pwc-archive/papers-with-abstracts huggingface dataset:
Learning Mixture of Gaussians with Streaming Data
Authors: Aditi Raghunathan, Ravishankar Krishnaswamy, Prateek Jain
Date: 2017-07-08 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Clustering

In this paper, we study the problem of learning a mixture of Gaussians with
streaming data: given a stream of $N$ points in $d$ dimensions generated by an
unknown mixture of $k$ spherical Gaussians, the goal is to estimate the model
parameters using a single pass over the data stream. We analyze a streaming
version of the popular Lloyd's heuristic and show that the algorithm estimates
all the unknown centers of the component Gaussians accurately if they are
sufficiently separated. Assuming each pair of centers are $C\sigma$ distant
with $C=\Omega((k\log k)^{1/4}\sigma)$ and where $\sigma^2$ is the maximum
variance of any Gaussian component, we show that asymptotically the algorithm
estimates the centers optimally (up to constants); our center separation
requirement matches the best known result for spherical Gaussians
\citep{vempalawang}. For finite samples, we show that a bias term based on the
initial estimate decreases at $O(1/{\rm poly}(N))$ rate while variance
decreases at nearly optimal rate of $\sigma^2 d/N$.
  Our analysis requires seeding the algorithm with a good initial estimate of
the true cluster centers for which we provide an online PCA based clustering
algorithm. Indeed, the asymptotic per-step time complexity of our algorithm is
the optimal $d\cdot k$ while space complexity of our algorithm is $O(dk\log
k)$.
  In addition to the bias and variance terms which tend to $0$, the
hard-thresholding based updates of streaming Lloyd's algorithm is agnostic to
the data distribution and hence incurs an approximation error that cannot be
avoided. However, by using a streaming version of the classical
(soft-thresholding-based) EM method that exploits the Gaussian distribution
explicitly, we show that for a mixture of two Gaussians the true means can be
estimated consistently, with estimation error decreasing at nearly optimal
rate, and tending to $0$ for $N\rightarrow \infty$.
Evaluating Social Networks Using Task-Focused Network Inference
Authors: Ivan Brugere, Chris Kanich, Tanya Y. Berger-Wolf
Date: 2017-07-08 00:00:00
Tasks: General Classification

Networks are representations of complex underlying social processes. However,
the same given network may be more suitable to model one behavior of
individuals than another. In many cases, aggregate population models may be
more effective than modeling on the network. We present a general framework for
evaluating the suitability of given networks for a set of predictive tasks of
interest, compared against alternative, networks inferred from data. We present
several interpretable network models and measures for our comparison. We apply
this general framework to the case study on collective classification of music
preferences in a newly available dataset of the Last.fm social network.
Efficient Vector Representation for Documents through Corruption
Authors: Minmin Chen
Date: 2017-07-08 00:00:00
Tasks: Document Classification, Representation Learning, Sentiment Analysis, Word Embeddings

We present an efficient document representation learning framework, Document
Vector through Corruption (Doc2VecC). Doc2VecC represents each document as a
simple average of word embeddings. It ensures a representation generated as
such captures the semantic meanings of the document during learning. A
corruption model is included, which introduces a data-dependent regularization
that favors informative or rare words while forcing the embeddings of common
and non-discriminative ones to be close to zero. Doc2VecC produces
significantly better word embeddings than Word2Vec. We compare Doc2VecC with
several state-of-the-art document representation learning algorithms. The
simple model architecture introduced by Doc2VecC matches or out-performs the
state-of-the-art in generating high-quality document representations for
sentiment analysis, document classification as well as semantic relatedness
tasks. The simplicity of the model enables training on billions of words per
hour on a single machine. At the same time, the model is very efficient in
generating representations of unseen documents at test time.
Correlational Dueling Bandits with Application to Clinical Treatment in Large Decision Spaces
Authors: Yanan Sui, Yisong Yue, Joel W. Burdick
Date: 2017-07-08 00:00:00
Tasks: Decision Making, Decision Making Under Uncertainty, Sequential Decision Making

We consider sequential decision making under uncertainty, where the goal is
to optimize over a large decision space using noisy comparative feedback. This
problem can be formulated as a $K$-armed Dueling Bandits problem where $K$ is
the total number of decisions. When $K$ is very large, existing dueling bandits
algorithms suffer huge cumulative regret before converging on the optimal arm.
This paper studies the dueling bandits problem with a large number of arms that
exhibit a low-dimensional correlation structure. Our problem is motivated by a
clinical decision making process in large decision space. We propose an
efficient algorithm CorrDuel which optimizes the exploration/exploitation
tradeoff in this large decision space of clinical treatments. More broadly, our
approach can be applied to other sequential decision problems with large and
structured decision spaces. We derive regret bounds, and evaluate performance
in simulation experiments as well as on a live clinical trial of therapeutic
spinal cord stimulation. To our knowledge, this marks the first time an online
learning algorithm was applied towards spinal cord injury treatments. Our
experimental results show the effectiveness and efficiency of our approach.
Sequential Dialogue Context Modeling for Spoken Language Understanding
Authors: Ankur Bapna, Gokhan Tur, Dilek Hakkani-Tur, Larry Heck
Date: 2017-05-08 00:00:00
Proceeding: WS 2017 8
Tasks: Goal-Oriented Dialogue Systems, Spoken Language Understanding

Spoken Language Understanding (SLU) is a key component of goal oriented
dialogue systems that would parse user utterances into semantic frame
representations. Traditionally SLU does not utilize the dialogue history beyond
the previous system turn and contextual ambiguities are resolved by the
downstream components. In this paper, we explore novel approaches for modeling
dialogue context in a recurrent neural network (RNN) based language
understanding system. We propose the Sequential Dialogue Encoder Network, that
allows encoding context from the dialogue history in chronological order. We
compare the performance of our proposed architecture with two context models,
one that uses just the previous turn context and another that encodes dialogue
context in a memory network, but loses the order of utterances in the dialogue
history. Experiments with a multi-domain dialogue dataset demonstrate that the
proposed architecture results in reduced semantic frame error rates.
Towards Zero-Shot Frame Semantic Parsing for Domain Scaling
Authors: Ankur Bapna, Gokhan Tur, Dilek Hakkani-Tur, Larry Heck
Date: 2017-07-07 00:00:00
Tasks: Semantic Parsing, slot-filling, Slot Filling

State-of-the-art slot filling models for goal-oriented human/machine
conversational language understanding systems rely on deep learning methods.
While multi-task training of such models alleviates the need for large
in-domain annotated datasets, bootstrapping a semantic parsing model for a new
domain using only the semantic frame, such as the back-end API or knowledge
graph schema, is still one of the holy grail tasks of language understanding
for dialogue systems. This paper proposes a deep learning based approach that
can utilize only the slot description in context without the need for any
labeled or unlabeled in-domain examples, to quickly bootstrap a new domain. The
main idea of this paper is to leverage the encoding of the slot names and
descriptions within a multi-task deep learned slot filling model, to implicitly
align slots across domains. The proposed approach is promising for solving the
domain scaling problem and eliminating the need for any manually annotated data
or explicit schema alignment. Furthermore, our experiments on multiple domains
show that this approach results in significantly better slot-filling
performance when compared to using only in-domain data, especially in the low
data regime.
Fast Stochastic Hierarchical Bayesian MAP for Tomographic Imaging
Authors: John McKay, Raghu G. Raj, Vishal Monga
Date: 2017-07-07 00:00:00

Any image recovery algorithm attempts to achieve the highest quality
reconstruction in a timely manner. The former can be achieved in several ways,
among which are by incorporating Bayesian priors that exploit natural image
tendencies to cue in on relevant phenomena. The Hierarchical Bayesian MAP
(HB-MAP) is one such approach which is known to produce compelling results
albeit at a substantial computational cost. We look to provide further analysis
and insights into what makes the HB-MAP work. While retaining the proficient
nature of HB-MAP's Type-I estimation, we propose a stochastic
approximation-based approach to Type-II estimation. The resulting algorithm,
fast stochastic HB-MAP (fsHBMAP), takes dramatically fewer operations while
retaining high reconstruction quality. We employ our fsHBMAP scheme towards the
problem of tomographic imaging and demonstrate that fsHBMAP furnishes promising
results when compared to many competing methods.
Simple to Complex Cross-modal Learning to Rank
Authors: Minnan Luo, Xiaojun Chang, Zhihui Li, Liqiang Nie, Alexander G. Hauptmann, Qinghua Zheng
Date: 2017-02-04 00:00:00
Tasks: Cross-Modal Retrieval, Information Retrieval, Learning Theory, Learning-To-Rank, Retrieval

The heterogeneity-gap between different modalities brings a significant
challenge to multimedia information retrieval. Some studies formalize the
cross-modal retrieval tasks as a ranking problem and learn a shared multi-modal
embedding space to measure the cross-modality similarity. However, previous
methods often establish the shared embedding space based on linear mapping
functions which might not be sophisticated enough to reveal more complicated
inter-modal correspondences. Additionally, current studies assume that the
rankings are of equal importance, and thus all rankings are used
simultaneously, or a small number of rankings are selected randomly to train
the embedding space at each iteration. Such strategies, however, always suffer
from outliers as well as reduced generalization capability due to their lack of
insightful understanding of procedure of human cognition. In this paper, we
involve the self-paced learning theory with diversity into the cross-modal
learning to rank and learn an optimal multi-modal embedding space based on
non-linear mapping functions. This strategy enhances the model's robustness to
outliers and achieves better generalization via training the model gradually
from easy rankings by diverse queries to more complex ones. An efficient
alternative algorithm is exploited to solve the proposed challenging problem
with fast convergence in practice. Extensive experimental results on several
benchmark datasets indicate that the proposed method achieves significant
improvements over the state-of-the-arts in this literature.
A Large-Scale Exploration of Factors Affecting Hand Hygiene Compliance Using Linear Predictive Models
Authors: Michael T. Lash, Jason Slater, Philip M. Polgreen, Alberto M. Segre
Date: 2017-05-06 00:00:00

This large-scale study, consisting of 24.5 million hand hygiene opportunities
spanning 19 distinct facilities in 10 different states, uses linear predictive
models to expose factors that may affect hand hygiene compliance. We examine
the use of features such as temperature, relative humidity, influenza severity,
day/night shift, federal holidays and the presence of new residents in
predicting daily hand hygiene compliance. The results suggest that colder
temperatures and federal holidays have an adverse effect on hand hygiene
compliance rates, and that individual cultures and attitudes regarding hand
hygiene seem to exist among facilities.
Learning Efficient Image Representation for Person Re-Identification
Authors: Yang Yang, Shengcai Liao, Zhen Lei, Stan Z. Li
Date: 2017-07-07 00:00:00
Tasks: Diversity, Person Re-Identification

Color names based image representation is successfully used in person
re-identification, due to the advantages of being compact, intuitively
understandable as well as being robust to photometric variance. However, there
exists the diversity between underlying distribution of color names' RGB values
and that of image pixels' RGB values, which may lead to inaccuracy when
directly comparing them in Euclidean space. In this paper, we propose a new
method named soft Gaussian mapping (SGM) to address this problem. We model the
discrepancies between color names and pixels using a Gaussian and utilize the
inverse of covariance matrix to bridge the gap between them. Based on SGM, an
image could be converted to several soft Gaussian maps. In each soft Gaussian
map, we further seek to establish stable and robust descriptors within a local
region through a max pooling operation. Then, a robust image representation
based on color names is obtained by concatenating the statistical descriptors
in each stripe. When labeled data are available, one discriminative subspace
projection matrix is learned to build efficient representations of an image via
cross-view coupling learning. Experiments on the public datasets - VIPeR,
PRID450S and CUHK03, demonstrate the effectiveness of our method.
Visual Search at eBay
Authors: Fan Yang, Ajinkya Kale, Yury Bubnov, Leon Stein, Qiaosong Wang, Hadi Kiapour, Robinson Piramuthu
Date: 2017-06-10 00:00:00

In this paper, we propose a novel end-to-end approach for scalable visual
search infrastructure. We discuss the challenges we faced for a massive
volatile inventory like at eBay and present our solution to overcome those. We
harness the availability of large image collection of eBay listings and
state-of-the-art deep learning techniques to perform visual search at scale.
Supervised approach for optimized search limited to top predicted categories
and also for compact binary signature are key to scale up without compromising
accuracy and precision. Both use a common deep neural network requiring only a
single forward inference. The system architecture is presented with in-depth
discussions of its basic components and optimizations for a trade-off between
search relevance and latency. This solution is currently deployed in a
distributed cloud infrastructure and fuels visual search in eBay ShopBot and
Close5. We show benchmark on ImageNet dataset on which our approach is faster
and more accurate than several unsupervised baselines. We share our learnings
with the hope that visual search becomes a first class citizen for all large
scale search engines rather than an afterthought.
A parallel corpus of Python functions and documentation strings for automated code documentation and code generation
Authors: Antonio Valerio Miceli Barone, Rico Sennrich
Date: 2017-07-07 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Code Generation, Data Augmentation, Machine Translation, Translation

Automated documentation of programming source code and automated code
generation from natural language are challenging tasks of both practical and
scientific interest. Progress in these areas has been limited by the low
availability of parallel corpora of code and natural language descriptions,
which tend to be small and constrained to specific domains.
  In this work we introduce a large and diverse parallel corpus of a hundred
thousands Python functions with their documentation strings ("docstrings")
generated by scraping open source repositories on GitHub. We describe baseline
results for the code documentation and code generation tasks obtained by neural
machine translation. We also experiment with data augmentation techniques to
further increase the amount of training data.
  We release our datasets and processing scripts in order to stimulate research
in these areas.
Fair Personalization
Authors: L. Elisa Celis, Nisheeth K. Vishnoi
Date: 2017-07-07 00:00:00

Personalization is pervasive in the online space as, when combined with
learning, it leads to higher efficiency and revenue by allowing the most
relevant content to be served to each user. However, recent studies suggest
that such personalization can propagate societal or systemic biases, which has
led to calls for regulatory mechanisms and algorithms to combat inequality.
Here we propose a rigorous algorithmic framework that allows for the
possibility to control biased or discriminatory personalization with respect to
sensitive attributes of users without losing all of the benefits of
personalization.
Full Resolution Image Compression with Recurrent Neural Networks
Authors: George Toderici, Damien Vincent, Nick Johnston, Sung Jin Hwang, David Minnen, Joel Shor, Michele Covell
Date: 2016-08-18 00:00:00
Proceeding: CVPR 2017 7
Tasks: Decoder, Image Compression

This paper presents a set of full-resolution lossy image compression methods
based on neural networks. Each of the architectures we describe can provide
variable compression rates during deployment without requiring retraining of
the network: each network need only be trained once. All of our architectures
consist of a recurrent neural network (RNN)-based encoder and decoder, a
binarizer, and a neural network for entropy coding. We compare RNN types (LSTM,
associative LSTM) and introduce a new hybrid of GRU and ResNet. We also study
"one-shot" versus additive reconstruction architectures and introduce a new
scaled-additive framework. We compare to previous work, showing improvements of
4.3%-8.8% AUC (area under the rate-distortion curve), depending on the
perceptual metric used. As far as we know, this is the first neural network
architecture that is able to outperform JPEG at image compression across most
bitrates on the rate-distortion curve on the Kodak dataset images, with and
without the aid of entropy coding.
Unifying local and non-local signal processing with graph CNNs
Authors: Gilles Puy, Srdan Kitic, Patrick Pérez
Date: 2017-02-24 00:00:00
Tasks: Style Transfer

This paper deals with the unification of local and non-local signal
processing on graphs within a single convolutional neural network (CNN)
framework. Building upon recent works on graph CNNs, we propose to use
convolutional layers that take as inputs two variables, a signal and a graph,
allowing the network to adapt to changes in the graph structure. In this
article, we explain how this framework allows us to design a novel method to
perform style transfer.
Composing graphical models with neural networks for structured representations and fast inference
Authors: Matthew J. Johnson, David Duvenaud, Alexander B. Wiltschko, Sandeep R. Datta, Ryan P. Adams
Date: 2016-03-20 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: Variational Inference

We propose a general modeling and inference framework that composes
probabilistic graphical models with deep learning methods and combines their
respective strengths. Our model family augments graphical structure in latent
variables with neural network observation models. For inference, we extend
variational autoencoders to use graphical model approximating distributions
with recognition networks that output conjugate potentials. All components of
these models are learned simultaneously with a single objective, giving a
scalable algorithm that leverages stochastic variational inference, natural
gradients, graphical model message passing, and the reparameterization trick.
We illustrate this framework with several example models and an application to
mouse behavioral phenotyping.
GPU-Accelerated Algorithms for Compressed Signals Recovery with Application to Astronomical Imagery Deblurring
Authors: Attilio Fiandrotti, Sophie M. Fosson, Chiara Ravazzi, Enrico Magli
Date: 2017-07-07 00:00:00
Tasks: Compressive Sensing, Deblurring, GPU

Compressive sensing promises to enable bandwidth-efficient on-board
compression of astronomical data by lifting the encoding complexity from the
source to the receiver. The signal is recovered off-line, exploiting GPUs
parallel computation capabilities to speedup the reconstruction process.
However, inherent GPU hardware constraints limit the size of the recoverable
signal and the speedup practically achievable. In this work, we design parallel
algorithms that exploit the properties of circulant matrices for efficient
GPU-accelerated sparse signals recovery. Our approach reduces the memory
requirements, allowing us to recover very large signals with limited memory. In
addition, it achieves a tenfold signal recovery speedup thanks to ad-hoc
parallelization of matrix-vector multiplications and matrix inversions.
Finally, we practically demonstrate our algorithms in a typical application of
circulant matrices: deblurring a sparse astronomical image in the compressed
domain.
Generative Adversarial Models for People Attribute Recognition in Surveillance
Authors: Matteo Fabbri, Simone Calderara, Rita Cucchiara
Date: 2017-07-07 00:00:00
Tasks: Attribute, General Classification

In this paper we propose a deep architecture for detecting people attributes
(e.g. gender, race, clothing ...) in surveillance contexts. Our proposal
explicitly deal with poor resolution and occlusion issues that often occur in
surveillance footages by enhancing the images by means of Deep Convolutional
Generative Adversarial Networks (DCGAN). Experiments show that by combining
both our Generative Reconstruction and Deep Attribute Classification Network we
can effectively extract attributes even when resolution is poor and in presence
of strong occlusions up to 80\% of the whole person figure.
The 2017 Hands in the Million Challenge on 3D Hand Pose Estimation
Authors: Shanxin Yuan, Qi Ye, Guillermo Garcia-Hernando, Tae-Kyun Kim
Date: 2017-07-07 00:00:00
Tasks: 3D Hand Pose Estimation, Hand Pose Estimation, Pose Estimation

We present the 2017 Hands in the Million Challenge, a public competition
designed for the evaluation of the task of 3D hand pose estimation. The goal of
this challenge is to assess how far is the state of the art in terms of solving
the problem of 3D hand pose estimation as well as detect major failure and
strength modes of both systems and evaluation metrics that can help to identify
future research directions. The challenge follows up the recent publication of
BigHand2.2M and First-Person Hand Action datasets, which have been designed to
exhaustively cover multiple hand, viewpoint, hand articulation, and occlusion.
The challenge consists of a standardized dataset, an evaluation protocol for
two different tasks, and a public competition. In this document we describe the
different aspects of the challenge and, jointly with the results of the
participants, it will be presented at the 3rd International Workshop on
Observing and Understanding Hands in Action, HANDS 2017, with ICCV 2017.
Computational Models of Tutor Feedback in Language Acquisition
Authors: Jens Nevens, Michael Spranger
Date: 2017-07-07 00:00:00
Tasks: Language Acquisition

This paper investigates the role of tutor feedback in language learning using
computational models. We compare two dominant paradigms in language learning:
interactive learning and cross-situational learning - which differ primarily in
the role of social feedback such as gaze or pointing. We analyze the
relationship between these two paradigms and propose a new mixed paradigm that
combines the two paradigms and allows to test algorithms in experiments that
combine no feedback and social feedback. To deal with mixed feedback
experiments, we develop new algorithms and show how they perform with respect
to traditional knn and prototype approaches.