pwc-archive/papers-with-abstracts huggingface dataset:
Towards Human-Machine Cooperation: Self-supervised Sample Mining for Object Detection
Authors: Keze Wang, Xiaopeng Yan, Dongyu Zhang, Lei Zhang, Liang Lin
Date: 2018-03-27 00:00:00
Proceeding: CVPR 2018 6
Tasks: Active Learning, Object, object-detection, Object Detection

Though quite challenging, leveraging large-scale unlabeled or partially
labeled images in a cost-effective way has increasingly attracted interests for
its great importance to computer vision. To tackle this problem, many Active
Learning (AL) methods have been developed. However, these methods mainly define
their sample selection criteria within a single image context, leading to the
suboptimal robustness and impractical solution for large-scale object
detection. In this paper, aiming to remedy the drawbacks of existing AL
methods, we present a principled Self-supervised Sample Mining (SSM) process
accounting for the real challenges in object detection. Specifically, our SSM
process concentrates on automatically discovering and pseudo-labeling reliable
region proposals for enhancing the object detector via the introduced cross
image validation, i.e., pasting these proposals into different labeled images
to comprehensively measure their values under different image contexts. By
resorting to the SSM process, we propose a new AL framework for gradually
incorporating unlabeled or partially labeled data into the model learning while
minimizing the annotating effort of users. Extensive experiments on two public
benchmarks clearly demonstrate our proposed framework can achieve the
comparable performance to the state-of-the-art methods with significantly fewer
annotations.
A0C: Alpha Zero in Continuous Action Space
Authors: Thomas M. Moerland, Joost Broekens, Aske Plaat, Catholijn M. Jonker
Date: 2018-05-24 00:00:00
Tasks: Board Games, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Self-Driving Cars

A core novelty of Alpha Zero is the interleaving of tree search and deep
learning, which has proven very successful in board games like Chess, Shogi and
Go. These games have a discrete action space. However, many real-world
reinforcement learning domains have continuous action spaces, for example in
robotic control, navigation and self-driving cars. This paper presents the
necessary theoretical extensions of Alpha Zero to deal with continuous action
space. We also provide some preliminary experiments on the Pendulum swing-up
task, empirically showing the feasibility of our approach. Thereby, this work
provides a first step towards the application of iterated search and learning
in domains with a continuous action space.
Measuring Item Similarity in Introductory Programming: Python and Robot Programming Case Studies
Authors: Radek Pelánek, Tomáš Effenberger, Matěj Vaněk, Vojtěch Sassmann, Dominik Gmiterko
Date: 2018-05-24 00:00:00

A personalized learning system needs a large pool of items for learners to
solve. When working with a large pool of items, it is useful to measure the
similarity of items. We outline a general approach to measuring the similarity
of items and discuss specific measures for items used in introductory
programming. Evaluation of quality of similarity measures is difficult. To this
end, we propose an evaluation approach utilizing three levels of abstraction.
We illustrate our approach to measuring similarity and provide evaluation using
items from three diverse programming environments.
A Corpus for Multilingual Document Classification in Eight Languages
Authors: Holger Schwenk, Xi-An Li
Date: 2018-05-24 00:00:00
Proceeding: LREC 2018 5
Tasks: Classification, Cross-Lingual Document Classification, Document Classification, General Classification, Sentence, Sentence Embeddings

Cross-lingual document classification aims at training a document classifier
on resources in one language and transferring it to a different language
without any additional resources. Several approaches have been proposed in the
literature and the current best practice is to evaluate them on a subset of the
Reuters Corpus Volume 2. However, this subset covers only few languages
(English, German, French and Spanish) and almost all published works focus on
the the transfer between English and German. In addition, we have observed that
the class prior distributions differ significantly between the languages. We
argue that this complicates the evaluation of the multilinguality. In this
paper, we propose a new subset of the Reuters corpus with balanced class priors
for eight languages. By adding Italian, Russian, Japanese and Chinese, we cover
languages which are very different with respect to syntax, morphology, etc. We
provide strong baselines for all language transfer directions using
multilingual word and sentence embeddings respectively. Our goal is to offer a
freely available framework to evaluate cross-lingual document classification,
and we hope to foster by these means, research in this important area.
Beyond Greedy Ranking: Slate Optimization via List-CVAE
Authors: Ray Jiang, Sven Gowal, Timothy A. Mann, Danilo J. Rezende
Date: 2018-03-05 00:00:00
Proceeding: ICLR 2019 5

The conventional solution to the recommendation problem greedily ranks
individual document candidates by prediction scores. However, this method fails
to optimize the slate as a whole, and hence, often struggles to capture biases
caused by the page layout and document interdepedencies. The slate
recommendation problem aims to directly find the optimally ordered subset of
documents (i.e. slates) that best serve users' interests. Solving this problem
is hard due to the combinatorial explosion in all combinations of document
candidates and their display positions on the page. Therefore we propose a
paradigm shift from the traditional viewpoint of solving a ranking problem to a
direct slate generation framework. In this paper, we introduce List Conditional
Variational Auto-Encoders (List-CVAE), which learns the joint distribution of
documents on the slate conditioned on user responses, and directly generates
full slates. Experiments on simulated and real-world data show that List-CVAE
outperforms popular comparable ranking methods consistently on various scales
of documents corpora.
Multi-Scale DenseNet-Based Electricity Theft Detection
Authors: Bo Li, Kele Xu, Xiaoyan Cui, Yiheng Wang, Xinbo Ai, Yanbo Wang
Date: 2018-05-24 00:00:00
Tasks: Feature Engineering

Electricity theft detection issue has drawn lots of attention during last
decades. Timely identification of the electricity theft in the power system is
crucial for the safety and availability of the system. Although sustainable
efforts have been made, the detection task remains challenging and falls short
of accuracy and efficiency, especially with the increase of the data size.
Recently, convolutional neural network-based methods have achieved better
performance in comparison with traditional methods, which employ handcrafted
features and shallow-architecture classifiers. In this paper, we present a
novel approach for automatic detection by using a multi-scale dense connected
convolution neural network (multi-scale DenseNet) in order to capture the
long-term and short-term periodic features within the sequential data. We
compare the proposed approaches with the classical algorithms, and the
experimental results demonstrate that the multiscale DenseNet approach can
significantly improve the accuracy of the detection. Moreover, our method is
scalable, enabling larger data processing while no handcrafted feature
engineering is needed.
Native Language Cognate Effects on Second Language Lexical Choice
Authors: Ella Rabinovich, Yulia Tsvetkov, Shuly Wintner
Date: 2018-05-24 00:00:00
Proceeding: TACL 2018 1

We present a computational analysis of cognate effects on the spontaneous
linguistic productions of advanced non-native speakers. Introducing a large
corpus of highly competent non-native English speakers, and using a set of
carefully selected lexical items, we show that the lexical choices of
non-natives are affected by cognates in their native language. This effect is
so powerful that we are able to reconstruct the phylogenetic language tree of
the Indo-European language family solely from the frequencies of specific
lexical items in the English of authors with various native languages. We
quantitatively analyze non-native lexical choice, highlighting cognate
facilitation as one of the important phenomena shaping the language of
non-native speakers.
Residual Networks as Geodesic Flows of Diffeomorphisms
Authors: Francois Rousseau, Ronan Fablet
Date: 2018-05-24 00:00:00

This paper addresses the understanding and characterization of residual
networks (ResNet), which are among the state-of-the-art deep learning
architectures for a variety of supervised learning problems. We focus on the
mapping component of ResNets, which map the embedding space towards a new
unknown space where the prediction or classification can be stated according to
linear criteria. We show that this mapping component can be regarded as the
numerical implementation of continuous flows of diffeomorphisms governed by
ordinary differential equations. Especially, ResNets with shared weights are
fully characterized as numerical approximation of exponential diffeomorphic
operators. We stress both theoretically and numerically the relevance of the
enforcement of diffeormorphic properties and the importance of numerical issues
to make consistent the continuous formulation and the discretized ResNet
implementation. We further discuss the resulting theoretical and computational
insights on ResNet architectures.
Coarse-to-fine Seam Estimation for Image Stitching
Authors: Tianli Liao, Jing Chen, Yifang Xu
Date: 2018-05-24 00:00:00
Tasks: Image Stitching

Seam-cutting and seam-driven techniques have been proven effective for
handling imperfect image series in image stitching. Generally, seam-driven is
to utilize seam-cutting to find a best seam from one or finite alignment
hypotheses based on a predefined seam quality metric. However, the quality
metrics in most methods are defined to measure the average performance of the
pixels on the seam without considering the relevance and variance among them.
This may cause that the seam with the minimal measure is not optimal
(perception-inconsistent) in human perception. In this paper, we propose a
novel coarse-to-fine seam estimation method which applies the evaluation in a
different way. For pixels on the seam, we develop a patch-point evaluation
algorithm concentrating more on the correlation and variation of them. The
evaluations are then used to recalculate the difference map of the overlapping
region and reestimate a stitching seam. This evaluation-reestimation procedure
iterates until the current seam changes negligibly comparing with the previous
seams. Experiments show that our proposed method can finally find a nearly
perception-consistent seam after several iterations, which outperforms the
conventional seam-cutting and other seam-driven methods.
Primal-Dual Wasserstein GAN
Authors: Mevlana Gemici, Zeynep Akata, Max Welling
Date: 2018-05-24 00:00:00
Tasks: Decoder

We introduce Primal-Dual Wasserstein GAN, a new learning algorithm for
building latent variable models of the data distribution based on the primal
and the dual formulations of the optimal transport (OT) problem. We utilize the
primal formulation to learn a flexible inference mechanism and to create an
optimal approximate coupling between the data distribution and the generative
model. In order to learn the generative model, we use the dual formulation and
train the decoder adversarially through a critic network that is regularized by
the approximate coupling obtained from the primal. Unlike previous methods that
violate various properties of the optimal critic, we regularize the norm and
the direction of the gradients of the critic function. Our model shares many of
the desirable properties of auto-encoding models in terms of mode coverage and
latent structure, while avoiding their undesirable averaging properties, e.g.
their inability to capture sharp visual features when modeling real images. We
compare our algorithm with several other generative modeling techniques that
utilize Wasserstein distances on Frechet Inception Distance (FID) and Inception
Scores (IS).
Corpus Conversion Service: A Machine Learning Platform to Ingest Documents at Scale
Authors: Peter W J Staar, Michele Dolfi, Christoph Auer, Costas Bekas
Date: 2018-05-24 00:00:00
Tasks: Articles, BIG-bench Machine Learning

Over the past few decades, the amount of scientific articles and technical
literature has increased exponentially in size. Consequently, there is a great
need for systems that can ingest these documents at scale and make the
contained knowledge discoverable. Unfortunately, both the format of these
documents (e.g. the PDF format or bitmap images) as well as the presentation of
the data (e.g. complex tables) make the extraction of qualitative and
quantitive data extremely challenging. In this paper, we present a modular,
cloud-based platform to ingest documents at scale. This platform, called the
Corpus Conversion Service (CCS), implements a pipeline which allows users to
parse and annotate documents (i.e. collect ground-truth), train
machine-learning classification algorithms and ultimately convert any type of
PDF or bitmap-documents to a structured content representation format. We will
show that each of the modules is scalable due to an asynchronous microservice
architecture and can therefore handle massive amounts of documents.
Furthermore, we will show that our capability to gather ground-truth is
accelerated by machine-learning algorithms by at least one order of magnitude.
This allows us to both gather large amounts of ground-truth in very little time
and obtain very good precision/recall metrics in the range of 99\% with regard
to content conversion to structured output. The CCS platform is currently
deployed on IBM internal infrastructure and serving more than 250 active users
for knowledge-engineering project engagements.
A Unified Probabilistic Model for Learning Latent Factors and Their Connectivities from High-Dimensional Data
Authors: Ricardo Pio Monti, Aapo Hyvärinen
Date: 2018-05-24 00:00:00
Tasks: Connectivity Estimation

Connectivity estimation is challenging in the context of high-dimensional
data. A useful preprocessing step is to group variables into clusters, however,
it is not always clear how to do so from the perspective of connectivity
estimation. Another practical challenge is that we may have data from multiple
related classes (e.g., multiple subjects or conditions) and wish to incorporate
constraints on the similarities across classes. We propose a probabilistic
model which simultaneously performs both a grouping of variables (i.e.,
detecting community structure) and estimation of connectivities between the
groups which correspond to latent variables. The model is essentially a factor
analysis model where the factors are allowed to have arbitrary correlations,
while the factor loading matrix is constrained to express a community
structure. The model can be applied on multiple classes so that the
connectivities can be different between the classes, while the community
structure is the same for all classes. We propose an efficient estimation
algorithm based on score matching, and prove the identifiability of the model.
Finally, we present an extension to directed (causal) connectivities over
latent variables. Simulations and experiments on fMRI data validate the
practical utility of the method.
DRPose3D: Depth Ranking in 3D Human Pose Estimation
Authors: Min Wang, Xipeng Chen, Wentao Liu, Chen Qian, Liang Lin, Lizhuang Ma
Date: 2018-05-23 00:00:00
Tasks: 3D Human Pose Estimation, 3D Pose Estimation, Pose Estimation

In this paper, we propose a two-stage depth ranking based method (DRPose3D)
to tackle the problem of 3D human pose estimation. Instead of accurate 3D
positions, the depth ranking can be identified by human intuitively and learned
using the deep neural network more easily by solving classification problems.
Moreover, depth ranking contains rich 3D information. It prevents the 2D-to-3D
pose regression in two-stage methods from being ill-posed. In our method,
firstly, we design a Pairwise Ranking Convolutional Neural Network (PRCNN) to
extract depth rankings of human joints from images. Secondly, a coarse-to-fine
3D Pose Network(DPNet) is proposed to estimate 3D poses from both depth
rankings and 2D human joint locations. Additionally, to improve the generality
of our model, we introduce a statistical method to augment depth rankings. Our
approach outperforms the state-of-the-art methods in the Human3.6M benchmark
for all three testing protocols, indicating that depth ranking is an essential
geometric feature which can be learned to improve the 3D pose estimation.
Correlation Heuristics for Constraint Programming
Authors: Ruiwei Wang, Wei Xia, Roland H. C. Yap
Date: 2018-05-06 00:00:00

Effective general-purpose search strategies are an important component in
Constraint Programming. We introduce a new idea, namely, using correlations
between variables to guide search. Variable correlations are measured and
maintained by using domain changes during constraint propagation. We propose
two variable heuristics based on the correlation matrix, crbs-sum and crbs-max.
We evaluate our correlation heuristics with well known heuristics, namely,
dom/wdeg, impact-based search and activity-based search. Experiments on a large
set of benchmarks show that our correlation heuristics are competitive with the
other heuristics, and can be the fastest on many series.
Interpretable and Compositional Relation Learning by Joint Training with an Autoencoder
Authors: Ryo Takahashi, Ran Tian, Kentaro Inui
Date: 2018-05-24 00:00:00
Proceeding: ACL 2018 7
Tasks: Dimensionality Reduction, Knowledge Base Completion, Relation

Embedding models for entities and relations are extremely useful for
recovering missing facts in a knowledge base. Intuitively, a relation can be
modeled by a matrix mapping entity vectors. However, relations reside on low
dimension sub-manifolds in the parameter space of arbitrary matrices---for one
reason, composition of two relations $\boldsymbol{M}_1,\boldsymbol{M}_2$ may
match a third $\boldsymbol{M}_3$ (e.g. composition of relations
currency_of_country and country_of_film usually matches
currency_of_film_budget), which imposes compositional constraints to be
satisfied by the parameters (i.e. $\boldsymbol{M}_1\cdot
\boldsymbol{M}_2\approx \boldsymbol{M}_3$). In this paper we investigate a
dimension reduction technique by training relations jointly with an
autoencoder, which is expected to better capture compositional constraints. We
achieve state-of-the-art on Knowledge Base Completion tasks with strongly
improved Mean Rank, and show that joint training with an autoencoder leads to
interpretable sparse codings of relations, helps discovering compositional
constraints and benefits from compositional training. Our source code is
released at github.com/tianran/glimvec.
On the Global Convergence of Gradient Descent for Over-parameterized Models using Optimal Transport
Authors: Lenaic Chizat, Francis Bach
Date: 2018-05-24 00:00:00
Proceeding: NeurIPS 2018 12

Many tasks in machine learning and signal processing can be solved by
minimizing a convex function of a measure. This includes sparse spikes
deconvolution or training a neural network with a single hidden layer. For
these problems, we study a simple minimization method: the unknown measure is
discretized into a mixture of particles and a continuous-time gradient descent
is performed on their weights and positions. This is an idealization of the
usual way to train neural networks with a large hidden layer. We show that,
when initialized correctly and in the many-particle limit, this gradient flow,
although non-convex, converges to global minimizers. The proof involves
Wasserstein gradient flows, a by-product of optimal transport theory. Numerical
experiments show that this asymptotic behavior is already at play for a
reasonable number of particles, even in high dimension.
Maize Haploid Identification via LSTM-CNN and Hyperspectral Imaging Technology
Authors: Xuan-Yu Wang, Wen-Xuan Liao, Dong An, Yao-Guang Wei
Date: 2018-05-23 00:00:00

Accurate and fast identification of seed cultivars is crucial to plant
breeding, with accelerating breeding of new products and increasing its
quality. In our study, the first attempt to design a high-accurate
identification model of maize haploid seeds from diploid ones based on optimum
waveband selection of the LSTM-CNN algorithm is realized via deep learning and
hyperspectral imaging technology, with accuracy reaching 97% in the determining
optimum waveband of 1367.6-1526.4nm. The verification of testing another
cultivar achieved an accuracy of 93% in the same waveband. The model collected
images of 256 wavebands of seeds in the spectral region of 862.9-1704.2nm. The
high-noise waveband intervals were found and deleted by the LSTM. The
optimum-data waveband intervals were determined by CNN's waveband-based
detection. The optimum sample set for network training only accounted for 1/5
of total sample data. The accuracy was significantly higher than the
full-waveband modeling or modeling of any other wavebands. Our study
demonstrates that the proposed model has outstanding effect on maize haploid
identification and it could be generalized to some extent.
Laplacian Networks: Bounding Indicator Function Smoothness for Neural Network Robustness
Authors: Carlos Eduardo Rosar Kos Lassance, Vincent Gripon, Antonio Ortega
Date: 2018-05-24 00:00:00

For the past few years, Deep Neural Network (DNN) robustness has become a
question of paramount importance. As a matter of fact, in sensitive settings
misclassification can lead to dramatic consequences. Such misclassifications
are likely to occur when facing adversarial attacks, hardware failures or
limitations, and imperfect signal acquisition. To address this question,
authors have proposed different approaches aiming at increasing the robustness
of DNNs, such as adding regularizers or training using noisy examples. In this
paper we propose a new regularizer built upon the Laplacian of similarity
graphs obtained from the representation of training data at each layer of the
DNN architecture. This regularizer penalizes large changes (across consecutive
layers in the architecture) in the distance between examples of different
classes, and as such enforces smooth variations of the class boundaries. Since
it is agnostic to the type of deformations that are expected when predicting
with the DNN, the proposed regularizer can be combined with existing ad-hoc
methods. We provide theoretical justification for this regularizer and
demonstrate its effectiveness to improve robustness of DNNs on classical
supervised learning vision datasets.
Deep Hashing with Category Mask for Fast Video Retrieval
Authors: Xu Liu, Lili Zhao, Dajun Ding, Yajiao Dong
Date: 2017-12-22 00:00:00
Tasks: Code Generation, Deep Hashing, Diversity, General Classification, Retrieval, Video Retrieval

This paper proposes an end-to-end deep hashing framework with category mask
for fast video retrieval. We train our network in a supervised way by fully
exploiting inter-class diversity and intra-class identity. Classification loss
is optimized to maximize inter-class diversity, while intra-pair is introduced
to learn representative intra-class identity. We investigate the binary bits
distribution related to categories and find out that the effectiveness of
binary bits is highly correlated with data categories, and some bits may
degrade classification performance of some categories. We then design hash code
generation scheme with category mask to filter out bits with negative
contribution. Experimental results demonstrate the proposed method outperforms
several state-of-the-arts under various evaluation metrics on public datasets.