pwc-archive/papers-with-abstracts huggingface dataset:
A Framework in CRM Customer Lifecycle: Identify Downward Trend and Potential Issues Detection
Authors: Kun Hu, Zhe Li, Ying Liu, Luyin Cheng, Qi Yang, Yan Li
Date: 2018-02-25 00:00:00
Tasks: Causal Inference, Management, Marketing

Customer retention is one of the primary goals in the area of customer
relationship management. A mass of work exists in which machine learning models
or business rules are established to predict churn. However, targeting users at
an early stage when they start to show a downward trend is a better strategy.
In downward trend prediction, the reasons why customers show a downward trend
is of great interest in the industry as it helps the business to understand the
pain points that customers suffer and to take early action to prevent them from
churning. A commonly used method is to collect feedback from customers by
either aggressively reaching out to them or by passively hearing from them.
However, it is believed that there are a large number of customers who have
unpleasant experiences and never speak out. In the literature, there is limited
research work that provides a comprehensive and scientific approach to identify
these "silent suffers". In this study, we propose a novel two-part framework:
developing the downward prediction process and establishing the methodology to
identify the reasons why customers are in the downward trend. In the first
prediction part, we focus on predicting the downward trend, which is an earlier
stage of the customer lifecycle compared to churn. In the second part, we
propose an approach to figuring out the cause (of the downward trend) based on
a causal inference method and semi-supervised learning. The proposed approach
is capable of identifying potential silent sufferers. We take bad shopping
experiences as inputs to develop the framework and validate it via a marketing
A/B test in the real world. The test readout demonstrates the effectiveness of
the framework by driving 88.5% incremental lift in purchase volume.
Incorporating Discriminator in Sentence Generation: a Gibbs Sampling Method
Authors: Jinyue Su, Jiacheng Xu, Xipeng Qiu, Xuanjing Huang
Date: 2018-02-25 00:00:00
Tasks: Sentence

Generating plausible and fluent sentence with desired properties has long
been a challenge. Most of the recent works use recurrent neural networks (RNNs)
and their variants to predict following words given previous sequence and
target label. In this paper, we propose a novel framework to generate
constrained sentences via Gibbs Sampling. The candidate sentences are revised
and updated iteratively, with sampled new words replacing old ones. Our
experiments show the effectiveness of the proposed method to generate plausible
and diverse sentences.
Meta Multi-Task Learning for Sequence Modeling
Authors: Junkun Chen, Xipeng Qiu, Pengfei Liu, Xuanjing Huang
Date: 2018-02-25 00:00:00
Tasks: Multi-Task Learning, Representation Learning, Semantic Composition, text-classification, Text Classification

Semantic composition functions have been playing a pivotal role in neural
representation learning of text sequences. In spite of their success, most
existing models suffer from the underfitting problem: they use the same shared
compositional function on all the positions in the sequence, thereby lacking
expressive power due to incapacity to capture the richness of compositionality.
Besides, the composition functions of different tasks are independent and
learned from scratch. In this paper, we propose a new sharing scheme of
composition function across multiple tasks. Specifically, we use a shared
meta-network to capture the meta-knowledge of semantic composition and generate
the parameters of the task-specific semantic composition models. We conduct
extensive experiments on two types of tasks, text classification and sequence
tagging, which demonstrate the benefits of our approach. Besides, we show that
the shared meta-knowledge learned by our proposed model can be regarded as
off-the-shelf knowledge and easily transferred to new tasks.
Overcoming Exploration in Reinforcement Learning with Demonstrations
Authors: Ashvin Nair, Bob McGrew, Marcin Andrychowicz, Wojciech Zaremba, Pieter Abbeel
Date: 2017-09-28 00:00:00
Tasks: continuous-control, Continuous Control, Reinforcement Learning, Reinforcement Learning (RL)

Exploration in environments with sparse rewards has been a persistent problem
in reinforcement learning (RL). Many tasks are natural to specify with a sparse
reward, and manually shaping a reward function can result in suboptimal
performance. However, finding a non-zero reward is exponentially more difficult
with increasing task horizon or action dimensionality. This puts many
real-world tasks out of practical reach of RL methods. In this work, we use
demonstrations to overcome the exploration problem and successfully learn to
perform long-horizon, multi-step robotics tasks with continuous control such as
stacking blocks with a robot arm. Our method, which builds on top of Deep
Deterministic Policy Gradients and Hindsight Experience Replay, provides an
order of magnitude of speedup over RL on simulated robotics tasks. It is simple
to implement and makes only the additional assumption that we can collect a
small set of demonstrations. Furthermore, our method is able to solve tasks not
solvable by either RL or behavior cloning alone, and often ends up
outperforming the demonstrator policy.
Bonnet: An Open-Source Training and Deployment Framework for Semantic Segmentation in Robotics using CNNs
Authors: Andres Milioto, Cyrill Stachniss
Date: 2018-02-25 00:00:00
Tasks: Semantic Segmentation

The ability to interpret a scene is an important capability for a robot that
is supposed to interact with its environment. The knowledge of what is in front
of the robot is, for example, relevant for navigation, manipulation, or
planning. Semantic segmentation labels each pixel of an image with a class
label and thus provides a detailed semantic annotation of the surroundings to
the robot. Convolutional neural networks (CNNs) are popular methods for
addressing this type of problem. The available software for training and the
integration of CNNs for real robots, however, is quite fragmented and often
difficult to use for non-experts, despite the availability of several
high-quality open-source frameworks for neural network implementation and
training. In this paper, we propose a tool called Bonnet, which addresses this
fragmentation problem by building a higher abstraction that is specific for the
semantic segmentation task. It provides a modular approach to simplify the
training of a semantic segmentation CNN independently of the used dataset and
the intended task. Furthermore, we also address the deployment on a real
robotic platform. Thus, we do not propose a new CNN approach in this paper.
Instead, we provide a stable and easy-to-use tool to make this technology more
approachable in the context of autonomous systems. In this sense, we aim at
closing a gap between computer vision research and its use in robotics
research. We provide an open-source codebase for training and deployment. The
training interface is implemented in Python using TensorFlow and the deployment
interface provides a C++ library that can be easily integrated in an existing
robotics codebase, a ROS node, and two standalone applications for label
prediction in images and videos.
Correlation Flow: Robust Optical Flow Using Kernel Cross-Correlators
Authors: Chen Wang, Tete Ji, Thien-Minh Nguyen, Lihua Xie
Date: 2018-02-20 00:00:00
Tasks: Autonomous Navigation, Optical Flow Estimation, Position, Robot Navigation

Robust velocity and position estimation is crucial for autonomous robot
navigation. The optical flow based methods for autonomous navigation have been
receiving increasing attentions in tandem with the development of micro
unmanned aerial vehicles. This paper proposes a kernel cross-correlator (KCC)
based algorithm to determine optical flow using a monocular camera, which is
named as correlation flow (CF). Correlation flow is able to provide reliable
and accurate velocity estimation and is robust to motion blur. In addition, it
can also estimate the altitude velocity and yaw rate, which are not available
by traditional methods. Autonomous flight tests on a quadcopter show that
correlation flow can provide robust trajectory estimation with very low
processing power. The source codes are released based on the ROS framework.
A Generic Inverted Index Framework for Similarity Search on the GPU - Technical Report
Authors: Jingbo Zhou, Qi Guo, H. V. Jagadish, Luboš Krčál, Siyuan Liu, Wenhao Luan, Anthony K. H. Tung, Yueji Yang, Yuxin Zheng
Date: 2016-03-28 00:00:00
Tasks: GPU

We propose a novel generic inverted index framework on the GPU (called
GENIE), aiming to reduce the programming complexity of the GPU for parallel
similarity search of different data types. Not every data type and similarity
measure are supported by GENIE, but many popular ones are. We present the
system design of GENIE, and demonstrate similarity search with GENIE on several
data types along with a theoretical analysis of search results. A new concept
of locality sensitive hashing (LSH) named $\tau$-ANN search, and a novel data
structure c-PQ on the GPU are also proposed for achieving this purpose.
Extensive experiments on different real-life datasets demonstrate the
efficiency and effectiveness of our framework. The implemented system has been
released as open source.
TAMU at KBP 2017: Event Nugget Detection and Coreference Resolution
Authors: Prafulla Kumar Choubey, Ruihong Huang
Date: 2017-11-06 00:00:00
Tasks: coreference-resolution, Coreference Resolution, Event Coreference Resolution

In this paper, we describe TAMU's system submitted to the TAC KBP 2017 event
nugget detection and coreference resolution task. Our system builds on the
statistical and empirical observations made on training and development data.
We found that modifiers of event nuggets tend to have unique syntactic
distribution. Their parts-of-speech tags and dependency relations provides them
essential characteristics that are useful in identifying their span and also
defining their types and realis status. We further found that the joint
modeling of event span detection and realis status identification performs
better than the individual models for both tasks. Our simple system designed
using minimal features achieved the micro-average F1 scores of 57.72, 44.27 and
42.47 for event span detection, type identification and realis status
classification tasks respectively. Also, our system achieved the CoNLL F1 score
of 27.20 in event coreference resolution task.
A Simple Neural Attentive Meta-Learner
Authors: Nikhil Mishra, Mostafa Rohaninejad, Xi Chen, Pieter Abbeel
Date: 2017-07-11 00:00:00
Proceeding: ICLR 2018 1
Tasks: Few-Shot Image Classification, Meta-Learning, Reinforcement Learning

Deep neural networks excel in regimes with large amounts of data, but tend to
struggle when data is scarce or when they need to adapt quickly to changes in
the task. In response, recent work in meta-learning proposes training a
meta-learner on a distribution of similar tasks, in the hopes of generalization
to novel but related tasks by learning a high-level strategy that captures the
essence of the problem it is asked to solve. However, many recent meta-learning
approaches are extensively hand-designed, either using architectures
specialized to a particular application, or hard-coding algorithmic components
that constrain how the meta-learner solves the task. We propose a class of
simple and generic meta-learner architectures that use a novel combination of
temporal convolutions and soft attention; the former to aggregate information
from past experience and the latter to pinpoint specific pieces of information.
In the most extensive set of meta-learning experiments to date, we evaluate the
resulting Simple Neural AttentIve Learner (or SNAIL) on several
heavily-benchmarked tasks. On all tasks, in both supervised and reinforcement
learning, SNAIL attains state-of-the-art performance by significant margins.
Teacher Improves Learning by Selecting a Training Subset
Authors: Yuzhe Ma, Robert Nowak, Philippe Rigollet, Xuezhou Zhang, Xiaojin Zhu
Date: 2018-02-25 00:00:00
Tasks: General Classification, regression

We call a learner super-teachable if a teacher can trim down an iid training
set while making the learner learn even better. We provide sharp super-teaching
guarantees on two learners: the maximum likelihood estimator for the mean of a
Gaussian, and the large margin classifier in 1D. For general learners, we
provide a mixed-integer nonlinear programming-based algorithm to find a super
teaching set. Empirical experiments show that our algorithm is able to find
good super-teaching sets for both regression and classification problems.
DID: Distributed Incremental Block Coordinate Descent for Nonnegative Matrix Factorization
Authors: Tianxiang Gao, Chris Chu
Date: 2018-02-25 00:00:00
Tasks: Dimensionality Reduction

Nonnegative matrix factorization (NMF) has attracted much attention in the
last decade as a dimension reduction method in many applications. Due to the
explosion in the size of data, naturally the samples are collected and stored
distributively in local computational nodes. Thus, there is a growing need to
develop algorithms in a distributed memory architecture. We propose a novel
distributed algorithm, called \textit{distributed incremental block coordinate
descent} (DID), to solve the problem. By adapting the block coordinate descent
framework, closed-form update rules are obtained in DID. Moreover, DID performs
updates incrementally based on the most recently updated residual matrix. As a
result, only one communication step per iteration is required. The correctness,
efficiency, and scalability of the proposed algorithm are verified in a series
of numerical experiments.
Online Boosting Algorithms for Multi-label Ranking
Authors: Young Hun Jung, Ambuj Tewari
Date: 2017-10-23 00:00:00
Tasks: Multi-Label Learning

We consider the multi-label ranking approach to multi-label learning.
Boosting is a natural method for multi-label ranking as it aggregates weak
predictions through majority votes, which can be directly used as scores to
produce a ranking of the labels. We design online boosting algorithms with
provable loss bounds for multi-label ranking. We show that our first algorithm
is optimal in terms of the number of learners required to attain a desired
accuracy, but it requires knowledge of the edge of the weak learners. We also
design an adaptive algorithm that does not require this knowledge and is hence
more practical. Experimental results on real data sets demonstrate that our
algorithms are at least as good as existing batch boosting algorithms.
Online Multiclass Boosting
Authors: Young Hun Jung, Jack Goetz, Ambuj Tewari
Date: 2017-02-23 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Binary Classification, General Classification

Recent work has extended the theoretical analysis of boosting algorithms to
multiclass problems and to online settings. However, the multiclass extension
is in the batch setting and the online extensions only consider binary
classification. We fill this gap in the literature by defining, and justifying,
a weak learning condition for online multiclass boosting. This condition leads
to an optimal boosting algorithm that requires the minimal number of weak
learners to achieve a certain accuracy. Additionally, we propose an adaptive
algorithm which is near optimal and enjoys an excellent performance on real
data due to its adaptive property.
On the Reconstruction Risk of Convolutional Sparse Dictionary Learning
Authors: Shashank Singh, Barnabás Póczos, Jian Ma
Date: 2017-08-29 00:00:00
Tasks: Dictionary Learning, Time Series, Time Series Analysis

Sparse dictionary learning (SDL) has become a popular method for adaptively
identifying parsimonious representations of a dataset, a fundamental problem in
machine learning and signal processing. While most work on SDL assumes a
training dataset of independent and identically distributed samples, a variant
known as convolutional sparse dictionary learning (CSDL) relaxes this
assumption, allowing more general sequential data sources, such as time series
or other dependent data. Although recent work has explored the statistical
properties of classical SDL, the statistical properties of CSDL remain
unstudied. This paper begins to study this by identifying the minimax
convergence rate of CSDL in terms of reconstruction risk, by both upper
bounding the risk of an established CSDL estimator and proving a matching
information-theoretic lower bound. Our results indicate that consistency in
reconstruction risk is possible precisely in the `ultra-sparse' setting, in
which the sparsity (i.e., the number of feature occurrences) is in $o(N)$ in
terms of the length N of the training sequence. Notably, our results make very
weak assumptions, allowing arbitrary dictionaries and dependent measurement
noise. Finally, we verify our theoretical results with numerical experiments on
synthetic data.
Generating retinal flow maps from structural optical coherence tomography with artificial intelligence
Authors: Cecilia S. Lee, Ariel J. Tyring, Yue Wu, Sa Xiao, Ariel S. Rokem, Nicolaas P. Deruyter, Qinqin Zhang, Adnan Tufail, Ruikang K. Wang, Aaron Y. Lee
Date: 2018-02-24 00:00:00

Despite significant advances in artificial intelligence (AI) for computer
vision, its application in medical imaging has been limited by the burden and
limits of expert-generated labels. We used images from optical coherence
tomography angiography (OCTA), a relatively new imaging modality that measures
perfusion of the retinal vasculature, to train an AI algorithm to generate
vasculature maps from standard structural optical coherence tomography (OCT)
images of the same retinae, both exceeding the ability and bypassing the need
for expert labeling. Deep learning was able to infer perfusion of
microvasculature from structural OCT images with similar fidelity to OCTA and
significantly better than expert clinicians (P < 0.00001). OCTA suffers from
need of specialized hardware, laborious acquisition protocols, and motion
artifacts; whereas our model works directly from standard OCT which are
ubiquitous and quick to obtain, and allows unlocking of large volumes of
previously collected standard OCT data both in existing clinical trials and
clinical practice. This finding demonstrates a novel application of AI to
medical imaging, whereby subtle regularities between different modalities are
used to image the same body part and AI is used to generate detailed and
accurate inferences of tissue function from structure imaging.
Time Series Learning using Monotonic Logical Properties
Authors: Marcell Vazquez-Chanlatte, Shromona Ghosh, Jyotirmoy V. Deshmukh, Alberto Sangiovanni-Vincentelli, Sanjit A. Seshia
Date: 2018-02-24 00:00:00
Tasks: Time Series, Time Series Analysis

Cyber-physical systems of today are generating large volumes of time-series
data. As manual inspection of such data is not tractable, the need for learning
methods to help discover logical structure in the data has increased. We
propose a logic-based framework that allows domain-specific knowledge to be
embedded into formulas in a parametric logical specification over time-series
data. The key idea is to then map a time series to a surface in the parameter
space of the formula. Given this mapping, we identify the Hausdorff distance
between boundaries as a natural distance metric between two time-series data
under the lens of the parametric specification. This enables embedding
non-trivial domain-specific knowledge into the distance metric and then using
off-the-shelf machine learning tools to label the data. After labeling the
data, we demonstrate how to extract a logical specification for each label.
Finally, we showcase our technique on real world traffic data to learn
classifiers/monitors for slow-downs and traffic jams.
An Adversarial Neuro-Tensorial Approach For Learning Disentangled Representations
Authors: Mengjiao Wang, Zhixin Shu, Shiyang Cheng, Yannis Panagakis, Dimitris Samaras, Stefanos Zafeiriou
Date: 2017-11-28 00:00:00
Tasks: 3D Face Reconstruction, Face Reconstruction

Several factors contribute to the appearance of an object in a visual scene,
including pose, illumination, and deformation, among others. Each factor
accounts for a source of variability in the data, while the multiplicative
interactions of these factors emulate the entangled variability, giving rise to
the rich structure of visual object appearance. Disentangling such unobserved
factors from visual data is a challenging task, especially when the data have
been captured in uncontrolled recording conditions (also referred to as
"in-the-wild") and label information is not available.
  In this paper, we propose the first unsupervised deep learning method (with
pseudo-supervision) for disentangling multiple latent factors of variation in
face images captured in-the-wild. To this end, we propose a deep latent
variable model, where the multiplicative interactions of multiple latent
factors of variation are explicitly modelled by means of multilinear (tensor)
structure. We demonstrate that the proposed approach indeed learns disentangled
representations of facial expressions and pose, which can be used in various
applications, including face editing, as well as 3D face reconstruction and
classification of facial expression, identity and pose.
Factorization tricks for LSTM networks
Authors: Oleksii Kuchaiev, Boris Ginsburg
Date: 2017-03-31 00:00:00
Tasks: Language Modelling

We present two simple ways of reducing the number of parameters and
accelerating the training of large Long Short-Term Memory (LSTM) networks: the
first one is "matrix factorization by design" of LSTM matrix into the product
of two smaller matrices, and the second one is partitioning of LSTM matrix, its
inputs and states into the independent groups. Both approaches allow us to
train large LSTM networks significantly faster to the near state-of the art
perplexity while using significantly less RNN parameters.
The Search Problem in Mixture Models
Authors: Avik Ray, Joe Neeman, Sujay Sanghavi, Sanjay Shakkottai
Date: 2016-10-04 00:00:00
Tasks: Clustering, Topic Models

We consider the task of learning the parameters of a {\em single} component
of a mixture model, for the case when we are given {\em side information} about
that component, we call this the "search problem" in mixture models. We would
like to solve this with computational and sample complexity lower than solving
the overall original problem, where one learns parameters of all components.
  Our main contributions are the development of a simple but general model for
the notion of side information, and a corresponding simple matrix-based
algorithm for solving the search problem in this general setting. We then
specialize this model and algorithm to four common scenarios: Gaussian mixture
models, LDA topic models, subspace clustering, and mixed linear regression. For
each one of these we show that if (and only if) the side information is
informative, we obtain parameter estimates with greater accuracy, and also
improved computation complexity than existing moment based mixture model
algorithms (e.g. tensor methods). We also illustrate several natural ways one
can obtain such side information, for specific problem instances. Our
experiments on real data sets (NY Times, Yelp, BSDS500) further demonstrate the
practicality of our algorithms showing significant improvement in runtime and
accuracy.