pwc-archive/papers-with-abstracts huggingface dataset:
Forecasting Economics and Financial Time Series: ARIMA vs. LSTM
Authors: Sima Siami-Namini, Akbar Siami Namin
Date: 2018-03-16 00:00:00
Tasks: Deep Learning, Time Series, Time Series Analysis

Forecasting time series data is an important subject in economics, business,
and finance. Traditionally, there are several techniques to effectively
forecast the next lag of time series data such as univariate Autoregressive
(AR), univariate Moving Average (MA), Simple Exponential Smoothing (SES), and
more notably Autoregressive Integrated Moving Average (ARIMA) with its many
variations. In particular, ARIMA model has demonstrated its outperformance in
precision and accuracy of predicting the next lags of time series. With the
recent advancement in computational power of computers and more importantly
developing more advanced machine learning algorithms and approaches such as
deep learning, new algorithms are developed to forecast time series data. The
research question investigated in this article is that whether and how the
newly developed deep learning-based algorithms for forecasting time series
data, such as "Long Short-Term Memory (LSTM)", are superior to the traditional
algorithms. The empirical studies conducted and reported in this article show
that deep learning-based algorithms such as LSTM outperform traditional-based
algorithms such as ARIMA model. More specifically, the average reduction in
error rates obtained by LSTM is between 84 - 87 percent when compared to ARIMA
indicating the superiority of LSTM to ARIMA. Furthermore, it was noticed that
the number of training times, known as "epoch" in deep learning, has no effect
on the performance of the trained forecast model and it exhibits a truly random
behavior.
Representation Learning over Dynamic Graphs
Authors: Rakshit Trivedi, Mehrdad Farajtabar, Prasenjeet Biswal, Hongyuan Zha
Date: 2018-03-11 00:00:00
Tasks: Dynamic Link Prediction, Link Prediction, Representation Learning

How can we effectively encode evolving information over dynamic graphs into
low-dimensional representations? In this paper, we propose DyRep, an inductive
deep representation learning framework that learns a set of functions to
efficiently produce low-dimensional node embeddings that evolves over time. The
learned embeddings drive the dynamics of two key processes namely,
communication and association between nodes in dynamic graphs. These processes
exhibit complex nonlinear dynamics that evolve at different time scales and
subsequently contribute to the update of node embeddings. We employ a
time-scale dependent multivariate point process model to capture these
dynamics. We devise an efficient unsupervised learning procedure and
demonstrate that our approach significantly outperforms representative
baselines on two real-world datasets for the problem of dynamic link prediction
and event time prediction.
Adversarial Logit Pairing
Authors: Harini Kannan, Alexey Kurakin, Ian Goodfellow
Date: 2018-03-16 00:00:00
Proceeding: NeurIPS 2018

In this paper, we develop improved techniques for defending against
adversarial examples at scale. First, we implement the state of the art version
of adversarial training at unprecedented scale on ImageNet and investigate
whether it remains effective in this setting - an important open scientific
question (Athalye et al., 2018). Next, we introduce enhanced defenses using a
technique we call logit pairing, a method that encourages logits for pairs of
examples to be similar. When applied to clean examples and their adversarial
counterparts, logit pairing improves accuracy on adversarial examples over
vanilla adversarial training; we also find that logit pairing on clean examples
only is competitive with adversarial training in terms of accuracy on two
datasets. Finally, we show that adversarial logit pairing achieves the state of
the art defense on ImageNet against PGD white box attacks, with an accuracy
improvement from 1.5% to 27.9%. Adversarial logit pairing also successfully
damages the current state of the art defense against black box attacks on
ImageNet (Tramer et al., 2018), dropping its accuracy from 66.6% to 47.1%. With
this new accuracy drop, adversarial logit pairing ties with Tramer et al.(2018)
for the state of the art on black box attacks on ImageNet.
Tensor2Tensor for Neural Machine Translation
Authors: Ashish Vaswani, Samy Bengio, Eugene Brevdo, Francois Chollet, Aidan N. Gomez, Stephan Gouws, Llion Jones, Łukasz Kaiser, Nal Kalchbrenner, Niki Parmar, Ryan Sepassi, Noam Shazeer, Jakob Uszkoreit
Date: 2018-03-16 00:00:00
Proceeding: WS 2018 3
Tasks: Deep Learning, Machine Translation, Translation

Tensor2Tensor is a library for deep learning models that is well-suited for
neural machine translation and includes the reference implementation of the
state-of-the-art Transformer model.
Fast Shadow Detection from a Single Image Using a Patched Convolutional Neural Network
Authors: Sepideh Hosseinzadeh, Moein Shakeri, Hong Zhang
Date: 2017-09-26 00:00:00
Tasks: Shadow Detection

In recent years, various shadow detection methods from a single image have
been proposed and used in vision systems; however, most of them are not
appropriate for the robotic applications due to the expensive time complexity.
This paper introduces a fast shadow detection method using a deep learning
framework, with a time cost that is appropriate for robotic applications. In
our solution, we first obtain a shadow prior map with the help of multi-class
support vector machine using statistical features. Then, we use a semantic-
aware patch-level Convolutional Neural Network that efficiently trains on
shadow examples by combining the original image and the shadow prior map.
Experiments on benchmark datasets demonstrate the proposed method significantly
decreases the time complexity of shadow detection, by one or two orders of
magnitude compared with state-of-the-art methods, without losing accuracy.
A Low-rank Tensor Regularization Strategy for Hyperspectral Unmixing
Authors: Tales Imbiriba, Ricardo Augusto Borsoi, José Carlos Moreira Bermudez
Date: 2018-03-16 00:00:00
Tasks: Hyperspectral Unmixing

Tensor-based methods have recently emerged as a more natural and effective
formulation to address many problems in hyperspectral imaging. In hyperspectral
unmixing (HU), low-rank constraints on the abundance maps have been shown to
act as a regularization which adequately accounts for the multidimensional
structure of the underlying signal. However, imposing a strict low-rank
constraint for the abundance maps does not seem to be adequate, as important
information that may be required to represent fine scale abundance behavior may
be discarded. This paper introduces a new low-rank tensor regularization that
adequately captures the low-rank structure underlying the abundance maps
without hindering the flexibility of the solution. Simulation results with
synthetic and real data show that the the extra flexibility introduced by the
proposed regularization significantly improves the unmixing results.
Semantic Adversarial Examples
Authors: Hossein Hosseini, Radha Poovendran
Date: 2018-03-16 00:00:00
Tasks: Denoising

Deep neural networks are known to be vulnerable to adversarial examples,
i.e., images that are maliciously perturbed to fool the model. Generating
adversarial examples has been mostly limited to finding small perturbations
that maximize the model prediction error. Such images, however, contain
artificial perturbations that make them somewhat distinguishable from natural
images. This property is used by several defense methods to counter adversarial
examples by applying denoising filters or training the model to be robust to
small perturbations.
  In this paper, we introduce a new class of adversarial examples, namely
"Semantic Adversarial Examples," as images that are arbitrarily perturbed to
fool the model, but in such a way that the modified image semantically
represents the same object as the original image. We formulate the problem of
generating such images as a constrained optimization problem and develop an
adversarial transformation based on the shape bias property of human cognitive
system. In our method, we generate adversarial images by first converting the
RGB image into the HSV (Hue, Saturation and Value) color space and then
randomly shifting the Hue and Saturation components, while keeping the Value
component the same. Our experimental results on CIFAR10 dataset show that the
accuracy of VGG16 network on adversarial color-shifted images is 5.7%.
Faces as Lighting Probes via Unsupervised Deep Highlight Extraction
Authors: Renjiao Yi, Chenyang Zhu, Ping Tan, Stephen Lin
Date: 2018-03-16 00:00:00
Proceeding: ECCV 2018 9

We present a method for estimating detailed scene illumination using human
faces in a single image. In contrast to previous works that estimate lighting
in terms of low-order basis functions or distant point lights, our technique
estimates illumination at a higher precision in the form of a non-parametric
environment map. Based on the observation that faces can exhibit strong
highlight reflections from a broad range of lighting directions, we propose a
deep neural network for extracting highlights from faces, and then trace these
reflections back to the scene to acquire the environment map. Since real
training data for highlight extraction is very limited, we introduce an
unsupervised scheme for finetuning the network on real images, based on the
consistent diffuse chromaticity of a given face seen in multiple real images.
In tracing the estimated highlights to the environment, we reduce the blurring
effect of skin reflectance on reflected light through a deconvolution
determined by prior knowledge on face material properties. Comparisons to
previous techniques for highlight extraction and illumination estimation show
the state-of-the-art performance of this approach on a variety of indoor and
outdoor scenes.
Calibration for the (Computationally-Identifiable) Masses
Authors: Úrsula Hébert-Johnson, Michael P. Kim, Omer Reingold, Guy N. Rothblum
Date: 2017-11-22 00:00:00
Tasks: Fairness

As algorithms increasingly inform and influence decisions made about
individuals, it becomes increasingly important to address concerns that these
algorithms might be discriminatory. The output of an algorithm can be
discriminatory for many reasons, most notably: (1) the data used to train the
algorithm might be biased (in various ways) to favor certain populations over
others; (2) the analysis of this training data might inadvertently or
maliciously introduce biases that are not borne out in the data. This work
focuses on the latter concern.
  We develop and study multicalbration -- a new measure of algorithmic fairness
that aims to mitigate concerns about discrimination that is introduced in the
process of learning a predictor from data. Multicalibration guarantees accurate
(calibrated) predictions for every subpopulation that can be identified within
a specified class of computations. We think of the class as being quite rich;
in particular, it can contain many overlapping subgroups of a protected group.
  We show that in many settings this strong notion of protection from
discrimination is both attainable and aligned with the goal of obtaining
accurate predictions. Along the way, we present new algorithms for learning a
multicalibrated predictor, study the computational complexity of this task, and
draw new connections to computational learning models such as agnostic
learning.
Listening to Chaotic Whispers: A Deep Learning Framework for News-oriented Stock Trend Prediction
Authors: Ziniu Hu, Weiqing Liu, Jiang Bian, Xuanzhe Liu, Tie-Yan Liu
Date: 2017-12-06 00:00:00
Tasks: Stock Market Prediction, Stock Trend Prediction

Stock trend prediction plays a critical role in seeking maximized profit from
stock investment. However, precise trend prediction is very difficult since the
highly volatile and non-stationary nature of stock market. Exploding
information on Internet together with advancing development of natural language
processing and text mining techniques have enable investors to unveil market
trends and volatility from online content. Unfortunately, the quality,
trustworthiness and comprehensiveness of online content related to stock market
varies drastically, and a large portion consists of the low-quality news,
comments, or even rumors. To address this challenge, we imitate the learning
process of human beings facing such chaotic online news, driven by three
principles: sequential content dependency, diverse influence, and effective and
efficient learning. In this paper, to capture the first two principles, we
designed a Hybrid Attention Networks to predict the stock trend based on the
sequence of recent related news. Moreover, we apply the self-paced learning
mechanism to imitate the third principle. Extensive experiments on real-world
stock market data demonstrate the effectiveness of our approach.
Learning deep structured active contours end-to-end
Authors: Diego Marcos, Devis Tuia, Benjamin Kellenberger, Lisa Zhang, Min Bai, Renjie Liao, Raquel Urtasun
Date: 2018-03-16 00:00:00
Proceeding: CVPR 2018 6
Tasks: Instance Segmentation, Segmentation, Semantic Segmentation

The world is covered with millions of buildings, and precisely knowing each
instance's position and extents is vital to a multitude of applications.
Recently, automated building footprint segmentation models have shown superior
detection accuracy thanks to the usage of Convolutional Neural Networks (CNN).
However, even the latest evolutions struggle to precisely delineating borders,
which often leads to geometric distortions and inadvertent fusion of adjacent
building instances. We propose to overcome this issue by exploiting the
distinct geometric properties of buildings. To this end, we present Deep
Structured Active Contours (DSAC), a novel framework that integrates priors and
constraints into the segmentation process, such as continuous boundaries,
smooth edges, and sharp corners. To do so, DSAC employs Active Contour Models
(ACM), a family of constraint- and prior-based polygonal models. We learn ACM
parameterizations per instance using a CNN, and show how to incorporate all
components in a structured output model, making DSAC trainable end-to-end. We
evaluate DSAC on three challenging building instance segmentation datasets,
where it compares favorably against state-of-the-art. Code will be made
available.
Nesting Probabilistic Programs
Authors: Tom Rainforth
Date: 2018-03-16 00:00:00
Tasks: Probabilistic Programming

We formalize the notion of nesting probabilistic programming queries and
investigate the resulting statistical implications. We demonstrate that while
query nesting allows the definition of models which could not otherwise be
expressed, such as those involving agents reasoning about other agents,
existing systems take approaches which lead to inconsistent estimates. We show
how to correct this by delineating possible ways one might want to nest queries
and asserting the respective conditions required for convergence. We further
introduce a new online nested Monte Carlo estimator that makes it substantially
easier to ensure these conditions are met, thereby providing a simple framework
for designing statistically correct inference engines. We prove the correctness
of this online estimator and show that, when using the recommended setup, its
asymptotic variance is always better than that of the equivalent fixed
estimator, while its bias is always within a factor of two.
A particle-based variational approach to Bayesian Non-negative Matrix Factorization
Authors: M. Arjumand Masood, Finale Doshi-Velez
Date: 2018-03-16 00:00:00

Bayesian Non-negative Matrix Factorization (NMF) is a promising approach for
understanding uncertainty and structure in matrix data. However, a large volume
of applied work optimizes traditional non-Bayesian NMF objectives that fail to
provide a principled understanding of the non-identifiability inherent in NMF--
an issue ideally addressed by a Bayesian approach. Despite their suitability,
current Bayesian NMF approaches have failed to gain popularity in an applied
setting; they sacrifice flexibility in modeling for tractable computation, tend
to get stuck in local modes, and require many thousands of samples for
meaningful uncertainty estimates. We address these issues through a
particle-based variational approach to Bayesian NMF that only requires the
joint likelihood to be differentiable for tractability, uses a novel
initialization technique to identify multiple modes in the posterior, and
allows domain experts to inspect a `small' set of factorizations that
faithfully represent the posterior. We introduce and employ a class of
likelihood and prior distributions for NMF that formulate a Bayesian model
using popular non-Bayesian NMF objectives. On several real datasets, we obtain
better particle approximations to the Bayesian NMF posterior in less time than
baselines and demonstrate the significant role that multimodality plays in
NMF-related tasks.
Synchronisation of Partial Multi-Matchings via Non-negative Factorisations
Authors: Florian Bernard, Johan Thunberg, Jorge Goncalves, Christian Theobalt
Date: 2018-03-16 00:00:00
Tasks: Clustering

In this work we study permutation synchronisation for the challenging case of
partial permutations, which plays an important role for the problem of matching
multiple objects (e.g. images or shapes). The term synchronisation refers to
the property that the set of pairwise matchings is cycle-consistent, i.e. in
the full matching case all compositions of pairwise matchings over cycles must
be equal to the identity. Motivated by clustering and matrix factorisation
perspectives of cycle-consistency, we derive an algorithm to tackle the
permutation synchronisation problem based on non-negative factorisations. In
order to deal with the inherent non-convexity of the permutation
synchronisation problem, we use an initialisation procedure based on a novel
rotation scheme applied to the solution of the spectral relaxation. Moreover,
this rotation scheme facilitates a convenient Euclidean projection to obtain a
binary solution after solving our relaxed problem. In contrast to
state-of-the-art methods, our approach is guaranteed to produce
cycle-consistent results. We experimentally demonstrate the efficacy of our
method and show that it achieves better results compared to existing methods.
Improved Part Segmentation Performance by Optimising Realism of Synthetic Images using Cycle Generative Adversarial Networks
Authors: Ruud Barth, Jochen Hemming, Eldert J. van Henten
Date: 2018-03-16 00:00:00
Tasks: Generative Adversarial Network, Translation

In this paper we report on improved part segmentation performance using
convolutional neural networks to reduce the dependency on the large amount of
manually annotated empirical images. This was achieved by optimising the visual
realism of synthetic agricultural images.In Part I, a cycle consistent
generative adversarial network was applied to synthetic and empirical images
with the objective to generate more realistic synthetic images by translating
them to the empirical domain. We first hypothesise and confirm that plant part
image features such as color and texture become more similar to the empirical
domain after translation of the synthetic images.Results confirm this with an
improved mean color distribution correlation with the empirical data prior of
0.62 and post translation of 0.90. Furthermore, the mean image features of
contrast, homogeneity, energy and entropy moved closer to the empirical mean,
post translation. In Part II, 7 experiments were performed using convolutional
neural networks with different combinations of synthetic, synthetic translated
to empirical and empirical images. We hypothesised that the translated images
can be used for (i) improved learning of empirical images, and (ii) that
learning without any fine-tuning with empirical images is improved by
bootstrapping with translated images over bootstrapping with synthetic images.
Results confirm our second and third hypotheses. First a maximum
intersection-over-union performance was achieved of 0.52 when bootstrapping
with translated images and fine-tuning with empirical images; an 8% increase
compared to only using synthetic images. Second, training without any empirical
fine-tuning resulted in an average IOU of 0.31; a 55% performance increase over
previous methods that only used synthetic images.
Graph Partition Neural Networks for Semi-Supervised Classification
Authors: Renjie Liao, Marc Brockschmidt, Daniel Tarlow, Alexander L. Gaunt, Raquel Urtasun, Richard Zemel
Date: 2018-03-16 00:00:00
Proceeding: ICLR 2018 1
Tasks: Classification, General Classification, Node Classification

We present graph partition neural networks (GPNN), an extension of graph
neural networks (GNNs) able to handle extremely large graphs. GPNNs alternate
between locally propagating information between nodes in small subgraphs and
globally propagating information between the subgraphs. To efficiently
partition graphs, we experiment with several partitioning algorithms and also
propose a novel variant for fast processing of large scale graphs. We
extensively test our model on a variety of semi-supervised node classification
tasks. Experimental results indicate that GPNNs are either superior or
comparable to state-of-the-art methods on a wide variety of datasets for
graph-based semi-supervised classification. We also show that GPNNs can achieve
similar performance as standard GNNs with fewer propagation steps.
Simulating the future urban growth in Xiongan New Area: a upcoming big city in China
Authors: Xun Liang
Date: 2018-03-16 00:00:00

China made the announement to create the Xiongan New Area in Hebei in April
1,2017. Thus a new magacity about 110km south west of Beijing will emerge.
Xiongan New Area is of great practial significant and historical significant
for transferring Beijing's non-capital function. Simulating the urban dynamics
in Xiongan New Area can help planners to decide where to build the new urban
and further manage the future urban growth. However, only a little research
focus on the future urban development in Xiongan New Area. In addition,
previous models are unable to simulate the urban dynamics in Xiongan New Area.
Because there are no original high density urbna for these models to learn the
transition rules.In this study, we proposed a C-FLUS model to solve such
problems. This framework was implemented by coupling a modified Cellular
automata(CA). An elaborately designed random planted seeds machanism based on
local maximums is addressed in the CA model to better simulate the occurrence
of the new urban. Through an analysis of the current driving forces, the C-FLUS
can detect the potential start zone and simulate the urban development under
different scenarios in Xiongan New Area. Our study shows that the new urban is
most likely to occur in northwest of Xiongxian, and it will rapidly extend to
Rongcheng and Anxin until almost cover the northern part of Xiongan New Area.
Moreover, the method can help planners to evaluate the impact of urban
expansion in Xiongan New Area.
Consistent sets of lines with no colorful incidence
Authors: Boris Bukh, Xavier Goaoc, Alfredo Hubard, Matthew Trager
Date: 2018-03-16 00:00:00
Tasks: 3D Reconstruction

We consider incidences among colored sets of lines in $\mathbb{R}^d$ and
examine whether the existence of certain concurrences between lines of $k$
colors force the existence of at least one concurrence between lines of $k+1$
colors. This question is relevant for problems in 3D reconstruction in computer
vision.