pwc-archive/papers-with-abstracts huggingface dataset:
Data Association with Gaussian Processes
Authors: Markus Kaiser, Clemens Otte, Thomas Runkler, Carl Henrik Ek
Date: 2018-10-16 00:00:00
Tasks: Gaussian Processes, Variational Inference

The data association problem is concerned with separating data coming from different generating processes, for example when data come from different data sources, contain significant noise, or exhibit multimodality. We present a fully Bayesian approach to this problem. Our model is capable of simultaneously solving the data association problem and the induced supervised learning problems. Underpinning our approach is the use of Gaussian process priors to encode the structure of both the data and the data associations. We present an efficient learning scheme based on doubly stochastic variational inference and discuss how it can be applied to deep Gaussian process priors.
Strategies for Language Identification in Code-Mixed Low Resource Languages
Authors: Soumil Mandal, Sankalp Sanand
Date: 2018-10-16 00:00:00
Tasks: Language Identification

In recent years, substantial work has been done on language tagging of
code-mixed data, but most of them use large amounts of data to build their
models. In this article, we present three strategies to build a word level
language tagger for code-mixed data using very low resources. Each of them
secured an accuracy higher than our baseline model, and the best performing
system got an accuracy around 91%. Combining all, the ensemble system achieved
an accuracy of around 92.6%.
Hunting for Discriminatory Proxies in Linear Regression Models
Authors: Samuel Yeom, Anupam Datta, Matt Fredrikson
Date: 2018-10-16 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Attribute, regression

A machine learning model may exhibit discrimination when used to make
decisions involving people. One potential cause for such outcomes is that the
model uses a statistical proxy for a protected demographic attribute. In this
paper we formulate a definition of proxy use for the setting of linear
regression and present algorithms for detecting proxies. Our definition follows
recent work on proxies in classification models, and characterizes a model's
constituent behavior that: 1) correlates closely with a protected random
variable, and 2) is causally influential in the overall behavior of the model.
We show that proxies in linear regression models can be efficiently identified
by solving a second-order cone program, and further extend this result to
account for situations where the use of a certain input variable is justified
as a `business necessity'. Finally, we present empirical results on two law
enforcement datasets that exhibit varying degrees of racial disparity in
prediction outcomes, demonstrating that proxies shed useful light on the causes
of discriminatory behavior in models.
Metropolis-Hastings view on variational inference and adversarial training
Authors: Kirill Neklyudov, Evgenii Egorov, Pavel Shvechikov, Dmitry Vetrov
Date: 2018-10-16 00:00:00
Proceeding: ICLR 2019 5
Tasks: Bayesian Inference, Variational Inference

A significant part of MCMC methods can be considered as the Metropolis-Hastings (MH) algorithm with different proposal distributions. From this point of view, the problem of constructing a sampler can be reduced to the question - how to choose a proposal for the MH algorithm? To address this question, we propose to learn an independent sampler that maximizes the acceptance rate of the MH algorithm, which, as we demonstrate, is highly related to the conventional variational inference. For Bayesian inference, the proposed method compares favorably against alternatives to sample from the posterior distribution. Under the same approach, we step beyond the scope of classical MCMC methods and deduce the Generative Adversarial Networks (GANs) framework from scratch, treating the generator as the proposal and the discriminator as the acceptance test. On real-world datasets, we improve Frechet Inception Distance and Inception Score, using different GANs as a proposal distribution for the MH algorithm. In particular, we demonstrate improvements of recently proposed BigGAN model on ImageNet.
Subword Semantic Hashing for Intent Classification on Small Datasets
Authors: Kumar Shridhar, Ayushman Dash, Amit Sahu, Gustav Grund Pihlgren, Pedro Alonso, Vinaychandran Pondenkandath, Gyorgy Kovacs, Foteini Simistira, Marcus Liwicki
Date: 2018-10-16 00:00:00
Tasks: Chatbot, General Classification, intent-classification, Intent Classification, Text Classification, Word Embeddings

In this paper, we introduce the use of Semantic Hashing as embedding for the task of Intent Classification and achieve state-of-the-art performance on three frequently used benchmarks. Intent Classification on a small dataset is a challenging task for data-hungry state-of-the-art Deep Learning based systems. Semantic Hashing is an attempt to overcome such a challenge and learn robust text classification. Current word embedding based are dependent on vocabularies. One of the major drawbacks of such methods is out-of-vocabulary terms, especially when having small training datasets and using a wider vocabulary. This is the case in Intent Classification for chatbots, where typically small datasets are extracted from internet communication. Two problems arise by the use of internet communication. First, such datasets miss a lot of terms in the vocabulary to use word embeddings efficiently. Second, users frequently make spelling errors. Typically, the models for intent classification are not trained with spelling errors and it is difficult to think about ways in which users will make mistakes. Models depending on a word vocabulary will always face such issues. An ideal classifier should handle spelling errors inherently. With Semantic Hashing, we overcome these challenges and achieve state-of-the-art results on three datasets: AskUbuntu, Chatbot, and Web Application. Our benchmarks are available online: https://github.com/kumar-shridhar/Know-Your-Intent
Joint Nonparametric Precision Matrix Estimation with Confounding
Authors: Sinong Geng, Mladen Kolar, Oluwasanmi Koyejo
Date: 2018-10-16 00:00:00

We consider the problem of precision matrix estimation where, due to extraneous confounding of the underlying precision matrix, the data are independent but not identically distributed. While such confounding occurs in many scientific problems, our approach is inspired by recent neuroscientific research suggesting that brain function, as measured using functional magnetic resonance imagine (fMRI), is susceptible to confounding by physiological noise such as breathing and subject motion. Following the scientific motivation, we propose a graphical model, which in turn motivates a joint nonparametric estimator. We provide theoretical guarantees for the consistency and the convergence rate of the proposed estimator. In addition, we demonstrate that the optimization of the proposed estimator can be transformed into a series of linear programming problems, and thus be efficiently solved in parallel. Empirical results are presented using simulated and real brain imaging data, which suggest that our approach improves precision matrix estimation, as compared to baselines, when confounding is present.
Improving Data Quality through Deep Learning and Statistical Models
Authors: Wei Dai, Kenji Yoshigoe, William Parsley
Date: 2018-10-16 00:00:00
Tasks: Deep Learning

Traditional data quality control methods are based on users experience or
previously established business rules, and this limits performance in addition
to being a very time consuming process with lower than desirable accuracy.
Utilizing deep learning, we can leverage computing resources and advanced
techniques to overcome these challenges and provide greater value to users. In
this paper, we, the authors, first review relevant works and discuss machine
learning techniques, tools, and statistical quality models. Second, we offer a
creative data quality framework based on deep learning and statistical model
algorithm for identifying data quality. Third, we use data involving salary
levels from an open dataset published by the state of Arkansas to demonstrate
how to identify outlier data and how to improve data quality via deep learning.
Finally, we discuss future work.
High-dimensional Varying Index Coefficient Models via Stein's Identity
Authors: Sen Na, Zhuoran Yang, Zhaoran Wang, Mladen Kolar
Date: 2018-10-16 00:00:00
Tasks: parameter estimation, Vocal Bursts Intensity Prediction

We study the parameter estimation problem for a varying index coefficient model in high dimensions. Unlike the most existing works that iteratively estimate the parameters and link functions, based on the generalized Stein's identity, we propose computationally efficient estimators for the high-dimensional parameters without estimating the link functions. We consider two different setups where we either estimate each sparse parameter vector individually or estimate the parameters simultaneously as a sparse or low-rank matrix. For all these cases, our estimators are shown to achieve optimal statistical rates of convergence (up to logarithmic terms in the low-rank setting). Moreover, throughout our analysis, we only require the covariate to satisfy certain moment conditions, which is significantly weaker than the Gaussian or elliptically symmetric assumptions that are commonly made in the existing literature. Finally, we conduct extensive numerical experiments to corroborate the theoretical results.
The CoNLL--SIGMORPHON 2018 Shared Task: Universal Morphological Reinflection
Authors: Ryan Cotterell, Christo Kirov, John Sylak-Glassman, Géraldine Walther, Ekaterina Vylomova, Arya D. McCarthy, Katharina Kann, Sabrina J. Mielke, Garrett Nicolai, Miikka Silfverberg, David Yarowsky, Jason Eisner, Mans Hulden
Date: 2018-10-16 00:00:00
Proceeding: CONLL 2018 10
Tasks: LEMMA, Task 2

The CoNLL--SIGMORPHON 2018 shared task on supervised learning of morphological generation featured data sets from 103 typologically diverse languages. Apart from extending the number of languages involved in earlier supervised tasks of generating inflected forms, this year the shared task also featured a new second task which asked participants to inflect words in sentential context, similar to a cloze task. This second task featured seven languages. Task 1 received 27 submissions and task 2 received 6 submissions. Both tasks featured a low, medium, and high data condition. Nearly all submissions featured a neural component and built on highly-ranked systems from the earlier 2017 shared task. In the inflection task (task 1), 41 of the 52 languages present in last year's inflection task showed improvement by the best systems in the low-resource setting. The cloze task (task 2) proved to be difficult, and few submissions managed to consistently improve upon both a simple neural baseline system and a lemma-repeating baseline.
Robust Gesture-Based Communication for Underwater Human-Robot Interaction in the context of Search and Rescue Diver Missions
Authors: Arturo Gomez Chavez, Christian A. Mueller, Tobias Doernbach, Davide Chiarella, Andreas Birk
Date: 2018-10-16 00:00:00
Tasks: General Classification, Gesture Recognition, Hand Gesture Recognition, Hand-Gesture Recognition

We propose a robust gesture-based communication pipeline for divers to
instruct an Autonomous Underwater Vehicle (AUV) to assist them in performing
high-risk tasks and helping in case of emergency. A gesture communication
language (CADDIAN) is developed, based on consolidated and standardized diver
gestures, including an alphabet, syntax and semantics, ensuring a logical
consistency. A hierarchical classification approach is introduced for hand
gesture recognition based on stereo imagery and multi-descriptor aggregation to
specifically cope with underwater image artifacts, e.g. light backscatter or
color attenuation. Once the classification task is finished, a syntax check is
performed to filter out invalid command sequences sent by the diver or
generated by errors in the classifier. Throughout this process, the diver
receives constant feedback from an underwater tablet to acknowledge or abort
the mission at any time. The objective is to prevent the AUV from executing
unnecessary, infeasible or potentially harmful motions. Experimental results
under different environmental conditions in archaeological exploration and
bridge inspection applications show that the system performs well in the field.
Multiple Interactions Made Easy (MIME): Large Scale Demonstrations Data for Imitation
Authors: Pratyusha Sharma, Lekha Mohan, Lerrel Pinto, Abhinav Gupta
Date: 2018-10-16 00:00:00
Tasks: Trajectory Prediction

In recent years, we have seen an emergence of data-driven approaches in
robotics. However, most existing efforts and datasets are either in simulation
or focus on a single task in isolation such as grasping, pushing or poking. In
order to make progress and capture the space of manipulation, we would need to
collect a large-scale dataset of diverse tasks such as pouring, opening
bottles, stacking objects etc. But how does one collect such a dataset? In this
paper, we present the largest available robotic-demonstration dataset (MIME)
that contains 8260 human-robot demonstrations over 20 different robotic tasks
(https://sites.google.com/view/mimedataset). These tasks range from the simple
task of pushing objects to the difficult task of stacking household objects.
Our dataset consists of videos of human demonstrations and kinesthetic
trajectories of robot demonstrations. We also propose to use this dataset for
the task of mapping 3rd person video features to robot trajectories.
Furthermore, we present two different approaches using this dataset and
evaluate the predicted robot trajectories against ground-truth trajectories. We
hope our dataset inspires research in multiple areas including visual
imitation, trajectory prediction, and multi-task robotic learning.
Salient Object Detection in Video using Deep Non-Local Neural Networks
Authors: Mohammad Shokri, Ahad Harati, Kimya Taba
Date: 2018-10-16 00:00:00
Tasks: object-detection, Object Detection, RGB Salient Object Detection, Saliency Detection, Salient Object Detection, Video Saliency Detection, Video Salient Object Detection

Detection of salient objects in image and video is of great importance in
many computer vision applications. In spite of the fact that the state of the
art in saliency detection for still images has been changed substantially over
the last few years, there have been few improvements in video saliency
detection. This paper investigates the use of recently introduced non-local
neural networks in video salient object detection. Non-local neural networks
are applied to capture global dependencies and hence determine the salient
objects. The effect of non-local operations is studied separately on static and
dynamic saliency detection in order to exploit both appearance and motion
features. A novel deep non-local neural network architecture is introduced for
video salient object detection and tested on two well-known datasets DAVIS and
FBMS. The experimental results show that the proposed algorithm outperforms
state-of-the-art video saliency detection methods.
Incremental learning abstract discrete planning domains and mappings to continuous perceptions
Authors: Luciano Serafini, Paolo Traverso
Date: 2018-10-16 00:00:00
Tasks: Incremental Learning, Model-based Reinforcement Learning, Reinforcement Learning

Most of the works on planning and learning, e.g., planning by (model based)
reinforcement learning, are based on two main assumptions: (i) the set of
states of the planning domain is fixed; (ii) the mapping between the
observations from the real word and the states is implicitly assumed or learned
offline, and it is not part of the planning domain. Consequently, the focus is
on learning the transitions between states. In this paper, we drop such
assumptions. We provide a formal framework in which (i) the agent can learn
dynamically new states of the planning domain; (ii) the mapping between
abstract states and the perception from the real world, represented by
continuous variables, is part of the planning domain; (iii) such mapping is
learned and updated along the "life" of the agent. We define an algorithm that
interleaves planning, acting, and learning, and allows the agent to update the
planning domain depending on how much it trusts the model w.r.t. the new
experiences learned by executing actions. We define a measure of coherence
between the planning domain and the real world as perceived by the agent. We
test our approach showing that the agent learns increasingly coherent models,
and that the system can scale to deal with models with an order of $10^6$
states.
INFODENS: An Open-source Framework for Learning Text Representations
Authors: Ahmad Taie, Raphael Rubino, Josef van Genabith
Date: 2018-10-16 00:00:00
Tasks: Feature Engineering, General Classification, Representation Learning, text-classification, Text Classification

The advent of representation learning methods enabled large performance gains
on various language tasks, alleviating the need for manual feature engineering.
While engineered representations are usually based on some linguistic
understanding and are therefore more interpretable, learned representations are
harder to interpret. Empirically studying the complementarity of both
approaches can provide more linguistic insights that would help reach a better
compromise between interpretability and performance. We present INFODENS, a
framework for studying learned and engineered representations of text in the
context of text classification tasks. It is designed to simplify the tasks of
feature engineering as well as provide the groundwork for extracting learned
features and combining both approaches. INFODENS is flexible, extensible, with
a short learning curve, and is easy to integrate with many of the available and
widely used natural language processing tools.
A Comparison of 1-D and 2-D Deep Convolutional Neural Networks in ECG Classification
Authors: Yunan Wu, Feng Yang, Ying Liu, Xuefan Zha, Shaofeng Yuan
Date: 2018-10-16 00:00:00
Tasks: ECG Classification, Electrocardiography (ECG), General Classification

Effective detection of arrhythmia is an important task in the remote
monitoring of electrocardiogram (ECG). The traditional ECG recognition depends
on the judgment of the clinicians' experience, but the results suffer from the
probability of human error due to the fatigue. To solve this problem, an ECG
signal classification method based on the images is presented to classify ECG
signals into normal and abnormal beats by using two-dimensional convolutional
neural networks (2D-CNNs). First, we compare the accuracy and robustness
between one-dimensional ECG signal input method and two-dimensional image input
method in AlexNet network. Then, in order to alleviate the overfitting problem
in two-dimensional network, we initialize AlexNet-like network with weights
trained on ImageNet, to fit the training ECG images and fine-tune the model,
and to further improve the accuracy and robustness of ECG classification. The
performance evaluated on the MIT-BIH arrhythmia database demonstrates that the
proposed method can achieve the accuracy of 98% and maintain high accuracy
within SNR range from 20 dB to 35 dB. The experiment shows that the 2D-CNNs
initialized with AlexNet weights performs better than one-dimensional signal
method without a large-scale dataset.
Real-Valued Evolutionary Multi-Modal Optimization driven by Hill-Valley Clustering
Authors: S. C. Maree, T. Alderliesten, D. Thierens, P. A. N. Bosman
Date: 2018-10-16 00:00:00
Tasks: Clustering, Evolutionary Algorithms

Model-based evolutionary algorithms (EAs) adapt an underlying search model to
features of the problem at hand, such as the linkage between problem variables.
The performance of EAs often deteriorates as multiple modes in the fitness
landscape are modelled with a unimodal search model. The number of modes is
however often unknown a priori, especially in a black-box setting, which
complicates adaptation of the search model. In this work, we focus on models
that can adapt to the multi-modality of the fitness landscape. Specifically, we
introduce Hill-Valley Clustering, a remarkably simple approach to adaptively
cluster the search space in niches, such that a single mode resides in each
niche. In each of the located niches, a core search algorithm is initialized to
optimize that niche. Combined with an EA and a restart scheme, the resulting
Hill-Valley EA (HillVallEA) is compared to current state-of-the-art niching
methods on a standard benchmark suite for multi-modal optimization. Numerical
results in terms of the detected number of global optima show that, in spite of
its simplicity, HillVallEA is competitive within the limited budget of the
benchmark suite, and shows superior performance in the long run.
Stochastic Negative Mining for Learning with Large Output Spaces
Authors: Sashank J. Reddi, Satyen Kale, Felix Yu, Dan Holtmann-Rice, Jiecao Chen, Sanjiv Kumar
Date: 2018-10-16 00:00:00
Tasks: Retrieval

We consider the problem of retrieving the most relevant labels for a given
input when the size of the output space is very large. Retrieval methods are
modeled as set-valued classifiers which output a small set of classes for each
input, and a mistake is made if the label is not in the output set. Despite its
practical importance, a statistically principled, yet practical solution to
this problem is largely missing. To this end, we first define a family of
surrogate losses and show that they are calibrated and convex under certain
conditions on the loss parameters and data distribution, thereby establishing a
statistical and analytical basis for using these losses. Furthermore, we
identify a particularly intuitive class of loss functions in the aforementioned
family and show that they are amenable to practical implementation in the large
output space setting (i.e. computation is possible without evaluating scores of
all labels) by developing a technique called Stochastic Negative Mining. We
also provide generalization error bounds for the losses in the family. Finally,
we conduct experiments which demonstrate that Stochastic Negative Mining yields
benefits over commonly used negative sampling approaches.
A Multi-stage Framework with Context Information Fusion Structure for Skin Lesion Segmentation
Authors: Yujiao Tang, Feng Yang, Shaofeng Yuan, Chang'an Zhan
Date: 2018-10-16 00:00:00
Tasks: Lesion Segmentation, Skin Lesion Segmentation

The computer-aided diagnosis (CAD) systems can highly improve the reliability
and efficiency of melanoma recognition. As a crucial step of CAD, skin lesion
segmentation has the unsatisfactory accuracy in existing methods due to large
variability in lesion appearance and artifacts. In this work, we propose a
framework employing multi-stage UNets (MS-UNet) in the auto-context scheme to
segment skin lesion accurately end-to-end. We apply two approaches to boost the
performance of MS-UNet. First, UNet is coupled with a context information
fusion structure (CIFS) to integrate the low-level and context information in
the multi-scale feature space. Second, to alleviate the gradient vanishing
problem, we use deep supervision mechanism through supervising MS-UNet by
minimizing a weighted Jaccard distance loss function. Four out of five commonly
used performance metrics, including Jaccard index and Dice coefficient, show
that our approach outperforms the state-ofthe-art deep learning based methods
on the ISBI 2016 Skin Lesion Challenge dataset.