pwc-archive/papers-with-abstracts huggingface dataset:
Asymptotically optimal private estimation under mean square loss
Authors: Min Ye, Alexander Barg
Date: 2017-07-31 00:00:00

We consider the minimax estimation problem of a discrete distribution with
support size $k$ under locally differential privacy constraints. A
privatization scheme is applied to each raw sample independently, and we need
to estimate the distribution of the raw samples from the privatized samples. A
positive number $\epsilon$ measures the privacy level of a privatization
scheme.
  In our previous work (arXiv:1702.00610), we proposed a family of new
privatization schemes and the corresponding estimator. We also proved that our
scheme and estimator are order optimal in the regime $e^{\epsilon} \ll k$ under
both $\ell_2^2$ and $\ell_1$ loss. In other words, for a large number of
samples the worst-case estimation loss of our scheme was shown to differ from
the optimal value by at most a constant factor. In this paper, we eliminate
this gap by showing asymptotic optimality of the proposed scheme and estimator
under the $\ell_2^2$ (mean square) loss. More precisely, we show that for any
$k$ and $\epsilon,$ the ratio between the worst-case estimation loss of our
scheme and the optimal value approaches $1$ as the number of samples tends to
infinity.
SemEval-2017 Task 1: Semantic Textual Similarity - Multilingual and Cross-lingual Focused Evaluation
Authors: Daniel Cer, Mona Diab, Eneko Agirre, Iñigo Lopez-Gazpio, Lucia Specia
Date: 2017-07-31 00:00:00
Tasks: Machine Translation, Question Answering, Semantic Textual Similarity, STS, STS Benchmark, Translation

Semantic Textual Similarity (STS) measures the meaning similarity of
sentences. Applications include machine translation (MT), summarization,
generation, question answering (QA), short answer grading, semantic search,
dialog and conversational systems. The STS shared task is a venue for assessing
the current state-of-the-art. The 2017 task focuses on multilingual and
cross-lingual pairs with one sub-track exploring MT quality estimation (MTQE)
data. The task obtained strong participation from 31 teams, with 17
participating in all language tracks. We summarize performance and review a
selection of well performing methods. Analysis highlights common errors,
providing insight into the limitations of existing models. To support ongoing
work on semantic representations, the STS Benchmark is introduced as a new
shared training and evaluation set carefully selected from the corpus of
English STS shared task data (2012-2017).
Interpretable Active Learning
Authors: Richard L. Phillips, Kyu Hyun Chang, Sorelle A. Friedler
Date: 2017-07-31 00:00:00
Tasks: Active Learning

Active learning has long been a topic of study in machine learning. However,
as increasingly complex and opaque models have become standard practice, the
process of active learning, too, has become more opaque. There has been little
investigation into interpreting what specific trends and patterns an active
learning strategy may be exploring. This work expands on the Local
Interpretable Model-agnostic Explanations framework (LIME) to provide
explanations for active learning recommendations. We demonstrate how LIME can
be used to generate locally faithful explanations for an active learning
strategy, and how these explanations can be used to understand how different
models and datasets explore a problem space over time. In order to quantify the
per-subgroup differences in how an active learning strategy queries spatial
regions, we introduce a notion of uncertainty bias (based on disparate impact)
to measure the discrepancy in the confidence for a model's predictions between
one subgroup and another. Using the uncertainty bias measure, we show that our
query explanations accurately reflect the subgroup focus of the active learning
queries, allowing for an interpretable explanation of what is being learned as
points with similar sources of uncertainty have their uncertainty bias
resolved. We demonstrate that this technique can be applied to track
uncertainty bias over user-defined clusters or automatically generated clusters
based on the source of uncertainty.
Guidefill: GPU Accelerated, Artist Guided Geometric Inpainting for 3D Conversion
Authors: L. Robert Hocking, Russell MacKenzie, Carola-Bibiane Schoenlieb
Date: 2016-11-16 00:00:00
Tasks: GPU

The conversion of traditional film into stereo 3D has become an important
problem in the past decade. One of the main bottlenecks is a disocclusion step,
which in commercial 3D conversion is usually done by teams of artists armed
with a toolbox of inpainting algorithms. A current difficulty in this is that
most available algorithms are either too slow for interactive use, or provide
no intuitive means for users to tweak the output. In this paper we present a
new fast inpainting algorithm based on transporting along automatically
detected splines, which the user may edit. Our algorithm is implemented on the
GPU and fills the inpainting domain in successive shells that adapt their shape
on the fly. In order to allocate GPU resources as efficiently as possible, we
propose a parallel algorithm to track the inpainting interface as it evolves,
ensuring that no resources are wasted on pixels that are not currently being
worked on. Theoretical analysis of the time and processor complexiy of our
algorithm without and with tracking (as well as numerous numerical experiments)
demonstrate the merits of the latter. Our transport mechanism is similar to the
one used in coherence transport, but improves upon it by corrected a "kinking"
phenomena whereby extrapolated isophotes may bend at the boundary of the
inpainting domain. Theoretical results explaining this phenomena and its
resolution are presented. Although our method ignores texture, in many cases
this is not a problem due to the thin inpainting domains in 3D conversion.
Experimental results show that our method can achieve a visual quality that is
competitive with the state-of-the-art while maintaining interactive speeds and
providing the user with an intuitive interface to tweak the results.
Statistics on the (compact) Stiefel manifold: Theory and Applications
Authors: Rudrasis Chakraborty, Baba Vemuri
Date: 2017-07-31 00:00:00

A Stiefel manifold of the compact type is often encountered in many fields of
Engineering including, signal and image processing, machine learning, numerical
optimization and others. The Stiefel manifold is a Riemannian homogeneous space
but not a symmetric space. In previous work, researchers have defined
probability distributions on symmetric spaces and performed statistical
analysis of data residing in these spaces. In this paper, we present original
work involving definition of Gaussian distributions on a homogeneous space and
show that the maximum-likelihood estimate of the location parameter of a
Gaussian distribution on the homogeneous space yields the Fr\'echet mean (FM)
of the samples drawn from this distribution. Further, we present an algorithm
to sample from the Gaussian distribution on the Stiefel manifold and
recursively compute the FM of these samples. We also prove the weak consistency
of this recursive FM estimator. Several synthetic and real data experiments are
then presented, demonstrating the superior computational performance of this
estimator over the gradient descent based non-recursive counter part as well as
the stochastic gradient descent based method prevalent in literature.
Spatio-Temporal Action Detection with Cascade Proposal and Location Anticipation
Authors: Zhenheng Yang, Jiyang Gao, Ram Nevatia
Date: 2017-07-31 00:00:00
Tasks: Action Detection, Region Proposal

In this work, we address the problem of spatio-temporal action detection in
temporally untrimmed videos. It is an important and challenging task as finding
accurate human actions in both temporal and spatial space is important for
analyzing large-scale video data. To tackle this problem, we propose a cascade
proposal and location anticipation (CPLA) model for frame-level action
detection. There are several salient points of our model: (1) a cascade region
proposal network (casRPN) is adopted for action proposal generation and shows
better localization accuracy compared with single region proposal network
(RPN); (2) action spatio-temporal consistencies are exploited via a location
anticipation network (LAN) and thus frame-level action detection is not
conducted independently. Frame-level detections are then linked by solving an
linking score maximization problem, and temporally trimmed into spatio-temporal
action tubes. We demonstrate the effectiveness of our model on the challenging
UCF101 and LIRIS-HARL datasets, both achieving state-of-the-art performance.
Universal Adversarial Perturbations Against Semantic Image Segmentation
Authors: Jan Hendrik Metzen, Mummadi Chaithanya Kumar, Thomas Brox, Volker Fischer
Date: 2017-04-19 00:00:00
Proceeding: ICCV 2017 10
Tasks: image-classification, Image Classification, Image Segmentation, Segmentation, Semantic Segmentation

While deep learning is remarkably successful on perceptual tasks, it was also
shown to be vulnerable to adversarial perturbations of the input. These
perturbations denote noise added to the input that was generated specifically
to fool the system while being quasi-imperceptible for humans. More severely,
there even exist universal perturbations that are input-agnostic but fool the
network on the majority of inputs. While recent work has focused on image
classification, this work proposes attacks against semantic image segmentation:
we present an approach for generating (universal) adversarial perturbations
that make the network yield a desired target segmentation as output. We show
empirically that there exist barely perceptible universal noise patterns which
result in nearly the same predicted segmentation for arbitrary inputs.
Furthermore, we also show the existence of universal noise which removes a
target class (e.g., all pedestrians) from the segmentation while leaving the
segmentation mostly unchanged otherwise.
Deep Joint Entity Disambiguation with Local Neural Attention
Authors: Octavian-Eugen Ganea, Thomas Hofmann
Date: 2017-04-17 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Deep Learning, Entity Disambiguation

We propose a novel deep learning model for joint document-level entity
disambiguation, which leverages learned neural representations. Key components
are entity embeddings, a neural attention mechanism over local context windows,
and a differentiable joint inference stage for disambiguation. Our approach
thereby combines benefits of deep learning with more traditional approaches
such as graphical models and probabilistic mention-entity maps. Extensive
experiments show that we are able to obtain competitive or state-of-the-art
accuracy at moderate computational costs.
Causes for Query Answers from Databases: Datalog Abduction, View-Updates, and Integrity Constraints
Authors: Leopoldo Bertossi, Babak Salimi
Date: 2016-11-06 00:00:00

Causality has been recently introduced in databases, to model, characterize,
and possibly compute causes for query answers. Connections between QA-causality
and consistency-based diagnosis and database repairs (wrt. integrity constraint
violations) have already been established. In this work we establish precise
connections between QA-causality and both abductive diagnosis and the
view-update problem in databases, allowing us to obtain new algorithmic and
complexity results for QA-causality. We also obtain new results on the
complexity of view-conditioned causality, and investigate the notion of
QA-causality in the presence of integrity constraints, obtaining complexity
results from a connection with view-conditioned causality. The abduction
connection under integrity constraints allows us to obtain algorithmic tools
for QA-causality.
QuotationFinder - Searching for Quotations and Allusions in Greek and Latin Texts and Establishing the Degree to Which a Quotation or Allusion Matches Its Source
Authors: Luc Herren
Date: 2016-02-28 00:00:00

The software programs generally used with the TLG (Thesaurus Linguae Graecae)
and the CLCLT (CETEDOC Library of Christian Latin Texts) CD-ROMs are not well
suited for finding quotations and allusions. QuotationFinder uses more
sophisticated criteria as it ranks search results based on how closely they
match the source text, listing search results with literal quotations first and
loose verbal parallels last.
Light Field Super-Resolution Via Graph-Based Regularization
Authors: Mattia Rossi, Pascal Frossard
Date: 2017-01-09 00:00:00
Tasks: Depth Estimation, Disparity Estimation, Multi-Frame Super-Resolution, Super-Resolution

Light field cameras capture the 3D information in a scene with a single
exposure. This special feature makes light field cameras very appealing for a
variety of applications: from post-capture refocus, to depth estimation and
image-based rendering. However, light field cameras suffer by design from
strong limitations in their spatial resolution, which should therefore be
augmented by computational methods. On the one hand, off-the-shelf single-frame
and multi-frame super-resolution algorithms are not ideal for light field data,
as they do not consider its particular structure. On the other hand, the few
super-resolution algorithms explicitly tailored for light field data exhibit
significant limitations, such as the need to estimate an explicit disparity map
at each view. In this work we propose a new light field super-resolution
algorithm meant to address these limitations. We adopt a multi-frame alike
super-resolution approach, where the complementary information in the different
light field views is used to augment the spatial resolution of the whole light
field. We show that coupling the multi-frame approach with a graph regularizer,
that enforces the light field structure via nonlocal self similarities, permits
to avoid the costly and challenging disparity estimation step for all the
views. Extensive experiments show that the new algorithm compares favorably to
the other state-of-the-art methods for light field super-resolution, both in
terms of PSNR and visual quality.
Generating High-Quality and Informative Conversation Responses with Sequence-to-Sequence Models
Authors: Louis Shao, Stephan Gouws, Denny Britz, Anna Goldie, Brian Strope, Ray Kurzweil
Date: 2017-01-11 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Decoder, Diversity, Reranking, Response Generation, Translation

Sequence-to-sequence models have been applied to the conversation response
generation problem where the source sequence is the conversation history and
the target sequence is the response. Unlike translation, conversation
responding is inherently creative. The generation of long, informative,
coherent, and diverse responses remains a hard task. In this work, we focus on
the single turn setting. We add self-attention to the decoder to maintain
coherence in longer responses, and we propose a practical approach, called the
glimpse-model, for scaling to large datasets. We introduce a stochastic
beam-search algorithm with segment-by-segment reranking which lets us inject
diversity earlier in the generation process. We trained on a combined data set
of over 2.3B conversation messages mined from the web. In human evaluation
studies, our method produces longer responses overall, with a higher proportion
rated as acceptable and excellent as length increases, compared to baseline
sequence-to-sequence models with explicit length-promotion. A back-off strategy
produces better responses overall, in the full spectrum of lengths.
Group Sparsity Residual Constraint for Image Denoising
Authors: Zhiyuan Zha, Xinggan Zhang, Qiong Wang, Lan Tang, Xin Liu
Date: 2017-03-01 00:00:00
Tasks: Denoising, Image Denoising

Group-based sparse representation has shown great potential in image
denoising. However, most existing methods only consider the nonlocal
self-similarity (NSS) prior of noisy input image. That is, the similar patches
are collected only from degraded input, which makes the quality of image
denoising largely depend on the input itself. However, such methods often
suffer from a common drawback that the denoising performance may degrade
quickly with increasing noise levels. In this paper we propose a new prior
model, called group sparsity residual constraint (GSRC). Unlike the
conventional group-based sparse representation denoising methods, two kinds of
prior, namely, the NSS priors of noisy and pre-filtered images, are used in
GSRC. In particular, we integrate these two NSS priors through the mechanism of
sparsity residual, and thus, the task of image denoising is converted to the
problem of reducing the group sparsity residual. To this end, we first obtain a
good estimation of the group sparse coefficients of the original image by
pre-filtering, and then the group sparse coefficients of the noisy image are
used to approximate this estimation. To improve the accuracy of the nonlocal
similar patch selection, an adaptive patch search scheme is designed.
Furthermore, to fuse these two NSS prior better, an effective iterative
shrinkage algorithm is developed to solve the proposed GSRC model. Experimental
results demonstrate that the proposed GSRC modeling outperforms many
state-of-the-art denoising methods in terms of the objective and the perceptual
metrics.
Contextual Motifs: Increasing the Utility of Motifs using Contextual Data
Authors: Ian Fox, Lynn Ang, Mamta Jaiswal, Rodica Pop-Busui, Jenna Wiens
Date: 2017-03-06 00:00:00

Motifs are a powerful tool for analyzing physiological waveform data.
Standard motif methods, however, ignore important contextual information (e.g.,
what the patient was doing at the time the data were collected). We hypothesize
that these additional contextual data could increase the utility of motifs.
Thus, we propose an extension to motifs, contextual motifs, that incorporates
context. Recognizing that, oftentimes, context may be unobserved or
unavailable, we focus on methods to jointly infer motifs and context. Applied
to both simulated and real physiological data, our proposed approach improves
upon existing motif methods in terms of the discriminative utility of the
discovered motifs. In particular, we discovered contextual motifs in continuous
glucose monitor (CGM) data collected from patients with type 1 diabetes.
Compared to their contextless counterparts, these contextual motifs led to
better predictions of hypo- and hyperglycemic events. Our results suggest that
even when inferred, context is useful in both a long- and short-term prediction
horizon when processing and interpreting physiological waveform data.
Multi-stage Multi-recursive-input Fully Convolutional Networks for Neuronal Boundary Detection
Authors: Wei Shen, Bin Wang, Yuan Jiang, Yan Wang, Alan Yuille
Date: 2017-03-24 00:00:00
Proceeding: ICCV 2017 10
Tasks: Boundary Detection, Segmentation

In the field of connectomics, neuroscientists seek to identify cortical
connectivity comprehensively. Neuronal boundary detection from the Electron
Microscopy (EM) images is often done to assist the automatic reconstruction of
neuronal circuit. But the segmentation of EM images is a challenging problem,
as it requires the detector to be able to detect both filament-like thin and
blob-like thick membrane, while suppressing the ambiguous intracellular
structure. In this paper, we propose multi-stage multi-recursive-input fully
convolutional networks to address this problem. The multiple recursive inputs
for one stage, i.e., the multiple side outputs with different receptive field
sizes learned from the lower stage, provide multi-scale contextual boundary
information for the consecutive learning. This design is
biologically-plausible, as it likes a human visual system to compare different
possible segmentation solutions to address the ambiguous boundary issue. Our
multi-stage networks are trained end-to-end. It achieves promising results on
two public available EM segmentation datasets, the mouse piriform cortex
dataset and the ISBI 2012 EM dataset.
A Generative Model of People in Clothing
Authors: Christoph Lassner, Gerard Pons-Moll, Peter V. Gehler
Date: 2017-05-11 00:00:00
Proceeding: ICCV 2017 10
Tasks: model, Semantic Segmentation

We present the first image-based generative model of people in clothing for
the full body. We sidestep the commonly used complex graphics rendering
pipeline and the need for high-quality 3D scans of dressed people. Instead, we
learn generative models from a large image database. The main challenge is to
cope with the high variance in human pose, shape and appearance. For this
reason, pure image-based approaches have not been considered so far. We show
that this challenge can be overcome by splitting the generating process in two
parts. First, we learn to generate a semantic segmentation of the body and
clothing. Second, we learn a conditional model on the resulting segments that
creates realistic images. The full model is differentiable and can be
conditioned on pose, shape or color. The result are samples of people in
different clothing items and styles. The proposed model can generate entirely
new people with realistic clothing. In several experiments we present
encouraging results that suggest an entirely data-driven approach to people
generation is possible.
A Framework for Super-Resolution of Scalable Video via Sparse Reconstruction of Residual Frames
Authors: Mohammad Hossein Moghaddam, Mohammad Javad Azizipour, Saeed Vahidian, Besma Smida
Date: 2017-07-31 00:00:00
Tasks: Compressive Sensing, Super-Resolution

This paper introduces a framework for super-resolution of scalable video
based on compressive sensing and sparse representation of residual frames in
reconnaissance and surveillance applications. We exploit efficient compressive
sampling and sparse reconstruction algorithms to super-resolve the video
sequence with respect to different compression rates. We use the sparsity of
residual information in residual frames as the key point in devising our
framework. Moreover, a controlling factor as the compressibility threshold to
control the complexity-performance trade-off is defined. Numerical experiments
confirm the efficiency of the proposed framework in terms of the compression
rate as well as the quality of reconstructed video sequence in terms of PSNR
measure. The framework leads to a more efficient compression rate and higher
video quality compared to other state-of-the-art algorithms considering
performance-complexity trade-offs.
Regularization techniques for fine-tuning in neural machine translation
Authors: Antonio Valerio Miceli Barone, Barry Haddow, Ulrich Germann, Rico Sennrich
Date: 2017-07-31 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Domain Adaptation, L2 Regularization, Machine Translation, NMT, Transfer Learning, Translation

We investigate techniques for supervised domain adaptation for neural machine
translation where an existing model trained on a large out-of-domain dataset is
adapted to a small in-domain dataset. In this scenario, overfitting is a major
challenge. We investigate a number of techniques to reduce overfitting and
improve transfer learning, including regularization techniques such as dropout
and L2-regularization towards an out-of-domain prior. In addition, we introduce
tuneout, a novel regularization technique inspired by dropout. We apply these
techniques, alone and in combination, to neural machine translation, obtaining
improvements on IWSLT datasets for English->German and English->Russian. We
also investigate the amounts of in-domain training data needed for domain
adaptation in NMT, and find a logarithmic relationship between the amount of
training data and gain in BLEU score.