pwc-archive/papers-with-abstracts huggingface dataset:
Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations
Authors: Carole H. Sudre, Wenqi Li, Tom Vercauteren, Sébastien Ourselin, M. Jorge Cardoso
Date: 2017-07-11 00:00:00
Tasks: Deep Learning, Segmentation, Sensitivity

Deep-learning has proved in recent years to be a powerful tool for image
analysis and is now widely used to segment both 2D and 3D medical images.
Deep-learning segmentation frameworks rely not only on the choice of network
architecture but also on the choice of loss function. When the segmentation
process targets rare observations, a severe class imbalance is likely to occur
between candidate labels, thus resulting in sub-optimal performance. In order
to mitigate this issue, strategies such as the weighted cross-entropy function,
the sensitivity function or the Dice loss function, have been proposed. In this
work, we investigate the behavior of these loss functions and their sensitivity
to learning rate tuning in the presence of different rates of label imbalance
across 2D and 3D segmentation tasks. We also propose to use the class
re-balancing properties of the Generalized Dice overlap, a known metric for
segmentation assessment, as a robust and accurate deep-learning loss function
for unbalanced tasks.
Temporal Modeling Approaches for Large-scale Youtube-8M Video Understanding
Authors: Fu Li, Chuang Gan, Xiao Liu, Yunlong Bian, Xiang Long, Yandong Li, Zhichao Li, Jie zhou, Shilei Wen
Date: 2017-07-14 00:00:00
Tasks: Video Recognition, Video Understanding

This paper describes our solution for the video recognition task of the
Google Cloud and YouTube-8M Video Understanding Challenge that ranked the 3rd
place. Because the challenge provides pre-extracted visual and audio features
instead of the raw videos, we mainly investigate various temporal modeling
approaches to aggregate the frame-level features for multi-label video
recognition. Our system contains three major components: two-stream sequence
model, fast-forward sequence model and temporal residual neural networks.
Experiment results on the challenging Youtube-8M dataset demonstrate that our
proposed temporal modeling approaches can significantly improve existing
temporal modeling approaches in the large-scale video recognition tasks. To be
noted, our fast-forward LSTM with a depth of 7 layers achieves 82.75% in term
of GAP@20 on the Kaggle Public test set.
CUNI System for the WMT17 Multimodal Translation Task
Authors: Jindřich Helcl, Jindřich Libovický
Date: 2017-07-14 00:00:00
Tasks: Image Captioning, Task 2, Translation

In this paper, we describe our submissions to the WMT17 Multimodal
Translation Task. For Task 1 (multimodal translation), our best scoring system
is a purely textual neural translation of the source image caption to the
target language. The main feature of the system is the use of additional data
that was acquired by selecting similar sentences from parallel corpora and by
data synthesis with back-translation. For Task 2 (cross-lingual image
captioning), our best submitted system generates an English caption which is
then translated by the best system used in Task 1. We also present negative
results, which are based on ideas that we believe have potential of making
improvements, but did not prove to be useful in our particular setup.
Large-Scale Shape Retrieval with Sparse 3D Convolutional Neural Networks
Authors: Alexandr Notchenko, Ermek Kapushev, Evgeny Burnaev
Date: 2016-11-28 00:00:00
Tasks: General Classification, Retrieval

In this paper we present results of performance evaluation of S3DCNN - a
Sparse 3D Convolutional Neural Network - on a large-scale 3D Shape benchmark
ModelNet40, and measure how it is impacted by voxel resolution of input shape.
We demonstrate comparable classification and retrieval performance to
state-of-the-art models, but with much less computational costs in training and
inference phases. We also notice that benefits of higher input resolution can
be limited by an ability of a neural network to generalize high level features.
Probabilistic Program Abstractions
Authors: Steven Holtzen, Todd Millstein, Guy Van Den Broeck
Date: 2017-05-28 00:00:00

Abstraction is a fundamental tool for reasoning about complex systems.
Program abstraction has been utilized to great effect for analyzing
deterministic programs. At the heart of program abstraction is the relationship
between a concrete program, which is difficult to analyze, and an abstract
program, which is more tractable. Program abstractions, however, are typically
not probabilistic. We generalize non-deterministic program abstractions to
probabilistic program abstractions by explicitly quantifying the
non-deterministic choices. Our framework upgrades key definitions and
properties of abstractions to the probabilistic context. We also discuss
preliminary ideas for performing inference on probabilistic abstractions and
general probabilistic programs.
Linguistic Markers of Influence in Informal Interactions
Authors: Shrimai Prabhumoye, Samridhi Choudhary, Evangelia Spiliopoulou, Christopher Bogart, Carolyn Penstein Rose, Alan W. black
Date: 2017-07-14 00:00:00
Proceeding: WS 2017 8
Tasks: General Classification

There has been a long standing interest in understanding `Social Influence'
both in Social Sciences and in Computational Linguistics. In this paper, we
present a novel approach to study and measure interpersonal influence in daily
interactions. Motivated by the basic principles of influence, we attempt to
identify indicative linguistic features of the posts in an online knitting
community. We present the scheme used to operationalize and label the posts
with indicator features. Experiments with the identified features show an
improvement in the classification accuracy of influence by 3.15%. Our results
illustrate the important correlation between the characteristics of the
language and its potential to influence others.
Cross-genre Document Retrieval: Matching between Conversational and Formal Writings
Authors: Tomasz Jurczyk, Jinho D. Choi
Date: 2017-07-14 00:00:00
Proceeding: WS 2017 9
Tasks: Reranking, Retrieval, Sentence

This paper challenges a cross-genre document retrieval task, where the
queries are in formal writing and the target documents are in conversational
writing. In this task, a query, is a sentence extracted from either a summary
or a plot of an episode in a TV show, and the target document consists of
transcripts from the corresponding episode. To establish a strong baseline, we
employ the current state-of-the-art search engine to perform document retrieval
on the dataset collected for this work. We then introduce a structure reranking
approach to improve the initial ranking by utilizing syntactic and semantic
structures generated by NLP tools. Our evaluation shows an improvement of more
than 4% when the structure reranking is applied, which is very promising.
A New Urban Objects Detection Framework Using Weakly Annotated Sets
Authors: Eric Keiji, Gabriel Ferreira, Claudio Silva, Roberto M. Cesar Jr
Date: 2017-06-28 00:00:00
Tasks: Object Recognition

Urban informatics explore data science methods to address different urban
issues intensively based on data. The large variety and quantity of data
available should be explored but this brings important challenges. For
instance, although there are powerful computer vision methods that may be
explored, they may require large annotated datasets. In this work we propose a
novel approach to automatically creating an object recognition system with
minimal manual annotation. The basic idea behind the method is to use large
input datasets using available online cameras on large cities. A off-the-shelf
weak classifier is used to detect an initial set of urban elements of interest
(e.g. cars, pedestrians, bikes, etc.). Such initial dataset undergoes a quality
control procedure and it is subsequently used to fine tune a strong classifier.
Quality control and comparative performance assessment are used as part of the
pipeline. We evaluate the method for detecting cars based on monitoring
cameras. Experimental results using real data show that despite losing
generality, the final detector provides better detection rates tailored to the
selected cameras. The programmed robot gathered 770 video hours from 24 online
city cameras (\~300GB), which has been fed to the proposed system. Our approach
has shown that the method nearly doubled the recall (93\%) with respect to
state-of-the-art methods using off-the-shelf algorithms.
LIUM Machine Translation Systems for WMT17 News Translation Task
Authors: Mercedes García-Martínez, Ozan Caglayan, Walid Aransa, Adrien Bardet, Fethi Bougares, Loïc Barrault
Date: 2017-07-14 00:00:00
Proceeding: WS 2017 9
Tasks: Machine Translation, Translation

This paper describes LIUM submissions to WMT17 News Translation Task for
English-German, English-Turkish, English-Czech and English-Latvian language
pairs. We train BPE-based attentive Neural Machine Translation systems with and
without factored outputs using the open source nmtpy framework. Competitive
scores were obtained by ensembling various systems and exploiting the
availability of target monolingual corpora for back-translation. The impact of
back-translation quantity and quality is also analyzed for English-Turkish
where our post-deadline submission surpassed the best entry by +1.6 BLEU.
Learning Active Learning from Data
Authors: Ksenia Konyushkova, Raphael Sznitman, Pascal Fua
Date: 2017-03-09 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Active Learning, regression

In this paper, we suggest a novel data-driven approach to active learning
(AL). The key idea is to train a regressor that predicts the expected error
reduction for a candidate sample in a particular learning state. By formulating
the query selection procedure as a regression problem we are not restricted to
working with existing AL heuristics; instead, we learn strategies based on
experience from previous AL outcomes. We show that a strategy can be learnt
either from simple synthetic 2D datasets or from a subset of domain-specific
data. Our method yields strategies that work well on real data from a wide
range of domains.
Freeway Merging in Congested Traffic based on Multipolicy Decision Making with Passive Actor Critic
Authors: Tomoki Nishi, Prashant Doshi, Danil Prokhorov
Date: 2017-07-14 00:00:00
Tasks: Decision Making, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Freeway merging in congested traffic is a significant challenge toward fully
automated driving. Merging vehicles need to decide not only how to merge into a
spot, but also where to merge. We present a method for the freeway merging
based on multi-policy decision making with a reinforcement learning method
called {\em passive actor-critic} (pAC), which learns with less knowledge of
the system and without active exploration. The method selects a merging spot
candidate by using the state value learned with pAC. We evaluate our method
using real traffic data. Our experiments show that pAC achieves 92\% success
rate to merge into a freeway, which is comparable to human decision making.
Guiding InfoGAN with Semi-Supervision
Authors: Adrian Spurr, Emre Aksan, Otmar Hilliges
Date: 2017-07-14 00:00:00
Tasks: Image Generation

In this paper we propose a new semi-supervised GAN architecture (ss-InfoGAN)
for image synthesis that leverages information from few labels (as little as
0.22%, max. 10% of the dataset) to learn semantically meaningful and
controllable data representations where latent variables correspond to label
categories. The architecture builds on Information Maximizing Generative
Adversarial Networks (InfoGAN) and is shown to learn both continuous and
categorical codes and achieves higher quality of synthetic samples compared to
fully unsupervised settings. Furthermore, we show that using small amounts of
labeled data speeds-up training convergence. The architecture maintains the
ability to disentangle latent variables for which no labels are available.
Finally, we contribute an information-theoretic reasoning on how introducing
semi-supervision increases mutual information between synthetic and real data.
LIUM-CVC Submissions for WMT17 Multimodal Translation Task
Authors: Ozan Caglayan, Walid Aransa, Adrien Bardet, Mercedes García-Martínez, Fethi Bougares, Loïc Barrault, Marc Masana, Luis Herranz, Joost Van de Weijer
Date: 2017-07-14 00:00:00
Proceeding: WS 2017 9
Tasks: Machine Translation, Translation

This paper describes the monomodal and multimodal Neural Machine Translation
systems developed by LIUM and CVC for WMT17 Shared Task on Multimodal
Translation. We mainly explored two multimodal architectures where either
global visual features or convolutional feature maps are integrated in order to
benefit from visual context. Our final systems ranked first for both En-De and
En-Fr language pairs according to the automatic evaluation metrics METEOR and
BLEU.
GLSR-VAE: Geodesic Latent Space Regularization for Variational AutoEncoder Architectures
Authors: Gaëtan Hadjeres, Frank Nielsen, François Pachet
Date: 2017-07-14 00:00:00
Tasks: Music Generation

VAEs (Variational AutoEncoders) have proved to be powerful in the context of
density modeling and have been used in a variety of contexts for creative
purposes. In many settings, the data we model possesses continuous attributes
that we would like to take into account at generation time. We propose in this
paper GLSR-VAE, a Geodesic Latent Space Regularization for the Variational
AutoEncoder architecture and its generalizations which allows a fine control on
the embedding of the data into the latent space. When augmenting the VAE loss
with this regularization, changes in the learned latent space reflects changes
of the attributes of the data. This deeper understanding of the VAE latent
space structure offers the possibility to modulate the attributes of the
generated data in a continuous way. We demonstrate its efficiency on a
monophonic music generation task where we manage to generate variations of
discrete sequences in an intended and playful way.
Negative Sampling Improves Hypernymy Extraction Based on Projection Learning
Authors: Dmitry Ustalov, Nikolay Arefyev, Chris Biemann, Alexander Panchenko
Date: 2017-07-12 00:00:00
Proceeding: EACL 2017 4
Tasks: General Classification, Relation Extraction, Word Embeddings

We present a new approach to extraction of hypernyms based on projection
learning and word embeddings. In contrast to classification-based approaches,
projection-based methods require no candidate hyponym-hypernym pairs. While it
is natural to use both positive and negative training examples in supervised
relation extraction, the impact of negative examples on hypernym prediction was
not studied so far. In this paper, we show that explicit negative examples used
for regularization of the model significantly improve performance compared to
the state-of-the-art approach of Fu et al. (2014) on three datasets from
different languages.
Generalizing the Convolution Operator in Convolutional Neural Networks
Authors: Kamaledin Ghiasi-Shirazi
Date: 2017-07-14 00:00:00

Convolutional neural networks have become a main tool for solving many
machine vision and machine learning problems. A major element of these networks
is the convolution operator which essentially computes the inner product
between a weight vector and the vectorized image patches extracted by sliding a
window in the image planes of the previous layer. In this paper, we propose two
classes of surrogate functions for the inner product operation inherent in the
convolution operator and so attain two generalizations of the convolution
operator. The first one is the class of positive definite kernel functions
where their application is justified by the kernel trick. The second one is the
class of similarity measures defined based on a distance function. We justify
this by tracing back to the basic idea behind the neocognitron which is the
ancestor of CNNs. Both methods are then further generalized by allowing a
monotonically increasing function to be applied subsequently. Like any
trainable parameter in a neural network, the template pattern and the
parameters of the kernel/distance function are trained with the
back-propagation algorithm. As an aside, we use the proposed framework to
justify the use of sine activation function in CNNs. Our experiments on the
MNIST dataset show that the performance of ordinary CNNs can be achieved by
generalized CNNs based on weighted L1/L2 distances, proving the applicability
of the proposed generalization of the convolutional neural networks.
Relative Entailment Among Probabilistic Implications
Authors: Albert Atserias, José L. Balcázar, Marie Ely Piceno
Date: 2015-01-20 00:00:00

We study a natural variant of the implicational fragment of propositional
logic. Its formulas are pairs of conjunctions of positive literals, related
together by an implicational-like connective; the semantics of this sort of
implication is defined in terms of a threshold on a conditional probability of
the consequent, given the antecedent: we are dealing with what the data
analysis community calls confidence of partial implications or association
rules. Existing studies of redundancy among these partial implications have
characterized so far only entailment from one premise and entailment from two
premises, both in the stand-alone case and in the case of presence of
additional classical implications (this is what we call "relative entailment").
By exploiting a previously noted alternative view of the entailment in terms of
linear programming duality, we characterize exactly the cases of entailment
from arbitrary numbers of premises, again both in the stand-alone case and in
the case of presence of additional classical implications. As a result, we
obtain decision algorithms of better complexity; additionally, for each
potential case of entailment, we identify a critical confidence threshold and
show that it is, actually, intrinsic to each set of premises and antecedent of
the conclusion.
Robust Imitation of Diverse Behaviors
Authors: Ziyu Wang, Josh Merel, Scott Reed, Greg Wayne, Nando de Freitas, Nicolas Heess
Date: 2017-07-10 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Imitation Learning, MuJoCo

Deep generative models have recently shown great promise in imitation
learning for motor control. Given enough data, even supervised approaches can
do one-shot imitation learning; however, they are vulnerable to cascading
failures when the agent trajectory diverges from the demonstrations. Compared
to purely supervised methods, Generative Adversarial Imitation Learning (GAIL)
can learn more robust controllers from fewer demonstrations, but is inherently
mode-seeking and more difficult to train. In this paper, we show how to combine
the favourable aspects of these two approaches. The base of our model is a new
type of variational autoencoder on demonstration trajectories that learns
semantic policy embeddings. We show that these embeddings can be learned on a 9
DoF Jaco robot arm in reaching tasks, and then smoothly interpolated with a
resulting smooth interpolation of reaching behavior. Leveraging these policy
representations, we develop a new version of GAIL that (1) is much more robust
than the purely-supervised controller, especially with few demonstrations, and
(2) avoids mode collapse, capturing many diverse behaviors when GAIL on its own
does not. We demonstrate our approach on learning diverse gaits from
demonstration on a 2D biped and a 62 DoF 3D humanoid in the MuJoCo physics
environment.
Exploiting saliency for object segmentation from image level labels
Authors: Seong Joon Oh, Rodrigo Benenson, Anna Khoreva, Zeynep Akata, Mario Fritz, Bernt Schiele
Date: 2017-01-28 00:00:00
Proceeding: CVPR 2017 7
Tasks: Object, Semantic Segmentation

There have been remarkable improvements in the semantic labelling task in the
recent years. However, the state of the art methods rely on large-scale
pixel-level annotations. This paper studies the problem of training a
pixel-wise semantic labeller network from image-level annotations of the
present object classes. Recently, it has been shown that high quality seeds
indicating discriminative object regions can be obtained from image-level
labels. Without additional information, obtaining the full extent of the object
is an inherently ill-posed problem due to co-occurrences. We propose using a
saliency model as additional information and hereby exploit prior knowledge on
the object extent and image statistics. We show how to combine both information
sources in order to recover 80% of the fully supervised performance - which is
the new state of the art in weakly supervised training for pixel-wise semantic
labelling. The code is available at https://goo.gl/KygSeb.
Smart Content Recognition from Images Using a Mixture of Convolutional Neural Networks
Authors: Tee Connie, Mundher Al-Shabi, Michael Goh
Date: 2016-12-30 00:00:00

With rapid development of the Internet, web contents become huge. Most of the
websites are publicly available, and anyone can access the contents from
anywhere such as workplace, home and even schools. Nevertheless, not all the
web contents are appropriate for all users, especially children. An example of
these contents is pornography images which should be restricted to certain age
group. Besides, these images are not safe for work (NSFW) in which employees
should not be seen accessing such contents during work. Recently, convolutional
neural networks have been successfully applied to many computer vision
problems. Inspired by these successes, we propose a mixture of convolutional
neural networks for adult content recognition. Unlike other works, our method
is formulated on a weighted sum of multiple deep neural network models. The
weights of each CNN models are expressed as a linear regression problem learned
using Ordinary Least Squares (OLS). Experimental results demonstrate that the
proposed model outperforms both single CNN model and the average sum of CNN
models in adult content recognition.
Spatially variant PSF modeling in confocal macroscopy
Authors: Anna Jezierska, Hugues Talbot, Jean-Christophe Pesquet, Gilbert Engler
Date: 2017-07-14 00:00:00
Tasks: Image Reconstruction

Point spread function (PSF) plays an essential role in image reconstruction.
In the context of confocal microscopy, optical performance degrades towards the
edge of the field of view as astigmatism, coma and vignetting. Thus, one should
expect the related artifacts to be even stronger in macroscopy, where the field
of view is much larger. The field aberrations in macroscopy fluorescence
imaging system was observed to be symmetrical and to increase with the distance
from the center of the field of view. In this paper we propose an experiment
and an optimization method for assessing the center of the field of view. The
obtained results constitute a step towards reducing the number of parameters in
macroscopy PSF model.
Evaluating Semantic Parsing against a Simple Web-based Question Answering Model
Authors: Alon Talmor, Mor Geva, Jonathan Berant
Date: 2017-07-14 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Question Answering, Semantic Parsing

Semantic parsing shines at analyzing complex natural language that involves
composition and computation over multiple pieces of evidence. However, datasets
for semantic parsing contain many factoid questions that can be answered from a
single web document. In this paper, we propose to evaluate semantic
parsing-based question answering models by comparing them to a question
answering baseline that queries the web and extracts the answer only from web
snippets, without access to the target knowledge-base. We investigate this
approach on COMPLEXQUESTIONS, a dataset designed to focus on compositional
language, and find that our model obtains reasonable performance (35 F1
compared to 41 F1 of state-of-the-art). We find in our analysis that our model
performs well on complex questions involving conjunctions, but struggles on
questions that involve relation composition and superlatives.