pwc-archive/papers-with-abstracts huggingface dataset:
Selfless Sequential Learning
Authors: Rahaf Aljundi, Marcus Rohrbach, Tinne Tuytelaars
Date: 2018-06-14 00:00:00
Proceeding: ICLR 2019 5
Tasks: Lifelong learning

Sequential learning, also called lifelong learning, studies the problem of
learning tasks in a sequence with access restricted to only the data of the
current task. In this paper we look at a scenario with fixed model capacity,
and postulate that the learning process should not be selfish, i.e. it should
account for future tasks to be added and thus leave enough capacity for them.
To achieve Selfless Sequential Learning we study different regularization
strategies and activation functions. We find that imposing sparsity at the
level of the representation (i.e.~neuron activations) is more beneficial for
sequential learning than encouraging parameter sparsity. In particular, we
propose a novel regularizer, that encourages representation sparsity by means
of neural inhibition. It results in few active neurons which in turn leaves
more free neurons to be utilized by upcoming tasks. As neural inhibition over
an entire layer can be too drastic, especially for complex tasks requiring
strong representations, our regularizer only inhibits other neurons in a local
neighbourhood, inspired by lateral inhibition processes in the brain. We
combine our novel regularizer, with state-of-the-art lifelong learning methods
that penalize changes to important previously learned parts of the network. We
show that our new regularizer leads to increased sparsity which translates in
consistent performance improvement %over alternative regularizers we studied on
diverse datasets.
Temporal Stability in Predictive Process Monitoring
Authors: Irene Teinemaa, Marlon Dumas, Anna Leontjeva, Fabrizio Maria Maggi
Date: 2017-12-12 00:00:00
Tasks: Binary Classification, Predictive Process Monitoring, Time Series, Time Series Analysis

Predictive process monitoring is concerned with the analysis of events
produced during the execution of a business process in order to predict as
early as possible the final outcome of an ongoing case. Traditionally,
predictive process monitoring methods are optimized with respect to accuracy.
However, in environments where users make decisions and take actions in
response to the predictions they receive, it is equally important to optimize
the stability of the successive predictions made for each case. To this end,
this paper defines a notion of temporal stability for binary classification
tasks in predictive process monitoring and evaluates existing methods with
respect to both temporal stability and accuracy. We find that methods based on
XGBoost and LSTM neural networks exhibit the highest temporal stability. We
then show that temporal stability can be enhanced by hyperparameter-optimizing
random forests and XGBoost classifiers with respect to inter-run stability.
Finally, we show that time series smoothing techniques can further enhance
temporal stability at the expense of slightly lower accuracy.
Three dimensional Deep Learning approach for remote sensing image classification
Authors: Amina Ben Hamida, A. Benoit, Patrick Lambert, Chokri Ben Amar
Date: 2018-06-15 00:00:00
Tasks: Classification, Deep Learning, General Classification, image-classification, Image Classification, Remote Sensing Image Classification, Specificity

Recently, a variety of approaches has been enriching the field of Remote
Sensing (RS) image processing and analysis. Unfortunately, existing methods
remain limited faced to the rich spatio-spectral content of today's large
datasets. It would seem intriguing to resort to Deep Learning (DL) based
approaches at this stage with regards to their ability to offer accurate
semantic interpretation of the data. However, the specificity introduced by the
coexistence of spectral and spatial content in the RS datasets widens the scope
of the challenges presented to adapt DL methods to these contexts. Therefore,
the aim of this paper is firstly to explore the performance of DL architectures
for the RS hyperspectral dataset classification and secondly to introduce a new
three-dimensional DL approach that enables a joint spectral and spatial
information process. A set of three-dimensional schemes is proposed and
evaluated. Experimental results based on well knownhyperspectral datasets
demonstrate that the proposed method is able to achieve a better classification
rate than state of the art methods with lower computational costs.
Primal-dual residual networks
Authors: Christoph Brauer, Dirk Lorenz
Date: 2018-06-15 00:00:00

In this work, we propose a deep neural network architecture motivated by
primal-dual splitting methods from convex optimization. We show theoretically
that there exists a close relation between the derived architecture and
residual networks, and further investigate this connection in numerical
experiments. Moreover, we demonstrate how our approach can be used to unroll
optimization algorithms for certain problems with hard constraints. Using the
example of speech dequantization, we show that our method can outperform
classical splitting methods when both are applied to the same task.
BubbleRank: Safe Online Learning to Re-Rank via Implicit Click Feedback
Authors: Chang Li, Branislav Kveton, Tor Lattimore, Ilya Markov, Maarten de Rijke, Csaba Szepesvari, Masrour Zoghi
Date: 2018-06-15 00:00:00
Tasks: Learning-To-Rank, Re-Ranking, Safe Exploration

In this paper, we study the problem of safe online learning to re-rank, where user feedback is used to improve the quality of displayed lists. Learning to rank has traditionally been studied in two settings. In the offline setting, rankers are typically learned from relevance labels created by judges. This approach has generally become standard in industrial applications of ranking, such as search. However, this approach lacks exploration and thus is limited by the information content of the offline training data. In the online setting, an algorithm can experiment with lists and learn from feedback on them in a sequential fashion. Bandit algorithms are well-suited for this setting but they tend to learn user preferences from scratch, which results in a high initial cost of exploration. This poses an additional challenge of safe exploration in ranked lists. We propose BubbleRank, a bandit algorithm for safe re-ranking that combines the strengths of both the offline and online settings. The algorithm starts with an initial base list and improves it online by gradually exchanging higher-ranked less attractive items for lower-ranked more attractive items. We prove an upper bound on the n-step regret of BubbleRank that degrades gracefully with the quality of the initial base list. Our theoretical findings are supported by extensive experiments on a large-scale real-world click dataset.
Safe Active Feature Selection for Sparse Learning
Authors: Shaogang Ren, Jianhua Z. Huang, Shuai Huang, Xiaoning Qian
Date: 2018-06-15 00:00:00
Tasks: feature selection, Sparse Learning

We present safe active incremental feature selection~(SAIF) to scale up the
computation of LASSO solutions. SAIF does not require a solution from a heavier
penalty parameter as in sequential screening or updating the full model for
each iteration as in dynamic screening. Different from these existing screening
methods, SAIF starts from a small number of features and incrementally recruits
active features and updates the significantly reduced model. Hence, it is much
more computationally efficient and scalable with the number of features. More
critically, SAIF has the safe guarantee as it has the convergence guarantee to
the optimal solution to the original full LASSO problem. Such an incremental
procedure and theoretical convergence guarantee can be extended to fused LASSO
problems. Compared with state-of-the-art screening methods as well as working
set and homotopy methods, which may not always guarantee the optimal solution,
SAIF can achieve superior or comparable efficiency and high scalability with
the safe guarantee when facing extremely high dimensional data sets.
Experiments with both synthetic and real-world data sets show that SAIF can be
up to 50 times faster than dynamic screening, and hundreds of times faster than
computing LASSO or fused LASSO solutions without screening.
SGM: Sequence Generation Model for Multi-label Classification
Authors: Pengcheng Yang, Xu sun, Wei Li, Shuming Ma, Wei Wu, Houfeng Wang
Date: 2018-06-13 00:00:00
Proceeding: COLING 2018 8
Tasks: Classification, Decoder, General Classification, model, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

Multi-label classification is an important yet challenging task in natural
language processing. It is more complex than single-label classification in
that the labels tend to be correlated. Existing methods tend to ignore the
correlations between labels. Besides, different parts of the text can
contribute differently for predicting different labels, which is not considered
by existing models. In this paper, we propose to view the multi-label
classification task as a sequence generation problem, and apply a sequence
generation model with a novel decoder structure to solve it. Extensive
experimental results show that our proposed methods outperform previous work by
a substantial margin. Further analysis of experimental results demonstrates
that the proposed methods not only capture the correlations between labels, but
also select the most informative words automatically when predicting different
labels.
Best sources forward: domain generalization through source-specific nets
Authors: Massimiliano Mancini, Samuel Rota Bulò, Barbara Caputo, Elisa Ricci
Date: 2018-06-15 00:00:00
Tasks: Domain Adaptation, Domain Generalization, Object Categorization

A long standing problem in visual object categorization is the ability of
algorithms to generalize across different testing conditions. The problem has
been formalized as a covariate shift among the probability distributions
generating the training data (source) and the test data (target) and several
domain adaptation methods have been proposed to address this issue. While these
approaches have considered the single source-single target scenario, it is
plausible to have multiple sources and require adaptation to any possible
target domain. This last scenario, named Domain Generalization (DG), is the
focus of our work. Differently from previous DG methods which learn domain
invariant representations from source data, we design a deep network with
multiple domain-specific classifiers, each associated to a source domain. At
test time we estimate the probabilities that a target sample belongs to each
source domain and exploit them to optimally fuse the classifiers predictions.
To further improve the generalization ability of our model, we also introduced
a domain agnostic component supporting the final classifier. Experiments on two
public benchmarks demonstrate the power of our approach.
Molecular generative model based on conditional variational autoencoder for de novo molecular design
Authors: Jaechang Lim, Seongok Ryu, Jin Woo Kim, Woo Youn Kim
Date: 2018-06-15 00:00:00

We propose a molecular generative model based on the conditional variational
autoencoder for de novo molecular design. It is specialized to control multiple
molecular properties simultaneously by imposing them on a latent space. As a
proof of concept, we demonstrate that it can be used to generate drug-like
molecules with five target properties. We were also able to adjust a single
property without changing the others and to manipulate it beyond the range of
the dataset.
Hybrid Approach of Relation Network and Localized Graph Convolutional Filtering for Breast Cancer Subtype Classification
Authors: Sungmin Rhee, Seokjun Seo, Sun Kim
Date: 2017-11-16 00:00:00
Tasks: Classification, Deep Learning, General Classification, Relation, Relation Network, Survival Analysis

Network biology has been successfully used to help reveal complex mechanisms
of disease, especially cancer. On the other hand, network biology requires
in-depth knowledge to construct disease-specific networks, but our current
knowledge is very limited even with the recent advances in human cancer
biology. Deep learning has shown a great potential to address the difficult
situation like this. However, deep learning technologies conventionally use
grid-like structured data, thus application of deep learning technologies to
the classification of human disease subtypes is yet to be explored. Recently,
graph based deep learning techniques have emerged, which becomes an opportunity
to leverage analyses in network biology. In this paper, we proposed a hybrid
model, which integrates two key components 1) graph convolution neural network
(graph CNN) and 2) relation network (RN). We utilize graph CNN as a component
to learn expression patterns of cooperative gene community, and RN as a
component to learn associations between learned patterns. The proposed model is
applied to the PAM50 breast cancer subtype classification task, the standard
breast cancer subtype classification of clinical utility. In experiments of
both subtype classification and patient survival analysis, our proposed method
achieved significantly better performances than existing methods. We believe
that this work is an important starting point to realize the upcoming
personalized medicine.
Learning to Act Properly: Predicting and Explaining Affordances from Images
Authors: Ching-Yao Chuang, Jiaman Li, Antonio Torralba, Sanja Fidler
Date: 2017-12-20 00:00:00
Proceeding: CVPR 2018 6
Tasks: Visual Reasoning

We address the problem of affordance reasoning in diverse scenes that appear
in the real world. Affordances relate the agent's actions to their effects when
taken on the surrounding objects. In our work, we take the egocentric view of
the scene, and aim to reason about action-object affordances that respect both
the physical world as well as the social norms imposed by the society. We also
aim to teach artificial agents why some actions should not be taken in certain
situations, and what would likely happen if these actions would be taken. We
collect a new dataset that builds upon ADE20k, referred to as ADE-Affordance,
which contains annotations enabling such rich visual reasoning. We propose a
model that exploits Graph Neural Networks to propagate contextual information
from the scene in order to perform detailed affordance reasoning about each
object. Our model is showcased through various ablation studies, pointing to
successes and challenges in this complex task.
Weakly Supervised Deep Image Hashing through Tag Embeddings
Authors: Vijetha Gattupalli, Yaoxin Zhuo, Baoxin Li
Date: 2018-06-15 00:00:00
Proceeding: CVPR 2019 6
Tasks: Deep Hashing, Image Retrieval, Retrieval, TAG, Weakly-supervised Learning

Many approaches to semantic image hashing have been formulated as supervised
learning problems that utilize images and label information to learn the binary
hash codes. However, large-scale labeled image data is expensive to obtain,
thus imposing a restriction on the usage of such algorithms. On the other hand,
unlabelled image data is abundant due to the existence of many Web image
repositories. Such Web images may often come with images tags that contain
useful information, although raw tags, in general, do not readily lead to
semantic labels. Motivated by this scenario, we formulate the problem of
semantic image hashing as a weakly-supervised learning problem. We utilize the
information contained in the user-generated tags associated with the images to
learn the hash codes. More specifically, we extract the word2vec semantic
embeddings of the tags and use the information contained in them for
constraining the learning. Accordingly, we name our model Weakly Supervised
Deep Hashing using Tag Embeddings (WDHT). WDHT is tested for the task of
semantic image retrieval and is compared against several state-of-art models.
Results show that our approach sets a new state-of-art in the area of weekly
supervised image hashing.
WikiRef: Wikilinks as a route to recommending appropriate references for scientific Wikipedia pages
Authors: Abhik Jana, Pranjal Kanojiya, Pawan Goyal, Animesh Mukherjee
Date: 2018-06-11 00:00:00
Proceeding: COLING 2018 8

The exponential increase in the usage of Wikipedia as a key source of
scientific knowledge among the researchers is making it absolutely necessary to
metamorphose this knowledge repository into an integral and self-contained
source of information for direct utilization. Unfortunately, the references
which support the content of each Wikipedia entity page, are far from complete.
Why are the reference section ill-formed for most Wikipedia pages? Is this
section edited as frequently as the other sections of a page? Can there be
appropriate surrogates that can automatically enhance the reference section? In
this paper, we propose a novel two step approach -- WikiRef -- that (i)
leverages the wikilinks present in a scientific Wikipedia target page and,
thereby, (ii) recommends highly relevant references to be included in that
target page appropriately and automatically borrowed from the reference section
of the wikilinks. In the first step, we build a classifier to ascertain whether
a wikilink is a potential source of reference or not. In the following step, we
recommend references to the target page from the reference section of the
wikilinks that are classified as potential sources of references in the first
step. We perform an extensive evaluation of our approach on datasets from two
different domains -- Computer Science and Physics. For Computer Science we
achieve a notably good performance with a precision@1 of 0.44 for reference
recommendation as opposed to 0.38 obtained from the most competitive baseline.
For the Physics dataset, we obtain a similar performance boost of 10% with
respect to the most competitive baseline.
Disentangled Person Image Generation
Authors: Liqian Ma, Qianru Sun, Stamatios Georgoulis, Luc van Gool, Bernt Schiele, Mario Fritz
Date: 2017-12-07 00:00:00
Proceeding: CVPR 2018 6
Tasks: Gesture-to-Gesture Translation, Image Generation, Person Re-Identification, Pose Transfer

Generating novel, yet realistic, images of persons is a challenging task due
to the complex interplay between the different image factors, such as the
foreground, background and pose information. In this work, we aim at generating
such images based on a novel, two-stage reconstruction pipeline that learns a
disentangled representation of the aforementioned image factors and generates
novel person images at the same time. First, a multi-branched reconstruction
network is proposed to disentangle and encode the three factors into embedding
features, which are then combined to re-compose the input image itself. Second,
three corresponding mapping functions are learned in an adversarial manner in
order to map Gaussian noise to the learned embedding feature space, for each
factor respectively. Using the proposed framework, we can manipulate the
foreground, background and pose of the input image, and also sample new
embedding features to generate such targeted manipulations, that provide more
control over the generation process. Experiments on Market-1501 and Deepfashion
datasets show that our model does not only generate realistic person images
with new foregrounds, backgrounds and poses, but also manipulates the generated
factors and interpolates the in-between states. Another set of experiments on
Market-1501 shows that our model can also be beneficial for the person
re-identification task.
Scalable Factorized Hierarchical Variational Autoencoder Training
Authors: Wei-Ning Hsu, James Glass
Date: 2018-04-09 00:00:00
Tasks: Disentanglement, Hyperparameter Optimization, Robust Speech Recognition, Speaker Verification, speech-recognition, Speech Recognition, Variational Inference, Voice Conversion

Deep generative models have achieved great success in unsupervised learning
with the ability to capture complex nonlinear relationships between latent
generating factors and observations. Among them, a factorized hierarchical
variational autoencoder (FHVAE) is a variational inference-based model that
formulates a hierarchical generative process for sequential data. Specifically,
an FHVAE model can learn disentangled and interpretable representations, which
have been proven useful for numerous speech applications, such as speaker
verification, robust speech recognition, and voice conversion. However, as we
will elaborate in this paper, the training algorithm proposed in the original
paper is not scalable to datasets of thousands of hours, which makes this model
less applicable on a larger scale. After identifying limitations in terms of
runtime, memory, and hyperparameter optimization, we propose a hierarchical
sampling training algorithm to address all three issues. Our proposed method is
evaluated comprehensively on a wide variety of datasets, ranging from 3 to
1,000 hours and involving different types of generating factors, such as
recording conditions and noise types. In addition, we also present a new
visualization method for qualitatively evaluating the performance with respect
to the interpretability and disentanglement. Models trained with our proposed
algorithm demonstrate the desired characteristics on all the datasets.
Learning 6-DOF Grasping Interaction via Deep Geometry-aware 3D Representations
Authors: Xinchen Yan, Jasmine Hsu, Mohi Khansari, Yunfei Bai, Arkanath Pathak, Abhinav Gupta, James Davidson, Honglak Lee
Date: 2017-08-24 00:00:00
Tasks: 3D geometry, 3D Geometry Prediction, 3D Shape Modeling, Data Augmentation

This paper focuses on the problem of learning 6-DOF grasping with a parallel
jaw gripper in simulation. We propose the notion of a geometry-aware
representation in grasping based on the assumption that knowledge of 3D
geometry is at the heart of interaction. Our key idea is constraining and
regularizing grasping interaction learning through 3D geometry prediction.
Specifically, we formulate the learning of deep geometry-aware grasping model
in two steps: First, we learn to build mental geometry-aware representation by
reconstructing the scene (i.e., 3D occupancy grid) from RGBD input via
generative 3D shape modeling. Second, we learn to predict grasping outcome with
its internal geometry-aware representation. The learned outcome prediction
model is used to sequentially propose grasping solutions via
analysis-by-synthesis optimization. Our contributions are fourfold: (1) To best
of our knowledge, we are presenting for the first time a method to learn a
6-DOF grasping net from RGBD input; (2) We build a grasping dataset from
demonstrations in virtual reality with rich sensory and interaction
annotations. This dataset includes 101 everyday objects spread across 7
categories, additionally, we propose a data augmentation strategy for effective
learning; (3) We demonstrate that the learned geometry-aware representation
leads to about 10 percent relative performance improvement over the baseline
CNN on grasping objects from our dataset. (4) We further demonstrate that the
model generalizes to novel viewpoints and object instances.