pwc-archive/papers-with-abstracts huggingface dataset:
Space-Filling Curve Indices as Acceleration Structure for Exemplar-Based Inpainting
Authors: Tim Dahmen, Patrick Trampert, Pascal Peter, Pinak Bheed, Joachim Weickert, Philipp Slusallek
Date: 2017-12-18 00:00:00
Tasks: Dimensionality Reduction

Exemplar-based inpainting is the process of reconstructing missing parts of an image by searching the remaining data for patches that fit seamlessly. The image is completed to a plausible-looking solution by repeatedly inserting the patch that is the best match according to some cost function. We present an acceleration structure that uses a multi-index scheme to accelerate this search procedure drastically, particularly in the case of very large datasets. The index scheme uses ideas such as dimensionality reduction and k-nearest neighbor search on space-filling curves that are well known in the field of multimedia databases. Our method has a theoretic runtime of O(log2 n) per iteration and reaches a speedup factor of up to 660 over the original method. The approach has the advantage of being agnostic to most modelbased parts of exemplar-based inpainting such as the order in which patches are processed and the cost function used to determine patch similarity. Thus, the acceleration structure can be used in conjunction with most exemplar-based inpainting algorithms.
Alternating Optimisation and Quadrature for Robust Control
Authors: Supratik Paul, Konstantinos Chatzilygeroudis, Kamil Ciosek, Jean-Baptiste Mouret, Michael A. Osborne, Shimon Whiteson
Date: 2016-05-24 00:00:00
Tasks: Bayesian Optimisation, Reinforcement Learning

Bayesian optimisation has been successfully applied to a variety of
reinforcement learning problems. However, the traditional approach for learning
optimal policies in simulators does not utilise the opportunity to improve
learning by adjusting certain environment variables: state features that are
unobservable and randomly determined by the environment in a physical setting
but are controllable in a simulator. This paper considers the problem of
finding a robust policy while taking into account the impact of environment
variables. We present Alternating Optimisation and Quadrature (ALOQ), which
uses Bayesian optimisation and Bayesian quadrature to address such settings.
ALOQ is robust to the presence of significant rare events, which may not be
observable under random sampling, but play a substantial role in determining
the optimal policy. Experimental results across different domains show that
ALOQ can learn more efficiently and robustly than existing methods.
A Chinese Dataset with Negative Full Forms for General Abbreviation Prediction
Authors: Yi Zhang, Xu sun
Date: 2017-12-18 00:00:00
Proceeding: LREC 2018 5
Tasks: Form, Prediction, valid

Abbreviation is a common phenomenon across languages, especially in Chinese.
In most cases, if an expression can be abbreviated, its abbreviation is used
more often than its fully expanded forms, since people tend to convey
information in a most concise way. For various language processing tasks,
abbreviation is an obstacle to improving the performance, as the textual form
of an abbreviation does not express useful information, unless it's expanded to
the full form. Abbreviation prediction means associating the fully expanded
forms with their abbreviations. However, due to the deficiency in the
abbreviation corpora, such a task is limited in current studies, especially
considering general abbreviation prediction should also include those full form
expressions that do not have valid abbreviations, namely the negative full
forms (NFFs). Corpora incorporating negative full forms for general
abbreviation prediction are few in number. In order to promote the research in
this area, we build a dataset for general Chinese abbreviation prediction,
which needs a few preprocessing steps, and evaluate several different models on
the built dataset. The dataset is available at
https://github.com/lancopku/Chinese-abbreviation-dataset
Low Resourced Machine Translation via Morpho-syntactic Modeling: The Case of Dialectal Arabic
Authors: Alexander Erdmann, Nizar Habash, Dima Taji, Houda Bouamor
Date: 2017-12-18 00:00:00
Proceeding: MTSummit 2017 9
Tasks: Machine Translation, Translation

We present the second ever evaluated Arabic dialect-to-dialect machine
translation effort, and the first to leverage external resources beyond a small
parallel corpus. The subject has not previously received serious attention due
to lack of naturally occurring parallel data; yet its importance is evidenced
by dialectal Arabic's wide usage and breadth of inter-dialect variation,
comparable to that of Romance languages. Our results suggest that modeling
morphology and syntax significantly improves dialect-to-dialect translation,
though optimizing such data-sparse models requires consideration of the
linguistic differences between dialects and the nature of available data and
resources. On a single-reference blind test set where untranslated input scores
6.5 BLEU and a model trained only on parallel data reaches 14.6, pivot
techniques and morphosyntactic modeling significantly improve performance to
17.5.
Automated flow for compressing convolution neural networks for efficient edge-computation with FPGA
Authors: Farhan Shafiq, Takato Yamada, Antonio T. Vilchez, Sakyasingha Dasgupta
Date: 2017-12-18 00:00:00
Tasks: CPU, object-detection, Object Detection, Quantization

Deep convolutional neural networks (CNN) based solutions are the current
state- of-the-art for computer vision tasks. Due to the large size of these
models, they are typically run on clusters of CPUs or GPUs. However, power
requirements and cost budgets can be a major hindrance in adoption of CNN for
IoT applications. Recent research highlights that CNN contain significant
redundancy in their structure and can be quantized to lower bit-width
parameters and activations, while maintaining acceptable accuracy. Low
bit-width and especially single bit-width (binary) CNN are particularly
suitable for mobile applications based on FPGA implementation, due to the
bitwise logic operations involved in binarized CNN. Moreover, the transition to
lower bit-widths opens new avenues for performance optimizations and model
improvement. In this paper, we present an automatic flow from trained
TensorFlow models to FPGA system on chip implementation of binarized CNN. This
flow involves quantization of model parameters and activations, generation of
network and model in embedded-C, followed by automatic generation of the FPGA
accelerator for binary convolutions. The automated flow is demonstrated through
implementation of binarized "YOLOV2" on the low cost, low power Cyclone- V FPGA
device. Experiments on object detection using binarized YOLOV2 demonstrate
significant performance benefit in terms of model size and inference speed on
FPGA as compared to CPU and mobile CPU platforms. Furthermore, the entire
automated flow from trained models to FPGA synthesis can be completed within
one hour.
Recurrent Ladder Networks
Authors: Isabeau Prémont-Schwarz, Alexander Ilin, Tele Hotloo Hao, Antti Rasmus, Rinu Boney, Harri Valpola
Date: 2017-07-28 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Music Modeling

We propose a recurrent extension of the Ladder networks whose structure is
motivated by the inference required in hierarchical latent variable models. We
demonstrate that the recurrent Ladder is able to handle a wide variety of
complex learning tasks that benefit from iterative inference and temporal
modeling. The architecture shows close-to-optimal results on temporal modeling
of video data, competitive results on music modeling, and improved perceptual
grouping based on higher order abstractions, such as stochastic textures and
motion cues. We present results for fully supervised, semi-supervised, and
unsupervised tasks. The results suggest that the proposed architecture and
principles are powerful tools for learning a hierarchy of abstractions,
learning iterative inference and handling temporal information.
Deep Neural Generative Model of Functional MRI Images for Psychiatric Disorder Diagnosis
Authors: Takashi Matsubara, Tetsuo Tashiro, Kuniaki Uehara
Date: 2017-12-18 00:00:00
Tasks: Diagnostic, Functional Connectivity

Accurate diagnosis of psychiatric disorders plays a critical role in
improving the quality of life for patients and potentially supports the
development of new treatments. Many studies have been conducted on machine
learning techniques that seek brain imaging data for specific biomarkers of
disorders. These studies have encountered the following dilemma: A direct
classification overfits to a small number of high-dimensional samples but
unsupervised feature-extraction has the risk of extracting a signal of no
interest. In addition, such studies often provided only diagnoses for patients
without presenting the reasons for these diagnoses. This study proposed a deep
neural generative model of resting-state functional magnetic resonance imaging
(fMRI) data. The proposed model is conditioned by the assumption of the
subject's state and estimates the posterior probability of the subject's state
given the imaging data, using Bayes' rule. This study applied the proposed
model to diagnose schizophrenia and bipolar disorders. Diagnostic accuracy was
improved by a large margin over competitive approaches, namely classifications
of functional connectivity, discriminative/generative models of region-wise
signals, and those with unsupervised feature-extractors. The proposed model
visualizes brain regions largely related to the disorders, thus motivating
further biological investigation.
Misspecified Nonconvex Statistical Optimization for Phase Retrieval
Authors: Zhuoran Yang, Lin F. Yang, Ethan X. Fang, Tuo Zhao, Zhaoran Wang, Matey Neykov
Date: 2017-12-18 00:00:00
Tasks: Retrieval

Existing nonconvex statistical optimization theory and methods crucially rely
on the correct specification of the underlying "true" statistical models. To
address this issue, we take a first step towards taming model misspecification
by studying the high-dimensional sparse phase retrieval problem with
misspecified link functions. In particular, we propose a simple variant of the
thresholded Wirtinger flow algorithm that, given a proper initialization,
linearly converges to an estimator with optimal statistical accuracy for a
broad family of unknown link functions. We further provide extensive numerical
experiments to support our theoretical findings.
Sparse hierarchical interaction learning with epigraphical projection
Authors: Mingyuan Jiu, Nelly Pustelnik, Stefan Janaqi, Mériam Chebre, Lin Qi, Philippe Ricoux
Date: 2017-05-22 00:00:00
Tasks: Additive models

This work focuses on learning optimization problems with quadratical interactions between variables, which go beyond the additive models of traditional linear learning. We investigate more specifically two different methods encountered in the literature to deal with this problem: "hierNet" and structured-sparsity regularization, and study their connections. We propose a primal-dual proximal algorithm based on an epigraphical projection to optimize a general formulation of these learning problems. The experimental setting first highlights the improvement of the proposed procedure compared to state-of-the-art methods based on fast iterative shrinkage-thresholding algorithm (i.e. FISTA) or alternating direction method of multipliers (i.e. ADMM), and then, using the proposed flexible optimization framework, we provide fair comparisons between the different hierarchical penalizations and their improvement over the standard $\ell_1$-norm penalization. The experiments are conducted both on synthetic and real data, and they clearly show that the proposed primal-dual proximal algorithm based on epigraphical projection is efficient and effective to solve and investigate the problem of hierarchical interaction learning.
Panoramic Robust PCA for Foreground-Background Separation on Noisy, Free-Motion Camera Video
Authors: Brian E. Moore, Chen Gao, Raj Rao Nadakuditi
Date: 2017-12-18 00:00:00

This work presents a new robust PCA method for foreground-background
separation on freely moving camera video with possible dense and sparse
corruptions. Our proposed method registers the frames of the corrupted video
and then encodes the varying perspective arising from camera motion as missing
data in a global model. This formulation allows our algorithm to produce a
panoramic background component that automatically stitches together corrupted
data from partially overlapping frames to reconstruct the full field of view.
We model the registered video as the sum of a low-rank component that captures
the background, a smooth component that captures the dynamic foreground of the
scene, and a sparse component that isolates possible outliers and other sparse
corruptions in the video. The low-rank portion of our model is based on a
recent low-rank matrix estimator (OptShrink) that has been shown to yield
superior low-rank subspace estimates in practice. To estimate the smooth
foreground component of our model, we use a weighted total variation framework
that enables our method to reliably decouple the true foreground of the video
from sparse corruptions. We perform extensive numerical experiments on both
static and moving camera video subject to a variety of dense and sparse
corruptions. Our experiments demonstrate the state-of-the-art performance of
our proposed method compared to existing methods both in terms of foreground
and background estimation accuracy.
Visual Explanations from Hadamard Product in Multimodal Deep Networks
Authors: Jin-Hwa Kim, Byoung-Tak Zhang
Date: 2017-12-18 00:00:00
Tasks: Question Answering, Visual Question Answering, Visual Question Answering (VQA)

The visual explanation of learned representation of models helps to
understand the fundamentals of learning. The attentional models of previous
works used to visualize the attended regions over an image or text using their
learned weights to confirm their intended mechanism. Kim et al. (2016) show
that the Hadamard product in multimodal deep networks, which is well-known for
the joint function of visual question answering tasks, implicitly performs an
attentional mechanism for visual inputs. In this work, we extend their work to
show that the Hadamard product in multimodal deep networks performs not only
for visual inputs but also for textual inputs simultaneously using the proposed
gradient-based visualization technique. The attentional effect of Hadamard
product is visualized for both visual and textual inputs by analyzing the two
inputs and an output of the Hadamard product with the proposed method and
compared with learned attentional weights of a visual question answering model.
Lung Nodule Classification by the Combination of Fusion Classifier and Cascaded Convolutional Neural Networks
Authors: Masaharu Sakamoto, Hiroki Nakano, Kun Zhao, Taro Sekiyama
Date: 2017-11-19 00:00:00
Tasks: General Classification, Lung Nodule Classification

Lung nodule classification is a class imbalanced problem, as nodules are
found with much lower frequency than non-nodules. In the class imbalanced
problem, conventional classifiers tend to be overwhelmed by the majority class
and ignore the minority class. We showed that cascaded convolutional neural
networks can classify the nodule candidates precisely for a class imbalanced
nodule candidate data set in our previous study. In this paper, we propose
Fusion classifier in conjunction with the cascaded convolutional neural network
models. To fuse the models, nodule probabilities are calculated by using the
convolutional neural network models at first. Then, Fusion classifier is
trained and tested by the nodule probabilities. The proposed method achieved
the sensitivity of 94.4% and 95.9% at 4 and 8 false positives per scan in Free
Receiver Operating Characteristics (FROC) curve analysis, respectively.
Predicting Individual Physiologically Acceptable States for Discharge from a Pediatric Intensive Care Unit
Authors: Cameron Carlin, Long Van Ho, David Ledbetter, Melissa Aczon, Randall Wetzel
Date: 2017-12-18 00:00:00
Tasks: ICU Admission, regression

Objective: Predict patient-specific vitals deemed medically acceptable for
discharge from a pediatric intensive care unit (ICU). Design: The means of each
patient's hr, sbp and dbp measurements between their medical and physical
discharge from the ICU were computed as a proxy for their physiologically
acceptable state space (PASS) for successful ICU discharge. These individual
PASS values were compared via root mean squared error (rMSE) to population
age-normal vitals, a polynomial regression through the PASS values of a
Pediatric ICU (PICU) population and predictions from two recurrent neural
network models designed to predict personalized PASS within the first twelve
hours following ICU admission. Setting: PICU at Children's Hospital Los Angeles
(CHLA). Patients: 6,899 PICU episodes (5,464 patients) collected between 2009
and 2016. Interventions: None. Measurements: Each episode data contained 375
variables representing vitals, labs, interventions, and drugs. They also
included a time indicator for PICU medical discharge and physical discharge.
Main Results: The rMSEs between individual PASS values and population
age-normals (hr: 25.9 bpm, sbp: 13.4 mmHg, dbp: 13.0 mmHg) were larger than the
rMSEs corresponding to the polynomial regression (hr: 19.1 bpm, sbp: 12.3 mmHg,
dbp: 10.8 mmHg). The rMSEs from the best performing RNN model were the lowest
(hr: 16.4 bpm; sbp: 9.9 mmHg, dbp: 9.0 mmHg). Conclusion: PICU patients are a
unique subset of the general population, and general age-normal vitals may not
be suitable as target values indicating physiologic stability at discharge.
Age-normal vitals that were specifically derived from the medical-to-physical
discharge window of ICU patients may be more appropriate targets for
'acceptable' physiologic state for critical care patients. Going beyond simple
age bins, an RNN model can provide more personalized target values.
Path-Based Spectral Clustering: Guarantees, Robustness to Outliers, and Fast Algorithms
Authors: Anna Little, Mauro Maggioni, James M. Murphy
Date: 2017-12-17 00:00:00
Tasks: Clustering

We consider the problem of clustering with the longest-leg path distance
(LLPD) metric, which is informative for elongated and irregularly shaped
clusters. We prove finite-sample guarantees on the performance of clustering
with respect to this metric when random samples are drawn from multiple
intrinsically low-dimensional clusters in high-dimensional space, in the
presence of a large number of high-dimensional outliers. By combining these
results with spectral clustering with respect to LLPD, we provide conditions
under which the Laplacian eigengap statistic correctly determines the number of
clusters for a large class of data sets, and prove guarantees on the labeling
accuracy of the proposed algorithm. Our methods are quite general and provide
performance guarantees for spectral clustering with any ultrametric. We also
introduce an efficient, easy to implement approximation algorithm for the LLPD
based on a multiscale analysis of adjacency graphs, which allows for the
runtime of LLPD spectral clustering to be quasilinear in the number of data
points.
Probabilistic Semantic Retrieval for Surveillance Videos with Activity Graphs
Authors: Yu-Ting Chen, Joseph Wang, Yannan Bai, Gregory Castañón, Venkatesh Saligrama
Date: 2017-12-17 00:00:00
Tasks: Diversity, Retrieval, Semantic Retrieval

We present a novel framework for finding complex activities matching
user-described queries in cluttered surveillance videos. The wide diversity of
queries coupled with unavailability of annotated activity data limits our
ability to train activity models. To bridge the semantic gap we propose to let
users describe an activity as a semantic graph with object attributes and
inter-object relationships associated with nodes and edges, respectively. We
learn node/edge-level visual predictors during training and, at test-time,
propose to retrieve activity by identifying likely locations that match the
semantic graph. We formulate a novel CRF based probabilistic activity
localization objective that accounts for mis-detections, mis-classifications
and track-losses, and outputs a likelihood score for a candidate grounded
location of the query in the video. We seek groundings that maximize overall
precision and recall. To handle the combinatorial search over all
high-probability groundings, we propose a highest precision subgraph matching
algorithm. Our method outperforms existing retrieval methods on benchmarked
datasets.
The xyz algorithm for fast interaction search in high-dimensional data
Authors: Gian-Andrea Thanei, Nicolai Meinshausen, Rajen D. Shah
Date: 2016-10-17 00:00:00
Tasks: CPU, Vocal Bursts Intensity Prediction

When performing regression on a dataset with $p$ variables, it is often of
interest to go beyond using main linear effects and include interactions as
products between individual variables. For small-scale problems, these
interactions can be computed explicitly but this leads to a computational
complexity of at least $\mathcal{O}(p^2)$ if done naively. This cost can be
prohibitive if $p$ is very large. We introduce a new randomised algorithm that
is able to discover interactions with high probability and under mild
conditions has a runtime that is subquadratic in $p$. We show that strong
interactions can be discovered in almost linear time, whilst finding weaker
interactions requires $\mathcal{O}(p^\alpha)$ operations for $1 < \alpha < 2$
depending on their strength. The underlying idea is to transform interaction
search into a closestpair problem which can be solved efficiently in
subquadratic time. The algorithm is called $\mathit{xyz}$ and is implemented in
the language R. We demonstrate its efficiency for application to genome-wide
association studies, where more than $10^{11}$ interactions can be screened in
under $280$ seconds with a single-core $1.2$ GHz CPU.
Structured Optimal Transport
Authors: David Alvarez-Melis, Tommi S. Jaakkola, Stefanie Jegelka
Date: 2017-12-17 00:00:00
Tasks: BIG-bench Machine Learning, Domain Adaptation, Sentence

Optimal Transport has recently gained interest in machine learning for
applications ranging from domain adaptation, sentence similarities to deep
learning. Yet, its ability to capture frequently occurring structure beyond the
"ground metric" is limited. In this work, we develop a nonlinear generalization
of (discrete) optimal transport that is able to reflect much additional
structure. We demonstrate how to leverage the geometry of this new model for
fast algorithms, and explore connections and properties. Illustrative
experiments highlight the benefit of the induced structured couplings for tasks
in domain adaptation and natural language processing.