pwc-archive/papers-with-abstracts huggingface dataset:
Fourier Policy Gradients
Authors: Matthew Fellows, Kamil Ciosek, Shimon Whiteson
Date: 2018-02-19 00:00:00
Proceeding: ICML 2018 7
Tasks: Reinforcement Learning, Reinforcement Learning (RL)

We propose a new way of deriving policy gradient updates for reinforcement
learning. Our technique, based on Fourier analysis, recasts integrals that
arise with expected policy gradients as convolutions and turns them into
multiplications. The obtained analytical solutions allow us to capture the low
variance benefits of EPG in a broad range of settings. For the critic, we treat
trigonometric and radial basis functions, two function families with the
universal approximation property. The choice of policy can be almost arbitrary,
including mixtures or hybrid continuous-discrete probability distributions.
Moreover, we derive a general family of sample-based estimators for stochastic
policy gradients, which unifies existing results on sample-based approximation.
We believe that this technique has the potential to shape the next generation
of policy gradient approaches, powered by analytical results.
Automatic, fast and robust characterization of noise distributions for diffusion MRI
Authors: Samuel St-Jean, Alberto De Luca, Max A. Viergever, Alexander Leemans
Date: 2018-05-30 00:00:00
Tasks: Diffusion  MRI, Noise Estimation

Knowledge of the noise distribution in magnitude diffusion MRI images is the
centerpiece to quantify uncertainties arising from the acquisition process. The
use of parallel imaging methods, the number of receiver coils and imaging
filters applied by the scanner, amongst other factors, dictate the resulting
signal distribution. Accurate estimation beyond textbook Rician or noncentral
chi distributions often requires information about the acquisition process
(e.g. coils sensitivity maps or reconstruction coefficients), which is not
usually available. We introduce a new method where a change of variable
naturally gives rise to a particular form of the gamma distribution for
background signals. The first moments and maximum likelihood estimators of this
gamma distribution explicitly depend on the number of coils, making it possible
to estimate all unknown parameters using only the magnitude data. A rejection
step is used to make the method automatic and robust to artifacts. Experiments
on synthetic datasets show that the proposed method can reliably estimate both
the degrees of freedom and the standard deviation. The worst case errors range
from below 2% (spatially uniform noise) to approximately 10% (spatially
variable noise). Repeated acquisitions of in vivo datasets show that the
estimated parameters are stable and have lower variances than compared methods.
Code-Switching Language Modeling using Syntax-Aware Multi-Task Learning
Authors: Genta Indra Winata, Andrea Madotto, Chien-Sheng Wu, Pascale Fung
Date: 2018-05-30 00:00:00
Proceeding: WS 2018 7
Tasks: Language Modeling, Language Modelling, Multi-Task Learning, Part-Of-Speech Tagging, Syntax Representation

Lack of text data has been the major issue on code-switching language
modeling. In this paper, we introduce multi-task learning based language model
which shares syntax representation of languages to leverage linguistic
information and tackle the low resource data issue. Our model jointly learns
both language modeling and Part-of-Speech tagging on code-switched utterances.
In this way, the model is able to identify the location of code-switching
points and improves the prediction of next word. Our approach outperforms
standard LSTM based language model, with an improvement of 9.7% and 7.4% in
perplexity on SEAME Phase I and Phase II dataset respectively.
RetainVis: Visual Analytics with Interpretable and Interactive Recurrent Neural Networks on Electronic Medical Records
Authors: Bum Chul Kwon, Min-Je Choi, Joanne Taery Kim, Edward Choi, Young Bin Kim, Soonwook Kwon, Jimeng Sun, Jaegul Choo
Date: 2018-05-28 00:00:00

We have recently seen many successful applications of recurrent neural
networks (RNNs) on electronic medical records (EMRs), which contain histories
of patients' diagnoses, medications, and other various events, in order to
predict the current and future states of patients. Despite the strong
performance of RNNs, it is often challenging for users to understand why the
model makes a particular prediction. Such black-box nature of RNNs can impede
its wide adoption in clinical practice. Furthermore, we have no established
methods to interactively leverage users' domain expertise and prior knowledge
as inputs for steering the model. Therefore, our design study aims to provide a
visual analytics solution to increase interpretability and interactivity of
RNNs via a joint effort of medical experts, artificial intelligence scientists,
and visual analytics researchers. Following the iterative design process
between the experts, we design, implement, and evaluate a visual analytics tool
called RetainVis, which couples a newly improved, interpretable and interactive
RNN-based model called RetainEX and visualizations for users' exploration of
EMR data in the context of prediction tasks. Our study shows the effective use
of RetainVis for gaining insights into how individual medical codes contribute
to making risk predictions, using EMRs of patients with heart failure and
cataract symptoms. Our study also demonstrates how we made substantial changes
to the state-of-the-art RNN model called RETAIN in order to make use of
temporal information and increase interactivity. This study will provide a
useful guideline for researchers that aim to design an interpretable and
interactive visual analytics tool for RNNs.
A Robust and Effective Approach Towards Accurate Metastasis Detection and pN-stage Classification in Breast Cancer
Authors: Byungjae Lee, Kyunghyun Paeng
Date: 2018-05-30 00:00:00
Tasks: Diagnostic, General Classification, Prognosis

Predicting TNM stage is the major determinant of breast cancer prognosis and
treatment. The essential part of TNM stage classification is whether the cancer
has metastasized to the regional lymph nodes (N-stage). Pathologic N-stage
(pN-stage) is commonly performed by pathologists detecting metastasis in
histological slides. However, this diagnostic procedure is prone to
misinterpretation and would normally require extensive time by pathologists
because of the sheer volume of data that needs a thorough review. Automated
detection of lymph node metastasis and pN-stage prediction has a great
potential to reduce their workload and help the pathologist. Recent advances in
convolutional neural networks (CNN) have shown significant improvements in
histological slide analysis, but accuracy is not optimized because of the
difficulty in the handling of gigapixel images. In this paper, we propose a
robust method for metastasis detection and pN-stage classification in breast
cancer from multiple gigapixel pathology images in an effective way. pN-stage
is predicted by combining patch-level CNN based metastasis detector and
slide-level lymph node classifier. The proposed framework achieves a
state-of-the-art quadratic weighted kappa score of 0.9203 on the Camelyon17
dataset, outperforming the previous winning method of the Camelyon17 challenge.
Human vs Automatic Metrics: on the Importance of Correlation Design
Authors: Anastasia Shimorina
Date: 2018-05-29 00:00:00
Tasks: Sentence, Text Generation

This paper discusses two existing approaches to the correlation analysis between automatic evaluation metrics and human scores in the area of natural language generation. Our experiments show that depending on the usage of a system- or sentence-level correlation analysis, correlation results between automatic scores and human judgments are inconsistent.
Stochastic Deep Compressive Sensing for the Reconstruction of Diffusion Tensor Cardiac MRI
Authors: Jo Schlemper, Guang Yang, Pedro Ferreira, Andrew Scott, Laura-Ann McGill, Zohya Khalique, Margarita Gorodezky, Malte Roehl, Jennifer Keegan, Dudley Pennell, David Firmin, Daniel Rueckert
Date: 2018-05-30 00:00:00
Tasks: Compressive Sensing

Understanding the structure of the heart at the microscopic scale of
cardiomyocytes and their aggregates provides new insights into the mechanisms
of heart disease and enables the investigation of effective therapeutics.
Diffusion Tensor Cardiac Magnetic Resonance (DT-CMR) is a unique non-invasive
technique that can resolve the microscopic structure, organisation, and
integrity of the myocardium without the need for exogenous contrast agents.
However, this technique suffers from relatively low signal-to-noise ratio (SNR)
and frequent signal loss due to respiratory and cardiac motion. Current DT-CMR
techniques rely on acquiring and averaging multiple signal acquisitions to
improve the SNR. Moreover, in order to mitigate the influence of respiratory
movement, patients are required to perform many breath holds which results in
prolonged acquisition durations (e.g., ~30 mins using the existing technology).
In this study, we propose a novel cascaded Convolutional Neural Networks (CNN)
based compressive sensing (CS) technique and explore its applicability to
improve DT-CMR acquisitions. Our simulation based studies have achieved high
reconstruction fidelity and good agreement between DT-CMR parameters obtained
with the proposed reconstruction and fully sampled ground truth. When compared
to other state-of-the-art methods, our proposed deep cascaded CNN method and
its stochastic variation demonstrated significant improvements. To the best of
our knowledge, this is the first study using deep CNN based CS for the DT-CMR
reconstruction. In addition, with relatively straightforward modifications to
the acquisition scheme, our method can easily be translated into a method for
online, at-the-scanner reconstruction enabling the deployment of accelerated
DT-CMR in various clinical applications.
Sobolev Descent
Authors: Youssef Mroueh, Tom Sercu, Anant Raj
Date: 2018-05-30 00:00:00

We study a simplification of GAN training: the problem of transporting particles from a source to a target distribution. Starting from the Sobolev GAN critic, part of the gradient regularized GAN family, we show a strong relation with Optimal Transport (OT). Specifically with the less popular dynamic formulation of OT that finds a path of distributions from source to target minimizing a ``kinetic energy''. We introduce Sobolev descent that constructs similar paths by following gradient flows of a critic function in a kernel space or parametrized by a neural network. In the kernel version, we show convergence to the target distribution in the MMD sense. We show in theory and experiments that regularization has an important role in favoring smooth transitions between distributions, avoiding large gradients from the critic. This analysis in a simplified particle setting provides insight in paths to equilibrium in GANs.
Bilingual Character Representation for Efficiently Addressing Out-of-Vocabulary Words in Code-Switching Named Entity Recognition
Authors: Genta Indra Winata, Chien-Sheng Wu, Andrea Madotto, Pascale Fung
Date: 2018-05-30 00:00:00
Proceeding: WS 2018 7
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Transfer Learning

We propose an LSTM-based model with hierarchical architecture on named entity recognition from code-switching Twitter data. Our model uses bilingual character representation and transfer learning to address out-of-vocabulary words. In order to mitigate data noise, we propose to use token replacement and normalization. In the 3rd Workshop on Computational Approaches to Linguistic Code-Switching Shared Task, we achieved second place with 62.76% harmonic mean F1-score for English-Spanish language pair without using any gazetteer and knowledge-based information.
Im2Flow: Motion Hallucination from Static Images for Action Recognition
Authors: Ruohan Gao, Bo Xiong, Kristen Grauman
Date: 2017-12-12 00:00:00
Proceeding: CVPR 2018 6
Tasks: Action Recognition, Activity Recognition, Decoder, Hallucination, Optical Flow Estimation, Temporal Action Localization

Existing methods to recognize actions in static images take the images at
their face value, learning the appearances---objects, scenes, and body
poses---that distinguish each action class. However, such models are deprived
of the rich dynamic structure and motions that also define human activity. We
propose an approach that hallucinates the unobserved future motion implied by a
single snapshot to help static-image action recognition. The key idea is to
learn a prior over short-term dynamics from thousands of unlabeled videos,
infer the anticipated optical flow on novel static images, and then train
discriminative models that exploit both streams of information. Our main
contributions are twofold. First, we devise an encoder-decoder convolutional
neural network and a novel optical flow encoding that can translate a static
image into an accurate flow map. Second, we show the power of hallucinated flow
for recognition, successfully transferring the learned motion into a standard
two-stream network for activity recognition. On seven datasets, we demonstrate
the power of the approach. It not only achieves state-of-the-art accuracy for
dense optical flow prediction, but also consistently enhances recognition of
actions and dynamic scenes.
Semi-Supervised Phone Classification using Deep Neural Networks and Stochastic Graph-Based Entropic Regularization
Authors: Sunil Thulasidasan, Jeffrey Bilmes
Date: 2016-12-15 00:00:00
Tasks: Diversity, General Classification

We describe a graph-based semi-supervised learning framework in the context
of deep neural networks that uses a graph-based entropic regularizer to favor
smooth solutions over a graph induced by the data. The main contribution of
this work is a computationally efficient, stochastic graph-regularization
technique that uses mini-batches that are consistent with the graph structure,
but also provides enough stochasticity (in terms of mini-batch data diversity)
for convergence of stochastic gradient descent methods to good solutions. For
this work, we focus on results of frame-level phone classification accuracy on
the TIMIT speech corpus but our method is general and scalable to much larger
data sets. Results indicate that our method significantly improves
classification accuracy compared to the fully-supervised case when the fraction
of labeled data is low, and it is competitive with other methods in the fully
labeled case.
Robust Place Categorization with Deep Domain Generalization
Authors: Massimiliano Mancini, Samuel Rota Bulò, Barbara Caputo, Elisa Ricci
Date: 2018-05-30 00:00:00
Tasks: Domain Adaptation, Domain Generalization, General Classification

Traditional place categorization approaches in robot vision assume that
training and test images have similar visual appearance. Therefore, any
seasonal, illumination and environmental changes typically lead to severe
degradation in performance. To cope with this problem, recent works have
proposed to adopt domain adaptation techniques. While effective, these methods
assume that some prior information about the scenario where the robot will
operate is available at training time. Unfortunately, in many cases this
assumption does not hold, as we often do not know where a robot will be
deployed. To overcome this issue, in this paper we present an approach which
aims at learning classification models able to generalize to unseen scenarios.
Specifically, we propose a novel deep learning framework for domain
generalization. Our method develops from the intuition that, given a set of
different classification models associated to known domains (e.g. corresponding
to multiple environments, robots), the best model for a new sample in the novel
domain can be computed directly at test time by optimally combining the known
models. To implement our idea, we exploit recent advances in deep domain
adaptation and design a Convolutional Neural Network architecture with novel
layers performing a weighted version of Batch Normalization. Our experiments,
conducted on three common datasets for robot place categorization, confirm the
validity of our contribution.
Classifying cooking object's state using a tuned VGG convolutional neural network
Authors: Rahul Paul
Date: 2018-05-23 00:00:00
Tasks: General Classification, Object

In robotics, knowing the object states and recognizing the desired states are
very important. Objects at different states would require different grasping.
To achieve different states, different manipulations would be required, as well
as different grasping. To analyze the objects at different states, a dataset of
cooking objects was created. Cooking consists of various cutting techniques
needed for different dishes (e.g. diced, julienne etc.). Identifying each of
this state of cooking objects by the human can be difficult sometimes too. In
this paper, we have analyzed seven different cooking object states by tuning a
convolutional neural network (CNN). For this task, images were downloaded and
annotated by students and they are divided into training and a completely
different test set. By tuning the vgg-16 CNN 77% accuracy was obtained. The
work presented in this paper focuses on classification between various object
states rather than task recognition or recipe prediction. This framework can be
easily adapted in any other object state classification activity.
End-to-end named entity extraction from speech
Authors: Sahar Ghannay, Antoine Caubrière, Yannick Estève, Antoine Laurent, Emmanuel Morin
Date: 2018-05-30 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Entity Extraction using GAN, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, speech-recognition, Speech Recognition

Named entity recognition (NER) is among SLU tasks that usually extract
semantic information from textual documents. Until now, NER from speech is made
through a pipeline process that consists in processing first an automatic
speech recognition (ASR) on the audio and then processing a NER on the ASR
outputs. Such approach has some disadvantages (error propagation, metric to
tune ASR systems sub-optimal in regards to the final task, reduced space search
at the ASR output level...) and it is known that more integrated approaches
outperform sequential ones, when they can be applied. In this paper, we present
a first study of end-to-end approach that directly extracts named entities from
speech, though a unique neural architecture. On a such way, a joint
optimization is able for both ASR and NER. Experiments are carried on French
data easily accessible, composed of data distributed in several evaluation
campaign. Experimental results show that this end-to-end approach provides
better results (F-measure=0.69 on test data) than a classical pipeline approach
to detect named entity categories (F-measure=0.65).
Bi-Directional Neural Machine Translation with Synthetic Parallel Data
Authors: Xing Niu, Michael Denkowski, Marine Carpuat
Date: 2018-05-29 00:00:00
Proceeding: WS 2018 7
Tasks: Machine Translation, NMT, Translation

Despite impressive progress in high-resource settings, Neural Machine
Translation (NMT) still struggles in low-resource and out-of-domain scenarios,
often failing to match the quality of phrase-based translation. We propose a
novel technique that combines back-translation and multilingual NMT to improve
performance in these difficult cases. Our technique trains a single model for
both directions of a language pair, allowing us to back-translate source or
target monolingual data without requiring an auxiliary model. We then continue
training on the augmented parallel data, enabling a cycle of improvement for a
single model that can incorporate any source, target, or parallel data to
improve both translation directions. As a byproduct, these models can reduce
training and deployment costs significantly compared to uni-directional models.
Extensive experiments show that our technique outperforms standard
back-translation in low-resource scenarios, improves quality on cross-domain
tasks, and effectively reduces costs across the board.
Predicting County Level Corn Yields Using Deep Long Short Term Memory Models
Authors: Zehui Jiang, Chao Liu, Nathan P. Hendricks, Baskar Ganapathysubramanian, Dermot J. Hayes, Soumik Sarkar
Date: 2018-05-30 00:00:00
Tasks: Prediction, Time Series, Time Series Analysis, Time Series Prediction

Corn yield prediction is beneficial as it provides valuable information about
production and prices prior the harvest. Publicly available high-quality corn
yield prediction can help address emergent information asymmetry problems and
in doing so improve price efficiency in futures markets. This paper is the
first to employ Long Short-Term Memory (LSTM), a special form of Recurrent
Neural Network (RNN) method to predict corn yields. A cross sectional time
series of county-level corn yield and hourly weather data made the sample space
large enough to use deep learning technics. LSTM is efficient in time series
prediction with complex inner relations, which makes it suitable for this task.
The empirical results from county level data in Iowa show promising predictive
power relative to existing survey based methods.
Adaptive System Identification Using LMS Algorithm Integrated with Evolutionary Computation
Authors: Ibraheem Kasim Ibraheem
Date: 2018-05-30 00:00:00

System identification is an exceptionally expansive topic and of remarkable
significance in the discipline of signal processing and communication. Our goal
in this paper is to show how simple adaptive FIR and IIR filters can be used in
system modeling and demonstrating the application of adaptive system
identification. The main objective of our research is to study the LMS
algorithm and its improvement by the genetic search approach, namely, LMS-GA,
to search the multi-modal error surface of the IIR filter to avoid local minima
and finding the optimal weight vector when only measured or estimated data are
available. Convergence analysis of the LMS algorithm in the case of coloured
input signal, i.e., correlated input signal is demonstrated on adaptive FIR
filter via power spectral density of the input signals and Fourier transform of
the autocorrelation matrix of the input signal. Simulations have been carried
out on adaptive filtering of FIR and IIR filters and tested on white and
coloured input signals to validate the powerfulness of the genetic-based LMS
algorithm.
Identifying and Understanding User Reactions to Deceptive and Trusted Social News Sources
Authors: Maria Glenski, Tim Weninger, Svitlana Volkova
Date: 2018-05-30 00:00:00
Proceeding: ACL 2018 7

In the age of social news, it is important to understand the types of
reactions that are evoked from news sources with various levels of credibility.
In the present work we seek to better understand how users react to trusted and
deceptive news sources across two popular, and very different, social media
platforms. To that end, (1) we develop a model to classify user reactions into
one of nine types, such as answer, elaboration, and question, etc, and (2) we
measure the speed and the type of reaction for trusted and deceptive news
sources for 10.8M Twitter posts and 6.2M Reddit comments. We show that there
are significant differences in the speed and the type of reactions between
trusted and deceptive news sources on Twitter, but far smaller differences on
Reddit.