pwc-archive/papers-with-abstracts huggingface dataset:
Minimax Lower Bounds for Noisy Matrix Completion Under Sparse Factor Models
Authors: Abhinav V. Sambasivan, Jarvis D. Haupt
Date: 2015-10-02 00:00:00
Tasks: Matrix Completion

This paper examines fundamental error characteristics for a general class of
matrix completion problems, where the matrix of interest is a product of two a
priori unknown matrices, one of which is sparse, and the observations are
noisy. Our main contributions come in the form of minimax lower bounds for the
expected per-element squared error for this problem under under several common
noise models. Specifically, we analyze scenarios where the corruptions are
characterized by additive Gaussian noise or additive heavier-tailed (Laplace)
noise, Poisson-distributed observations, and highly-quantized (e.g., one-bit)
observations, as instances of our general result. Our results establish that
the error bounds derived in (Soni et al., 2016) for complexity-regularized
maximum likelihood estimators achieve, up to multiplicative constants and
logarithmic factors, the minimax error rates in each of these noise scenarios,
provided that the nominal number of observations is large enough, and the
sparse factor has (on an average) at least one non-zero per column.
Lip2AudSpec: Speech reconstruction from silent lip movements video
Authors: Hassan Akbari, Himani Arora, Liangliang Cao, Nima Mesgarani
Date: 2017-10-26 00:00:00
Tasks: Lip Reading

In this study, we propose a deep neural network for reconstructing
intelligible speech from silent lip movement videos. We use auditory
spectrogram as spectral representation of speech and its corresponding sound
generation method resulting in a more natural sounding reconstructed speech.
Our proposed network consists of an autoencoder to extract bottleneck features
from the auditory spectrogram which is then used as target to our main lip
reading network comprising of CNN, LSTM and fully connected layers. Our
experiments show that the autoencoder is able to reconstruct the original
auditory spectrogram with a 98% correlation and also improves the quality of
reconstructed speech from the main lip reading network. Our model, trained
jointly on different speakers is able to extract individual speaker
characteristics and gives promising results of reconstructing intelligible
speech with superior word recognition accuracy.
FashionBrain Project: A Vision for Understanding Europe's Fashion Data Universe
Authors: Alessandro Checco, Gianluca Demartini, Alexander Loeser, Ines Arous, Mourad Khayati, Matthias Dantone, Richard Koopmanschap, Svetlin Stalinov, Martin Kersten, Ying Zhang
Date: 2017-10-26 00:00:00

A core business in the fashion industry is the understanding and prediction
of customer needs and trends. Search engines and social networks are at the
same time a fundamental bridge and a costly middleman between the customer's
purchase intention and the retailer. To better exploit Europe's distinctive
characteristics e.g., multiple languages, fashion and cultural differences, it
is pivotal to reduce retailers' dependence to search engines. This goal can be
achieved by harnessing various data channels (manufacturers and distribution
networks, online shops, large retailers, social media, market observers, call
centers, press/magazines etc.) that retailers can leverage in order to gain
more insight about potential buyers, and on the industry trends as a whole.
This can enable the creation of novel on-line shopping experiences, the
detection of influencers, and the prediction of upcoming fashion trends.
  In this paper, we provide an overview of the main research challenges and an
analysis of the most promising technological solutions that we are
investigating in the FashionBrain project.
From Distance Correlation to Multiscale Graph Correlation
Authors: Cencheng Shen, Carey E. Priebe, Joshua T. Vogelstein
Date: 2017-10-26 00:00:00
Tasks: scientific discovery

Understanding and developing a correlation measure that can detect general
dependencies is not only imperative to statistics and machine learning, but
also crucial to general scientific discovery in the big data age. In this
paper, we establish a new framework that generalizes distance correlation --- a
correlation measure that was recently proposed and shown to be universally
consistent for dependence testing against all joint distributions of finite
moments --- to the Multiscale Graph Correlation (MGC). By utilizing the
characteristic functions and incorporating the nearest neighbor machinery, we
formalize the population version of local distance correlations, define the
optimal scale in a given dependency, and name the optimal local correlation as
MGC. The new theoretical framework motivates a theoretically sound Sample MGC
and allows a number of desirable properties to be proved, including the
universal consistency, convergence and almost unbiasedness of the sample
version. The advantages of MGC are illustrated via a comprehensive set of
simulations with linear, nonlinear, univariate, multivariate, and noisy
dependencies, where it loses almost no power in monotone dependencies while
achieving better performance in general dependencies, compared to distance
correlation and other popular methods.
Meta Learning Shared Hierarchies
Authors: Kevin Frans, Jonathan Ho, Xi Chen, Pieter Abbeel, John Schulman
Date: 2017-10-26 00:00:00
Proceeding: ICLR 2018 1
Tasks: Meta-Learning, Reinforcement Learning

We develop a metalearning approach for learning hierarchically structured
policies, improving sample efficiency on unseen tasks through the use of shared
primitives---policies that are executed for large numbers of timesteps.
Specifically, a set of primitives are shared within a distribution of tasks,
and are switched between by task-specific policies. We provide a concrete
metric for measuring the strength of such hierarchies, leading to an
optimization problem for quickly reaching high reward on unseen tasks. We then
present an algorithm to solve this problem end-to-end through the use of any
off-the-shelf reinforcement learning method, by repeatedly sampling new tasks
and resetting task-specific policies. We successfully discover meaningful motor
primitives for the directional movement of four-legged robots, solely by
interacting with distributions of mazes. We also demonstrate the
transferability of primitives to solve long-timescale sparse-reward obstacle
courses, and we enable 3D humanoid robots to robustly walk and crawl with the
same policy.
Variational Grid Setting Network
Authors: Yu-Neng Chuang, Zi-Yu Huang, Yen-Lung Tsai
Date: 2017-09-30 00:00:00

We propose a new neural network architecture for automatic generation of
missing characters in a Chinese font set. We call the neural network
architecture the Variational Grid Setting Network which is based on the
variational autoencoder (VAE) with some tweaks. The neural network model is
able to generate missing characters relatively large in size ($256 \times 256$
pixels). Moreover, we show that one can use very few samples for training data
set, and get a satisfied result.
Deep Spatial Regression Model for Image Crowd Counting
Authors: Haiyan Yao, Kang Han, Wanggen Wan, Li Hou
Date: 2017-10-26 00:00:00
Tasks: Crowd Counting, Density Estimation, model, regression

Computer vision techniques have been used to produce accurate and generic
crowd count estimators in recent years. Due to severe occlusions, appearance
variations, perspective distortions and illumination conditions, crowd counting
is a very challenging task. To this end, we propose a deep spatial regression
model(DSRM) for counting the number of individuals present in a still image
with arbitrary perspective and arbitrary resolution. Our proposed model is
based on Convolutional Neural Network (CNN) and long short term memory (LSTM).
First, we put the images into a pretrained CNN to extract a set of high-level
features. Then the features in adjacent regions are used to regress the local
counts with a LSTM structure which takes the spatial information into
consideration. The final global count is obtained by a sum of the local
patches. We apply our framework on several challenging crowd counting datasets,
and the experiment results illustrate that our method on the crowd counting and
density estimation problem outperforms state-of-the-art methods in terms of
reliability and effectiveness.
Impact of Coreference Resolution on Slot Filling
Authors: Heike Adel, Hinrich Schütze
Date: 2017-10-26 00:00:00
Tasks: coreference-resolution, Coreference Resolution, slot-filling, Slot Filling

In this paper, we demonstrate the importance of coreference resolution for
natural language processing on the example of the TAC Slot Filling shared task.
We illustrate the strengths and weaknesses of automatic coreference resolution
systems and provide experimental results to show that they improve performance
in the slot filling end-to-end setting. Finally, we publish KBPchains, a
resource containing automatically extracted coreference chains from the TAC
source corpus in order to support other researchers working on this topic.
Generalized Value Iteration Networks: Life Beyond Lattices
Authors: Sufeng. Niu, Siheng Chen, Hanyu Guo, Colin Targonski, Melissa C. Smith, Jelena Kovačević
Date: 2017-06-08 00:00:00
Tasks: Q-Learning

In this paper, we introduce a generalized value iteration network (GVIN),
which is an end-to-end neural network planning module. GVIN emulates the value
iteration algorithm by using a novel graph convolution operator, which enables
GVIN to learn and plan on irregular spatial graphs. We propose three novel
differentiable kernels as graph convolution operators and show that the
embedding based kernel achieves the best performance. We further propose
episodic Q-learning, an improvement upon traditional n-step Q-learning that
stabilizes training for networks that contain a planning module. Lastly, we
evaluate GVIN on planning problems in 2D mazes, irregular graphs, and
real-world street networks, showing that GVIN generalizes well for both
arbitrary graphs and unseen graphs of larger scale and outperforms a naive
generalization of VIN (discretizing a spatial graph into a 2D image).
On the Use of Machine Translation-Based Approaches for Vietnamese Diacritic Restoration
Authors: Thai-Hoang Pham, Xuan-Khoai Pham, Phuong Le-Hong
Date: 2017-09-20 00:00:00
Tasks: Machine Translation, Translation, Word Embeddings

This paper presents an empirical study of two machine translation-based
approaches for Vietnamese diacritic restoration problem, including phrase-based
and neural-based machine translation models. This is the first work that
applies neural-based machine translation method to this problem and gives a
thorough comparison to the phrase-based machine translation method which is the
current state-of-the-art method for this problem. On a large dataset, the
phrase-based approach has an accuracy of 97.32% while that of the neural-based
approach is 96.15%. While the neural-based method has a slightly lower
accuracy, it is about twice faster than the phrase-based method in terms of
inference speed. Moreover, neural-based machine translation method has much
room for future improvement such as incorporating pre-trained word embeddings
and collecting more training data.
phi-LSTM: A Phrase-based Hierarchical LSTM Model for Image Captioning
Authors: Ying Hua Tan, Chee Seng Chan
Date: 2016-08-20 00:00:00
Tasks: Image Captioning, Image Description, Sentence

A picture is worth a thousand words. Not until recently, however, we noticed
some success stories in understanding of visual scenes: a model that is able to
detect/name objects, describe their attributes, and recognize their
relationships/interactions. In this paper, we propose a phrase-based
hierarchical Long Short-Term Memory (phi-LSTM) model to generate image
description. The proposed model encodes sentence as a sequence of combination
of phrases and words, instead of a sequence of words alone as in those
conventional solutions. The two levels of this model are dedicated to i) learn
to generate image relevant noun phrases, and ii) produce appropriate image
description from the phrases and other words in the corpus. Adopting a
convolutional neural network to learn image features and the LSTM to learn the
word sequence in a sentence, the proposed model has shown better or competitive
results in comparison to the state-of-the-art models on Flickr8k and Flickr30k
datasets.
WaterGAN: Unsupervised Generative Network to Enable Real-time Color Correction of Monocular Underwater Images
Authors: Jie Li, Katherine A. Skinner, Ryan M. Eustice, Matthew Johnson-Roberson
Date: 2017-02-23 00:00:00
Tasks: Generative Adversarial Network

This paper reports on WaterGAN, a generative adversarial network (GAN) for
generating realistic underwater images from in-air image and depth pairings in
an unsupervised pipeline used for color correction of monocular underwater
images. Cameras onboard autonomous and remotely operated vehicles can capture
high resolution images to map the seafloor, however, underwater image formation
is subject to the complex process of light propagation through the water
column. The raw images retrieved are characteristically different than images
taken in air due to effects such as absorption and scattering, which cause
attenuation of light at different rates for different wavelengths. While this
physical process is well described theoretically, the model depends on many
parameters intrinsic to the water column as well as the objects in the scene.
These factors make recovery of these parameters difficult without simplifying
assumptions or field calibration, hence, restoration of underwater images is a
non-trivial problem. Deep learning has demonstrated great success in modeling
complex nonlinear systems but requires a large amount of training data, which
is difficult to compile in deep sea environments. Using WaterGAN, we generate a
large training dataset of paired imagery, both raw underwater and true color
in-air, as well as depth data. This data serves as input to a novel end-to-end
network for color correction of monocular underwater images. Due to the
depth-dependent water column effects inherent to underwater environments, we
show that our end-to-end network implicitly learns a coarse depth estimate of
the underwater scene from monocular underwater images. Our proposed pipeline is
validated with testing on real data collected from both a pure water tank and
from underwater surveys in field testing. Source code is made publicly
available with sample datasets and pretrained models.
Learning Approximate Stochastic Transition Models
Authors: Yuhang Song, Christopher Grimm, Xianming Wang, Michael L. Littman
Date: 2017-10-26 00:00:00
Tasks: Model-based Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We examine the problem of learning mappings from state to state, suitable for
use in a model-based reinforcement-learning setting, that simultaneously
generalize to novel states and can capture stochastic transitions. We show that
currently popular generative adversarial networks struggle to learn these
stochastic transition models but a modification to their loss functions results
in a powerful learning algorithm for this class of problems.
Improved Workflow for Unsupervised Multiphase Image Segmentation
Authors: Brendan A. West, Taylor S. Hodgdon, Matthew D. Parno, Arnold J. Song
Date: 2017-10-26 00:00:00
Tasks: General Classification, Image Segmentation, Segmentation, Semantic Segmentation

Quantitative image analysis often depends on accurate classification of
pixels through a segmentation process. However, imaging artifacts such as the
partial volume effect and sensor noise complicate the classification process.
These effects increase the pixel intensity variance of each constituent class,
causing intensities from one class to overlap with another. This increased
variance makes threshold based segmentation methods insufficient due to
ambiguous overlap regions in the pixel intensity distributions. The class
ambiguity becomes even more complex for systems with more than two
constituents, such as unsaturated moist granular media. In this paper, we
propose an image processing workflow that improves segmentation accuracy for
multiphase systems. First, the ambiguous transition regions between classes are
identified and removed, which allows for global thresholding of single-class
regions. Then the transition regions are classified using a distance function,
and finally both segmentations are combined into one classified image. This
workflow includes three methodologies for identifying transition pixels and we
demonstrate on a variety of synthetic images that these approaches are able to
accurately separate the ambiguous transition pixels from the single-class
regions. For situations with typical amounts of image noise, misclassification
errors and area differences calculated between each class of the synthetic
images and the resultant segmented images range from 0.69-1.48% and 0.01-0.74%,
respectively, showing the segmentation accuracy of this approach. We
demonstrate that we are able to accurately segment x-ray microtomography images
of moist granular media using these computationally efficient methodologies.
Segment Parameter Labelling in MCMC Mean-Shift Change Detection
Authors: Alireza Ahrabian, Shirin Enshaeifar, Clive Cheong-Took, Payam Barnaghi
Date: 2017-10-26 00:00:00
Tasks: Change Detection, Change Point Detection, Time Series, Time Series Analysis

This work addresses the problem of segmentation in time series data with
respect to a statistical parameter of interest in Bayesian models. It is common
to assume that the parameters are distinct within each segment. As such, many
Bayesian change point detection models do not exploit the segment parameter
patterns, which can improve performance. This work proposes a Bayesian
mean-shift change point detection algorithm that makes use of repetition in
segment parameters, by introducing segment class labels that utilise a
Dirichlet process prior. The performance of the proposed approach was assessed
on both synthetic and real world data, highlighting the enhanced performance
when using parameter labelling.
SRE: Semantic Rules Engine For the Industrial Internet-Of-Things Gateways
Authors: Charbel El Kaed, Imran Khan, Andre Van Den Berg, Hicham Hossayni, Christophe Saint-Marcel
Date: 2017-10-26 00:00:00
Tasks: energy management, Management

The Advent of the Internet-of-Things (IoT) paradigm has brought opportunities
to solve many real-world problems. Energy management, for example, has
attracted huge interest from academia, industries, governments and regulatory
bodies. It involves collecting energy usage data, analyzing it, and optimizing
the energy consumption by applying control strategies. However, in industrial
environments, performing such optimization is not trivial. The changes in
business rules, process control, and customer requirements make it much more
challenging. In this paper, a Semantic Rules Engine (SRE) for industrial
gateways is presented that allows implementing dynamic and flexible rule-based
control strategies. It is simple, expressive, and allows managing rules
on-the-fly without causing any service interruption. Additionally, it can
handle semantic queries and provide results by inferring additional knowledge
from previously defined concepts in ontologies. SRE has been validated and
tested on different hardware platforms and in commercial products. Performance
evaluations are also presented to validate its conformance to the customer
requirements.
Streaming Small-Footprint Keyword Spotting using Sequence-to-Sequence Models
Authors: Yanzhang He, Rohit Prabhavalkar, Kanishka Rao, Wei Li, Anton Bakhtin, Ian McGraw
Date: 2017-10-26 00:00:00
Tasks: General Classification, Keyword Spotting, Language Modeling, Language Modelling, Small-Footprint Keyword Spotting

We develop streaming keyword spotting systems using a recurrent neural
network transducer (RNN-T) model: an all-neural, end-to-end trained,
sequence-to-sequence model which jointly learns acoustic and language model
components. Our models are trained to predict either phonemes or graphemes as
subword units, thus allowing us to detect arbitrary keyword phrases, without
any out-of-vocabulary words. In order to adapt the models to the requirements
of keyword spotting, we propose a novel technique which biases the RNN-T system
towards a specific keyword of interest.
  Our systems are compared against a strong sequence-trained, connectionist
temporal classification (CTC) based "keyword-filler" baseline, which is
augmented with a separate phoneme language model. Overall, our RNN-T system
with the proposed biasing technique significantly improves performance over the
baseline system.
Watch Your Step: Learning Node Embeddings via Graph Attention
Authors: Sami Abu-El-Haija, Bryan Perozzi, Rami Al-Rfou, Alex Alemi
Date: 2017-10-26 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Graph Attention, Graph Embedding, Link Prediction, Node Classification

Graph embedding methods represent nodes in a continuous vector space,
preserving information from the graph (e.g. by sampling random walks). There
are many hyper-parameters to these methods (such as random walk length) which
have to be manually tuned for every graph. In this paper, we replace random
walk hyper-parameters with trainable parameters that we automatically learn via
backpropagation. In particular, we learn a novel attention model on the power
series of the transition matrix, which guides the random walk to optimize an
upstream objective. Unlike previous approaches to attention models, the method
that we propose utilizes attention parameters exclusively on the data (e.g. on
the random walk), and not used by the model for inference. We experiment on
link prediction tasks, as we aim to produce embeddings that best-preserve the
graph structure, generalizing to unseen information. We improve
state-of-the-art on a comprehensive suite of real world datasets including
social, collaboration, and biological networks. Adding attention to random
walks can reduce the error by 20% to 45% on datasets we attempted. Further, our
learned attention parameters are different for every graph, and our
automatically-found values agree with the optimal choice of hyper-parameter if
we manually tune existing methods.
Escape from Cells: Deep Kd-Networks for the Recognition of 3D Point Cloud Models
Authors: Roman Klokov, Victor Lempitsky
Date: 2017-04-04 00:00:00
Proceeding: ICCV 2017 10
Tasks: 3D Part Segmentation, 3D Point Cloud Classification, General Classification, Retrieval

We present a new deep learning architecture (called Kd-network) that is
designed for 3D model recognition tasks and works with unstructured point
clouds. The new architecture performs multiplicative transformations and share
parameters of these transformations according to the subdivisions of the point
clouds imposed onto them by Kd-trees. Unlike the currently dominant
convolutional architectures that usually require rasterization on uniform
two-dimensional or three-dimensional grids, Kd-networks do not rely on such
grids in any way and therefore avoid poor scaling behaviour. In a series of
experiments with popular shape recognition benchmarks, Kd-networks demonstrate
competitive performance in a number of shape recognition tasks such as shape
classification, shape retrieval and shape part segmentation.
ALL-IN-1: Short Text Classification with One Model for All Languages
Authors: Barbara Plank
Date: 2017-10-26 00:00:00
Tasks: All, General Classification, Multilingual text classification, Multilingual Word Embeddings, text-classification, Text Classification, Word Embeddings

We present ALL-IN-1, a simple model for multilingual text classification that
does not require any parallel data. It is based on a traditional Support Vector
Machine classifier exploiting multilingual word embeddings and character
n-grams. Our model is simple, easily extendable yet very effective, overall
ranking 1st (out of 12 teams) in the IJCNLP 2017 shared task on customer
feedback analysis in four languages: English, French, Japanese and Spanish.