pwc-archive/papers-with-abstracts huggingface dataset:
Reinforcement Learning based Curriculum Optimization for Neural Machine Translation
Authors: Gaurav Kumar, George Foster, Colin Cherry, Maxim Krikun
Date: 2019-02-28 00:00:00
Proceeding: NAACL 2019 6
Tasks: Machine Translation, NMT, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Sentence, Translation

We consider the problem of making efficient use of heterogeneous training
data in neural machine translation (NMT). Specifically, given a training
dataset with a sentence-level feature such as noise, we seek an optimal
curriculum, or order for presenting examples to the system during training. Our
curriculum framework allows examples to appear an arbitrary number of times,
and thus generalizes data weighting, filtering, and fine-tuning schemes. Rather
than relying on prior knowledge to design a curriculum, we use reinforcement
learning to learn one automatically, jointly with the NMT system, in the course
of a single training run. We show that this approach can beat uniform and
filtering baselines on Paracrawl and WMT English-to-French datasets by up to
+3.4 BLEU, and match the performance of a hand-designed, state-of-the-art
curriculum.
SPDA: Superpixel-based Data Augmentation for Biomedical Image Segmentation
Authors: Yizhe Zhang, Lin Yang, Hao Zheng, Peixian Liang, Colleen Mangold, Raquel G. Loreto, David. P. Hughes, Danny Z. Chen
Date: 2019-02-28 00:00:00
Tasks: Data Augmentation, Deep Learning, Image Segmentation, Semantic Segmentation

Supervised training a deep neural network aims to "teach" the network to
mimic human visual perception that is represented by image-and-label pairs in
the training data. Superpixelized (SP) images are visually perceivable to
humans, but a conventionally trained deep learning model often performs poorly
when working on SP images. To better mimic human visual perception, we think it
is desirable for the deep learning model to be able to perceive not only raw
images but also SP images. In this paper, we propose a new superpixel-based
data augmentation (SPDA) method for training deep learning models for
biomedical image segmentation. Our method applies a superpixel generation
scheme to all the original training images to generate superpixelized images.
The SP images thus obtained are then jointly used with the original training
images to train a deep learning model. Our experiments of SPDA on four
biomedical image datasets show that SPDA is effective and can consistently
improve the performance of state-of-the-art fully convolutional networks for
biomedical image segmentation in 2D and 3D images. Additional studies also
demonstrate that SPDA can practically reduce the generalization gap.
Catalyst.RL: A Distributed Framework for Reproducible RL Research
Authors: Sergey Kolesnikov, Oleksii Hrinchuk
Date: 2019-02-28 00:00:00
Tasks: continuous-control, Continuous Control, Deep Reinforcement Learning, Reinforcement Learning

Despite the recent progress in deep reinforcement learning field (RL), and,
arguably because of it, a large body of work remains to be done in reproducing
and carefully comparing different RL algorithms. We present catalyst.RL, an
open source framework for RL research with a focus on reproducibility and
flexibility. Main features of our library include large-scale asynchronous
distributed training, easy-to-use configuration files with the complete list of
hyperparameters for the particular experiments, efficient implementations of
various RL algorithms and auxiliary tricks, such as frame stacking, n-step
returns, value distributions, etc. To vindicate the usefulness of our
framework, we evaluate it on a range of benchmarks in a continuous control, as
well as on the task of developing a controller to enable a
physiologically-based human model with a prosthetic leg to walk and run. The
latter task was introduced at NeurIPS 2018 AI for Prosthetics Challenge, where
our team took the 3rd place, capitalizing on the ability of catalyst.RL to
train high-quality and sample-efficient RL agents.
Novel and Efficient Approximations for Zero-One Loss of Linear Classifiers
Authors: Hiva Ghanbari, Minhan Li, Katya Scheinberg
Date: 2019-02-28 00:00:00

The predictive quality of machine learning models is typically measured in
terms of their (approximate) expected prediction accuracy or the so-called Area
Under the Curve (AUC). Minimizing the reciprocals of these measures are the
goals of supervised learning. However, when the models are constructed by the
means of empirical risk minimization (ERM), surrogate functions such as the
logistic loss or hinge loss are optimized instead. In this work, we show that
in the case of linear predictors, the expected error and the expected ranking
loss can be effectively approximated by smooth functions whose closed form
expressions and those of their first (and second) order derivatives depend on
the first and second moments of the data distribution, which can be
precomputed. Hence, the complexity of an optimization algorithm applied to
these functions does not depend on the size of the training data. These
approximation functions are derived under the assumption that the output of the
linear classifier for a given data set has an approximately normal
distribution. We argue that this assumption is significantly weaker than the
Gaussian assumption on the data itself and we support this claim by
demonstrating that our new approximation is quite accurate on data sets that
are not necessarily Gaussian. We present computational results that show that
our proposed approximations and related optimization algorithms can produce
linear classifiers with similar or better test accuracy or AUC, than those
obtained using state-of-the-art approaches, in a fraction of the time.
Large-Scale Object Mining for Object Discovery from Unlabeled Video
Authors: Aljosa Osep, Paul Voigtlaender, Jonathon Luiten, Stefan Breuers, Bastian Leibe
Date: 2019-02-28 00:00:00
Tasks: Clustering, Object, Object Discovery

This paper addresses the problem of object discovery from unlabeled driving
videos captured in a realistic automotive setting. Identifying recurring object
categories in such raw video streams is a very challenging problem. Not only do
object candidates first have to be localized in the input images, but many
interesting object categories occur relatively infrequently. Object discovery
will therefore have to deal with the difficulties of operating in the long tail
of the object distribution. We demonstrate the feasibility of performing fully
automatic object discovery in such a setting by mining object tracks using a
generic object tracker. In order to facilitate further research in object
discovery, we release a collection of more than 360,000 automatically mined
object tracks from 10+ hours of video data (560,000 frames). We use this
dataset to evaluate the suitability of different feature representations and
clustering strategies for object discovery.
Interpreting, axiomatising and representing coherent choice functions in terms of desirability
Authors: Jasper De Bock, Gert de Cooman
Date: 2019-02-28 00:00:00
Tasks: Decision Making

Choice functions constitute a simple, direct and very general mathematical framework for modelling choice under uncertainty. In particular, they are able to represent the set-valued choices that appear in imprecise-probabilistic decision making. We provide these choice functions with a clear interpretation in terms of desirability, use this interpretation to derive a set of basic coherence axioms, and show that this notion of coherence leads to a representation in terms of sets of strict preference orders. By imposing additional properties such as totality, the mixing property and Archimedeanity, we obtain representation in terms of sets of strict total orders, lexicographic probability systems, coherent lower previsions or linear previsions.
Deep learning in bioinformatics: introduction, application, and perspective in big data era
Authors: Yu Li, Chao Huang, Lizhong Ding, Zhongxiao Li, Yijie Pan, Xin Gao
Date: 2019-02-28 00:00:00
Tasks: Deep Learning

Deep learning, which is especially formidable in handling big data, has
achieved great success in various fields, including bioinformatics. With the
advances of the big data era in biology, it is foreseeable that deep learning
will become increasingly important in the field and will be incorporated in
vast majorities of analysis pipelines. In this review, we provide both the
exoteric introduction of deep learning, and concrete examples and
implementations of its representative applications in bioinformatics. We start
from the recent achievements of deep learning in the bioinformatics field,
pointing out the problems which are suitable to use deep learning. After that,
we introduce deep learning in an easy-to-understand fashion, from shallow
neural networks to legendary convolutional neural networks, legendary recurrent
neural networks, graph neural networks, generative adversarial networks,
variational autoencoder, and the most recent state-of-the-art architectures.
After that, we provide eight examples, covering five bioinformatics research
directions and all the four kinds of data type, with the implementation written
in Tensorflow and Keras. Finally, we discuss the common issues, such as
overfitting and interpretability, that users will encounter when adopting deep
learning methods and provide corresponding suggestions. The implementations are
freely available at \url{https://github.com/lykaust15/Deep_learning_examples}.
Probabilistic smallest enclosing ball in high dimensions via subgradient sampling
Authors: Amer Krivošija, Alexander Munteanu
Date: 2019-02-28 00:00:00
Tasks: Clustering, Vocal Bursts Intensity Prediction

We study a variant of the median problem for a collection of point sets in
high dimensions. This generalizes the geometric median as well as the
(probabilistic) smallest enclosing ball (pSEB) problems. Our main objective and
motivation is to improve the previously best algorithm for the pSEB problem by
reducing its exponential dependence on the dimension to linear. This is
achieved via a novel combination of sampling techniques for clustering problems
in metric spaces with the framework of stochastic subgradient descent. As a
result, the algorithm becomes applicable to shape fitting problems in Hilbert
spaces of unbounded dimension via kernel functions. We present an exemplary
application by extending the support vector data description (SVDD) shape
fitting method to the probabilistic case. This is done by simulating the pSEB
algorithm implicitly in the feature space induced by the kernel function.
On the usage of the probability integral transform to reduce the complexity of multi-way fuzzy decision trees in Big Data classification problems
Authors: Mikel Elkano, Mikel Uriz, Humberto Bustince, Mikel Galar
Date: 2019-02-28 00:00:00
Tasks: Attribute, General Classification

We present a new distributed fuzzy partitioning method to reduce the
complexity of multi-way fuzzy decision trees in Big Data classification
problems. The proposed algorithm builds a fixed number of fuzzy sets for all
variables and adjusts their shape and position to the real distribution of
training data. A two-step process is applied : 1) transformation of the
original distribution into a standard uniform distribution by means of the
probability integral transform. Since the original distribution is generally
unknown, the cumulative distribution function is approximated by computing the
q-quantiles of the training set; 2) construction of a Ruspini strong fuzzy
partition in the transformed attribute space using a fixed number of equally
distributed triangular membership functions. Despite the aforementioned
transformation, the definition of every fuzzy set in the original space can be
recovered by applying the inverse cumulative distribution function (also known
as quantile function). The experimental results reveal that the proposed
methodology allows the state-of-the-art multi-way fuzzy decision tree (FMDT)
induction algorithm to maintain classification accuracy with up to 6 million
fewer leaves.
Improving fraud prediction with incremental data balancing technique for massive data streams
Authors: Rafiq Ahmed Mohammed, Kok-Wai Wong, Mohd Fairuz Shiratuddin, Xuequn Wang
Date: 2019-02-28 00:00:00
Tasks: General Classification

The performance of classification algorithms with a massive and highly imbalanced data stream depends upon efficient balancing strategy. Some techniques of balancing strategy have been applied in the past with Batch data to resolve the class imbalance problem. This paper proposes a new incremental data balancing framework which can work with massive imbalanced data streams. In this paper, we choose Racing Algorithm as an automated data balancing technique which optimizes the balancing techniques. We applied Random Forest classification algorithm which can deal with the massive data stream. We investigated the suitability of Racing Algorithm and Random Forest in the proposed framework. Applying new technique in the proposed framework on the European Credit Card dataset, provided better results than the Batch mode. The proposed framework is more scalable to handle online massive data streams.
Transformation Consistent Self-ensembling Model for Semi-supervised Medical Image Segmentation
Authors: Xiaomeng Li, Lequan Yu, Hao Chen, Chi-Wing Fu, Lei Xing, Pheng-Ann Heng
Date: 2019-02-28 00:00:00
Tasks: Image Segmentation, Lesion Segmentation, Liver Segmentation, Medical Image Segmentation, Optic Disc Segmentation, Segmentation, Semantic Segmentation, Semi-supervised Medical Image Segmentation, Skin Lesion Segmentation, Tumor Segmentation

Deep convolutional neural networks have achieved remarkable progress on a variety of medical image computing tasks. A common problem when applying supervised deep learning methods to medical images is the lack of labeled data, which is very expensive and time-consuming to be collected. In this paper, we present a novel semi-supervised method for medical image segmentation, where the network is optimized by the weighted combination of a common supervised loss for labeled inputs only and a regularization loss for both labeled and unlabeled data. To utilize the unlabeled data, our method encourages the consistent predictions of the network-in-training for the same input under different regularizations. Aiming for the semi-supervised segmentation problem, we enhance the effect of regularization for pixel-level predictions by introducing a transformation, including rotation and flipping, consistent scheme in our self-ensembling model. With the aim of semi-supervised segmentation tasks, we introduce a transformation consistent strategy in our self-ensembling model to enhance the regularization effect for pixel-level predictions. We have extensively validated the proposed semi-supervised method on three typical yet challenging medical image segmentation tasks: (i) skin lesion segmentation from dermoscopy images on International Skin Imaging Collaboration (ISIC) 2017 dataset, (ii) optic disc segmentation from fundus images on Retinal Fundus Glaucoma Challenge (REFUGE) dataset, and (iii) liver segmentation from volumetric CT scans on Liver Tumor Segmentation Challenge (LiTS) dataset. Compared to the state-of-the-arts, our proposed method shows superior segmentation performance on challenging 2D/3D medical images, demonstrating the effectiveness of our semi-supervised method for medical image segmentation.
Ranking in Genealogy: Search Results Fusion at Ancestry
Authors: Peng Jiang, Yingrui Yang, Gann Bierner, Fengjie Alex Li, Ruhan Wang, Azadeh Moghtaderi
Date: 2019-02-27 00:00:00
Tasks: Diversity, Information Retrieval, Retrieval

Genealogy research is the study of family history using available resources
such as historical records. Ancestry provides its customers with one of the
world's largest online genealogical index with billions of records from a wide
range of sources, including vital records such as birth and death certificates,
census records, court and probate records among many others. Search at Ancestry
aims to return relevant records from various record types, allowing our
subscribers to build their family trees, research their family history, and
make meaningful discoveries about their ancestors from diverse perspectives. In
a modern search engine designed for genealogical study, the appropriate ranking
of search results to provide highly relevant information represents a daunting
challenge. In particular, the disparity in historical records makes it
inherently difficult to score records in an equitable fashion. Herein, we
provide an overview of our solutions to overcome such record disparity problems
in the Ancestry search engine. Specifically, we introduce customized coordinate
ascent (customized CA) to speed up ranking within a specific record type. We
then propose stochastic search (SS) that linearly combines ranked results
federated across contents from various record types. Furthermore, we propose a
novel information retrieval metric, normalized cumulative entropy (NCE), to
measure the diversity of results. We demonstrate the effectiveness of these two
algorithms in terms of relevance (by NDCG) and diversity (by NCE) if applicable
in the offline experiments using real customer data at Ancestry.
Fast Periodicity Estimation and Reconstruction of hidden components from noisy periodic signal
Authors: Bharadwaj Aryasomayajula, Dibakar Sil, Sarbani Palit
Date: 2019-01-26 00:00:00
Tasks: Period Estimation

Periodicity estimation from an arbitrary length noisy signal is
computationally very costly. A recently developed Ramanujan Fat Dictionary is
one of the ways to find the hidden components from an arbitrary length (non
integral multiple of period) of the signal. This method suffers from high run
time due to the lack of information about the period and effect of noise on the
signal. We propose a new method that efficiently estimates the period of the
signal and finding the hidden components thus becomes easy from it. Our method
works well with significantly low SNR values and runs in O(n) time complexity,
n being length of the signal. Comparision of run time analysis between our
method for period estimation of a given signal and SVD method at various SNR
values has been made and the corresponding hidden components are there by
extracted by projecting onto the factor-Ramanujan Subspaces.
A Long-Short Demands-Aware Model for Next-Item Recommendation
Authors: Ting Bai, Pan Du, Wayne Xin Zhao, Ji-Rong Wen, Jian-Yun Nie
Date: 2019-02-12 00:00:00
Tasks: Recommendation Systems

Recommending the right products is the central problem in recommender
systems, but the right products should also be recommended at the right time to
meet the demands of users, so as to maximize their values. Users' demands,
implying strong purchase intents, can be the most useful way to promote
products sales if well utilized. Previous recommendation models mainly focused
on user's general interests to find the right products. However, the aspect of
meeting users' demands at the right time has been much less explored. To
address this problem, we propose a novel Long-Short Demands-aware Model (LSDM),
in which both user's interests towards items and user's demands over time are
incorporated. We summarize two aspects: termed as long-time demands (e.g.,
purchasing the same product repetitively showing a long-time persistent
interest) and short-time demands (e.g., co-purchase like buying paintbrushes
after pigments). To utilize such long-short demands of users, we create
different clusters to group the successive product purchases together according
to different time spans, and use recurrent neural networks to model each
sequence of clusters at a time scale. The long-short purchase demands with
multi-time scales are finally aggregated by joint learning strategies.
Experimental results on three real-world commerce datasets demonstrate the
effectiveness of our model for next-item recommendation, showing the usefulness
of modeling users' long-short purchase demands of items with multi-time scales.
Saec: Similarity-Aware Embedding Compression in Recommendation Systems
Authors: Xiaorui Wu, Hong Xu, Honglin Zhang, Huaming Chen, Jian Wang
Date: 2019-02-26 00:00:00
Tasks: Clustering, Recommendation Systems

Production recommendation systems rely on embedding methods to represent
various features. An impeding challenge in practice is that the large embedding
matrix incurs substantial memory footprint in serving as the number of features
grows over time. We propose a similarity-aware embedding matrix compression
method called Saec to address this challenge. Saec clusters similar features
within a field to reduce the embedding matrix size. Saec also adopts a fast
clustering optimization based on feature frequency to drastically improve
clustering time. We implement and evaluate Saec on Numerous, the production
distributed machine learning system in Tencent, with 10-day worth of feature
data from QQ mobile browser. Testbed experiments show that Saec reduces the
number of embedding vectors by two orders of magnitude, compresses the
embedding size by ~27x, and delivers the same AUC and log loss performance.
A detailed comparative study of open source deep learning frameworks
Authors: Ghadeer Al-Bdour, Raffi Al-Qurran, Mahmoud Al-Ayyoub, Ali Shatnawi
Date: 2019-02-25 00:00:00
Tasks: Deep Learning

Deep Learning (DL) is one of the hottest trends in machine learning as DL approaches produced results superior to the state-of-the-art in problematic areas such as image processing and natural language processing (NLP). To foster the growth of DL, several open source frameworks appeared providing implementations of the most common DL algorithms. These frameworks vary in the algorithms they support and in the quality of their implementations. The purpose of this work is to provide a qualitative and quantitative comparison among three of the most popular and most comprehensive DL frameworks (namely Google's TensorFlow, University of Montreal's Theano and Microsoft's CNTK). The ultimate goal of this work is to help end users make an informed decision about the best DL framework that suits their needs and resources. To ensure that our study is as comprehensive as possible, we conduct several experiments using multiple benchmark datasets from different fields (image processing, NLP, etc.) and measure the performance of the frameworks' implementations of different DL algorithms. For most of our experiments, we find out that CNTK's implementations are superior to the other ones under consideration.
Neuromodulated Goal-Driven Perception in Uncertain Domains
Authors: Xinyun Zou, Soheil Kolouri, Praveen K. Pilly, Jeffrey L. Krichmar
Date: 2019-02-16 00:00:00
Tasks: valid

In uncertain domains, the goals are often unknown and need to be predicted by
the organism or system. In this paper, contrastive excitation backprop (c-EB)
was used in a goal-driven perception task with pairs of noisy MNIST digits,
where the system had to increase attention to one of the two digits
corresponding to a goal (i.e., even, odd, low value, or high value) and
decrease attention to the distractor digit or noisy background pixels. Because
the valid goal was unknown, an online learning model based on the cholinergic
and noradrenergic neuromodulatory systems was used to predict a noisy goal
(expected uncertainty) and re-adapt when the goal changed (unexpected
uncertainty). This neurobiologically plausible model demonstrates how
neuromodulatory systems can predict goals in uncertain domains and how
attentional mechanisms can enhance the perception of that goal.