pwc-archive/papers-with-abstracts huggingface dataset:
On Robustness of Principal Component Regression
Authors: Anish Agarwal, Devavrat Shah, Dennis Shen, Dogyoon Song
Date: 2019-02-28 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Art Analysis, Causal Inference, counterfactual, regression, Time Series Analysis

Principal Component Regression (PCR) is a simple, but powerful and ubiquitously utilized method. Its effectiveness is well established when the covariates exhibit low-rank structure. However, its ability to handle settings with noisy, missing, and mixed-valued covariates is not understood and remains an important open challenge. As the main contribution of this work we establish the robustness of PCR in this respect and provide meaningful finite-sample analysis. In the process, we establish that PCR is equivalent to performing Linear Regression after pre-processing the covariate matrix via Hard Singular Value Thresholding (HSVT). That is, PCR is equivalent to the recently proposed robust variant of the Synthetic Control method in the context of counterfactual analysis using observational data. As an immediate consequence, we obtain finite-sample analysis of the Robust Synthetic Control (RSC) estimator that was previously absent. As an important contribution to the Synthetic Control literature, we establish that an (approximate) linear synthetic control exists in the setting of a generalized factor model; traditionally, the existence of a synthetic control needs to be assumed to exist as an axiom. We further discuss a surprising implication of the robustness property of PCR with respect to noise, i.e., PCR can learn a good predictive model even if the covariates are tactfully transformed to preserve differential privacy. Finally, this work advances the state-of-the-art analysis for HSVT by establishing stronger guarantees with respect to the $\ell_{2, \infty}$-norm rather than the Frobenius norm as is commonly done in the matrix estimation literature, which may be of interest in its own right.
Meta Dynamic Pricing: Transfer Learning Across Experiments
Authors: Hamsa Bastani, David Simchi-Levi, Ruihao Zhu
Date: 2019-02-28 00:00:00
Tasks: Thompson Sampling, Transfer Learning

We study the problem of learning shared structure \emph{across} a sequence of dynamic pricing experiments for related products. We consider a practical formulation where the unknown demand parameters for each product come from an unknown distribution (prior) that is shared across products. We then propose a meta dynamic pricing algorithm that learns this prior online while solving a sequence of Thompson sampling pricing experiments (each with horizon $T$) for $N$ different products. Our algorithm addresses two challenges: (i) balancing the need to learn the prior (\emph{meta-exploration}) with the need to leverage the estimated prior to achieve good performance (\emph{meta-exploitation}), and (ii) accounting for uncertainty in the estimated prior by appropriately "widening" the estimated prior as a function of its estimation error. We introduce a novel prior alignment technique to analyze the regret of Thompson sampling with a mis-specified prior, which may be of independent interest. Unlike prior-independent approaches, our algorithm's meta regret grows sublinearly in $N$, demonstrating that the price of an unknown prior in Thompson sampling can be negligible in experiment-rich environments (large $N$). Numerical experiments on synthetic and real auto loan data demonstrate that our algorithm significantly speeds up learning compared to prior-independent algorithms.
BERT for Joint Intent Classification and Slot Filling
Authors: Qian Chen, Zhu Zhuo, Wen Wang
Date: 2019-02-28 00:00:00
Tasks: General Classification, intent-classification, Intent Classification, Intent Detection, Natural Language Understanding, Sentence, Slot Filling

Intent classification and slot filling are two essential tasks for natural
language understanding. They often suffer from small-scale human-labeled
training data, resulting in poor generalization capability, especially for rare
words. Recently a new language representation model, BERT (Bidirectional
Encoder Representations from Transformers), facilitates pre-training deep
bidirectional representations on large-scale unlabeled corpora, and has created
state-of-the-art models for a wide variety of natural language processing tasks
after simple fine-tuning. However, there has not been much effort on exploring
BERT for natural language understanding. In this work, we propose a joint
intent classification and slot filling model based on BERT. Experimental
results demonstrate that our proposed model achieves significant improvement on
intent classification accuracy, slot filling F1, and sentence-level semantic
frame accuracy on several public benchmark datasets, compared to the
attention-based recurrent neural network models and slot-gated models.
PixelSteganalysis: Pixel-wise Hidden Information Removal with Low Visual Degradation
Authors: Dahuin Jung, Ho Bae, Hyun-Soo Choi, Sungroh Yoon
Date: 2019-02-28 00:00:00
Tasks: Steganalysis

Recently, the field of steganography has experienced rapid developments based on deep learning (DL). DL based steganography distributes secret information over all the available bits of the cover image, thereby posing difficulties in using conventional steganalysis methods to detect, extract or remove hidden secret images. However, our proposed framework is the first to effectively disable covert communications and transactions that use DL based steganography. We propose a DL based steganalysis technique that effectively removes secret images by restoring the distribution of the original images. We formulate a problem and address it by exploiting sophisticated pixel distributions and an edge distribution of images by using a deep neural network. Based on the given information, we remove the hidden secret information at the pixel level. We evaluate our technique by comparing it with conventional steganalysis methods using three public benchmarks. As the decoding method of DL based steganography is approximate (lossy) and is different from the decoding method of conventional steganography, we also introduce a new quantitative metric called the destruction rate (DT). The experimental results demonstrate performance improvements of 10-20% in both the decoded rate and the DT.
SweepNet: Wide-baseline Omnidirectional Depth Estimation
Authors: Changhee Won, Jongbin Ryu, Jongwoo Lim
Date: 2019-02-28 00:00:00
Tasks: Depth Estimation

Omnidirectional depth sensing has its advantage over the conventional stereo systems since it enables us to recognize the objects of interest in all directions without any blind regions. In this paper, we propose a novel wide-baseline omnidirectional stereo algorithm which computes the dense depth estimate from the fisheye images using a deep convolutional neural network. The capture system consists of multiple cameras mounted on a wide-baseline rig with ultrawide field of view (FOV) lenses, and we present the calibration algorithm for the extrinsic parameters based on the bundle adjustment. Instead of estimating depth maps from multiple sets of rectified images and stitching them, our approach directly generates one dense omnidirectional depth map with full 360-degree coverage at the rig global coordinate system. To this end, the proposed neural network is designed to output the cost volume from the warped images in the sphere sweeping method, and the final depth map is estimated by taking the minimum cost indices of the aggregated cost volume by SGM. For training the deep neural network and testing the entire system, realistic synthetic urban datasets are rendered using Blender. The experiments using the synthetic and real-world datasets show that our algorithm outperforms the conventional depth estimation methods and generate highly accurate depth maps.
Bi-Directional Cascade Network for Perceptual Edge Detection
Authors: Jianzhong He, Shiliang Zhang, Ming Yang, Yanhu Shan, Tiejun Huang
Date: 2019-02-28 00:00:00
Proceeding: CVPR 2019 6
Tasks: Edge Detection

Exploiting multi-scale representations is critical to improve edge detection
for objects at different scales. To extract edges at dramatically different
scales, we propose a Bi-Directional Cascade Network (BDCN) structure, where an
individual layer is supervised by labeled edges at its specific scale, rather
than directly applying the same supervision to all CNN outputs. Furthermore, to
enrich multi-scale representations learned by BDCN, we introduce a Scale
Enhancement Module (SEM) which utilizes dilated convolution to generate
multi-scale features, instead of using deeper CNNs or explicitly fusing
multi-scale edge maps. These new approaches encourage the learning of
multi-scale representations in different layers and detect edges that are well
delineated by their scales. Learning scale dedicated layers also results in
compact network with a fraction of parameters. We evaluate our method on three
datasets, i.e., BSDS500, NYUDv2, and Multicue, and achieve ODS Fmeasure of
0.828, 1.3% higher than current state-of-the art on BSDS500. The code has been
available at https://github.com/pkuCactus/BDCN.
Adversarial Attack and Defense on Point Sets
Authors: Jiancheng Yang, Qiang Zhang, Rongyao Fang, Bingbing Ni, Jinxian Liu, Qi Tian
Date: 2019-02-28 00:00:00
Tasks: Adversarial Attack

Emergence of the utility of 3D point cloud data in safety-critical vision tasks (e.g., ADAS) urges researchers to pay more attention to the robustness of 3D representations and deep networks. To this end, we develop an attack and defense scheme, dedicated to 3D point cloud data, for preventing 3D point clouds from manipulated as well as pursuing noise-tolerable 3D representation. A set of novel 3D point cloud attack operations are proposed via pointwise gradient perturbation and adversarial point attachment / detachment. We then develop a flexible perturbation-measurement scheme for 3D point cloud data to detect potential attack data or noisy sensing data. Notably, the proposed defense methods are even effective to detect the adversarial point clouds generated by a proof-of-concept attack directly targeting the defense. Transferability of adversarial attacks between several point cloud networks is addressed, and we propose an momentum-enhanced pointwise gradient to improve the attack transferability. We further analyze the transferability from adversarial point clouds to grid CNNs and the inverse. Extensive experimental results on common point cloud benchmarks demonstrate the validity of the proposed 3D attack and defense framework.
What you get is not always what you see: pitfalls in solar array assessment using overhead imagery
Authors: Wei Hu, Kyle Bradbury, Jordan M. Malof, Boning Li, Bohao Huang, Artem Streltsov, K. Sydny Fujita, Ben Hoen
Date: 2019-02-28 00:00:00

Effective integration planning for small, distributed solar photovoltaic (PV) arrays into electric power grids requires access to high quality data: the location and power capacity of individual solar PV arrays. Unfortunately, national databases of small-scale solar PV do not exist; those that do are limited in their spatial resolution, typically aggregated up to state or national levels. While several promising approaches for solar PV detection have been published, strategies for evaluating the performance of these models are often highly heterogeneous from study to study. The resulting comparison of these methods for practical applications for energy assessments becomes challenging and may imply that the reported performance evaluations are overly optimistic. The heterogeneity comes in many forms, each of which we explore in this work: the level of spatial aggregation, the validation of ground truth, inconsistencies in the training and validation datasets, and the degree of diversity of the locations and sensors from which the training and validation data originate. For each, we discuss emerging practices from the literature to address them or suggest directions of future research. As part of our investigation, we evaluate solar PV identification performance in two large regions. Our findings suggest that traditional performance evaluation of the automated identification of solar PV from satellite imagery may be optimistic due to common limitations in the validation process. The takeaways from this work are intended to inform and catalyze the large-scale practical application of automated solar PV assessment techniques by energy researchers and professionals.
Supervised ML Solution for Band Assignment in Dual-Band Systems with Omnidirectional and Directional Antennas
Authors: Daoud Burghal, Rui Wang, Abdullah Alghafis, Andreas F. Molisch
Date: 2019-02-28 00:00:00
Tasks: Binary Classification

Many wireless networks, including 5G NR (New Radio) and future beyond 5G cellular systems, are expected to operate on multiple frequency bands. This paper considers the band assignment (BA) problem in dual-band systems, where the basestation (BS) chooses one of the two available frequency bands (centimeter-wave and millimeter-wave bands) to communicate with the user equipment (UE). While the millimeter-wave band might offer higher data rate, there is a significant probability of outage during which the communication should be carried on the (more reliable) centimeter-wave band. With mobility, the BA can be perceived as a sequential problem, where the BS uses previously observed information to predict the best band for a future time step. We formulate the BA as a binary classification problem and propose supervised Machine Learning (ML) solutions. We study the problem when both the BS and the UE use (i) omnidirectional antennas and (ii) both use directional antennas. In the omnidirectional case, we derive analytical benchmark solutions based on the Gaussian Process (GP) assumption for the inter-band shadow fading. In the directional case, where the labeling is shown to be complex, we propose an efficient labeling approach based on the Viterbi Algorithm (VA). We compare the performances for two channel models: (i) a stochastic channel and (ii) a ray-tracing based channel.
Towards Robust ResNet: A Small Step but A Giant Leap
Authors: Jingfeng Zhang, Bo Han, Laura Wynter, Kian Hsiang Low, Mohan Kankanhalli
Date: 2019-02-28 00:00:00

This paper presents a simple yet principled approach to boosting the robustness of the residual network (ResNet) that is motivated by the dynamical system perspective. Namely, a deep neural network can be interpreted using a partial differential equation, which naturally inspires us to characterize ResNet by an explicit Euler method. Our analytical studies reveal that the step factor h in the Euler method is able to control the robustness of ResNet in both its training and generalization. Specifically, we prove that a small step factor h can benefit the training robustness for back-propagation; from the view of forward-propagation, a small h can aid in the robustness of the model generalization. A comprehensive empirical evaluation on both vision CIFAR-10 and text AG-NEWS datasets confirms that a small h aids both the training and generalization robustness.
Face Recognition Under Varying Blur, Illumination and Expression in an Unconstrained Environment
Authors: Anubha Pearline. S, Hemalatha. M
Date: 2019-02-28 00:00:00
Tasks: Face Recognition, Robust Face Recognition

Face recognition system is one of the esteemed research areas in pattern
recognition and computer vision as long as its major challenges. A few
challenges in recognizing faces are blur, illumination, and varied expressions.
Blur is natural while taking photographs using cameras, mobile phones, etc.
Blur can be uniform and non-uniform. Usually non-uniform blur happens in images
taken using handheld image devices. Distinguishing or handling a blurred image
in a face recognition system is generally tough. Under varying lighting
conditions, it is challenging to identify the person correctly. Diversified
facial expressions such as happiness, sad, surprise, fear, anger changes or
deforms the faces from normal images. Identifying faces with facial expressions
is also a challenging task, due to the deformation caused by the facial
expressions. To solve these issues, a pre-processing step was carried out after
which Blur and Illumination-Robust Face recognition (BIRFR) algorithm was
performed. The test image and training images with facial expression are
transformed to neutral face using Facial expression removal (FER) peration.
Every training image is transformed based on the optimal Transformation Spread
Function (TSF), and illumination coefficients. Local Binary Pattern (LBP)
features extracted from test image and transformed training image is used for
classification.
Financial series prediction using Attention LSTM
Authors: Sangyeon Kim, Myungjoo Kang
Date: 2019-02-28 00:00:00
Tasks: BIG-bench Machine Learning, Deep Learning, Prediction, Time Series, Time Series Analysis, Time Series Prediction

Financial time series prediction, especially with machine learning
techniques, is an extensive field of study. In recent times, deep learning
methods (especially time series analysis) have performed outstandingly for
various industrial problems, with better prediction than machine learning
methods. Moreover, many researchers have used deep learning methods to predict
financial time series with various models in recent years. In this paper, we
will compare various deep learning models, such as multilayer perceptron (MLP),
one-dimensional convolutional neural networks (1D CNN), stacked long short-term
memory (stacked LSTM), attention networks, and weighted attention networks for
financial time series prediction. In particular, attention LSTM is not only
used for prediction, but also for visualizing intermediate outputs to analyze
the reason of prediction; therefore, we will show an example for understanding
the model prediction intuitively with attention vectors. In addition, we focus
on time and factors, which lead to an easy understanding of why certain trends
are predicted when accessing a given time series table. We also modify the loss
functions of the attention models with weighted categorical cross entropy; our
proposed model produces a 0.76 hit ratio, which is superior to those of other
methods for predicting the trends of the KOSPI 200.
Real-time tree search with pessimistic scenarios
Authors: Takayuki Osogami, Toshihiro Takahashi
Date: 2019-02-28 00:00:00

Autonomous agents need to make decisions in a sequential manner, under partially observable environment, and in consideration of how other agents behave. In critical situations, such decisions need to be made in real time for example to avoid collisions and recover to safe conditions. We propose a technique of tree search where a deterministic and pessimistic scenario is used after a specified depth. Because there is no branching with the deterministic scenario, the proposed technique allows us to take into account the events that can occur far ahead in the future. The effectiveness of the proposed technique is demonstrated in Pommerman, a multi-agent environment used in a NeurIPS 2018 competition, where the agents that implement the proposed technique have won the first and third places.
PFLD: A Practical Facial Landmark Detector
Authors: Xiaojie Guo, Siyuan Li, Jinke Yu, Jiawan Zhang, Jiayi Ma, Lin Ma, Wei Liu, Haibin Ling
Date: 2019-02-28 00:00:00
Tasks: Face Alignment, Facial Landmark Detection

Being accurate, efficient, and compact is essential to a facial landmark
detector for practical use. To simultaneously consider the three concerns, this
paper investigates a neat model with promising detection accuracy under wild
environments e.g., unconstrained pose, expression, lighting, and occlusion
conditions) and super real-time speed on a mobile device. More concretely, we
customize an end-to-end single stage network associated with acceleration
techniques. During the training phase, for each sample, rotation information is
estimated for geometrically regularizing landmark localization, which is then
NOT involved in the testing phase. A novel loss is designed to, besides
considering the geometrical regularization, mitigate the issue of data
imbalance by adjusting weights of samples to different states, such as large
pose, extreme lighting, and occlusion, in the training set. Extensive
experiments are conducted to demonstrate the efficacy of our design and reveal
its superior performance over state-of-the-art alternatives on widely-adopted
challenging benchmarks, i.e., 300W (including iBUG, LFPW, AFW, HELEN, and
XM2VTS) and AFLW. Our model can be merely 2.1Mb of size and reach over 140 fps
per face on a mobile phone (Qualcomm ARM 845 processor) with high precision,
making it attractive for large-scale or real-time applications. We have made
our practical system based on PFLD 0.25X model publicly available at
\url{http://sites.google.com/view/xjguo/fld} for encouraging comparisons and
improvements from the community.
Cascaded Recurrent Neural Networks for Hyperspectral Image Classification
Authors: Renlong Hang, Qingshan Liu, Danfeng Hong, Pedram Ghamisi
Date: 2019-02-28 00:00:00
Tasks: Classification, General Classification, Hyperspectral Image Classification, image-classification, Image Classification

By considering the spectral signature as a sequence, recurrent neural
networks (RNNs) have been successfully used to learn discriminative features
from hyperspectral images (HSIs) recently. However, most of these models only
input the whole spectral bands into RNNs directly, which may not fully explore
the specific properties of HSIs. In this paper, we propose a cascaded RNN model
using gated recurrent units (GRUs) to explore the redundant and complementary
information of HSIs. It mainly consists of two RNN layers. The first RNN layer
is used to eliminate redundant information between adjacent spectral bands,
while the second RNN layer aims to learn the complementary information from
non-adjacent spectral bands. To improve the discriminative ability of the
learned features, we design two strategies for the proposed model. Besides,
considering the rich spatial information contained in HSIs, we further extend
the proposed model to its spectral-spatial counterpart by incorporating some
convolutional layers. To test the effectiveness of our proposed models, we
conduct experiments on two widely used HSIs. The experimental results show that
our proposed models can achieve better results than the compared models.
A novel dynamic asset allocation system using Feature Saliency Hidden Markov models for smart beta investing
Authors: Elizabeth Fons, Paula Dawson, Jeffrey Yau, Xiao-jun Zeng, John Keane
Date: 2019-02-28 00:00:00
Tasks: feature selection

The financial crisis of 2008 generated interest in more transparent,
rules-based strategies for portfolio construction, with Smart beta strategies
emerging as a trend among institutional investors. While they perform well in
the long run, these strategies often suffer from severe short-term drawdown
(peak-to-trough decline) with fluctuating performance across cycles. To address
cyclicality and underperformance, we build a dynamic asset allocation system
using Hidden Markov Models (HMMs). We test our system across multiple
combinations of smart beta strategies and the resulting portfolios show an
improvement in risk-adjusted returns, especially on more return oriented
portfolios (up to 50$\%$ in excess of market annually). In addition, we propose
a novel smart beta allocation system based on the Feature Saliency HMM (FSHMM)
algorithm that performs feature selection simultaneously with the training of
the HMM, to improve regime identification. We evaluate our systematic trading
system with real life assets using MSCI indices; further, the results (up to
60$\%$ in excess of market annually) show model performance improvement with
respect to portfolios built using full feature HMMs.