pwc-archive/papers-with-abstracts huggingface dataset:
Hyperparameter Learning via Distributional Transfer
Authors: Ho Chung Leon Law, Peilin Zhao, Lucian Chan, Junzhou Huang, Dino Sejdinovic
Date: 2018-10-15 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Bayesian Optimisation

Bayesian optimisation is a popular technique for hyperparameter learning but typically requires initial exploration even in cases where similar prior tasks have been solved. We propose to transfer information across tasks using learnt representations of training datasets used in those tasks. This results in a joint Gaussian process model on hyperparameters and data representations. Representations make use of the framework of distribution embeddings into reproducing kernel Hilbert spaces. The developed method has a faster convergence compared to existing baselines, in some cases requiring only a few evaluations of the target objective.
Dimensionality Reduction and (Bucket) Ranking: a Mass Transportation Approach
Authors: Mastane Achab, Anna Korba, Stephan Clémençon
Date: 2018-10-15 00:00:00
Tasks: Dimensionality Reduction

Whereas most dimensionality reduction techniques (e.g. PCA, ICA, NMF) for multivariate data essentially rely on linear algebra to a certain extent, summarizing ranking data, viewed as realizations of a random permutation $\Sigma$ on a set of items indexed by $i\in \{1,\ldots,\; n\}$, is a great statistical challenge, due to the absence of vector space structure for the set of permutations $\mathfrak{S}_n$. It is the goal of this article to develop an original framework for possibly reducing the number of parameters required to describe the distribution of a statistical population composed of rankings/permutations, on the premise that the collection of items under study can be partitioned into subsets/buckets, such that, with high probability, items in a certain bucket are either all ranked higher or else all ranked lower than items in another bucket. In this context, $\Sigma$'s distribution can be hopefully represented in a sparse manner by a bucket distribution, i.e. a bucket ordering plus the ranking distributions within each bucket. More precisely, we introduce a dedicated distortion measure, based on a mass transportation metric, in order to quantify the accuracy of such representations. The performance of buckets minimizing an empirical version of the distortion is investigated through a rate bound analysis. Complexity penalization techniques are also considered to select the shape of a bucket order with minimum expected distortion. Beyond theoretical concepts and results, numerical experiments on real ranking data are displayed in order to provide empirical evidence of the relevance of the approach promoted.
CURIOUS: Intrinsically Motivated Modular Multi-Goal Reinforcement Learning
Authors: Cédric Colas, Pierre Fournier, Olivier Sigaud, Mohamed Chetouani, Pierre-Yves Oudeyer
Date: 2018-10-15 00:00:00
Tasks: Diversity, Efficient Exploration, Multi-Goal Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In open-ended environments, autonomous learning agents must set their own goals and build their own curriculum through an intrinsically motivated exploration. They may consider a large diversity of goals, aiming to discover what is controllable in their environments, and what is not. Because some goals might prove easy and some impossible, agents must actively select which goal to practice at any moment, to maximize their overall mastery on the set of learnable goals. This paper proposes CURIOUS, an algorithm that leverages 1) a modular Universal Value Function Approximator with hindsight learning to achieve a diversity of goals of different kinds within a unique policy and 2) an automated curriculum learning mechanism that biases the attention of the agent towards goals maximizing the absolute learning progress. Agents focus sequentially on goals of increasing complexity, and focus back on goals that are being forgotten. Experiments conducted in a new modular-goal robotic environment show the resulting developmental self-organization of a learning curriculum, and demonstrate properties of robustness to distracting goals, forgetting and changes in body properties.
Feature Representation Analysis of Deep Convolutional Neural Network using Two-stage Feature Transfer -An Application for Diffuse Lung Disease Classification-
Authors: Aiga Suzuki, Hidenori Sakanashi, Shoji Kido, Hayaru Shouno
Date: 2018-10-15 00:00:00
Tasks: General Classification, Lung Disease Classification, Transfer Learning

Transfer learning is a machine learning technique designed to improve
generalization performance by using pre-trained parameters obtained from other
learning tasks. For image recognition tasks, many previous studies have
reported that, when transfer learning is applied to deep neural networks,
performance improves, despite having limited training data. This paper proposes
a two-stage feature transfer learning method focusing on the recognition of
textural medical images. During the proposed method, a model is successively
trained with massive amounts of natural images, some textural images, and the
target images. We applied this method to the classification task of textural
X-ray computed tomography images of diffuse lung diseases. In our experiment,
the two-stage feature transfer achieves the best performance compared to a
from-scratch learning and a conventional single-stage feature transfer. We also
investigated the robustness of the target dataset, based on size. Two-stage
feature transfer shows better robustness than the other two learning methods.
Moreover, we analyzed the feature representations obtained from DLDs imagery
inputs for each feature transfer models using a visualization method. We showed
that the two-stage feature transfer obtains both edge and textural features of
DLDs, which does not occur in conventional single-stage feature transfer
models.
Playing for Depth
Authors: Mohammad Mahdi Haji-Esmaeili, Gholamali Montazer
Date: 2018-10-15 00:00:00
Tasks: Depth Estimation, Monocular Depth Estimation

Estimating the relative depth of a scene is a significant step towards
understanding the general structure of the depicted scenery, the relations of
entities in the scene and their interactions. When faced with the task of
estimating depth without the use of Stereo images, we are dependent on the
availability of large-scale depth datasets and high-capacity models to capture
the intrinsic nature of depth. Unfortunately, creating datasets of depth images
is not a trivial task as the requirements for the camera mainly limits us to
areas where we can provide the necessities for the camera to work.
  In this work, we present a new depth dataset captured from Video Games in an
easy and reproducible way. The nature of open-world video games gives us the
ability to capture high-quality depth maps in the wild without the
constrictions of Stereo cameras. Experiments on this dataset shows that using
such synthetic datasets increases the accuracy of Monocular Depth Estimation in
the wild where other approaches usually fail to generalize.
Bringing back simplicity and lightliness into neural image captioning
Authors: Jean-Benoit Delbrouck, Stéphane Dupont
Date: 2018-10-15 00:00:00
Tasks: Caption Generation, Image Captioning, Machine Translation, Translation

Neural Image Captioning (NIC) or neural caption generation has attracted a
lot of attention over the last few years. Describing an image with a natural
language has been an emerging challenge in both fields of computer vision and
language processing. Therefore a lot of research has focused on driving this
task forward with new creative ideas. So far, the goal has been to maximize
scores on automated metric and to do so, one has to come up with a plurality of
new modules and techniques. Once these add up, the models become complex and
resource-hungry. In this paper, we take a small step backwards in order to
study an architecture with interesting trade-off between performance and
computational complexity. To do so, we tackle every component of a neural
captioning model and propose one or more solution that lightens the model
overall. Our ideas are inspired by two related tasks: Multimodal and Monomodal
Neural Machine Translation.
Comparing Temporal Graphs Using Dynamic Time Warping
Authors: Vincent Froese, Brijnesh Jain, Rolf Niedermeier, Malte Renken
Date: 2018-10-15 00:00:00
Tasks: Dynamic Time Warping, Time Series Analysis

Within many real-world networks the links between pairs of nodes change over time. Thus, there has been a recent boom in studying temporal graphs. Recognizing patterns in temporal graphs requires a proximity measure to compare different temporal graphs. To this end, we propose to study dynamic time warping on temporal graphs. We define the dynamic temporal graph warping distance (dtgw) to determine the dissimilarity of two temporal graphs. Our novel measure is flexible and can be applied in various application domains. We show that computing the dtgw-distance is a challenging (in general) NP-hard optimization problem and identify some polynomial-time solvable special cases. Moreover, we develop a quadratic programming formulation and an efficient heuristic. In experiments on real-word data we show that the heuristic performs very well and that our dtgw-distance performs favorably in de-anonymizing networks compared to other approaches.
UMONS Submission for WMT18 Multimodal Translation Task
Authors: Jean-Benoit Delbrouck, Stéphane Dupont
Date: 2018-10-15 00:00:00
Tasks: Image Captioning, Machine Translation, Multimodal Machine Translation, Translation

This paper describes the UMONS solution for the Multimodal Machine
Translation Task presented at the third conference on machine translation
(WMT18). We explore a novel architecture, called deepGRU, based on recent
findings in the related task of Neural Image Captioning (NIC). The models
presented in the following sections lead to the best METEOR translation score
for both constrained (English, image) -> German and (English, image) -> French
sub-tasks.
A Context-aware Capsule Network for Multi-label Classification
Authors: Sameera Ramasinghe, C. D. Athuralya, Salman Khan
Date: 2018-10-15 00:00:00
Tasks: Classification, General Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

Recently proposed Capsule Network is a brain inspired architecture that
brings a new paradigm to deep learning by modelling input domain variations
through vector based representations. Despite being a seminal contribution,
CapsNet does not explicitly model structured relationships between the detected
entities and among the capsule features for related inputs. Motivated by the
working of cortical network in human visual system, we seek to resolve CapsNet
limitations by proposing several intuitive modifications to the CapsNet
architecture. We introduce, (1) a novel routing weight initialization
technique, (2) an improved CapsNet design that exploits semantic relationships
between the primary capsule activations using a densely connected Conditional
Random Field and (3) a Cholesky transformation based correlation module to
learn a general priority scheme. Our proposed design allows CapsNet to scale
better to more complex problems, such as the multi-label classification task,
where semantically related categories co-exist with various interdependencies.
We present theoretical bases for our extensions and demonstrate significant
improvements on ADE20K scene dataset.
Sparse-View CT Reconstruction via Convolutional Sparse Coding
Authors: Peng Bao, Wenjun Xia, Kang Yang, Jiliu Zhou, Yi Zhang
Date: 2018-10-15 00:00:00
Tasks: CT Reconstruction, Dictionary Learning

Traditional dictionary learning based CT reconstruction methods are
patch-based and the features learned with these methods often contain shifted
versions of the same features. To deal with these problems, the convolutional
sparse coding (CSC) has been proposed and introduced into various applications.
In this paper, inspired by the successful applications of CSC in the field of
signal processing, we propose a novel sparse-view CT reconstruction method
based on CSC with gradient regularization on feature maps. By directly working
on whole image, which need not to divide the image into overlapped patches like
dictionary learning based methods, the proposed method can maintain more
details and avoid the artifacts caused by patch aggregation. Experimental
results demonstrate that the proposed method has better performance than
several existing algorithms in both qualitative and quantitative aspects.
Supervised COSMOS Autoencoder: Learning Beyond the Euclidean Loss!
Authors: Maneet Singh, Shruti Nagpal, Mayank Vatsa, Richa Singh, Afzel Noore
Date: 2018-10-15 00:00:00
Tasks: Attribute, Face Recognition, General Classification

Autoencoders are unsupervised deep learning models used for learning
representations. In literature, autoencoders have shown to perform well on a
variety of tasks spread across multiple domains, thereby establishing
widespread applicability. Typically, an autoencoder is trained to generate a
model that minimizes the reconstruction error between the input and the
reconstructed output, computed in terms of the Euclidean distance. While this
can be useful for applications related to unsupervised reconstruction, it may
not be optimal for classification. In this paper, we propose a novel Supervised
COSMOS Autoencoder which utilizes a multi-objective loss function to learn
representations that simultaneously encode the (i) "similarity" between the
input and reconstructed vectors in terms of their direction, (ii)
"distribution" of pixel values of the reconstruction with respect to the input
sample, while also incorporating (iii) "discriminability" in the feature
learning pipeline. The proposed autoencoder model incorporates a Cosine
similarity and Mahalanobis distance based loss function, along with supervision
via Mutual Information based loss. Detailed analysis of each component of the
proposed model motivates its applicability for feature learning in different
classification tasks. The efficacy of Supervised COSMOS autoencoder is
demonstrated via extensive experimental evaluations on different image
datasets. The proposed model outperforms existing algorithms on MNIST,
CIFAR-10, and SVHN databases. It also yields state-of-the-art results on
CelebA, LFWA, Adience, and IJB-A databases for attribute prediction and face
recognition, respectively.
The Focus-Aspect-Polarity Model for Predicting Subjective Noun Attributes in Images
Authors: Tushar Karayil, Philipp Blandfort, Jörn Hees, Andreas Dengel
Date: 2018-10-15 00:00:00
Tasks: Attribute

Subjective visual interpretation is a challenging yet important topic in
computer vision. Many approaches reduce this problem to the prediction of
adjective- or attribute-labels from images. However, most of these do not take
attribute semantics into account, or only process the image in a holistic
manner. Furthermore, there is a lack of relevant datasets with fine-grained
subjective labels. In this paper, we propose the Focus-Aspect-Polarity model to
structure the process of capturing subjectivity in image processing, and
introduce a novel dataset following this way of modeling. We run experiments on
this dataset to compare several deep learning methods and find that
incorporating context information based on tensor multiplication in several
cases outperforms the default way of information fusion (concatenation).
Solution for Large-Scale Hierarchical Object Detection Datasets with Incomplete Annotation and Data Imbalance
Authors: Yuan Gao, Xingyuan Bu, Yang Hu, Hui Shen, Ti Bai, Xubin Li, Shilei Wen
Date: 2018-10-15 00:00:00
Tasks: object-detection, Object Detection, TAG

This report demonstrates our solution for the Open Images 2018 Challenge.
Based on our detailed analysis on the Open Images Datasets (OID), it is found
that there are four typical features: large-scale, hierarchical tag system,
severe annotation incompleteness and data imbalance. Considering these
characteristics, an amount of strategies are employed, including SNIPER, soft
sampling, class-aware sampling (CAS), hierarchical non-maximum suppression
(HNMS) and so on. In virtue of these effective strategies, and further using
the powerful SENet154 armed with feature pyramid module and deformable ROIalign
as the backbone, our best single model could achieve a mAP of 56.9%. After a
further ensemble with 9 models, the final mAP is boosted to 62.2% in the public
leaderboard (ranked the 2nd place) and 58.6% in the private leaderboard (ranked
the 3rd place, slightly inferior to the 1st place by only 0.04 point).
Robust descent using smoothed multiplicative noise
Authors: Matthew J. Holland
Date: 2018-10-15 00:00:00

To improve the off-sample generalization of classical procedures minimizing
the empirical risk under potentially heavy-tailed data, new robust learning
algorithms have been proposed in recent years, with generalized median-of-means
strategies being particularly salient. These procedures enjoy performance
guarantees in the form of sharp risk bounds under weak moment assumptions on
the underlying loss, but typically suffer from a large computational overhead
and substantial bias when the data happens to be sub-Gaussian, limiting their
utility. In this work, we propose a novel robust gradient descent procedure
which makes use of a smoothed multiplicative noise applied directly to
observations before constructing a sum of soft-truncated gradient coordinates.
We show that the procedure has competitive theoretical guarantees, with the
major advantage of a simple implementation that does not require an iterative
sub-routine for robustification. Empirical tests reinforce the theory, showing
more efficient generalization over a much wider class of data distributions.
Learning to Jointly Translate and Predict Dropped Pronouns with a Shared Reconstruction Mechanism
Authors: Long-Yue Wang, Zhaopeng Tu, Andy Way, Qun Liu
Date: 2018-10-15 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Decoder, Machine Translation, Translation

Pronouns are frequently omitted in pro-drop languages, such as Chinese,
generally leading to significant challenges with respect to the production of
complete translations. Recently, Wang et al. (2018) proposed a novel
reconstruction-based approach to alleviating dropped pronoun (DP) translation
problems for neural machine translation models. In this work, we improve the
original model from two perspectives. First, we employ a shared reconstructor
to better exploit encoder and decoder representations. Second, we jointly learn
to translate and predict DPs in an end-to-end manner, to avoid the errors
propagated from an external DP prediction model. Experimental results show that
our approach significantly improves both translation performance and DP
prediction accuracy.
3D Feature Pyramid Attention Module for Robust Visual Speech Recognition
Authors: Jing-Yun Xiao
Date: 2018-10-15 00:00:00
Tasks: Lipreading, Sentence, speech-recognition, Speech Recognition, Visual Speech Recognition

Visual speech recognition is the task to decode the speech content from a
video based on visual information, especially the movements of lips. It is also
referenced as lipreading. Motivated by two problems existing in lipreading,
words with similar pronunciation and the variation of word duration, we propose
a novel 3D Feature Pyramid Attention (3D-FPA) module to jointly improve the
representation power of features in both the spatial and temporal domains.
Specifically, the input features are downsampled for 3 times in both the
spatial and temporal dimensions to construct spatiotemporal feature pyramids.
Then high-level features are upsampled and combined with low-level features,
finally generating a pixel-level soft attention mask to be multiplied with the
input features.It enhances the discriminative power of features and exploits
the temporal multi-scale information while decoding the visual speeches. Also,
this module provides a new method to construct and utilize temporal pyramid
structures in video analysis tasks. The field of temporal featrue pyramids are
still under exploring compared to the plentiful works on spatial feature
pyramids for image analysis tasks. To validate the effectiveness and
adaptability of our proposed module, we embed the module in a sentence-level
lipreading model, LipNet, with the result of 3.6% absolute decrease in word
error rate, and a word-level model, with the result of 1.4% absolute
improvement in accuracy.
An Optimal Control Approach to Sequential Machine Teaching
Authors: Laurent Lessard, Xuezhou Zhang, Xiaojin Zhu
Date: 2018-10-15 00:00:00

Given a sequential learning algorithm and a target model, sequential machine
teaching aims to find the shortest training sequence to drive the learning
algorithm to the target model. We present the first principled way to find such
shortest training sequences. Our key insight is to formulate sequential machine
teaching as a time-optimal control problem. This allows us to solve sequential
teaching by leveraging key theoretical and computational tools developed over
the past 60 years in the optimal control community. Specifically, we study the
Pontryagin Maximum Principle, which yields a necessary condition for optimality
of a training sequence. We present analytic, structural, and numerical
implications of this approach on a case study with a least-squares loss
function and gradient descent learner. We compute optimal training sequences
for this problem, and although the sequences seem circuitous, we find that they
can vastly outperform the best available heuristics for generating training
sequences.
Traffic Signs in the Wild: Highlights from the IEEE Video and Image Processing Cup 2017 Student Competition [SP Competitions]
Authors: Dogancan Temel, Ghassan AlRegib
Date: 2018-10-15 00:00:00
Tasks: Autonomous Vehicles, Traffic Sign Detection

Robust and reliable traffic sign detection is necessary to bring autonomous
vehicles onto our roads. State-of-the-art algorithms successfully perform
traffic sign detection over existing databases that mostly lack severe
challenging conditions. VIP Cup 2017 competition focused on detecting such
traffic signs under challenging conditions. To facilitate such task and
competition, we introduced a video dataset denoted as CURE-TSD that includes a
variety of challenging conditions. The goal of this challenge was to implement
traffic sign detection algorithms that can robustly perform under such
challenging conditions. In this article, we share an overview of the VIP Cup
2017 experience including competition setup, teams, technical approaches,
participation statistics, and competition experience through finalist teams
members' and organizers' eyes.
ABACUS: Unsupervised Multivariate Change Detection via Bayesian Source Separation
Authors: Wenyu Zhang, Daniel Gilbert, David Matteson
Date: 2018-10-15 00:00:00
Tasks: Change Detection, Dimensionality Reduction

Change detection involves segmenting sequential data such that observations
in the same segment share some desired properties. Multivariate change
detection continues to be a challenging problem due to the variety of ways
change points can be correlated across channels and the potentially poor
signal-to-noise ratio on individual channels. In this paper, we are interested
in locating additive outliers (AO) and level shifts (LS) in the unsupervised
setting. We propose ABACUS, Automatic BAyesian Changepoints Under Sparsity, a
Bayesian source separation technique to recover latent signals while also
detecting changes in model parameters. Multi-level sparsity achieves both
dimension reduction and modeling of signal changes. We show ABACUS has
competitive or superior performance in simulation studies against
state-of-the-art change detection methods and established latent variable
models. We also illustrate ABACUS on two real application, modeling genomic
profiles and analyzing household electricity consumption.