pwc-archive/papers-with-abstracts huggingface dataset:
A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applications
Authors: Dongyeop Kang, Waleed Ammar, Bhavana Dalvi, Madeleine van Zuylen, Sebastian Kohlmeier, Eduard Hovy, Roy Schwartz
Date: 2018-04-25 00:00:00
Proceeding: NAACL 2018 6

Peer reviewing is a central component in the scientific publishing process.
We present the first public dataset of scientific peer reviews available for
research purposes (PeerRead v1) providing an opportunity to study this
important artifact. The dataset consists of 14.7K paper drafts and the
corresponding accept/reject decisions in top-tier venues including ACL, NIPS
and ICLR. The dataset also includes 10.7K textual peer reviews written by
experts for a subset of the papers. We describe the data collection process and
report interesting observed phenomena in the peer reviews. We also propose two
novel NLP tasks based on this dataset and provide simple baseline models. In
the first task, we show that simple models can predict whether a paper is
accepted with up to 21% error reduction compared to the majority baseline. In
the second task, we predict the numerical scores of review aspects and show
that simple models can outperform the mean baseline for aspects with high
variance such as 'originality' and 'impact'.
Convergence guarantees for a class of non-convex and non-smooth optimization problems
Authors: Koulik Khamaru, Martin J. Wainwright
Date: 2018-04-25 00:00:00
Proceeding: ICML 2018 7
Tasks: Density Estimation

We consider the problem of finding critical points of functions that are
non-convex and non-smooth. Studying a fairly broad class of such problems, we
analyze the behavior of three gradient-based methods (gradient descent,
proximal update, and Frank-Wolfe update). For each of these methods, we
establish rates of convergence for general problems, and also prove faster
rates for continuous sub-analytic functions. We also show that our algorithms
can escape strict saddle points for a class of non-smooth functions, thereby
generalizing known results for smooth functions. Our analysis leads to a
simplification of the popular CCCP algorithm, used for optimizing functions
that can be written as a difference of two convex functions. Our simplified
algorithm retains all the convergence properties of CCCP, along with a
significantly lower cost per iteration. We illustrate our methods and theory
via applications to the problems of best subset selection, robust estimation,
mixture density estimation, and shape-from-shading reconstruction.
Actor and Observer: Joint Modeling of First and Third-Person Videos
Authors: Gunnar A. Sigurdsson, Abhinav Gupta, Cordelia Schmid, Ali Farhadi, Karteek Alahari
Date: 2018-04-25 00:00:00
Proceeding: CVPR 2018 6
Tasks: Action Recognition, Temporal Action Localization

Several theories in cognitive neuroscience suggest that when people interact
with the world, or simulate interactions, they do so from a first-person
egocentric perspective, and seamlessly transfer knowledge between third-person
(observer) and first-person (actor). Despite this, learning such models for
human action recognition has not been achievable due to the lack of data. This
paper takes a step in this direction, with the introduction of Charades-Ego, a
large-scale dataset of paired first-person and third-person videos, involving
112 people, with 4000 paired videos. This enables learning the link between the
two, actor and observer perspectives. Thereby, we address one of the biggest
bottlenecks facing egocentric vision research, providing a link from
first-person to the abundant third-person data on the web. We use this data to
learn a joint representation of first and third-person videos, with only weak
supervision, and show its effectiveness for transferring knowledge from the
third-person to the first-person domain.
Identifying and Alleviating Concept Drift in Streaming Tensor Decomposition
Authors: Ravdeep Pasricha, Ekta Gujral, Evangelos E. Papalexakis
Date: 2018-04-25 00:00:00
Tasks: Tensor Decomposition

Tensor decompositions are used in various data mining applications from
social network to medical applications and are extremely useful in discovering
latent structures or concepts in the data. Many real-world applications are
dynamic in nature and so are their data. To deal with this dynamic nature of
data, there exist a variety of online tensor decomposition algorithms. A
central assumption in all those algorithms is that the number of latent
concepts remains fixed throughout the entire stream. However, this need not be
the case. Every incoming batch in the stream may have a different number of
latent concepts, and the difference in latent concepts from one tensor batch to
another can provide insights into how our findings in a particular application
behave and deviate over time. In this paper, we define "concept" and "concept
drift" in the context of streaming tensor decomposition, as the manifestation
of the variability of latent concepts throughout the stream. Furthermore, we
introduce SeekAndDestroy, an algorithm that detects concept drift in streaming
tensor decomposition and is able to produce results robust to that drift. To
the best of our knowledge, this is the first work that investigates concept
drift in streaming tensor decomposition. We extensively evaluate SeekAndDestroy
on synthetic datasets, which exhibit a wide variety of realistic drift. Our
experiments demonstrate the effectiveness of SeekAndDestroy, both in the
detection of concept drift and in the alleviation of its effects, producing
results with similar quality to decomposing the entire tensor in one shot.
Additionally, in real datasets, SeekAndDestroy outperforms other streaming
baselines, while discovering novel useful components.
t-DCF: a Detection Cost Function for the Tandem Assessment of Spoofing Countermeasures and Automatic Speaker Verification
Authors: Tomi Kinnunen, Kong Aik Lee, Hector Delgado, Nicholas Evans, Massimiliano Todisco, Md Sahidullah, Junichi Yamagishi, Douglas A. Reynolds
Date: 2018-04-25 00:00:00
Tasks: Speaker Verification

The ASVspoof challenge series was born to spearhead research in anti-spoofing
for automatic speaker verification (ASV). The two challenge editions in 2015
and 2017 involved the assessment of spoofing countermeasures (CMs) in isolation
from ASV using an equal error rate (EER) metric. While a strategic approach to
assessment at the time, it has certain shortcomings. First, the CM EER is not
necessarily a reliable predictor of performance when ASV and CMs are combined.
Second, the EER operating point is ill-suited to user authentication
applications, e.g. telephone banking, characterised by a high target user prior
but a low spoofing attack prior. We aim to migrate from CM- to ASV-centric
assessment with the aid of a new tandem detection cost function (t-DCF) metric.
It extends the conventional DCF used in ASV research to scenarios involving
spoofing attacks. The t-DCF metric has 6 parameters: (i) false alarm and miss
costs for both systems, and (ii) prior probabilities of target and spoof trials
(with an implied third, nontarget prior). The study is intended to serve as a
self-contained, tutorial-like presentation. We analyse with the t-DCF a
selection of top-performing CM submissions to the 2015 and 2017 editions of
ASVspoof, with a focus on the spoofing attack prior. Whereas there is little to
choose between countermeasure systems for lower priors, system rankings derived
with the EER and t-DCF show differences for higher priors. We observe some
ranking changes. Findings support the adoption of the DCF-based metric into the
roadmap for future ASVspoof challenges, and possibly for other biometric
anti-spoofing evaluations.
When is there a Representer Theorem? Nondifferentiable Regularisers and Banach spaces
Authors: Kevin Schlegel
Date: 2018-04-25 00:00:00

We consider a general regularised interpolation problem for learning a
parameter vector from data. The well known representer theorem says that under
certain conditions on the regulariser there exists a solution in the linear
span of the data points. This is the core of kernel methods in machine learning
as it makes the problem computationally tractable. Necessary and sufficient
conditions for differentiable regularisers on Hilbert spaces to admit a
representer theorem have been proved. We extend those results to
nondifferentiable regularisers on uniformly convex and uniformly smooth Banach
spaces. This gives a (more) complete answer to the question when there is a
representer theorem. We then note that for regularised interpolation in fact
the solution is determined by the function space alone and independent of the
regulariser, making the extension to Banach spaces even more valuable.
Speaker-independent raw waveform model for glottal excitation
Authors: Lauri Juvela, Vassilis Tsiaras, Bajibabu Bollepalli, Manu Airaksinen, Junichi Yamagishi, Paavo Alku
Date: 2018-04-25 00:00:00
Tasks: model, Speech Synthesis, text-to-speech, Text to Speech, Text-To-Speech Synthesis, Voice Conversion

Recent speech technology research has seen a growing interest in using
WaveNets as statistical vocoders, i.e., generating speech waveforms from
acoustic features. These models have been shown to improve the generated speech
quality over classical vocoders in many tasks, such as text-to-speech synthesis
and voice conversion. Furthermore, conditioning WaveNets with acoustic features
allows sharing the waveform generator model across multiple speakers without
additional speaker codes. However, multi-speaker WaveNet models require large
amounts of training data and computation to cover the entire acoustic space.
This paper proposes leveraging the source-filter model of speech production to
more effectively train a speaker-independent waveform generator with limited
resources. We present a multi-speaker 'GlotNet' vocoder, which utilizes a
WaveNet to generate glottal excitation waveforms, which are then used to excite
the corresponding vocal tract filter to produce speech. Listening tests show
that the proposed model performs favourably to a direct WaveNet vocoder trained
with the same model architecture and data.
Matrix Completion and Related Problems via Strong Duality
Authors: Maria-Florina Balcan, YIngyu Liang, David P. Woodruff, Hongyang Zhang
Date: 2017-04-27 00:00:00
Tasks: Matrix Completion

This work studies the strong duality of non-convex matrix factorization
problems: we show that under certain dual conditions, these problems and its
dual have the same optimum. This has been well understood for convex
optimization, but little was known for non-convex problems. We propose a novel
analytical framework and show that under certain dual conditions, the optimal
solution of the matrix factorization program is the same as its bi-dual and
thus the global optimality of the non-convex program can be achieved by solving
its bi-dual which is convex. These dual conditions are satisfied by a wide
class of matrix factorization problems, although matrix factorization problems
are hard to solve in full generality. This analytical framework may be of
independent interest to non-convex optimization more broadly.
  We apply our framework to two prototypical matrix factorization problems:
matrix completion and robust Principal Component Analysis (PCA). These are
examples of efficiently recovering a hidden matrix given limited reliable
observations of it. Our framework shows that exact recoverability and strong
duality hold with nearly-optimal sample complexity guarantees for matrix
completion and robust PCA.
Covariate Regularized Community Detection in Sparse Graphs
Authors: Bowei Yan, Purnamrita Sarkar
Date: 2016-07-10 00:00:00
Tasks: Clustering, Community Detection

In this paper, we investigate community detection in networks in the presence
of node covariates. In many instances, covariates and networks individually
only give a partial view of the cluster structure. One needs to jointly infer
the full cluster structure by considering both. In statistics, an emerging body
of work has been focused on combining information from both the edges in the
network and the node covariates to infer community memberships. However, so far
the theoretical guarantees have been established in the dense regime, where the
network can lead to perfect clustering under a broad parameter regime, and
hence the role of covariates is often not clear. In this paper, we examine
sparse networks in conjunction with finite dimensional sub-gaussian mixtures as
covariates under moderate separation conditions. In this setting each
individual source can only cluster a non-vanishing fraction of nodes correctly.
We propose a simple optimization framework which provably improves clustering
accuracy when the two sources carry partial information about the cluster
memberships, and hence perform poorly on their own. Our optimization problem
can be solved using scalable convex optimization algorithms. Using a variety of
simulated and real data examples, we show that the proposed method outperforms
other existing methodology.
Unsupervised Domain Adaptation with Adversarial Residual Transform Networks
Authors: Guanyu Cai, Yuqin Wang, Mengchu Zhou, Lianghua He
Date: 2018-04-25 00:00:00
Tasks: Domain Adaptation, Unsupervised Domain Adaptation

Domain adaptation is widely used in learning problems lacking labels. Recent studies show that deep adversarial domain adaptation models can make markable improvements in performance, which include symmetric and asymmetric architectures. However, the former has poor generalization ability whereas the latter is very hard to train. In this paper, we propose a novel adversarial domain adaptation method named Adversarial Residual Transform Networks (ARTNs) to improve the generalization ability, which directly transforms the source features into the space of target features. In this model, residual connections are used to share features and adversarial loss is reconstructed, thus making the model more generalized and easier to train. Moreover, a special regularization term is added to the loss function to alleviate a vanishing gradient problem, which enables its training process stable. A series of experiments based on Amazon review dataset, digits datasets and Office-31 image datasets are conducted to show that the proposed ARTN can be comparable with the methods of the state-of-the-art.
SegMap: 3D Segment Mapping using Data-Driven Descriptors
Authors: Renaud Dubé, Andrei Cramariuc, Daniel Dugas, Juan Nieto, Roland Siegwart, Cesar Cadena
Date: 2018-04-25 00:00:00
Tasks: Data Compression

When performing localization and mapping, working at the level of structure
can be advantageous in terms of robustness to environmental changes and
differences in illumination. This paper presents SegMap: a map representation
solution to the localization and mapping problem based on the extraction of
segments in 3D point clouds. In addition to facilitating the computationally
intensive task of processing 3D point clouds, working at the level of segments
addresses the data compression requirements of real-time single- and
multi-robot systems. While current methods extract descriptors for the single
task of localization, SegMap leverages a data-driven descriptor in order to
extract meaningful features that can also be used for reconstructing a dense 3D
map of the environment and for extracting semantic information. This is
particularly interesting for navigation tasks and for providing visual feedback
to end-users such as robot operators, for example in search and rescue
scenarios. These capabilities are demonstrated in multiple urban driving and
search and rescue experiments. Our method leads to an increase of area under
the ROC curve of 28.3% over current state of the art using eigenvalue based
features. We also obtain very similar reconstruction capabilities to a model
specifically trained for this task. The SegMap implementation will be made
available open-source along with easy to run demonstrations at
www.github.com/ethz-asl/segmap. A video demonstration is available at
https://youtu.be/CMk4w4eRobg.
Prototypical Priors: From Improving Classification to Zero-Shot Learning
Authors: Saumya Jetley, Bernardino Romera-Paredes, Sadeep Jayasumana, Philip Torr
Date: 2015-12-03 00:00:00
Tasks: Classification, General Classification, Zero-Shot Learning

Recent works on zero-shot learning make use of side information such as
visual attributes or natural language semantics to define the relations between
output visual classes and then use these relationships to draw inference on new
unseen classes at test time. In a novel extension to this idea, we propose the
use of visual prototypical concepts as side information. For most real-world
visual object categories, it may be difficult to establish a unique prototype.
However, in cases such as traffic signs, brand logos, flags, and even natural
language characters, these prototypical templates are available and can be
leveraged for an improved recognition performance. The present work proposes a
way to incorporate this prototypical information in a deep learning framework.
Using prototypes as prior information, the deepnet pipeline learns the input
image projections into the prototypical embedding space subject to minimization
of the final classification loss. Based on our experiments with two different
datasets of traffic signs and brand logos, prototypical embeddings incorporated
in a conventional convolutional neural network improve the recognition
performance. Recognition accuracy on the Belga logo dataset is especially
noteworthy and establishes a new state-of-the-art. In zero-shot learning
scenarios, the same system can be directly deployed to draw inference on unseen
classes by simply adding the prototypical information for these new classes at
test time. Thus, unlike earlier approaches, testing on seen and unseen classes
is handled using the same pipeline, and the system can be tuned for a trade-off
of seen and unseen class performance as per task requirement. Comparison with
one of the latest works in the zero-shot learning domain yields top results on
the two datasets mentioned above.
Applying Faster R-CNN for Object Detection on Malaria Images
Authors: Jane Hung, Deepali Ravel, Stefanie C. P. Lopes, Gabriel Rangel, Odailton Amaral Nery, Benoit Malleret, Francois Nosten, Marcus V. G. Lacerda, Marcelo U. Ferreira, Laurent Rénia, Manoj T. Duraisingh, Fabio T. M. Costa, Matthias Marti, Anne E. Carpenter
Date: 2018-04-25 00:00:00
Tasks: Cell Segmentation, Object, object-detection, Object Detection

Deep learning based models have had great success in object detection, but
the state of the art models have not yet been widely applied to biological
image data. We apply for the first time an object detection model previously
used on natural images to identify cells and recognize their stages in
brightfield microscopy images of malaria-infected blood. Many micro-organisms
like malaria parasites are still studied by expert manual inspection and hand
counting. This type of object detection task is challenging due to factors like
variations in cell shape, density, and color, and uncertainty of some cell
classes. In addition, annotated data useful for training is scarce, and the
class distribution is inherently highly imbalanced due to the dominance of
uninfected red blood cells. We use Faster Region-based Convolutional Neural
Network (Faster R-CNN), one of the top performing object detection models in
recent years, pre-trained on ImageNet but fine tuned with our data, and compare
it to a baseline, which is based on a traditional approach consisting of cell
segmentation, extraction of several single-cell features, and classification
using random forests. To conduct our initial study, we collect and label a
dataset of 1300 fields of view consisting of around 100,000 individual cells.
We demonstrate that Faster R-CNN outperforms our baseline and put the results
in context of human performance.
Deep Convolutional AutoEncoder-based Lossy Image Compression
Authors: Zhengxue Cheng, Heming Sun, Masaru Takeuchi, Jiro Katto
Date: 2018-04-25 00:00:00
Tasks: Image Compression, Quantization

Image compression has been investigated as a fundamental research topic for
many decades. Recently, deep learning has achieved great success in many
computer vision tasks, and is gradually being used in image compression. In
this paper, we present a lossy image compression architecture, which utilizes
the advantages of convolutional autoencoder (CAE) to achieve a high coding
efficiency. First, we design a novel CAE architecture to replace the
conventional transforms and train this CAE using a rate-distortion loss
function. Second, to generate a more energy-compact representation, we utilize
the principal components analysis (PCA) to rotate the feature maps produced by
the CAE, and then apply the quantization and entropy coder to generate the
codes. Experimental results demonstrate that our method outperforms traditional
image coding algorithms, by achieving a 13.7% BD-rate decrement on the Kodak
database images compared to JPEG2000. Besides, our method maintains a moderate
complexity similar to JPEG2000.
Hand Pose Estimation via Latent 2.5D Heatmap Regression
Authors: Umar Iqbal, Pavlo Molchanov, Thomas Breuel, Juergen Gall, Jan Kautz
Date: 2018-04-25 00:00:00
Proceeding: ECCV 2018 9
Tasks: 3D Hand Pose Estimation, Hand Pose Estimation, Pose Estimation, regression

Estimating the 3D pose of a hand is an essential part of human-computer
interaction. Estimating 3D pose using depth or multi-view sensors has become
easier with recent advances in computer vision, however, regressing pose from a
single RGB image is much less straightforward. The main difficulty arises from
the fact that 3D pose requires some form of depth estimates, which are
ambiguous given only an RGB image. In this paper we propose a new method for 3D
hand pose estimation from a monocular image through a novel 2.5D pose
representation. Our new representation estimates pose up to a scaling factor,
which can be estimated additionally if a prior of the hand size is given. We
implicitly learn depth maps and heatmap distributions with a novel CNN
architecture. Our system achieves the state-of-the-art estimation of 2D and 3D
hand pose on several challenging datasets in presence of severe occlusions.
Strong Baselines for Neural Semi-supervised Learning under Domain Shift
Authors: Sebastian Ruder, Barbara Plank
Date: 2018-04-25 00:00:00
Proceeding: ACL 2018 7
Tasks: Domain Adaptation, Multi-Task Learning, Part-Of-Speech Tagging, Sentiment Analysis

Novel neural models have been proposed in recent years for learning under
domain shift. Most models, however, only evaluate on a single task, on
proprietary datasets, or compare to weak baselines, which makes comparison of
models difficult. In this paper, we re-evaluate classic general-purpose
bootstrapping approaches in the context of neural networks under domain shifts
vs. recent neural approaches and propose a novel multi-task tri-training method
that reduces the time and space complexity of classic tri-training. Extensive
experiments on two benchmarks are negative: while our novel method establishes
a new state-of-the-art for sentiment analysis, it does not fare consistently
the best. More importantly, we arrive at the somewhat surprising conclusion
that classic tri-training, with some additions, outperforms the state of the
art. We conclude that classic approaches constitute an important and strong
baseline.