pwc-archive/papers-with-abstracts huggingface dataset:
auditor: an R Package for Model-Agnostic Visual Validation and Diagnostics
Authors: Alicja Gosiewska, Przemyslaw Biecek
Date: 2018-09-19 00:00:00
Tasks: Diagnostic

Machine learning models have spread to almost every area of life. They are successfully applied in biology, medicine, finance, physics, and other fields. With modern software it is easy to train even a~complex model that fits the training data and results in high accuracy on the test set. The problem arises when models fail confronted with real-world data. This paper describes methodology and tools for model-agnostic audit. Introduced techniques facilitate assessing and comparing the goodness of fit and performance of models. In~addition, they may be used for the analysis of the similarity of residuals and for identification of~outliers and influential observations. The examination is carried out by diagnostic scores and visual verification. Presented methods were implemented in the auditor package for R. Due to flexible and~consistent grammar, it is simple to validate models of any classes.
Inferring short-term volatility indicators from Bitcoin blockchain
Authors: Nino Antulov-Fantulin, Dijana Tolic, Matija Piskorec, Zhang Ce, Irena Vodenska
Date: 2018-09-19 00:00:00

In this paper, we study the possibility of inferring early warning indicators
(EWIs) for periods of extreme bitcoin price volatility using features obtained
from Bitcoin daily transaction graphs. We infer the low-dimensional
representations of transaction graphs in the time period from 2012 to 2017
using Bitcoin blockchain, and demonstrate how these representations can be used
to predict extreme price volatility events. Our EWI, which is obtained with a
non-negative decomposition, contains more predictive information than those
obtained with singular value decomposition or scalar value of the total Bitcoin
transaction volume.
Machine Learning for Forecasting Mid Price Movement using Limit Order Book Data
Authors: Paraskevi Nousi, Avraam Tsantekidis, Nikolaos Passalis, Adamantios Ntakaris, Juho Kanniainen, Anastasios Tefas, Moncef Gabbouj, Alexandros Iosifidis
Date: 2018-09-19 00:00:00
Tasks: BIG-bench Machine Learning

Forecasting the movements of stock prices is one the most challenging
problems in financial markets analysis. In this paper, we use Machine Learning
(ML) algorithms for the prediction of future price movements using limit order
book data. Two different sets of features are combined and evaluated:
handcrafted features based on the raw order book data and features extracted by
ML algorithms, resulting in feature vectors with highly variant
dimensionalities. Three classifiers are evaluated using combinations of these
sets of features on two different evaluation setups and three prediction
scenarios. Even though the large scale and high frequency nature of the limit
order book poses several challenges, the scope of the conducted experiments and
the significance of the experimental results indicate that Machine Learning
highly befits this task carving the path towards future research in this field.
In-Edge AI: Intelligentizing Mobile Edge Computing, Caching and Communication by Federated Learning
Authors: Xiaofei Wang, Yiwen Han, Chenyang Wang, Qiyang Zhao, Xu Chen, Min Chen
Date: 2018-09-19 00:00:00
Tasks: Deep Reinforcement Learning, Edge-computing, Federated Learning, Reinforcement Learning

Recently, along with the rapid development of mobile communication technology, edge computing theory and techniques have been attracting more and more attentions from global researchers and engineers, which can significantly bridge the capacity of cloud and requirement of devices by the network edges, and thus can accelerate the content deliveries and improve the quality of mobile services. In order to bring more intelligence to the edge systems, compared to traditional optimization methodology, and driven by the current deep learning techniques, we propose to integrate the Deep Reinforcement Learning techniques and Federated Learning framework with the mobile edge systems, for optimizing the mobile edge computing, caching and communication. And thus, we design the "In-Edge AI" framework in order to intelligently utilize the collaboration among devices and edge nodes to exchange the learning parameters for a better training and inference of the models, and thus to carry out dynamic system-level optimization and application-level enhancement while reducing the unnecessary system communication load. "In-Edge AI" is evaluated and proved to have near-optimal performance but relatively low overhead of learning, while the system is cognitive and adaptive to the mobile communication systems. Finally, we discuss several related challenges and opportunities for unveiling a promising upcoming future of "In-Edge AI".
Power Market Price Forecasting via Deep Learning
Authors: Yongli Zhu, Songtao Lu, Renchang Dai, Guangyi Liu, Zhiwei Wang
Date: 2018-09-18 00:00:00
Tasks: Deep Learning

A study on power market price forecasting by deep learning is presented. As
one of the most successful deep learning frameworks, the LSTM (Long short-term
memory) neural network is utilized. The hourly prices data from the New England
and PJM day-ahead markets are used in this study. First, a LSTM network is
formulated and trained. Then the raw input and output data are preprocessed by
unit scaling, and the trained network is tested on the real price data under
different input lengths, forecasting horizons and data sizes. Its performance
is also compared with other existing methods. The forecasted results
demonstrate that, the LSTM deep neural network can outperform the others under
different application settings in this problem.
Implicit Maximum Likelihood Estimation
Authors: Ke Li, Jitendra Malik
Date: 2018-09-24 00:00:00
Proceeding: ICLR 2019 5

Implicit probabilistic models are models defined naturally in terms of a
sampling procedure and often induces a likelihood function that cannot be
expressed explicitly. We develop a simple method for estimating parameters in
implicit models that does not require knowledge of the form of the likelihood
function or any derived quantities, but can be shown to be equivalent to
maximizing likelihood under some conditions. Our result holds in the
non-asymptotic parametric setting, where both the capacity of the model and the
number of data examples are finite. We also demonstrate encouraging
experimental results.
Autonomous Deep Learning: Incremental Learning of Denoising Autoencoder for Evolving Data Streams
Authors: Mahardhika Pratama, Andri Ashfahani, Yew Soon Ong, Savitha Ramasamy, Edwin Lughofer
Date: 2018-09-24 00:00:00
Tasks: Denoising, Incremental Learning

The generative learning phase of Autoencoder (AE) and its successor Denosing
Autoencoder (DAE) enhances the flexibility of data stream method in exploiting
unlabelled samples. Nonetheless, the feasibility of DAE for data stream
analytic deserves in-depth study because it characterizes a fixed network
capacity which cannot adapt to rapidly changing environments. An automated
construction of a denoising autoeconder, namely deep evolving denoising
autoencoder (DEVDAN), is proposed in this paper. DEVDAN features an open
structure both in the generative phase and in the discriminative phase where
input features can be automatically added and discarded on the fly. A network
significance (NS) method is formulated in this paper and is derived from the
bias-variance concept. This method is capable of estimating the statistical
contribution of the network structure and its hidden units which precursors an
ideal state to add or prune input features. Furthermore, DEVDAN is free of the
problem- specific threshold and works fully in the single-pass learning
fashion. The efficacy of DEVDAN is numerically validated using nine
non-stationary data stream problems simulated under the prequential
test-then-train protocol where DEVDAN is capable of delivering an improvement
of classification accuracy to recently published online learning works while
having flexibility in the automatic extraction of robust input features and in
adapting to rapidly changing environments.
Jointly Multiple Events Extraction via Attention-based Graph Information Aggregation
Authors: Xiao Liu, Zhunchen Luo, He-Yan Huang
Date: 2018-09-24 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Event Extraction, Sentence

Event extraction is of practical utility in natural language processing. In
the real world, it is a common phenomenon that multiple events existing in the
same sentence, where extracting them are more difficult than extracting a
single event. Previous works on modeling the associations between events by
sequential modeling methods suffer a lot from the low efficiency in capturing
very long-range dependencies. In this paper, we propose a novel Jointly
Multiple Events Extraction (JMEE) framework to jointly extract multiple event
triggers and arguments by introducing syntactic shortcut arcs to enhance
information flow and attention-based graph convolution networks to model graph
information. The experiment results demonstrate that our proposed framework
achieves competitive results compared with state-of-the-art methods.
Incorporating Luminance, Depth and Color Information by a Fusion-based Network for Semantic Segmentation
Authors: Shang-Wei Hung, Shao-Yuan Lo, Hsueh-Ming Hang
Date: 2018-09-24 00:00:00
Tasks: Autonomous Driving, Real-Time Semantic Segmentation, Scene Understanding, Segmentation, Semantic Segmentation

Semantic segmentation has made encouraging progress due to the success of deep convolutional networks in recent years. Meanwhile, depth sensors become prevalent nowadays, so depth maps can be acquired more easily. However, there are few studies that focus on the RGB-D semantic segmentation task. Exploiting the depth information effectiveness to improve performance is a challenge. In this paper, we propose a novel solution named LDFNet, which incorporates Luminance, Depth and Color information by a fusion-based network. It includes a sub-network to process depth maps and employs luminance images to assist the depth information in processes. LDFNet outperforms the other state-of-art systems on the Cityscapes dataset, and its inference speed is faster than most of the existing networks. The experimental results show the effectiveness of the proposed multi-modal fusion network and its potential for practical applications.
Sparse-to-Continuous: Enhancing Monocular Depth Estimation using Occupancy Maps
Authors: Nícolas Rosa, Vitor Guizilini, Valdir Grassi Jr
Date: 2018-09-24 00:00:00
Tasks: Depth Estimation, Monocular Depth Estimation

This paper addresses the problem of single image depth estimation (SIDE), focusing on improving the quality of deep neural network predictions. In a supervised learning scenario, the quality of predictions is intrinsically related to the training labels, which guide the optimization process. For indoor scenes, structured-light-based depth sensors (e.g. Kinect) are able to provide dense, albeit short-range, depth maps. On the other hand, for outdoor scenes, LiDARs are considered the standard sensor, which comparatively provides much sparser measurements, especially in areas further away. Rather than modifying the neural network architecture to deal with sparse depth maps, this article introduces a novel densification method for depth maps, using the Hilbert Maps framework. A continuous occupancy map is produced based on 3D points from LiDAR scans, and the resulting reconstructed surface is projected into a 2D depth map with arbitrary resolution. Experiments conducted with various subsets of the KITTI dataset show a significant improvement produced by the proposed Sparse-to-Continuous technique, without the introduction of extra information into the training stage.
Deep Confidence: A Computationally Efficient Framework for Calculating Reliable Errors for Deep Neural Networks
Authors: Isidro Cortes-Ciriano, Andreas Bender
Date: 2018-09-24 00:00:00
Tasks: Conformal Prediction, Deep Learning, Drug Discovery, Prediction

Deep learning architectures have proved versatile in a number of drug
discovery applications, including the modelling of in vitro compound activity.
While controlling for prediction confidence is essential to increase the trust,
interpretability and usefulness of virtual screening models in drug discovery,
techniques to estimate the reliability of the predictions generated with deep
learning networks remain largely underexplored. Here, we present Deep
Confidence, a framework to compute valid and efficient confidence intervals for
individual predictions using the deep learning technique Snapshot Ensembling
and conformal prediction. Specifically, Deep Confidence generates an ensemble
of deep neural networks by recording the network parameters throughout the
local minima visited during the optimization phase of a single neural network.
This approach serves to derive a set of base learners (i.e., snapshots) with
comparable predictive power on average, that will however generate slightly
different predictions for a given instance. The variability across base
learners and the validation residuals are in turn harnessed to compute
confidence intervals using the conformal prediction framework. Using a set of
24 diverse IC50 data sets from ChEMBL 23, we show that Snapshot Ensembles
perform on par with Random Forest (RF) and ensembles of independently trained
deep neural networks. In addition, we find that the confidence regions
predicted using the Deep Confidence framework span a narrower set of values.
Overall, Deep Confidence represents a highly versatile error prediction
framework that can be applied to any deep learning-based application at no
extra computational cost.
Weakly-Supervised Learning of Metric Aggregations for Deformable Image Registration
Authors: Enzo Ferrante, Puneet K. Dokania, Rafael Marini Silva, Nikos Paragios
Date: 2018-09-24 00:00:00
Tasks: Image Registration, Weakly-supervised Learning

Deformable registration has been one of the pillars of biomedical image
computing. Conventional approaches refer to the definition of a similarity
criterion that, once endowed with a deformation model and a smoothness
constraint, determines the optimal transformation to align two given images.
The definition of this metric function is among the most critical aspects of
the registration process. We argue that incorporating semantic information (in
the form of anatomical segmentation maps) into the registration process will
further improve the accuracy of the results. In this paper, we propose a novel
weakly supervised approach to learn domain specific aggregations of
conventional metrics using anatomical segmentations. This combination is
learned using latent structured support vector machines (LSSVM). The learned
matching criterion is integrated within a metric free optimization framework
based on graphical models, resulting in a multi-metric algorithm endowed with a
spatially varying similarity metric function conditioned on the anatomical
structures. We provide extensive evaluation on three different datasets of CT
and MRI images, showing that learned multi-metric registration outperforms
single-metric approaches based on conventional similarity measures.
SDN Flow Entry Management Using Reinforcement Learning
Authors: Ting-Yu Mu, Ala Al-Fuqaha, Khaled Shuaib, Farag M. Sallabi, Junaid Qadir
Date: 2018-09-24 00:00:00
Tasks: Deep Reinforcement Learning, Management, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Modern information technology services largely depend on cloud
infrastructures to provide their services. These cloud infrastructures are
built on top of datacenter networks (DCNs) constructed with high-speed links,
fast switching gear, and redundancy to offer better flexibility and resiliency.
In this environment, network traffic includes long-lived (elephant) and
short-lived (mice) flows with partitioned and aggregated traffic patterns.
Although SDN-based approaches can efficiently allocate networking resources for
such flows, the overhead due to network reconfiguration can be significant.
With limited capacity of Ternary Content-Addressable Memory (TCAM) deployed in
an OpenFlow enabled switch, it is crucial to determine which forwarding rules
should remain in the flow table, and which rules should be processed by the SDN
controller in case of a table-miss on the SDN switch. This is needed in order
to obtain the flow entries that satisfy the goal of reducing the long-term
control plane overhead introduced between the controller and the switches. To
achieve this goal, we propose a machine learning technique that utilizes two
variations of reinforcement learning (RL) algorithms-the first of which is
traditional reinforcement learning algorithm based while the other is deep
reinforcement learning based. Emulation results using the RL algorithm show
around 60% improvement in reducing the long-term control plane overhead, and
around 14% improvement in the table-hit ratio compared to the Multiple Bloom
Filters (MBF) method given a fixed size flow table of 4KB.
Fast Geometrically-Perturbed Adversarial Faces
Authors: Ali Dabouei, Sobhan Soleymani, Jeremy Dawson, Nasser M. Nasrabadi
Date: 2018-09-24 00:00:00
Tasks: Face Recognition

The state-of-the-art performance of deep learning algorithms has led to a
considerable increase in the utilization of machine learning in
security-sensitive and critical applications. However, it has recently been
shown that a small and carefully crafted perturbation in the input space can
completely fool a deep model. In this study, we explore the extent to which
face recognition systems are vulnerable to geometrically-perturbed adversarial
faces. We propose a fast landmark manipulation method for generating
adversarial faces, which is approximately 200 times faster than the previous
geometric attacks and obtains 99.86% success rate on the state-of-the-art face
recognition models. To further force the generated samples to be natural, we
introduce a second attack constrained on the semantic structure of the face
which has the half speed of the first attack with the success rate of 99.96%.
Both attacks are extremely robust against the state-of-the-art defense methods
with the success rate of equal or greater than 53.59%. Code is available at
https://github.com/alldbi/FLM
Improved Semantic Stixels via Multimodal Sensor Fusion
Authors: Florian Piewak, Peter Pinggera, Markus Enzweiler, David Pfeiffer, Marius Zöllner
Date: 2018-09-24 00:00:00
Tasks: Sensor Fusion

This paper presents a compact and accurate representation of 3D scenes that
are observed by a LiDAR sensor and a monocular camera. The proposed method is
based on the well-established Stixel model originally developed for stereo
vision applications. We extend this Stixel concept to incorporate data from
multiple sensor modalities. The resulting mid-level fusion scheme takes full
advantage of the geometric accuracy of LiDAR measurements as well as the high
resolution and semantic detail of RGB images. The obtained environment model
provides a geometrically and semantically consistent representation of the 3D
scene at a significantly reduced amount of data while minimizing information
loss at the same time. Since the different sensor modalities are considered as
input to a joint optimization problem, the solution is obtained with only minor
computational overhead. We demonstrate the effectiveness of the proposed
multimodal Stixel algorithm on a manually annotated ground truth dataset. Our
results indicate that the proposed mid-level fusion of LiDAR and camera data
improves both the geometric and semantic accuracy of the Stixel model
significantly while reducing the computational overhead as well as the amount
of generated data in comparison to using a single modality on its own.
On The Utility of Conditional Generation Based Mutual Information for Characterizing Adversarial Subspaces
Authors: Chia-Yi Hsu, Pei-Hsuan Lu, Pin-Yu Chen, Chia-Mu Yu
Date: 2018-09-24 00:00:00

Recent studies have found that deep learning systems are vulnerable to
adversarial examples; e.g., visually unrecognizable adversarial images can
easily be crafted to result in misclassification. The robustness of neural
networks has been studied extensively in the context of adversary detection,
which compares a metric that exhibits strong discriminate power between natural
and adversarial examples. In this paper, we propose to characterize the
adversarial subspaces through the lens of mutual information (MI) approximated
by conditional generation methods. We use MI as an information-theoretic metric
to strengthen existing defenses and improve the performance of adversary
detection. Experimental results on MagNet defense demonstrate that our proposed
MI detector can strengthen its robustness against powerful adversarial attacks.
WiRe57 : A Fine-Grained Benchmark for Open Information Extraction
Authors: William Léchelle, Fabrizio Gotti, Philippe Langlais
Date: 2018-09-24 00:00:00
Proceeding: WS 2019 8
Tasks: Open Information Extraction

We build a reference for the task of Open Information Extraction, on five documents. We tentatively resolve a number of issues that arise, including inference and granularity. We seek to better pinpoint the requirements for the task. We produce our annotation guidelines specifying what is correct to extract and what is not. In turn, we use this reference to score existing Open IE systems. We address the non-trivial problem of evaluating the extractions produced by systems against the reference tuples, and share our evaluation script. Among seven compared extractors, we find the MinIE system to perform best.
Joint Multitask Learning for Community Question Answering Using Task-Specific Embeddings
Authors: Shafiq Joty, Lluis Marquez, Preslav Nakov
Date: 2018-09-24 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Community Question Answering, Question Answering

We address jointly two important tasks for Question Answering in community
forums: given a new question, (i) find related existing questions, and (ii)
find relevant answers to this new question. We further use an auxiliary task to
complement the previous two, i.e., (iii) find good answers with respect to the
thread question in a question-comment thread. We use deep neural networks
(DNNs) to learn meaningful task-specific embeddings, which we then incorporate
into a conditional random field (CRF) model for the multitask setting,
performing joint learning over a complex graph structure. While DNNs alone
achieve competitive results when trained to produce the embeddings, the CRF,
which makes use of the embeddings and the dependencies between the tasks,
improves the results significantly and consistently across a variety of
evaluation metrics, thus showing the complementarity of DNNs and structured
learning.