pwc-archive/papers-with-abstracts huggingface dataset:
Semi-dual Regularized Optimal Transport
Authors: Marco Cuturi, Gabriel Peyré
Date: 2018-11-13 00:00:00

Variational problems that involve Wasserstein distances and more generally
optimal transport (OT) theory are playing an increasingly important role in
data sciences. Such problems can be used to form an examplar measure out of
various probability measures, as in the Wasserstein barycenter problem, or to
carry out parametric inference and density fitting, where the loss is measured
in terms of an optimal transport cost to the measure of observations. Despite
being conceptually simple, such problems are computationally challenging
because they involve minimizing over quantities (Wasserstein distances) that
are themselves hard to compute. Entropic regularization has recently emerged as
an efficient tool to approximate the solution of such variational Wasserstein
problems. In this paper, we give a thorough duality tour of these
regularization techniques. In particular, we show how important concepts from
classical OT such as c-transforms and semi-discrete approaches translate into
similar ideas in a regularized setting. These dual formulations lead to smooth
variational problems, which can be solved using smooth, differentiable and
convex optimization problems that are simpler to implement and numerically more
stable that their un-regularized counterparts. We illustrate the versatility of
this approach by applying it to the computation of Wasserstein barycenters and
gradient flows of spatial regularization functionals.
Deep Q learning for fooling neural networks
Authors: Mandar Kulkarni
Date: 2018-11-13 00:00:00
Tasks: Q-Learning, Reinforcement Learning, Reinforcement Learning (RL)

Deep learning models are vulnerable to external attacks. In this paper, we
propose a Reinforcement Learning (RL) based approach to generate adversarial
examples for the pre-trained (target) models. We assume a semi black-box
setting where the only access an adversary has to the target model is the class
probabilities obtained for the input queries. We train a Deep Q Network (DQN)
agent which, with experience, learns to attack only a small portion of image
pixels to generate non-targeted adversarial images. Initially, an agent
explores an environment by sequentially modifying random sets of image pixels
and observes its effect on the class probabilities. At the end of an episode,
it receives a positive (negative) reward if it succeeds (fails) to alter the
label of the image. Experimental results with MNIST, CIFAR-10 and Imagenet
datasets demonstrate that our RL framework is able to learn an effective attack
policy.
A domain agnostic measure for monitoring and evaluating GANs
Authors: Paulina Grnarova, Kfir. Y. Levy, Aurelien Lucchi, Nathanael Perraudin, Ian Goodfellow, Thomas Hofmann, Andreas Krause
Date: 2018-11-13 00:00:00
Proceeding: NeurIPS 2019 12

Generative Adversarial Networks (GANs) have shown remarkable results in modeling complex distributions, but their evaluation remains an unsettled issue. Evaluations are essential for: (i) relative assessment of different models and (ii) monitoring the progress of a single model throughout training. The latter cannot be determined by simply inspecting the generator and discriminator loss curves as they behave non-intuitively. We leverage the notion of duality gap from game theory to propose a measure that addresses both (i) and (ii) at a low computational cost. Extensive experiments show the effectiveness of this measure to rank different GAN models and capture the typical GAN failure scenarios, including mode collapse and non-convergent behaviours. This evaluation metric also provides meaningful monitoring on the progression of the loss during training. It highly correlates with FID on natural image datasets, and with domain specific scores for text, sound and cosmology data where FID is not directly suitable. In particular, our proposed metric requires no labels or a pretrained classifier, making it domain agnostic.
Reduced-order modeling with artificial neurons for gravitational-wave inference
Authors: Alvin J. K. Chua, Chad R. Galley, Michele Vallisneri
Date: 2018-11-13 00:00:00
Tasks: parameter estimation, Time Series, Time Series Analysis

Gravitational-wave data analysis is rapidly absorbing techniques from deep learning, with a focus on convolutional networks and related methods that treat noisy time series as images. We pursue an alternative approach, in which waveforms are first represented as weighted sums over reduced bases (reduced-order modeling); we then train artificial neural networks to map gravitational-wave source parameters into basis coefficients. Statistical inference proceeds directly in coefficient space, where it is theoretically straightforward and computationally efficient. The neural networks also provide analytic waveform derivatives, which are useful for gradient-based sampling schemes. We demonstrate fast and accurate coefficient interpolation for the case of a four-dimensional binary-inspiral waveform family, and discuss promising applications of our framework in parameter estimation.
ML-Net: multi-label classification of biomedical texts with deep neural networks
Authors: Jingcheng Du, Qingyu Chen, Yifan Peng, Yang Xiang, Cui Tao, Zhiyong Lu
Date: 2018-11-13 00:00:00
Tasks: Benchmarking, Classification, Feature Engineering, General Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Multi-Label Classification Of Biomedical Texts, Multi-Label Text Classification, Text Classification

In multi-label text classification, each textual document can be assigned
with one or more labels. Due to this nature, the multi-label text
classification task is often considered to be more challenging compared to the
binary or multi-class text classification problems. As an important task with
broad applications in biomedicine such as assigning diagnosis codes, a number
of different computational methods (e.g. training and combining binary
classifiers for each label) have been proposed in recent years. However, many
suffered from modest accuracy and efficiency, with only limited success in
practical use. We propose ML-Net, a novel deep learning framework, for
multi-label classification of biomedical texts. As an end-to-end system, ML-Net
combines a label prediction network with an automated label count prediction
mechanism to output an optimal set of labels by leveraging both predicted
confidence score of each label and the contextual information in the target
document. We evaluate ML-Net on three independent, publicly-available corpora
in two kinds of text genres: biomedical literature and clinical notes. For
evaluation, example-based measures such as precision, recall and f-measure are
used. ML-Net is compared with several competitive machine learning baseline
models. Our benchmarking results show that ML-Net compares favorably to the
state-of-the-art methods in multi-label classification of biomedical texts.
ML-NET is also shown to be robust when evaluated on different text genres in
biomedicine. Unlike traditional machine learning methods, ML-Net does not
require human efforts in feature engineering and is highly efficient and
scalable approach to tasks with a large set of labels (no need to build
individual classifiers for each separate label). Finally, ML-NET is able to
dynamically estimate the label count based on the document context in a more
systematic and accurate manner.
Few-shot Learning for Named Entity Recognition in Medical Text
Authors: Maximilian Hofer, Andrey Kormilitzin, Paul Goldberg, Alejo Nevado-Holgado
Date: 2018-11-13 00:00:00
Tasks: Few-Shot Learning, Medical Named Entity Recognition, Named Entity Recognition, Named Entity Recognition (NER), Word Embeddings

Deep neural network models have recently achieved state-of-the-art
performance gains in a variety of natural language processing (NLP) tasks
(Young, Hazarika, Poria, & Cambria, 2017). However, these gains rely on the
availability of large amounts of annotated examples, without which
state-of-the-art performance is rarely achievable. This is especially
inconvenient for the many NLP fields where annotated examples are scarce, such
as medical text. To improve NLP models in this situation, we evaluate five
improvements on named entity recognition (NER) tasks when only ten annotated
examples are available: (1) layer-wise initialization with pre-trained weights,
(2) hyperparameter tuning, (3) combining pre-training data, (4) custom word
embeddings, and (5) optimizing out-of-vocabulary (OOV) words. Experimental
results show that the F1 score of 69.3% achievable by state-of-the-art models
can be improved to 78.87%.
Two-stream convolutional networks for end-to-end learning of self-driving cars
Authors: Nelson Fernandez
Date: 2018-11-13 00:00:00
Tasks: regression, Self-Driving Cars

We propose a methodology to extend the concept of Two-Stream Convolutional
Networks to perform end-to-end learning for self-driving cars with temporal
cues. The system has the ability to learn spatiotemporal features by
simultaneously mapping raw images and pre-calculated optical flows directly to
steering commands. Although optical flows encode temporal-rich information, we
found that 2D-CNNs are prone to capturing features only as spatial
representations. We show how the use of Multitask Learning favors the learning
of temporal features via inductive transfer from a shared spatiotemporal
representation. Preliminary results demonstrate a competitive improvement of
30% in prediction accuracy and stability compared to widely used regression
methods trained on the Comma.ai dataset.
Learning to Compensate Photovoltaic Power Fluctuations from Images of the Sky by Imitating an Optimal Policy
Authors: Robin Spiess, Felix Berkenkamp, Jan Poland, Andreas Krause
Date: 2018-11-13 00:00:00
Tasks: Imitation Learning

The energy output of photovoltaic (PV) power plants depends on the
environment and thus fluctuates over time. As a result, PV power can cause
instability in the power grid, in particular when increasingly used. Limiting
the rate of change of the power output is a common way to mitigate these
fluctuations, often with the help of large batteries. A reactive controller
that uses these batteries to compensate ramps works in practice, but causes
stress on the battery due to a high energy throughput. In this paper, we
present a deep learning approach that uses images of the sky to compensate
power fluctuations predictively and reduces battery stress. In particular, we
show that the optimal control policy can be computed using information that is
only available in hindsight. Based on this, we use imitation learning to train
a neural network that approximates this hindsight-optimal policy, but uses only
currently available sky images and sensor data. We evaluate our method on a
large dataset of measurements and images from a real power plant and show that
the trained policy reduces stress on the battery.
Jointly identifying opinion mining elements and fuzzy measurement of opinion intensity to analyze product features
Authors: Haiqing Zhang, Aicha Sekhari, Yacine Ouzrout, Abdelaziz Bouras
Date: 2018-11-13 00:00:00
Tasks: Opinion Mining

Opinion mining mainly involves three elements: feature and feature-of
relations, opinion expressions and the related opinion attributes (e.g.
Polarity), and feature-opinion relations. Although many works have emerged to
achieve its aim of gaining information, the previous researches typically
handled each of the three elements in isolation, which cannot give sufficient
information extraction results; hence, the complexity and the running time of
information extraction is increased. In this paper, we propose an opinion
mining extraction algorithm to jointly discover the main opinion mining
elements. Specifically, the algorithm automatically builds kernels to combine
closely related words into new terms from word level to phrase level based on
dependency relations; and we ensure the accuracy of opinion expressions and
polarity based on: fuzzy measurements, opinion degree intensifiers, and opinion
patterns. The 3458 analyzed reviews show that the proposed algorithm can
effectively identify the main elements simultaneously and outperform the
baseline methods. The proposed algorithm is used to analyze the features among
heterogeneous products in the same category. The feature-by-feature comparison
can help to select the weaker features and recommend the correct specifications
from the beginning life of a product. From this comparison, some interesting
observations are revealed. For example, the negative polarity of video
dimension is higher than the product usability dimension for a product. Yet,
enhancing the dimension of product usability can more effectively improve the
product (C) 2015 Elsevier Ltd. All rights reserved.
Towards Neural Machine Translation for African Languages
Authors: Jade Z. Abbott, Laura Martinus
Date: 2018-11-13 00:00:00
Tasks: Machine Translation, NMT, Translation

Given that South African education is in crisis, strategies for improvement
and sustainability of high-quality, up-to-date education must be explored. In
the migration of education online, inclusion of machine translation for
low-resourced local languages becomes necessary. This paper aims to spur the
use of current neural machine translation (NMT) techniques for low-resourced
local languages. The paper demonstrates state-of-the-art performance on
English-to-Setswana translation using the Autshumato dataset. The use of the
Transformer architecture beat previous techniques by 5.33 BLEU points. This
demonstrates the promise of using current NMT techniques for African languages.
An Introductory Survey on Attention Mechanisms in NLP Problems
Authors: Dichao Hu
Date: 2018-11-12 00:00:00
Tasks: Dependency Parsing, General Classification, Machine Translation, Question Answering, Sentiment Analysis, Sentiment Classification, Survey, Text Summarization, Translation

First derived from human intuition, later adapted to machine translation for
automatic token alignment, attention mechanism, a simple method that can be
used for encoding sequence data based on the importance score each element is
assigned, has been widely applied to and attained significant improvement in
various tasks in natural language processing, including sentiment
classification, text summarization, question answering, dependency parsing,
etc. In this paper, we survey through recent works and conduct an introductory
summary of the attention mechanism in different NLP problems, aiming to provide
our readers with basic knowledge on this widely used method, discuss its
different variants for different tasks, explore its association with other
techniques in machine learning, and examine methods for evaluating its
performance.
Learning to Predict the Cosmological Structure Formation
Authors: Siyu He, Yin Li, Yu Feng, Shirley Ho, Siamak Ravanbakhsh, Wei Chen, Barnabás Póczos
Date: 2018-11-15 00:00:00

Matter evolved under influence of gravity from minuscule density fluctuations. Non-perturbative structure formed hierarchically over all scales, and developed non-Gaussian features in the Universe, known as the Cosmic Web. To fully understand the structure formation of the Universe is one of the holy grails of modern astrophysics. Astrophysicists survey large volumes of the Universe and employ a large ensemble of computer simulations to compare with the observed data in order to extract the full information of our own Universe. However, to evolve trillions of galaxies over billions of years even with the simplest physics is a daunting task. We build a deep neural network, the Deep Density Displacement Model (hereafter D$^3$M), to predict the non-linear structure formation of the Universe from simple linear perturbation theory. Our extensive analysis, demonstrates that D$^3$M outperforms the second order perturbation theory (hereafter 2LPT), the commonly used fast approximate simulation method, in point-wise comparison, 2-point correlation, and 3-point correlation. We also show that D$^3$M is able to accurately extrapolate far beyond its training data, and predict structure formation for significantly different cosmological parameters. Our study proves, for the first time, that deep learning is a practical and accurate alternative to approximate simulations of the gravitational structure formation of the Universe.
On transfer learning using a MAC model variant
Authors: Vincent Marois, T. S. Jayram, Vincent Albouy, Tomasz Kornuta, Younes Bouhadjar, Ahmet S. Ozcan
Date: 2018-11-15 00:00:00
Tasks: model, Transfer Learning

We introduce a variant of the MAC model (Hudson and Manning, ICLR 2018) with
a simplified set of equations that achieves comparable accuracy, while training
faster. We evaluate both models on CLEVR and CoGenT, and show that, transfer
learning with fine-tuning results in a 15 point increase in accuracy, matching
the state of the art. Finally, in contrast, we demonstrate that improper
fine-tuning can actually reduce a model's accuracy as well.
Exploiting Class Learnability in Noisy Data
Authors: Matthew Klawonn, Eric Heim, James Hendler
Date: 2018-11-15 00:00:00

In many domains, collecting sufficient labeled training data for supervised
machine learning requires easily accessible but noisy sources, such as
crowdsourcing services or tagged Web data. Noisy labels occur frequently in
data sets harvested via these means, sometimes resulting in entire classes of
data on which learned classifiers generalize poorly. For real world
applications, we argue that it can be beneficial to avoid training on such
classes entirely. In this work, we aim to explore the classes in a given data
set, and guide supervised training to spend time on a class proportional to its
learnability. By focusing the training process, we aim to improve model
generalization on classes with a strong signal. To that end, we develop an
online algorithm that works in conjunction with classifier and training
algorithm, iteratively selecting training data for the classifier based on how
well it appears to generalize on each class. Testing our approach on a variety
of data sets, we show our algorithm learns to focus on classes for which the
model has low generalization error relative to strong baselines, yielding a
classifier with good performance on learnable classes.
Reward learning from human preferences and demonstrations in Atari
Authors: Borja Ibarz, Jan Leike, Tobias Pohlen, Geoffrey Irving, Shane Legg, Dario Amodei
Date: 2018-11-15 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Atari Games, Deep Reinforcement Learning, Imitation Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

To solve complex real-world problems with reinforcement learning, we cannot
rely on manually specified reward functions. Instead, we can have humans
communicate an objective to the agent directly. In this work, we combine two
approaches to learning from human feedback: expert demonstrations and
trajectory preferences. We train a deep neural network to model the reward
function and use its predicted reward to train an DQN-based deep reinforcement
learning agent on 9 Atari games. Our approach beats the imitation learning
baseline in 7 games and achieves strictly superhuman performance on 2 games
without using game rewards. Additionally, we investigate the goodness of fit of
the reward model, present some reward hacking problems, and study the effects
of noise in the human labels.
Tight Bayesian Ambiguity Sets for Robust MDPs
Authors: Reazul Hasan Russel, Marek Petrik
Date: 2018-11-15 00:00:00
Tasks: Decision Making, Reinforcement Learning, Reinforcement Learning (RL), Sequential Decision Making

Robustness is important for sequential decision making in a stochastic
dynamic environment with uncertain probabilistic parameters. We address the
problem of using robust MDPs (RMDPs) to compute policies with provable
worst-case guarantees in reinforcement learning. The quality and robustness of
an RMDP solution is determined by its ambiguity set. Existing methods construct
ambiguity sets that lead to impractically conservative solutions. In this
paper, we propose RSVF, which achieves less conservative solutions with the
same worst-case guarantees by 1) leveraging a Bayesian prior, 2) optimizing the
size and location of the ambiguity set, and, most importantly, 3) relaxing the
requirement that the set is a confidence interval. Our theoretical analysis
shows the safety of RSVF, and the empirical results demonstrate its practical
promise.
Adjusting for Confounding in Unsupervised Latent Representations of Images
Authors: Craig A. Glastonbury, Michael Ferlaino, Christoffer Nellåker, Cecilia M. Lindgren
Date: 2018-11-15 00:00:00

Biological imaging data are often partially confounded or contain unwanted
variability. Examples of such phenomena include variable lighting across
microscopy image captures, stain intensity variation in histological slides,
and batch effects for high throughput drug screening assays. Therefore, to
develop "fair" models which generalise well to unseen examples, it is crucial
to learn data representations that are insensitive to nuisance factors of
variation. In this paper, we present a strategy based on adversarial training,
capable of learning unsupervised representations invariant to confounders. As
an empirical validation of our method, we use deep convolutional autoencoders
to learn unbiased cellular representations from microscopy imaging.
Development and Validation of a Deep Learning Algorithm for Improving Gleason Scoring of Prostate Cancer
Authors: Kunal Nagpal, Davis Foote, Yun Liu, Po-Hsuan, Chen, Ellery Wulczyn, Fraser Tan, Niels Olson, Jenny L. Smith, Arash Mohtashamian, James H. Wren, Greg S. Corrado, Robert MacDonald, Lily H. Peng, Mahul B. Amin, Andrew J. Evans, Ankur R. Sangoi, Craig H. Mermel, Jason D. Hipp, Martin C. Stumpe
Date: 2018-11-15 00:00:00
Tasks: Diagnostic, whole slide images

For prostate cancer patients, the Gleason score is one of the most important
prognostic factors, potentially determining treatment independent of the stage.
However, Gleason scoring is based on subjective microscopic examination of
tumor morphology and suffers from poor reproducibility. Here we present a deep
learning system (DLS) for Gleason scoring whole-slide images of
prostatectomies. Our system was developed using 112 million
pathologist-annotated image patches from 1,226 slides, and evaluated on an
independent validation dataset of 331 slides, where the reference standard was
established by genitourinary specialist pathologists. On the validation
dataset, the mean accuracy among 29 general pathologists was 0.61. The DLS
achieved a significantly higher diagnostic accuracy of 0.70 (p=0.002) and
trended towards better patient risk stratification in correlations to clinical
follow-up data. Our approach could improve the accuracy of Gleason scoring and
subsequent therapy decisions, particularly where specialist expertise is
unavailable. The DLS also goes beyond the current Gleason system to more finely
characterize and quantitate tumor morphology, providing opportunities for
refinement of the Gleason system itself.
Mathematical Analysis of Adversarial Attacks
Authors: Zehao Dou, Stanley J. Osher, Bao Wang
Date: 2018-11-15 00:00:00
Tasks: General Classification

In this paper, we analyze efficacy of the fast gradient sign method (FGSM)
and the Carlini-Wagner's L2 (CW-L2) attack. We prove that, within a certain
regime, the untargeted FGSM can fool any convolutional neural nets (CNNs) with
ReLU activation; the targeted FGSM can mislead any CNNs with ReLU activation to
classify any given image into any prescribed class. For a special two-layer
neural network: a linear layer followed by the softmax output activation, we
show that the CW-L2 attack increases the ratio of the classification
probability between the target and ground truth classes. Moreover, we provide
numerical results to verify all our theoretical results.