pwc-archive/papers-with-abstracts huggingface dataset:
Super-Resolution for Hyperspectral and Multispectral Image Fusion Accounting for Seasonal Spectral Variability
Authors: Ricardo Augusto Borsoi, Tales Imbiriba, José Carlos Moreira Bermudez
Date: 2018-08-30 00:00:00
Tasks: Super-Resolution

Image fusion combines data from different heterogeneous sources to obtain more precise information about an underlying scene. Hyperspectral-multispectral (HS-MS) image fusion is currently attracting great interest in remote sensing since it allows the generation of high spatial resolution HS images, circumventing the main limitation of this imaging modality. Existing HS-MS fusion algorithms, however, neglect the spectral variability often existing between images acquired at different time instants. This time difference causes variations in spectral signatures of the underlying constituent materials due to different acquisition and seasonal conditions. This paper introduces a novel HS-MS image fusion strategy that combines an unmixing-based formulation with an explicit parametric model for typical spectral variability between the two images. Simulations with synthetic and real data show that the proposed strategy leads to a significant performance improvement under spectral variability and state-of-the-art performance otherwise.
Zero-Shot Adaptive Transfer for Conversational Language Understanding
Authors: Sungjin Lee, Rahul Jha
Date: 2018-08-29 00:00:00
Tasks: Domain Adaptation

Conversational agents such as Alexa and Google Assistant constantly need to
increase their language understanding capabilities by adding new domains. A
massive amount of labeled data is required for training each new domain. While
domain adaptation approaches alleviate the annotation cost, prior approaches
suffer from increased training time and suboptimal concept alignments. To
tackle this, we introduce a novel Zero-Shot Adaptive Transfer method for slot
tagging that utilizes the slot description for transferring reusable concepts
across domains, and enjoys efficient training without any explicit concept
alignments. Extensive experimentation over a dataset of 10 domains relevant to
our commercial personal digital assistant shows that our model outperforms
previous state-of-the-art systems by a large margin, and achieves an even
higher improvement in the low data regime.
Differentially Private Change-Point Detection
Authors: Rachel Cummings, Sara Krehbiel, Yajun Mei, Rui Tuo, Wanrong Zhang
Date: 2018-08-29 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Change Point Detection, Fault Detection

The change-point detection problem seeks to identify distributional changes
at an unknown change-point k* in a stream of data. This problem appears in many
important practical settings involving personal data, including
biosurveillance, fault detection, finance, signal detection, and security
systems. The field of differential privacy offers data analysis tools that
provide powerful worst-case privacy guarantees. We study the statistical
problem of change-point detection through the lens of differential privacy. We
give private algorithms for both online and offline change-point detection,
analyze these algorithms theoretically, and provide empirical validation of our
results.
AAD: Adaptive Anomaly Detection through traffic surveillance videos
Authors: Mohammmad Farhadi Bajestani, Seyed Soroush Heidari Rahmat Abadi, Seyed Mostafa Derakhshandeh Fard, Roozbeh Khodadadeh
Date: 2018-08-29 00:00:00
Tasks: Action Detection, Activity Detection, Anomaly Detection, Object Recognition, Optical Flow Estimation

Anomaly detection through video analysis is of great importance to detect any
anomalous vehicle/human behavior at a traffic intersection. While most existing
works use neural networks and conventional machine learning methods based on
provided dataset, we will use object recognition (Faster R-CNN) to identify
objects labels and their corresponding location in the video scene as the first
step to implement anomaly detection. Then, the optical flow will be utilized to
identify adaptive traffic flows in each region of the frame. Basically, we
propose an alternative method for unusual activity detection using an adaptive
anomaly detection framework. Compared to the baseline method described in the
reference paper, our method is more efficient and yields the comparable
accuracy.
Theoretical Linear Convergence of Unfolded ISTA and its Practical Weights and Thresholds
Authors: Xiaohan Chen, Jialin Liu, Zhangyang Wang, Wotao Yin
Date: 2018-08-29 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Compressive Sensing

In recent years, unfolding iterative algorithms as neural networks has become
an empirical success in solving sparse recovery problems. However, its
theoretical understanding is still immature, which prevents us from fully
utilizing the power of neural networks. In this work, we study unfolded ISTA
(Iterative Shrinkage Thresholding Algorithm) for sparse signal recovery. We
introduce a weight structure that is necessary for asymptotic convergence to
the true sparse signal. With this structure, unfolded ISTA can attain a linear
convergence, which is better than the sublinear convergence of ISTA/FISTA in
general cases. Furthermore, we propose to incorporate thresholding in the
network to perform support selection, which is easy to implement and able to
boost the convergence rate both theoretically and empirically. Extensive
simulations, including sparse vector recovery and a compressive sensing
experiment on real image data, corroborate our theoretical results and
demonstrate their practical usefulness. We have made our codes publicly
available: https://github.com/xchen-tamu/linear-lista-cpss.
The Impact of Preprocessing on Deep Representations for Iris Recognition on Unconstrained Environments
Authors: Luiz A. Zanlorensi, Eduardo Luz, Rayson Laroca, Alceu S. Britto Jr., Luiz S. Oliveira, David Menotti
Date: 2018-08-29 00:00:00
Tasks: Data Augmentation, Iris Recognition, Iris Segmentation, Transfer Learning

The use of iris as a biometric trait is widely used because of its high level
of distinction and uniqueness. Nowadays, one of the major research challenges
relies on the recognition of iris images obtained in visible spectrum under
unconstrained environments. In this scenario, the acquired iris are affected by
capture distance, rotation, blur, motion blur, low contrast and specular
reflection, creating noises that disturb the iris recognition systems. Besides
delineating the iris region, usually preprocessing techniques such as
normalization and segmentation of noisy iris images are employed to minimize
these problems. But these techniques inevitably run into some errors. In this
context, we propose the use of deep representations, more specifically,
architectures based on VGG and ResNet-50 networks, for dealing with the images
using (and not) iris segmentation and normalization. We use transfer learning
from the face domain and also propose a specific data augmentation technique
for iris images. Our results show that the approach using non-normalized and
only circle-delimited iris images reaches a new state of the art in the
official protocol of the NICE.II competition, a subset of the UBIRIS database,
one of the most challenging databases on unconstrained environments, reporting
an average Equal Error Rate (EER) of 13.98% which represents an absolute
reduction of about 5%.
Physically-Inspired Gaussian Process Models for Post-Transcriptional Regulation in Drosophila
Authors: Andrés F. López-Lopera, Nicolas Durrande, Mauricio A. Alvarez
Date: 2018-08-29 00:00:00
Tasks: Gaussian Processes

The regulatory process of Drosophila is thoroughly studied for understanding a great variety of biological principles. While pattern-forming gene networks are analysed in the transcription step, post-transcriptional events (e.g. translation, protein processing) play an important role in establishing protein expression patterns and levels. Since the post-transcriptional regulation of Drosophila depends on spatiotemporal interactions between mRNAs and gap proteins, proper physically-inspired stochastic models are required to study the link between both quantities. Previous research attempts have shown that using Gaussian processes (GPs) and differential equations lead to promising predictions when analysing regulatory networks. Here we aim at further investigating two types of physically-inspired GP models based on a reaction-diffusion equation where the main difference lies in where the prior is placed. While one of them has been studied previously using protein data only, the other is novel and yields a simple approach requiring only the differentiation of kernel functions. In contrast to other stochastic frameworks, discretising the spatial space is not required here. Both GP models are tested under different conditions depending on the availability of gap gene mRNA expression data. Finally, their performances are assessed on a high-resolution dataset describing the blastoderm stage of the early embryo of Drosophila melanogaster
Retrieval-Based Neural Code Generation
Authors: Shirley Anugrah Hayati, Raphael Olivier, Pravalika Avvaru, Pengcheng Yin, Anthony Tomasic, Graham Neubig
Date: 2018-08-29 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Code Generation, Retrieval, Sentence, Sentence Similarity

In models to generate program source code from natural language, representing
this code in a tree structure has been a common approach. However, existing
methods often fail to generate complex code correctly due to a lack of ability
to memorize large and complex structures. We introduce ReCode, a method based
on subtree retrieval that makes it possible to explicitly reference existing
code examples within a neural code generation model. First, we retrieve
sentences that are similar to input sentences using a dynamic-programming-based
sentence similarity scoring method. Next, we extract n-grams of action
sequences that build the associated abstract syntax tree. Finally, we increase
the probability of actions that cause the retrieved n-gram action subtree to be
in the predicted code. We show that our approach improves the performance on
two code generation tasks by up to +2.6 BLEU.
Hard Non-Monotonic Attention for Character-Level Transduction
Authors: Shijie Wu, Pamela Shapiro, Ryan Cotterell
Date: 2018-08-29 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Hard Attention, Image Captioning

Character-level string-to-string transduction is an important component of various NLP tasks. The goal is to map an input string to an output string, where the strings may be of different lengths and have characters taken from different alphabets. Recent approaches have used sequence-to-sequence models with an attention mechanism to learn which parts of the input string the model should focus on during the generation of the output string. Both soft attention and hard monotonic attention have been used, but hard non-monotonic attention has only been used in other sequence modeling tasks such as image captioning (Xu et al., 2015), and has required a stochastic approximation to compute the gradient. In this work, we introduce an exact, polynomial-time algorithm for marginalizing over the exponential number of non-monotonic alignments between two strings, showing that hard attention models can be viewed as neural reparameterizations of the classical IBM Model 1. We compare soft and hard non-monotonic attention experimentally and find that the exact algorithm significantly improves performance over the stochastic approximation and outperforms soft attention. Code is available at https://github. com/shijie-wu/neural-transducer.
The implicit fairness criterion of unconstrained learning
Authors: Lydia T. Liu, Max Simchowitz, Moritz Hardt
Date: 2018-08-29 00:00:00
Tasks: BIG-bench Machine Learning, Fairness

We clarify what fairness guarantees we can and cannot expect to follow from
unconstrained machine learning. Specifically, we characterize when
unconstrained learning on its own implies group calibration, that is, the
outcome variable is conditionally independent of group membership given the
score. We show that under reasonable conditions, the deviation from satisfying
group calibration is upper bounded by the excess risk of the learned score
relative to the Bayes optimal score function. A lower bound confirms the
optimality of our upper bound. Moreover, we prove that as the excess risk of
the learned score decreases, it strongly violates separation and independence,
two other standard fairness criteria.
  Our results show that group calibration is the fairness criterion that
unconstrained learning implicitly favors. On the one hand, this means that
calibration is often satisfied on its own without the need for active
intervention, albeit at the cost of violating other criteria that are at odds
with calibration. On the other hand, it suggests that we should be satisfied
with calibration as a fairness criterion only if we are at ease with the use of
unconstrained machine learning in a given application.
Reasoning about Actions and State Changes by Injecting Commonsense Knowledge
Authors: Niket Tandon, Bhavana Dalvi Mishra, Joel Grus, Wen-tau Yih, Antoine Bosselut, Peter Clark
Date: 2018-08-29 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Reading Comprehension, Structured Prediction

Comprehending procedural text, e.g., a paragraph describing photosynthesis,
requires modeling actions and the state changes they produce, so that questions
about entities at different timepoints can be answered. Although several recent
systems have shown impressive progress in this task, their predictions can be
globally inconsistent or highly improbable. In this paper, we show how the
predicted effects of actions in the context of a paragraph can be improved in
two ways: (1) by incorporating global, commonsense constraints (e.g., a
non-existent entity cannot be destroyed), and (2) by biasing reading with
preferences from large-scale corpora (e.g., trees rarely move). Unlike earlier
methods, we treat the problem as a neural structured prediction task, allowing
hard and soft constraints to steer the model away from unlikely predictions. We
show that the new model significantly outperforms earlier systems on a
benchmark dataset for procedural text comprehension (+8% relative gain), and
that it also avoids some of the nonsensical predictions that earlier systems
make.
Learning a Policy for Opportunistic Active Learning
Authors: Aishwarya Padmakumar, Peter Stone, Raymond J. Mooney
Date: 2018-08-29 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Active Learning, Object, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Retrieval

Active learning identifies data points to label that are expected to be the
most useful in improving a supervised model. Opportunistic active learning
incorporates active learning into interactive tasks that constrain possible
queries during interactions. Prior work has shown that opportunistic active
learning can be used to improve grounding of natural language descriptions in
an interactive object retrieval task. In this work, we use reinforcement
learning for such an object retrieval task, to learn a policy that effectively
trades off task completion with model improvement that would benefit future
tasks.
Correcting Length Bias in Neural Machine Translation
Authors: Kenton Murray, David Chiang
Date: 2018-08-29 00:00:00
Proceeding: WS 2018 10
Tasks: Machine Translation, NMT, Translation

We study two problems in neural machine translation (NMT). First, in beam
search, whereas a wider beam should in principle help translation, it often
hurts NMT. Second, NMT has a tendency to produce translations that are too
short. Here, we argue that these problems are closely related and both rooted
in label bias. We show that correcting the brevity problem almost eliminates
the beam problem; we compare some commonly-used methods for doing this, finding
that a simple per-word reward works well; and we introduce a simple and quick
way to tune this reward using the perceptron algorithm.
Interpretable Intuitive Physics Model
Authors: Tian Ye, Xiaolong Wang, James Davidson, Abhinav Gupta
Date: 2018-08-29 00:00:00
Proceeding: ECCV 2018 9
Tasks: Friction, model

Humans have a remarkable ability to use physical commonsense and predict the
effect of collisions. But do they understand the underlying factors? Can they
predict if the underlying factors have changed? Interestingly, in most cases
humans can predict the effects of similar collisions with different conditions
such as changes in mass, friction, etc. It is postulated this is primarily
because we learn to model physics with meaningful latent variables. This does
not imply we can estimate the precise values of these meaningful variables
(estimate exact values of mass or friction). Inspired by this observation, we
propose an interpretable intuitive physics model where specific dimensions in
the bottleneck layers correspond to different physical properties. In order to
demonstrate that our system models these underlying physical properties, we
train our model on collisions of different shapes (cube, cone, cylinder,
spheres etc.) and test on collisions of unseen combinations of shapes.
Furthermore, we demonstrate our model generalizes well even when similar scenes
are simulated with different underlying properties.
Grammar Induction with Neural Language Models: An Unusual Replication
Authors: Phu Mon Htut, Kyunghyun Cho, Samuel R. Bowman
Date: 2018-08-29 00:00:00
Proceeding: EMNLP (ACL) 2018 10
Tasks: Constituency Parsing, Language Modeling, Language Modelling

A substantial thread of recent work on latent tree learning has attempted to
develop neural network models with parse-valued latent variables and train them
on non-parsing tasks, in the hope of having them discover interpretable tree
structure. In a recent paper, Shen et al. (2018) introduce such a model and
report near-state-of-the-art results on the target task of language modeling,
and the first strong latent tree learning result on constituency parsing. In an
attempt to reproduce these results, we discover issues that make the original
results hard to trust, including tuning and even training on what is
effectively the test set. Here, we attempt to reproduce these results in a fair
experiment and to extend them to two new datasets. We find that the results of
this work are robust: All variants of the model under study outperform all
latent tree learning baselines, and perform competitively with symbolic grammar
induction systems. We find that this model represents the first empirical
success for latent tree learning, and that neural network language modeling
warrants further study as a setting for grammar induction.
Centroid estimation based on symmetric KL divergence for Multinomial text classification problem
Authors: Jiangning Chen, Heinrich Matzinger, Haoyan Zhai, Mi Zhou
Date: 2018-08-29 00:00:00
Tasks: General Classification, text-classification, Text Classification

We define a new method to estimate centroid for text classification based on
the symmetric KL-divergence between the distribution of words in training
documents and their class centroids. Experiments on several standard data sets
indicate that the new method achieves substantial improvements over the
traditional classifiers.
Semi-Metrification of the Dynamic Time Warping Distance
Authors: Brijnesh J. Jain
Date: 2018-08-29 00:00:00
Tasks: Dynamic Time Warping

The dynamic time warping (dtw) distance fails to satisfy the triangle
inequality and the identity of indiscernibles. As a consequence, the
dtw-distance is not warping-invariant, which in turn results in peculiarities
in data mining applications. This article converts the dtw-distance to a
semi-metric and shows that its canonical extension is warping-invariant.
Empirical results indicate that the nearest-neighbor classifier in the proposed
semi-metric space performs comparably to the same classifier in the standard
dtw-space. To overcome the undesirable peculiarities of dtw-spaces, this result
suggests to further explore the semi-metric space for data mining applications.
Symbolic regression based genetic approximations of the Colebrook equation for flow friction
Authors: Pavel Praks, Dejan Brkic
Date: 2018-08-29 00:00:00
Tasks: Friction, regression, Symbolic Regression

Widely used in hydraulics, the Colebrook equation for flow friction relates
implicitly to the input parameters; the Reynolds number, and the relative
roughness of inner pipe surface, with the output unknown parameter; the flow
friction factor. In this paper, a few explicit approximations to the Colebrook
equation are generated using the ability of artificial intelligence to make
inner patterns to connect input and output parameters in explicit way not
knowing their nature or the physical law that connects them, but only knowing
raw numbers. The fact that the used genetic programming tool does not know the
structure of the Colebrook equation which is based on computationally expensive
logarithmic law, is used to obtain better structure of the approximations which
is less demanding for calculation but also enough accurate. All generated
approximations are with low computational cost because they contain a limited
number of logarithmic forms used although for normalization of input parameters
or for acceleration, but they are also sufficiently accurate. The relative
error regarding the friction factor in best case is up to 0.13% with only two
logarithmic forms used. As the second logarithm can be accurately approximated
by the Pade approximation, practically the same error is obtained also using
only one logarithm.
Analyze Unstructured Data Patterns for Conceptual Representation
Authors: Aboubakr Aqle, Dena Al-Thani, Ali Jaoua
Date: 2018-08-29 00:00:00

Online news media provides aggregated news and stories from different sources
all over the world and up-to-date news coverage. The main goal of this study is
to have a solution that considered as a homogeneous source for the news and to
represent the news in a new conceptual framework. Furthermore, the user can
easily find different updated news in a fast way through the designed
interface. The Mobile App implementation is based on modeling the multi-level
conceptual analysis discipline. Discovering main concepts of any domain is
captured from the hidden unstructured data that are analyzed by the proposed
solution. Concepts are discovered through analyzing data patterns to be
structured into a tree-based interface for easy navigation for the end user,
through the discovered news concepts. Our final experiment results showing that
analyzing the news before displaying to the end-user and restructuring the
final output in a conceptual multilevel structure, that producing new display
frame for the end user to find the related information to his interest.
Understanding Latent Factors Using a GWAP
Authors: Johannes Kunkel, Benedikt Loepp, Jürgen Ziegler
Date: 2018-08-29 00:00:00
Tasks: Recommendation Systems

Recommender systems relying on latent factor models often appear as black
boxes to their users. Semantic descriptions for the factors might help to
mitigate this problem. Achieving this automatically is, however, a
non-straightforward task due to the models' statistical nature. We present an
output-agreement game that represents factors by means of sample items and
motivates players to create such descriptions. A user study shows that the
collected output actually reflects real-world characteristics of the factors.