pwc-archive/papers-with-abstracts huggingface dataset:
Tensor-Based Classifiers for Hyperspectral Data Analysis
Authors: Konstantinos Makantasis, Anastasios Doulamis, Nikolaos Doulamis, Antonis Nikitakis
Date: 2017-09-24 00:00:00
Tasks: Classification, Dimensionality Reduction, General Classification, tensor algebra

In this work, we present tensor-based linear and nonlinear models for
hyperspectral data classification and analysis. By exploiting principles of
tensor algebra, we introduce new classification architectures, the weight
parameters of which satisfies the {\it rank}-1 canonical decomposition
property. Then, we introduce learning algorithms to train both the linear and
the non-linear classifier in a way to i) to minimize the error over the
training samples and ii) the weight coefficients satisfies the {\it rank}-1
canonical decomposition property. The advantages of the proposed classification
model is that i) it reduces the number of parameters required and thus reduces
the respective number of training samples required to properly train the model,
ii) it provides a physical interpretation regarding the model coefficients on
the classification output and iii) it retains the spatial and spectral
coherency of the input samples. To address issues related with linear
classification, characterizing by low capacity, since it can produce rules that
are linear in the input space, we introduce non-linear classification models
based on a modification of a feedforward neural network. We call the proposed
architecture {\it rank}-1 Feedfoward Neural Network (FNN), since their weights
satisfy the {\it rank}-1 caconical decomposition property. Appropriate learning
algorithms are also proposed to train the network. Experimental results and
comparisons with state of the art classification methods, either linear (e.g.,
SVM) and non-linear (e.g., deep learning) indicates the outperformance of the
proposed scheme, especially in cases where a small number of training samples
are available. Furthermore, the proposed tensor-based classfiers are evaluated
against their capabilities in dimensionality reduction.
A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community
Authors: John E. Ball, Derek T. Anderson, Chee Seng Chan
Date: 2017-09-01 00:00:00
Tasks: speech-recognition, Speech Recognition, Transfer Learning

In recent years, deep learning (DL), a re-branding of neural networks (NNs),
has risen to the top in numerous areas, namely computer vision (CV), speech
recognition, natural language processing, etc. Whereas remote sensing (RS)
possesses a number of unique challenges, primarily related to sensors and
applications, inevitably RS draws from many of the same theories as CV; e.g.,
statistics, fusion, and machine learning, to name a few. This means that the RS
community should be aware of, if not at the leading edge of, of advancements
like DL. Herein, we provide the most comprehensive survey of state-of-the-art
RS DL research. We also review recent new developments in the DL field that can
be used in DL for RS. Namely, we focus on theories, tools and challenges for
the RS community. Specifically, we focus on unsolved challenges and
opportunities as it relates to (i) inadequate data sets, (ii)
human-understandable solutions for modelling physical phenomena, (iii) Big
Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and
learning algorithms for spectral, spatial and temporal data, (vi) transfer
learning, (vii) an improved theoretical understanding of DL systems, (viii)
high barriers to entry, and (ix) training and optimizing the DL.
Rapid and Robust Automated Macroscopic Wood Identification System using Smartphone with Macro-lens
Authors: Xin Jie Tang, Yong Haur Tay, Nordahlia Abdullah Siam, Seng Choon Lim
Date: 2017-09-24 00:00:00

Wood Identification has never been more important to serve the purpose of
global forest species protection and timber regulation. Macroscopic level wood
identification practiced by wood anatomists can identify wood up to genus
level. This is sufficient to serve as a frontline identification to fight
against illegal wood logging and timber trade for law enforcement authority.
However, frontline enforcement official may lack of the accuracy and confidence
of a well trained wood anatomist. Hence, computer assisted method such as
machine vision methods are developed to do rapid field identification for law
enforcement official. In this paper, we proposed a rapid and robust macroscopic
wood identification system using machine vision method with off-the-shelf
smartphone and retrofitted macro-lens. Our system is cost effective, easily
accessible, fast and scalable at the same time provides human-level accuracy on
identification. Camera-enabled smartphone with Internet connectivity coupled
with a macro-lens provides a simple and effective digital acquisition of
macroscopic wood images which are essential for macroscopic wood
identification. The images are immediately streamed to a cloud server via
Internet connection for identification which are done within seconds.
On the Optimality of Kernel-Embedding Based Goodness-of-Fit Tests
Authors: Krishnakumar Balasubramanian, Tong Li, Ming Yuan
Date: 2017-09-24 00:00:00

The reproducing kernel Hilbert space (RKHS) embedding of distributions offers
a general and flexible framework for testing problems in arbitrary domains and
has attracted considerable amount of attention in recent years. To gain
insights into their operating characteristics, we study here the statistical
performance of such approaches within a minimax framework. Focusing on the case
of goodness-of-fit tests, our analyses show that a vanilla version of the
kernel-embedding based test could be suboptimal, and suggest a simple remedy by
moderating the embedding. We prove that the moderated approach provides optimal
tests for a wide range of deviations from the null and can also be made
adaptive over a large collection of interpolation spaces. Numerical experiments
are presented to further demonstrate the merits of our approach.
Scan $B$-Statistic for Kernel Change-Point Detection
Authors: Shuang Li, Yao Xie, Hanjun Dai, Le Song
Date: 2015-07-05 00:00:00
Tasks: Change Point Detection

Detecting the emergence of an abrupt change-point is a classic problem in
statistics and machine learning. Kernel-based nonparametric statistics have
been used for this task which enjoy fewer assumptions on the distributions than
the parametric approach and can handle high-dimensional data. In this paper we
focus on the scenario when the amount of background data is large, and propose
two related computationally efficient kernel-based statistics for change-point
detection, which are inspired by the recently developed $B$-statistics. A novel
theoretical result of the paper is the characterization of the tail probability
of these statistics using the change-of-measure technique, which focuses on
characterizing the tail of the detection statistics rather than obtaining its
asymptotic distribution under the null distribution. Such approximations are
crucial to control the false alarm rate, which corresponds to the significance
level in offline change-point detection and the average-run-length in online
change-point detection. Our approximations are shown to be highly accurate.
Thus, they provide a convenient way to find detection thresholds for both
offline and online cases without the need to resort to the more expensive
simulations or bootstrapping. We show that our methods perform well on both
synthetic data and real data.
Adversarial Learning for Neural Dialogue Generation
Authors: Jiwei Li, Will Monroe, Tianlin Shi, Sébastien Jean, Alan Ritter, Dan Jurafsky
Date: 2017-01-23 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Dialogue Evaluation, Dialogue Generation, Reinforcement Learning, Reinforcement Learning (RL)

In this paper, drawing intuition from the Turing test, we propose using
adversarial training for open-domain dialogue generation: the system is trained
to produce sequences that are indistinguishable from human-generated dialogue
utterances. We cast the task as a reinforcement learning (RL) problem where we
jointly train two systems, a generative model to produce response sequences,
and a discriminator---analagous to the human evaluator in the Turing test--- to
distinguish between the human-generated dialogues and the machine-generated
ones. The outputs from the discriminator are then used as rewards for the
generative model, pushing the system to generate dialogues that mostly resemble
human dialogues.
  In addition to adversarial training we describe a model for adversarial {\em
evaluation} that uses success in fooling an adversary as a dialogue evaluation
metric, while avoiding a number of potential pitfalls. Experimental results on
several metrics, including adversarial evaluation, demonstrate that the
adversarially-trained system generates higher-quality responses than previous
baselines.
A Question Answering Approach to Emotion Cause Extraction
Authors: Lin Gui, Jiannan Hu, Yulan He, Ruifeng Xu, Qin Lu, Jiachen Du
Date: 2017-08-18 00:00:00
Tasks: Emotion Cause Extraction, Emotion Classification, General Classification, Question Answering, Reading Comprehension

Emotion cause extraction aims to identify the reasons behind a certain
emotion expressed in text. It is a much more difficult task compared to emotion
classification. Inspired by recent advances in using deep memory networks for
question answering (QA), we propose a new approach which considers emotion
cause identification as a reading comprehension task in QA. Inspired by
convolutional neural networks, we propose a new mechanism to store relevant
context in different memory slots to model context information. Our proposed
approach can extract both word level sequence features and lexical features.
Performance evaluation shows that our method achieves the state-of-the-art
performance on a recently released emotion cause dataset, outperforming a
number of competitive baselines by at least 3.01% in F-measure.
Neural Net Models for Open-Domain Discourse Coherence
Authors: Jiwei Li, Dan Jurafsky
Date: 2016-06-05 00:00:00
Tasks: Text Generation

Discourse coherence is strongly associated with text quality, making it
important to natural language generation and understanding. Yet existing models
of coherence focus on measuring individual aspects of coherence (lexical
overlap, rhetorical structure, entity centering) in narrow domains.
  In this paper, we describe domain-independent neural models of discourse
coherence that are capable of measuring multiple aspects of coherence in
existing sentences and can maintain coherence while generating new sentences.
We study both discriminative models that learn to distinguish coherent from
incoherent discourse, and generative models that produce coherent text,
including a novel neural latent-variable Markovian generative model that
captures the latent discourse dependencies between sentences in a text.
  Our work achieves state-of-the-art performance on multiple coherence
evaluations, and marks an initial step in generating coherent texts given
discourse contexts.
Weather Forecasting Error in Solar Energy Forecasting
Authors: Hossein Sangrody, Morteza Sarailoo, Ning Zhou, Nhu Tran, Mahdi Motalleb, Elham Foruzan
Date: 2017-09-24 00:00:00
Tasks: Weather Forecasting

As renewable distributed energy resources (DERs) penetrate the power grid at
an accelerating speed, it is essential for operators to have accurate solar
photovoltaic (PV) energy forecasting for efficient operations and planning.
Generally, observed weather data are applied in the solar PV generation
forecasting model while in practice the energy forecasting is based on
forecasted weather data. In this paper, a study on the uncertainty in weather
forecasting for the most commonly used weather variables is presented. The
forecasted weather data for six days ahead is compared with the observed data
and the results of analysis are quantified by statistical metrics. In addition,
the most influential weather predictors in energy forecasting model are
selected. The performance of historical and observed weather data errors is
assessed using a solar PV generation forecasting model. Finally, a sensitivity
test is performed to identify the influential weather variables whose accurate
values can significantly improve the results of energy forecasting.
Simultaneous Facial Landmark Detection, Pose and Deformation Estimation under Facial Occlusion
Authors: Yue Wu, Chao Gou, Qiang Ji
Date: 2017-09-23 00:00:00
Proceeding: CVPR 2017 7
Tasks: Facial Landmark Detection, Head Pose Estimation, Pose Estimation

Facial landmark detection, head pose estimation, and facial deformation
analysis are typical facial behavior analysis tasks in computer vision. The
existing methods usually perform each task independently and sequentially,
ignoring their interactions. To tackle this problem, we propose a unified
framework for simultaneous facial landmark detection, head pose estimation, and
facial deformation analysis, and the proposed model is robust to facial
occlusion. Following a cascade procedure augmented with model-based head pose
estimation, we iteratively update the facial landmark locations, facial
occlusion, head pose and facial de- formation until convergence. The
experimental results on benchmark databases demonstrate the effectiveness of
the proposed method for simultaneous facial landmark detection, head pose and
facial deformation estimation, even if the images are under facial occlusion.
Constrained Joint Cascade Regression Framework for Simultaneous Facial Action Unit Recognition and Facial Landmark Detection
Authors: Yue Wu, Qiang Ji
Date: 2017-09-23 00:00:00
Proceeding: CVPR 2016 6
Tasks: Facial Action Unit Detection, Facial Landmark Detection, regression

Cascade regression framework has been shown to be effective for facial
landmark detection. It starts from an initial face shape and gradually predicts
the face shape update from the local appearance features to generate the facial
landmark locations in the next iteration until convergence. In this paper, we
improve upon the cascade regression framework and propose the Constrained Joint
Cascade Regression Framework (CJCRF) for simultaneous facial action unit
recognition and facial landmark detection, which are two related face analysis
tasks, but are seldomly exploited together. In particular, we first learn the
relationships among facial action units and face shapes as a constraint. Then,
in the proposed constrained joint cascade regression framework, with the help
from the constraint, we iteratively update the facial landmark locations and
the action unit activation probabilities until convergence. Experimental
results demonstrate that the intertwined relationships of facial action units
and face shapes boost the performances of both facial action unit recognition
and facial landmark detection. The experimental results also demonstrate the
effectiveness of the proposed method comparing to the state-of-the-art works.
Constrained Deep Transfer Feature Learning and its Applications
Authors: Yue Wu, Qiang Ji
Date: 2017-09-23 00:00:00
Proceeding: CVPR 2016 6
Tasks: Facial Expression Recognition, Facial Expression Recognition (FER), Transfer Learning

Feature learning with deep models has achieved impressive results for both
data representation and classification for various vision tasks. Deep feature
learning, however, typically requires a large amount of training data, which
may not be feasible for some application domains. Transfer learning can be one
of the approaches to alleviate this problem by transferring data from data-rich
source domain to data-scarce target domain. Existing transfer learning methods
typically perform one-shot transfer learning and often ignore the specific
properties that the transferred data must satisfy. To address these issues, we
introduce a constrained deep transfer feature learning method to perform
simultaneous transfer learning and feature learning by performing transfer
learning in a progressively improving feature space iteratively in order to
better narrow the gap between the target domain and the source domain for
effective transfer of the data from the source domain to target domain.
Furthermore, we propose to exploit the target domain knowledge and incorporate
such prior knowledge as a constraint during transfer learning to ensure that
the transferred data satisfies certain properties of the target domain. To
demonstrate the effectiveness of the proposed constrained deep transfer feature
learning method, we apply it to thermal feature learning for eye detection by
transferring from the visible domain. We also applied the proposed method for
cross-view facial expression recognition as a second application. The
experimental results demonstrate the effectiveness of the proposed method for
both applications.
Robust Facial Landmark Detection under Significant Head Poses and Occlusion
Authors: Yue Wu, Qiang Ji
Date: 2017-09-23 00:00:00
Proceeding: ICCV 2015 12
Tasks: Facial Landmark Detection, Occlusion Estimation, regression

There have been tremendous improvements for facial landmark detection on
general "in-the-wild" images. However, it is still challenging to detect the
facial landmarks on images with severe occlusion and images with large head
poses (e.g. profile face). In fact, the existing algorithms usually can only
handle one of them. In this work, we propose a unified robust cascade
regression framework that can handle both images with severe occlusion and
images with large head poses. Specifically, the method iteratively predicts the
landmark occlusions and the landmark locations. For occlusion estimation,
instead of directly predicting the binary occlusion vectors, we introduce a
supervised regression method that gradually updates the landmark visibility
probabilities in each iteration to achieve robustness. In addition, we
explicitly add occlusion pattern as a constraint to improve the performance of
occlusion prediction. For landmark detection, we combine the landmark
visibility probabilities, the local appearances, and the local shapes to
iteratively update their positions. The experimental results show that the
proposed method is significantly better than state-of-the-art works on images
with severe occlusion and images with large head poses. It is also comparable
to other methods on general "in-the-wild" images.
Self-supervised learning: When is fusion of the primary and secondary sensor cue useful?
Authors: G. C. H. E. de Croon
Date: 2017-09-23 00:00:00
Tasks: Self-Supervised Learning

Self-supervised learning (SSL) is a reliable learning mechanism in which a
robot enhances its perceptual capabilities. Typically, in SSL a trusted,
primary sensor cue provides supervised training data to a secondary sensor cue.
In this article, a theoretical analysis is performed on the fusion of the
primary and secondary cue in a minimal model of SSL. A proof is provided that
determines the specific conditions under which it is favorable to perform
fusion. In short, it is favorable when (i) the prior on the target value is
strong or (ii) the secondary cue is sufficiently accurate. The theoretical
findings are validated with computational experiments. Subsequently, a
real-world case study is performed to investigate if fusion in SSL is also
beneficial when assumptions of the minimal model are not met. In particular, a
flying robot learns to map pressure measurements to sonar height measurements
and then fuses the two, resulting in better height estimation. Fusion is also
beneficial in the opposite case, when pressure is the primary cue. The analysis
and results are encouraging to study SSL fusion also for other robots and
sensors.
A probabilistic data-driven model for planar pushing
Authors: Maria Bauza, Alberto Rodriguez
Date: 2017-04-10 00:00:00
Tasks: Gaussian Processes, model

This paper presents a data-driven approach to model planar pushing
interaction to predict both the most likely outcome of a push and its expected
variability. The learned models rely on a variation of Gaussian processes with
input-dependent noise called Variational Heteroscedastic Gaussian processes
(VHGP) that capture the mean and variance of a stochastic function. We show
that we can learn accurate models that outperform analytical models after less
than 100 samples and saturate in performance with less than 1000 samples. We
validate the results against a collected dataset of repeated trajectories, and
use the learned models to study questions such as the nature of the variability
in pushing, and the validity of the quasi-static assumption.
Orthogonalized ALS: A Theoretically Principled Tensor Decomposition Algorithm for Practical Use
Authors: Vatsal Sharan, Gregory Valiant
Date: 2017-03-06 00:00:00
Proceeding: ICML 2017 8
Tasks: Tensor Decomposition, Word Embeddings

The popular Alternating Least Squares (ALS) algorithm for tensor
decomposition is efficient and easy to implement, but often converges to poor
local optima---particularly when the weights of the factors are non-uniform. We
propose a modification of the ALS approach that is as efficient as standard
ALS, but provably recovers the true factors with random initialization under
standard incoherence assumptions on the factors of the tensor. We demonstrate
the significant practical superiority of our approach over traditional ALS for
a variety of tasks on synthetic data---including tensor factorization on exact,
noisy and over-complete tensors, as well as tensor completion---and for
computing word embeddings from a third-order word tri-occurrence tensor.
Robust Probabilistic Modeling with Bayesian Data Reweighting
Authors: Yixin Wang, Alp Kucukelbir, David M. Blei
Date: 2016-06-13 00:00:00
Proceeding: ICML 2017 8

Probabilistic models analyze data by relying on a set of assumptions. Data
that exhibit deviations from these assumptions can undermine inference and
prediction quality. Robust models offer protection against mismatch between a
model's assumptions and reality. We propose a way to systematically detect and
mitigate mismatch of a large class of probabilistic models. The idea is to
raise the likelihood of each observation to a weight and then to infer both the
latent variables and the weights from data. Inferring the weights allows a
model to identify observations that match its assumptions and down-weight
others. This enables robust inference and improves predictive accuracy. We
study four different forms of mismatch with reality, ranging from missing
latent groups to structure misspecification. A Poisson factorization analysis
of the Movielens 1M dataset shows the benefits of this approach in a practical
scenario.
Nonconvex Low-Rank Matrix Recovery with Arbitrary Outliers via Median-Truncated Gradient Descent
Authors: Yuanxin Li, Yuejie Chi, Huishuai Zhang, Yingbin Liang
Date: 2017-09-23 00:00:00

Recent work has demonstrated the effectiveness of gradient descent for
directly recovering the factors of low-rank matrices from random linear
measurements in a globally convergent manner when initialized properly.
However, the performance of existing algorithms is highly sensitive in the
presence of outliers that may take arbitrary values. In this paper, we propose
a truncated gradient descent algorithm to improve the robustness against
outliers, where the truncation is performed to rule out the contributions of
samples that deviate significantly from the {\em sample median} of measurement
residuals adaptively in each iteration. We demonstrate that, when initialized
in a basin of attraction close to the ground truth, the proposed algorithm
converges to the ground truth at a linear rate for the Gaussian measurement
model with a near-optimal number of measurements, even when a constant fraction
of the measurements are arbitrarily corrupted. In addition, we propose a new
truncated spectral method that ensures an initialization in the basin of
attraction at slightly higher requirements. We finally provide numerical
experiments to validate the superior performance of the proposed approach.