steady-State Multiobjective Evolutionary Algorithms
Authors: Ilya Yakupov, Maxim Buzdalov
Date: 2018-04-14 00:00:00
Tasks: Blocking, Evolutionary Algorithms

In parallel and distributed environments, generational evolutionary
algorithms often do not exploit the full potential of the computation system
since they have to wait until the entire population is evaluated before
starting selection procedures. Steady-state algorithms are often seen as a
solution to this problem, since fitness evaluation can be done by multiple
threads in an asynchronous way. However, if the algorithm updates its state in
a complicated way, the threads will eventually have to wait until this update
finishes. State update procedures that are computationally expensive are common
in multiobjective evolutionary algorithms.
  We have implemented an asynchronous steady-state version of the NSGA-II
algorithm. Its most expensive part, non-dominated sorting, determines the time
needed to update the state. We turned the existing incremental non-dominated
sorting algorithm into an asynchronous one using several concurrency
techniques: a single entry-level lock, finer-grained locks working with
non-domination levels, and a non-blocking approach using compare-and-set
operations. Our experimental results reveal the trade-off between the
work-efficiency of the algorithm and the achieved amount of parallelism.
An optimal unrestricted learning procedure
Authors: Shahar Mendelson
Date: 2017-07-17 00:00:00

We study learning problems involving arbitrary classes of functions $F$,
distributions $X$ and targets $Y$. Because proper learning procedures, i.e.,
procedures that are only allowed to select functions in $F$, tend to perform
poorly unless the problem satisfies some additional structural property (e.g.,
that $F$ is convex), we consider unrestricted learning procedures that are free
to choose functions outside the given class.
  We present a new unrestricted procedure that is optimal in a very strong
sense: the required sample complexity is essentially the best one can hope for,
and the estimate holds for (almost) any problem, including heavy-tailed
situations. Moreover, the sample complexity coincides with the what one would
expect if $F$ were convex, even when $F$ is not. And if $F$ is convex, the
procedure turns out to be proper. Thus, the unrestricted procedure is actually
optimal in both realms, for convex classes as a proper procedure and for
arbitrary classes as an unrestricted procedure.
Detecting Malicious PowerShell Commands using Deep Neural Networks
Authors: Danny Hendler, Shay Kels, Amir Rubin
Date: 2018-04-11 00:00:00

Microsoft's PowerShell is a command-line shell and scripting language that is
installed by default on Windows machines. While PowerShell can be configured by
administrators for restricting access and reducing vulnerabilities, these
restrictions can be bypassed. Moreover, PowerShell commands can be easily
generated dynamically, executed from memory, encoded and obfuscated, thus
making the logging and forensic analysis of code executed by PowerShell
challenging.For all these reasons, PowerShell is increasingly used by
cybercriminals as part of their attacks' tool chain, mainly for downloading
malicious contents and for lateral movement. Indeed, a recent comprehensive
technical report by Symantec dedicated to PowerShell's abuse by cybercrimials
reported on a sharp increase in the number of malicious PowerShell samples they
received and in the number of penetration tools and frameworks that use
PowerShell. This highlights the urgent need of developing effective methods for
detecting malicious PowerShell commands.In this work, we address this challenge
by implementing several novel detectors of malicious PowerShell commands and
evaluating their performance. We implemented both "traditional" natural
language processing (NLP) based detectors and detectors based on
character-level convolutional neural networks (CNNs). Detectors' performance
was evaluated using a large real-world dataset.Our evaluation results show
that, although our detectors individually yield high performance, an ensemble
detector that combines an NLP-based classifier with a CNN-based classifier
provides the best performance, since the latter classifier is able to detect
malicious commands that succeed in evading the former. Our analysis of these
evasive commands reveals that some obfuscation patterns automatically detected
by the CNN classifier are intrinsically difficult to detect using the NLP
techniques we applied.
Panoptic Segmentation
Authors: Alexander Kirillov, Kaiming He, Ross Girshick, Carsten Rother, Piotr Dollár
Date: 2018-01-03 00:00:00
Proceeding: CVPR 2019 6
Tasks: Image Segmentation, Instance Segmentation, Panoptic Segmentation, Scene Parsing, Scene Segmentation, Segmentation, Semantic Segmentation

We propose and study a task we name panoptic segmentation (PS). Panoptic
segmentation unifies the typically distinct tasks of semantic segmentation
(assign a class label to each pixel) and instance segmentation (detect and
segment each object instance). The proposed task requires generating a coherent
scene segmentation that is rich and complete, an important step toward
real-world vision systems. While early work in computer vision addressed
related image/scene parsing tasks, these are not currently popular, possibly
due to lack of appropriate metrics or associated recognition challenges. To
address this, we propose a novel panoptic quality (PQ) metric that captures
performance for all classes (stuff and things) in an interpretable and unified
manner. Using the proposed metric, we perform a rigorous study of both human
and machine performance for PS on three existing datasets, revealing
interesting insights about the task. The aim of our work is to revive the
interest of the community in a more unified view of image segmentation.
Motion-based Object Segmentation based on Dense RGB-D Scene Flow
Authors: Lin Shao, Parth Shah, Vikranth Dwaracherla, Jeannette Bohg
Date: 2018-04-14 00:00:00
Tasks: Motion Segmentation, Object, Robot Manipulation, Segmentation, Semantic Segmentation

Given two consecutive RGB-D images, we propose a model that estimates a dense
3D motion field, also known as scene flow. We take advantage of the fact that
in robot manipulation scenarios, scenes often consist of a set of rigidly
moving objects. Our model jointly estimates (i) the segmentation of the scene
into an unknown but finite number of objects, (ii) the motion trajectories of
these objects and (iii) the object scene flow. We employ an hourglass, deep
neural network architecture. In the encoding stage, the RGB and depth images
undergo spatial compression and correlation. In the decoding stage, the model
outputs three images containing a per-pixel estimate of the corresponding
object center as well as object translation and rotation. This forms the basis
for inferring the object segmentation and final object scene flow. To evaluate
our model, we generated a new and challenging, large-scale, synthetic dataset
that is specifically targeted at robotic manipulation: It contains a large
number of scenes with a very diverse set of simultaneously moving 3D objects
and is recorded with a simulated, static RGB-D camera. In quantitative
experiments, we show that we outperform state-of-the-art scene flow and
motion-segmentation methods on this data set. In qualitative experiments, we
show how our learned model transfers to challenging real-world scenes, visually
generating better results than existing methods.
Not all Embeddings are created Equal: Extracting Entity-specific Substructures for RDF Graph Embedding
Authors: Muhammad Rizwan Saeed, Charalampos Chelmis, Viktor K. Prasanna
Date: 2018-04-14 00:00:00
Tasks: All, Graph Embedding, Knowledge Graphs, Specificity

Knowledge Graphs (KGs) are becoming essential to information systems that
require access to structured data. Several approaches have been recently
proposed, for obtaining vector representations of KGs suitable for Machine
Learning tasks, based on identifying and extracting relevant graph
substructures using uniform and biased random walks. However, such approaches
lead to representations comprising mostly "popular", instead of "relevant",
entities in the KG. In KGs, in which different types of entities often exist
(such as in Linked Open Data), a given target entity may have its own distinct
set of most "relevant" nodes and edges. We propose specificity as an accurate
measure of identifying most relevant, entity-specific, nodes and edges. We
develop a scalable method based on bidirectional random walks to compute
specificity. Our experimental evaluation results show that specificity-based
biased random walks extract more "meaningful" (in terms of size and relevance)
RDF substructures compared to the state-of-the-art and, the graph embedding
learned from the extracted substructures, outperform existing techniques in the
task of entity recommendation in DBpedia.
Pedestrian-Synthesis-GAN: Generating Pedestrian Data in Real Scene and Beyond
Authors: Xi Ouyang, Yu Cheng, Yifan Jiang, Chun-Liang Li, Pan Zhou
Date: 2018-04-05 00:00:00
Tasks: Generative Adversarial Network, Pedestrian Detection, Scene Text Recognition

State-of-the-art pedestrian detection models have achieved great success in
many benchmarks. However, these models require lots of annotation information
and the labeling process usually takes much time and efforts. In this paper, we
propose a method to generate labeled pedestrian data and adapt them to support
the training of pedestrian detectors. The proposed framework is built on the
Generative Adversarial Network (GAN) with multiple discriminators, trying to
synthesize realistic pedestrians and learn the background context
simultaneously. To handle the pedestrians of different sizes, we adopt the
Spatial Pyramid Pooling (SPP) layer in the discriminator. We conduct
experiments on two benchmarks. The results show that our framework can smoothly
synthesize pedestrians on background images of variations and different levels
of details. To quantitatively evaluate our approach, we add the generated
samples into training data of the baseline pedestrian detectors and show the
synthetic images are able to improve the detectors' performance.
LiDAR and Camera Calibration using Motion Estimated by Sensor Fusion Odometry
Authors: Ryoichi Ishikawa, Takeshi Oishi, Katsushi Ikeuchi
Date: 2018-04-14 00:00:00
Tasks: Camera Calibration, Sensor Fusion

In this paper, we propose a method of targetless and automatic Camera-LiDAR
calibration. Our approach is an extension of hand-eye calibration framework to
2D-3D calibration. By using the sensor fusion odometry method, the scaled
camera motions are calculated with high accuracy. In addition to this, we
clarify the suitable motion for this calibration method.
  The proposed method only requires the three-dimensional point cloud and the
camera image and does not need other information such as reflectance of LiDAR
and to give initial extrinsic parameter. In the experiments, we demonstrate our
method using several sensor configurations in indoor and outdoor scenes to
verify the effectiveness. The accuracy of our method achieves more than other
comparable state-of-the-art methods.
All-Optical Machine Learning Using Diffractive Deep Neural Networks
Authors: Xing Lin, Yair Rivenson, Nezih T. Yardimci, Muhammed Veli, Mona Jarrahi, Aydogan Ozcan
Date: 2018-04-14 00:00:00
Tasks: All, BIG-bench Machine Learning, Deep Learning, General Classification

We introduce an all-optical Diffractive Deep Neural Network (D2NN)
architecture that can learn to implement various functions after deep
learning-based design of passive diffractive layers that work collectively. We
experimentally demonstrated the success of this framework by creating
3D-printed D2NNs that learned to implement handwritten digit classification and
the function of an imaging lens at terahertz spectrum. With the existing
plethora of 3D-printing and other lithographic fabrication methods as well as
spatial-light-modulators, this all-optical deep learning framework can perform,
at the speed of light, various complex functions that computer-based neural
networks can implement, and will find applications in all-optical image
analysis, feature detection and object classification, also enabling new camera
designs and optical components that can learn to perform unique tasks using
D2NNs.
Developing Far-Field Speaker System Via Teacher-Student Learning
Authors: Jinyu Li, Rui Zhao, Zhuo Chen, Changliang Liu, Xiong Xiao, Guoli Ye, Yifan Gong
Date: 2018-04-14 00:00:00
Tasks: Keyword Spotting, Model Compression

In this study, we develop the keyword spotting (KWS) and acoustic model (AM)
components in a far-field speaker system. Specifically, we use teacher-student
(T/S) learning to adapt a close-talk well-trained production AM to far-field by
using parallel close-talk and simulated far-field data. We also use T/S
learning to compress a large-size KWS model into a small-size one to fit the
device computational cost. Without the need of transcription, T/S learning well
utilizes untranscribed data to boost the model performance in both the AM
adaptation and KWS model compression. We further optimize the models with
sequence discriminative training and live data to reach the best performance of
systems. The adapted AM improved from the baseline by 72.60% and 57.16%
relative word error rate reduction on play-back and live test data,
respectively. The final KWS model size was reduced by 27 times from a
large-size KWS model without losing accuracy.
Road Segmentation Using CNN with GRU
Authors: Yecheng Lyu, Xinming Huang
Date: 2018-04-14 00:00:00
Tasks: Autonomous Vehicles, Decoder, Road Segmentation, Segmentation

This paper presents an accurate and fast algorithm for road segmentation
using convolutional neural network (CNN) and gated recurrent units (GRU). For
autonomous vehicles, road segmentation is a fundamental task that can provide
the drivable area for path planning. The existing deep neural network based
segmentation algorithms usually take a very deep encoder-decoder structure to
fuse pixels, which requires heavy computations, large memory and long
processing time. Hereby, a CNN-GRU network model is proposed and trained to
perform road segmentation using data captured by the front camera of a vehicle.
GRU network obtains a long spatial sequence with lower computational
complexity, comparing to traditional encoder-decoder architecture. The proposed
road detector is evaluated on the KITTI road benchmark and achieves high
accuracy for road segmentation at real-time processing speed.
Exploring the Encoding Layer and Loss Function in End-to-End Speaker and Language Recognition System
Authors: Weicheng Cai, Jinkun Chen, Ming Li
Date: 2018-04-14 00:00:00
Tasks: Speaker Verification

In this paper, we explore the encoding/pooling layer and loss function in the
end-to-end speaker and language recognition system. First, a unified and
interpretable end-to-end system for both speaker and language recognition is
developed. It accepts variable-length input and produces an utterance level
result. In the end-to-end system, the encoding layer plays a role in
aggregating the variable-length input sequence into an utterance level
representation. Besides the basic temporal average pooling, we introduce a
self-attentive pooling layer and a learnable dictionary encoding layer to get
the utterance level representation. In terms of loss function for open-set
speaker verification, to get more discriminative speaker embedding, center loss
and angular softmax loss is introduced in the end-to-end system. Experimental
results on Voxceleb and NIST LRE 07 datasets show that the performance of
end-to-end learning system could be significantly improved by the proposed
encoding layer and loss function.