pwc-archive/papers-with-abstracts huggingface dataset:
Personalizing a Dialogue System with Transfer Reinforcement Learning
Authors: Kaixiang Mo, Shuangyin Li, Yu Zhang, Jiajun Li, Qiang Yang
Date: 2016-10-10 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Transfer Learning, Transfer Reinforcement Learning

It is difficult to train a personalized task-oriented dialogue system because
the data collected from each individual is often insufficient. Personalized
dialogue systems trained on a small dataset can overfit and make it difficult
to adapt to different user needs. One way to solve this problem is to consider
a collection of multiple users' data as a source domain and an individual
user's data as a target domain, and to perform a transfer learning from the
source to the target domain. By following this idea, we propose
"PETAL"(PErsonalized Task-oriented diALogue), a transfer-learning framework
based on POMDP to learn a personalized dialogue system. The system first learns
common dialogue knowledge from the source domain and then adapts this knowledge
to the target user. This framework can avoid the negative transfer problem by
considering differences between source and target users. The policy in the
personalized POMDP can learn to choose different actions appropriately for
different users. Experimental results on a real-world coffee-shopping data and
simulation data show that our personalized dialogue system can choose different
optimal actions for different users, and thus effectively improve the dialogue
quality under the personalized setting.
Detecting and Explaining Crisis
Authors: Rohan Kshirsagar, Robert Morris, Sam Bowman
Date: 2017-05-26 00:00:00
Proceeding: WS 2017 8

Individuals on social media may reveal themselves to be in various states of
crisis (e.g. suicide, self-harm, abuse, or eating disorders). Detecting crisis
from social media text automatically and accurately can have profound
consequences. However, detecting a general state of crisis without explaining
why has limited applications. An explanation in this context is a coherent,
concise subset of the text that rationalizes the crisis detection. We explore
several methods to detect and explain crisis using a combination of neural and
non-neural techniques. We evaluate these techniques on a unique data set
obtained from Koko, an anonymous emotional support network available through
various messaging applications. We annotate a small subset of the samples
labeled with crisis with corresponding explanations. Our best technique
significantly outperforms the baseline for detection and explanation.
Risk-Sensitive Cooperative Games for Human-Machine Systems
Authors: Agostino Capponi, Reza Ghanadan, Matt Stern
Date: 2017-05-26 00:00:00

Autonomous systems can substantially enhance a human's efficiency and
effectiveness in complex environments. Machines, however, are often unable to
observe the preferences of the humans that they serve. Despite the fact that
the human's and machine's objectives are aligned, asymmetric information, along
with heterogeneous sensitivities to risk by the human and machine, make their
joint optimization process a game with strategic interactions. We propose a
framework based on risk-sensitive dynamic games; the human seeks to optimize
her risk-sensitive criterion according to her true preferences, while the
machine seeks to adaptively learn the human's preferences and at the same time
provide a good service to the human. We develop a class of performance measures
for the proposed framework based on the concept of regret. We then evaluate
their dependence on the risk-sensitivity and the degree of uncertainty. We
present applications of our framework to self-driving taxis, and robo-financial
advising.
Two-Sample Tests for Large Random Graphs Using Network Statistics
Authors: Debarghya Ghoshdastidar, Maurilio Gutzeit, Alexandra Carpentier, Ulrike Von Luxburg
Date: 2017-05-17 00:00:00
Tasks: Two-sample testing, Vocal Bursts Valence Prediction

We consider a two-sample hypothesis testing problem, where the distributions
are defined on the space of undirected graphs, and one has access to only one
observation from each model. A motivating example for this problem is comparing
the friendship networks on Facebook and LinkedIn. The practical approach to
such problems is to compare the networks based on certain network statistics.
In this paper, we present a general principle for two-sample hypothesis testing
in such scenarios without making any assumption about the network generation
process. The main contribution of the paper is a general formulation of the
problem based on concentration of network statistics, and consequently, a
consistent two-sample test that arises as the natural solution for this
problem. We also show that the proposed test is minimax optimal for certain
network statistics.
Proceedings of the Workshop on Data Mining for Oil and Gas
Authors: Alipio Jorge, German Larrazabal, Pablo Guillen, Rui L. Lopes
Date: 2017-05-09 00:00:00
Tasks: BIG-bench Machine Learning, Decision Making

The process of exploring and exploiting Oil and Gas (O&G) generates a lot of
data that can bring more efficiency to the industry. The opportunities for
using data mining techniques in the "digital oil-field" remain largely
unexplored or uncharted. With the high rate of data expansion, companies are
scrambling to develop ways to develop near-real-time predictive analytics, data
mining and machine learning capabilities, and are expanding their data storage
infrastructure and resources. With these new goals, come the challenges of
managing data growth, integrating intelligence tools, and analyzing the data to
glean useful insights. Oil and Gas companies need data solutions to
economically extract value from very large volumes of a wide variety of data
generated from exploration, well drilling and production devices and sensors.
  Data mining for oil and gas industry throughout the lifecycle of the
reservoir includes the following roles: locating hydrocarbons, managing
geological data, drilling and formation evaluation, well construction, well
completion, and optimizing production through the life of the oil field. For
each of these phases during the lifecycle of oil field, data mining play a
significant role. Based on which phase were talking about, knowledge creation
through scientific models, data analytics and machine learning, a effective,
productive, and on demand data insight is critical for decision making within
the organization.
  The significant challenges posed by this complex and economically vital field
justify a meeting of data scientists that are willing to share their experience
and knowledge. Thus, the Worskhop on Data Mining for Oil and Gas (DM4OG) aims
to provide a quality forum for researchers that work on the significant
challenges arising from the synergy between data science, machine learning, and
the modeling and optimization problems in the O&G industry.
Robustness of classifiers to universal perturbations: a geometric perspective
Authors: Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, Omar Fawzi, Pascal Frossard, Stefano Soatto
Date: 2017-05-26 00:00:00
Proceeding: ICLR 2018 1

Deep networks have recently been shown to be vulnerable to universal perturbations: there exist very small image-agnostic perturbations that cause most natural images to be misclassified by such classifiers. In this paper, we propose the first quantitative analysis of the robustness of classifiers to universal perturbations, and draw a formal link between the robustness to universal perturbations, and the geometry of the decision boundary. Specifically, we establish theoretical bounds on the robustness of classifiers under two decision boundary models (flat and curved models). We show in particular that the robustness of deep networks to universal perturbations is driven by a key property of their curvature: there exists shared directions along which the decision boundary of deep networks is systematically positively curved. Under such conditions, we prove the existence of small universal perturbations. Our analysis further provides a novel geometric method for computing universal perturbations, in addition to explaining their properties.
Classification regions of deep neural networks
Authors: Alhussein Fawzi, Seyed-Mohsen Moosavi-Dezfooli, Pascal Frossard, Stefano Soatto
Date: 2017-05-26 00:00:00
Tasks: Classification, General Classification

The goal of this paper is to analyze the geometric properties of deep neural
network classifiers in the input space. We specifically study the topology of
classification regions created by deep networks, as well as their associated
decision boundary. Through a systematic empirical investigation, we show that
state-of-the-art deep nets learn connected classification regions, and that the
decision boundary in the vicinity of datapoints is flat along most directions.
We further draw an essential connection between two seemingly unrelated
properties of deep networks: their sensitivity to additive perturbations in the
inputs, and the curvature of their decision boundary. The directions where the
decision boundary is curved in fact remarkably characterize the directions to
which the classifier is the most vulnerable. We finally leverage a fundamental
asymmetry in the curvature of the decision boundary of deep nets, and propose a
method to discriminate between original images, and images perturbed with small
adversarial examples. We show the effectiveness of this purely geometric
approach for detecting small adversarial perturbations in images, and for
recovering the labels of perturbed images.
Adaptive Classification for Prediction Under a Budget
Authors: Feng Nan, Venkatesh Saligrama
Date: 2017-05-26 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Classification, General Classification, Prediction

We propose a novel adaptive approximation approach for test-time
resource-constrained prediction. Given an input instance at test-time, a gating
function identifies a prediction model for the input among a collection of
models. Our objective is to minimize overall average cost without sacrificing
accuracy. We learn gating and prediction models on fully labeled training data
by means of a bottom-up strategy. Our novel bottom-up method first trains a
high-accuracy complex model. Then a low-complexity gating and prediction model
are subsequently learned to adaptively approximate the high-accuracy model in
regions where low-cost models are capable of making highly accurate
predictions. We pose an empirical loss minimization problem with cost
constraints to jointly train gating and prediction models. On a number of
benchmark datasets our method outperforms state-of-the-art achieving higher
accuracy for the same cost.
Residual Expansion Algorithm: Fast and Effective Optimization for Nonconvex Least Squares Problems
Authors: Daiki Ikami, Toshihiko Yamasaki, Kiyoharu Aizawa
Date: 2017-05-26 00:00:00
Proceeding: CVPR 2017 7
Tasks: Blind Image Deblurring, Clustering, Deblurring, global-optimization, Image Deblurring, Quantization

We propose the residual expansion (RE) algorithm: a global (or near-global)
optimization method for nonconvex least squares problems. Unlike most existing
nonconvex optimization techniques, the RE algorithm is not based on either
stochastic or multi-point searches; therefore, it can achieve fast global
optimization. Moreover, the RE algorithm is easy to implement and successful in
high-dimensional optimization. The RE algorithm exhibits excellent empirical
performance in terms of k-means clustering, point-set registration, optimized
product quantization, and blind image deblurring.
Logical and Inequality Implications for Reducing the Size and Complexity of Quadratic Unconstrained Binary Optimization Problems
Authors: Fred Glover, Mark Lewis, Gary Kochenberger
Date: 2017-05-26 00:00:00

The quadratic unconstrained binary optimization (QUBO) problem arises in
diverse optimization applications ranging from Ising spin problems to classical
problems in graph theory and binary discrete optimization. The use of
preprocessing to transform the graph representing the QUBO problem into a
smaller equivalent graph is important for improving solution quality and time
for both exact and metaheuristic algorithms and is a step towards mapping large
scale QUBO to hardware graphs used in quantum annealing computers. In an
earlier paper (Lewis and Glover, 2016) a set of rules was introduced that
achieved significant QUBO reductions as verified through computational testing.
Here this work is extended with additional rules that provide further
reductions that succeed in exactly solving 10% of the benchmark QUBO problems.
An algorithm and associated data structures to efficiently implement the entire
set of rules is detailed and computational experiments are reported that
demonstrate their efficacy.
Fully Automatic Segmentation and Objective Assessment of Atrial Scars for Longstanding Persistent Atrial Fibrillation Patients Using Late Gadolinium-Enhanced MRI
Authors: Guang Yang, Xiahai Zhuang, Habib Khan, Shouvik Haldar, Eva Nyktari, Lei LI, Rick Wage, Xujiong Ye, Greg Slabaugh, Raad Mohiaddin, Tom Wong, Jennifer Keegan, David Firmin
Date: 2017-05-26 00:00:00
Tasks: Anatomy, Heart Segmentation, Segmentation

Purpose: Atrial fibrillation (AF) is the most common cardiac arrhythmia and
is correlated with increased morbidity and mortality. It is associated with
atrial fibrosis, which may be assessed non-invasively using late
gadolinium-enhanced (LGE) magnetic resonance imaging (MRI) where scar tissue is
visualised as a region of signal enhancement. In this study, we proposed a
novel fully automatic pipeline to achieve an accurate and objective atrial
scarring segmentation and assessment of LGE MRI scans for the AF patients.
Methods: Our fully automatic pipeline uniquely combined: (1) a multi-atlas
based whole heart segmentation (MA-WHS) to determine the cardiac anatomy from
an MRI Roadmap acquisition which is then mapped to LGE MRI, and (2) a
super-pixel and supervised learning based approach to delineate the
distribution and extent of atrial scarring in LGE MRI. Results: Both our MA-WHS
and atrial scarring segmentation showed accurate delineations of cardiac
anatomy (mean Dice = 89%) and atrial scarring (mean Dice =79%) respectively
compared to the established ground truth from manual segmentation. Compared
with previously studied methods with manual interventions, our innovative
pipeline demonstrated comparable results, but was computed fully automatically.
Conclusion: The proposed segmentation methods allow LGE MRI to be used as an
objective assessment tool for localisation, visualisation and quantification of
atrial scarring.
Towards meaningful physics from generative models
Authors: Marco Cristoforetti, Giuseppe Jurman, Andrea I. Nardelli, Cesare Furlanello
Date: 2017-05-26 00:00:00
Tasks: valid

In several physical systems, important properties characterizing the system
itself are theoretically related with specific degrees of freedom. Although
standard Monte Carlo simulations provide an effective tool to accurately
reconstruct the physical configurations of the system, they are unable to
isolate the different contributions corresponding to different degrees of
freedom. Here we show that unsupervised deep learning can become a valid
support to MC simulation, coupling useful insights in the phases detection task
with good reconstruction performance. As a testbed we consider the 2D XY model,
showing that a deep neural network based on variational autoencoders can detect
the continuous Kosterlitz-Thouless (KT) transitions, and that, if endowed with
the appropriate constrains, they generate configurations with meaningful
physical content.
A Sampling Theory Perspective of Graph-based Semi-supervised Learning
Authors: Aamir Anis, Aly El Gamal, Salman Avestimehr, Antonio Ortega
Date: 2017-05-26 00:00:00
Tasks: Graph Sampling

Graph-based methods have been quite successful in solving unsupervised and
semi-supervised learning problems, as they provide a means to capture the
underlying geometry of the dataset. It is often desirable for the constructed
graph to satisfy two properties: first, data points that are similar in the
feature space should be strongly connected on the graph, and second, the class
label information should vary smoothly with respect to the graph, where
smoothness is measured using the spectral properties of the graph Laplacian
matrix. Recent works have justified some of these smoothness conditions by
showing that they are strongly linked to the semi-supervised smoothness
assumption and its variants. In this work, we reinforce this connection by
viewing the problem from a graph sampling theoretic perspective, where class
indicator functions are treated as bandlimited graph signals (in the
eigenvector basis of the graph Laplacian) and label prediction as a bandlimited
reconstruction problem. Our approach involves analyzing the bandwidth of class
indicator signals generated from statistical data models with separable and
nonseparable classes. These models are quite general and mimic the nature of
most real-world datasets. Our results show that in the asymptotic limit, the
bandwidth of any class indicator is also closely related to the geometry of the
dataset. This allows one to theoretically justify the assumption of
bandlimitedness of class indicator signals, thereby providing a sampling
theoretic interpretation of graph-based semi-supervised classification.
Biomedical Event Trigger Identification Using Bidirectional Recurrent Neural Network Based Models
Authors: Patchigolla V S S Rahul, Sunil Kumar Sahu, Ashish Anand
Date: 2017-05-26 00:00:00
Proceeding: WS 2017 8
Tasks: Event Extraction, Sentence

Biomedical events describe complex interactions between various biomedical
entities. Event trigger is a word or a phrase which typically signifies the
occurrence of an event. Event trigger identification is an important first step
in all event extraction methods. However many of the current approaches either
rely on complex hand-crafted features or consider features only within a
window. In this paper we propose a method that takes the advantage of recurrent
neural network (RNN) to extract higher level features present across the
sentence. Thus hidden state representation of RNN along with word and entity
type embedding as features avoid relying on the complex hand-crafted features
generated using various NLP toolkits. Our experiments have shown to achieve
state-of-art F1-score on Multi Level Event Extraction (MLEE) corpus. We have
also performed category-wise analysis of the result and discussed the
importance of various features in trigger identification task.
ASR error management for improving spoken language understanding
Authors: Edwin Simonnet, Sahar Ghannay, Nathalie Camelin, Yannick Estève, Renato de Mori
Date: 2017-05-26 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Decoder, Management, speech-recognition, Speech Recognition, Spoken Language Understanding, Word Embeddings

This paper addresses the problem of automatic speech recognition (ASR) error
detection and their use for improving spoken language understanding (SLU)
systems. In this study, the SLU task consists in automatically extracting, from
ASR transcriptions , semantic concepts and concept/values pairs in a e.g
touristic information system. An approach is proposed for enriching the set of
semantic labels with error specific labels and by using a recently proposed
neural approach based on word embeddings to compute well calibrated ASR
confidence measures. Experimental results are reported showing that it is
possible to decrease significantly the Concept/Value Error Rate with a state of
the art system, outperforming previously published results performance on the
same experimental data. It also shown that combining an SLU approach based on
conditional random fields with a neural encoder/decoder attention based
architecture , it is possible to effectively identifying confidence islands and
uncertain semantic output segments useful for deciding appropriate error
handling actions by the dialogue manager strategy .
Learning Robust Features with Incremental Auto-Encoders
Authors: Yanan Li, Donghui Wang
Date: 2017-05-26 00:00:00

Automatically learning features, especially robust features, has attracted
much attention in the machine learning community. In this paper, we propose a
new method to learn non-linear robust features by taking advantage of the data
manifold structure. We first follow the commonly used trick of the trade, that
is learning robust features with artificially corrupted data, which are
training samples with manually injected noise. Following the idea of the
auto-encoder, we first assume features should contain much information to well
reconstruct the input from its corrupted copies. However, merely reconstructing
clean input from its noisy copies could make data manifold in the feature space
noisy. To address this problem, we propose a new method, called Incremental
Auto-Encoders, to iteratively denoise the extracted features. We assume the
noisy manifold structure is caused by a diffusion process. Consequently, we
reverse this specific diffusion process to further contract this noisy
manifold, which results in an incremental optimization of model parameters .
Furthermore, we show these learned non-linear features can be stacked into a
hierarchy of features. Experimental results on real-world datasets demonstrate
the proposed method can achieve better classification performances.
Zero-Shot Learning with Generative Latent Prototype Model
Authors: Yanan Li, Donghui Wang
Date: 2017-05-26 00:00:00
Tasks: model, Transfer Learning, Zero-Shot Learning

Zero-shot learning, which studies the problem of object classification for
categories for which we have no training examples, is gaining increasing
attention from community. Most existing ZSL methods exploit deterministic
transfer learning via an in-between semantic embedding space. In this paper, we
try to attack this problem from a generative probabilistic modelling
perspective. We assume for any category, the observed representation, e.g.
images or texts, is developed from a unique prototype in a latent space, in
which the semantic relationship among prototypes is encoded via linear
reconstruction. Taking advantage of this assumption, virtual instances of
unseen classes can be generated from the corresponding prototype, giving rise
to a novel ZSL model which can alleviate the domain shift problem existing in
the way of direct transfer learning. Extensive experiments on three benchmark
datasets show our proposed model can achieve state-of-the-art results.
Better Text Understanding Through Image-To-Text Transfer
Authors: Karol Kurach, Sylvain Gelly, Michal Jastrzebski, Philip Haeusser, Olivier Teytaud, Damien Vincent, Olivier Bousquet
Date: 2017-05-23 00:00:00
Tasks: Image to text

Generic text embeddings are successfully used in a variety of tasks. However,
they are often learnt by capturing the co-occurrence structure from pure text
corpora, resulting in limitations of their ability to generalize. In this
paper, we explore models that incorporate visual information into the text
representation. Based on comprehensive ablation studies, we propose a
conceptually simple, yet well performing architecture. It outperforms previous
multimodal approaches on a set of well established benchmarks. We also improve
the state-of-the-art results for image-related text datasets, using orders of
magnitude less data.