pwc-archive/papers-with-abstracts huggingface dataset:
Disentangling Motion, Foreground and Background Features in Videos
Authors: Xunyu Lin, Victor Campos, Xavier Giro-i-Nieto, Jordi Torres, Cristian Canton Ferrer
Date: 2017-07-13 00:00:00

This paper introduces an unsupervised framework to extract semantically rich
features for video representation. Inspired by how the human visual system
groups objects based on motion cues, we propose a deep convolutional neural
network that disentangles motion, foreground and background information. The
proposed architecture consists of a 3D convolutional feature encoder for blocks
of 16 frames, which is trained for reconstruction tasks over the first and last
frames of the sequence. A preliminary supervised experiment was conducted to
verify the feasibility of proposed method by training the model with a fraction
of videos from the UCF-101 dataset taking as ground truth the bounding boxes
around the activity regions. Qualitative results indicate that the network can
successfully segment foreground and background in videos as well as update the
foreground appearance based on disentangled motion features. The benefits of
these learned features are shown in a discriminative classification task, where
initializing the network with the proposed pretraining method outperforms both
random initialization and autoencoder pretraining. Our model and source code
are publicly available at https://imatge-upc.github.io/unsupervised-2017-cvprw/ .
When You Must Forget: beyond strong persistence when forgetting in answer set programming
Authors: Ricardo Gonçalves, Matthias Knorr, João Leite, Stefan Woltran
Date: 2017-07-17 00:00:00
Tasks: Open-Ended Question Answering

Among the myriad of desirable properties discussed in the context of
forgetting in Answer Set Programming (ASP), strong persistence naturally
captures its essence. Recently, it has been shown that it is not always
possible to forget a set of atoms from a program while obeying this property,
and a precise criterion regarding what can be forgotten has been presented,
accompanied by a class of forgetting operators that return the correct result
when forgetting is possible.
  However, it is an open question what to do when we have to forget a set of
atoms, but cannot without violating this property. In this paper, we address
this issue and investigate three natural alternatives to forget when forgetting
without violating strong persistence is not possible, which turn out to
correspond to the different possible relaxations of the characterization of
strong persistence. Additionally, we discuss their preferable usage, shed light
on the relation between forgetting and notions of relativized equivalence
established earlier in the context of ASP, and present a detailed study on
their computational complexity.
Fully Automatic and Real-Time Catheter Segmentation in X-Ray Fluoroscopy
Authors: Pierre Ambrosini, Daniel Ruijters, Wiro J. Niessen, Adriaan Moelker, Theo van Walsum
Date: 2017-07-17 00:00:00
Tasks: Data Augmentation

Augmenting X-ray imaging with 3D roadmap to improve guidance is a common
strategy. Such approaches benefit from automated analysis of the X-ray images,
such as the automatic detection and tracking of instruments. In this paper, we
propose a real-time method to segment the catheter and guidewire in 2D X-ray
fluoroscopic sequences. The method is based on deep convolutional neural
networks. The network takes as input the current image and the three previous
ones, and segments the catheter and guidewire in the current image.
Subsequently, a centerline model of the catheter is constructed from the
segmented image. A small set of annotated data combined with data augmentation
is used to train the network. We trained the method on images from 182 X-ray
sequences from 23 different interventions. On a testing set with images of 55
X-ray sequences from 5 other interventions, a median centerline distance error
of 0.2 mm and a median tip distance error of 0.9 mm was obtained. The
segmentation of the instruments in 2D X-ray sequences is performed in a
real-time fully-automatic manner.
Current-mode Memristor Crossbars for Neuromemristive Systems
Authors: Cory Merkel
Date: 2017-07-17 00:00:00

Motivated by advantages of current-mode design, this brief contribution
explores the implementation of weight matrices in neuromemristive systems via
current-mode memristor crossbar circuits. After deriving theoretical results
for the range and distribution of weights in the current-mode design, it is
shown that any weight matrix based on voltage-mode crossbars can be mapped to a
current-mode crossbar if the voltage-mode weights are carefully bounded. Then,
a modified gradient descent rule is derived for the current-mode design that
can be used to perform backpropagation training. Behavioral simulations on the
MNIST dataset indicate that both voltage and current-mode designs are able to
achieve similar accuracy and have similar defect tolerance. However, analysis
of trained weight distributions reveals that current-mode and voltage-mode
designs may use different feature representations.
Sub-domain Modelling for Dialogue Management with Hierarchical Reinforcement Learning
Authors: Paweł Budzianowski, Stefan Ultes, Pei-Hao Su, Nikola Mrkšić, Tsung-Hsien Wen, Iñigo Casanueva, Lina Rojas-Barahona, Milica Gašić
Date: 2017-06-19 00:00:00
Proceeding: WS 2017 8
Tasks: Dialogue Management, Hierarchical Reinforcement Learning, Management, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Human conversation is inherently complex, often spanning many different
topics/domains. This makes policy learning for dialogue systems very
challenging. Standard flat reinforcement learning methods do not provide an
efficient framework for modelling such dialogues. In this paper, we focus on
the under-explored problem of multi-domain dialogue management. First, we
propose a new method for hierarchical reinforcement learning using the option
framework. Next, we show that the proposed architecture learns faster and
arrives at a better policy than the existing flat ones do. Moreover, we show
how pretrained policies can be adapted to more complex systems with an
additional set of new actions. In doing that, we show that our approach has the
potential to facilitate policy optimisation for more sophisticated multi-domain
dialogue systems.
Neural Reranking for Named Entity Recognition
Authors: Jie Yang, Yue Zhang, Fei Dong
Date: 2017-07-17 00:00:00
Proceeding: RANLP 2017 9
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Reranking, Sentence

We propose a neural reranking system for named entity recognition (NER). The
basic idea is to leverage recurrent neural network models to learn
sentence-level patterns that involve named entity mentions. In particular,
given an output sentence produced by a baseline NER model, we replace all
entity mentions, such as \textit{Barack Obama}, into their entity types, such
as \textit{PER}. The resulting sentence patterns contain direct output
information, yet is less sparse without specific named entities. For example,
"PER was born in LOC" can be such a pattern. LSTM and CNN structures are
utilised for learning deep representations of such sentences for reranking.
Results show that our system can significantly improve the NER accuracies over
two different baselines, giving the best reported results on a standard
benchmark.
LIG-CRIStAL System for the WMT17 Automatic Post-Editing Task
Authors: Alexandre Berard, Olivier Pietquin, Laurent Besacier
Date: 2017-07-17 00:00:00
Tasks: Automatic Post-Editing, de-en, Sentence

This paper presents the LIG-CRIStAL submission to the shared Automatic Post-
Editing task of WMT 2017. We propose two neural post-editing models: a
monosource model with a task-specific attention mechanism, which performs
particularly well in a low-resource scenario; and a chained architecture which
makes use of the source sentence to provide extra context. This latter
architecture manages to slightly improve our results when more training data is
available. We present and discuss our results on two datasets (en-de and de-en)
that are made available for the task.
To Normalize, or Not to Normalize: The Impact of Normalization on Part-of-Speech Tagging
Authors: Rob van der Goot, Barbara Plank, Malvina Nissim
Date: 2017-07-17 00:00:00
Proceeding: WS 2017 9
Tasks: Part-Of-Speech Tagging, POS, POS Tagging

Does normalization help Part-of-Speech (POS) tagging accuracy on noisy,
non-canonical data? To the best of our knowledge, little is known on the actual
impact of normalization in a real-world scenario, where gold error detection is
not available. We investigate the effect of automatic normalization on POS
tagging of tweets. We also compare normalization to strategies that leverage
large amounts of unlabeled data kept in its raw form. Our results show that
normalization helps, but does not add consistently beyond just word embedding
layer initialization. The latter approach yields a tagging model that is
competitive with a Twitter state-of-the-art tagger.
The Power of Constraint Grammars Revisited
Authors: Anssi Yli-Jyrä
Date: 2017-07-17 00:00:00

Sequential Constraint Grammar (SCG) (Karlsson, 1990) and its extensions have
lacked clear connections to formal language theory. The purpose of this article
is to lay a foundation for these connections by simplifying the definition of
strings processed by the grammar and by showing that Nonmonotonic SCG is
undecidable and that derivations similar to the Generative Phonology exist. The
current investigations propose resource bounds that restrict the generative
power of SCG to a subset of context sensitive languages and present a strong
finite-state condition for grammars as wholes. We show that a grammar is
equivalent to a finite-state transducer if it is implemented with a Turing
machine that runs in o(n log n) time. This condition opens new finite-state
hypotheses and avenues for deeper analysis of SCG instances in the way inspired
by Finite-State Phonology.
Towards Bidirectional Hierarchical Representations for Attention-Based Neural Machine Translation
Authors: Baosong Yang, Derek F. Wong, Tong Xiao, Lidia S. Chao, Jingbo Zhu
Date: 2017-07-17 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Machine Translation, Translation

This paper proposes a hierarchical attentional neural translation model which
focuses on enhancing source-side hierarchical representations by covering both
local and global semantic information using a bidirectional tree-based encoder.
To maximize the predictive likelihood of target words, a weighted variant of an
attention mechanism is used to balance the attentive information between
lexical and phrase vectors. Using a tree-based rare word encoding, the proposed
model is extended to sub-word level to alleviate the out-of-vocabulary (OOV)
problem. Empirical results reveal that the proposed model significantly
outperforms sequence-to-sequence attention-based and tree-based neural
translation models in English-Chinese translation tasks.
PAC-Bayes and Domain Adaptation
Authors: Pascal Germain, Amaury Habrard, François Laviolette, Emilie Morvant
Date: 2017-07-17 00:00:00
Tasks: Domain Adaptation, Generalization Bounds

We provide two main contributions in PAC-Bayesian theory for domain adaptation where the objective is to learn, from a source distribution, a well-performing majority vote on a different, but related, target distribution. Firstly, we propose an improvement of the previous approach we proposed in Germain et al. (2013), which relies on a novel distribution pseudodistance based on a disagreement averaging, allowing us to derive a new tighter domain adaptation bound for the target risk. While this bound stands in the spirit of common domain adaptation works, we derive a second bound (introduced in Germain et al., 2016) that brings a new perspective on domain adaptation by deriving an upper bound on the target risk where the distributions' divergence-expressed as a ratio-controls the trade-off between a source error measure and the target voters' disagreement. We discuss and compare both results, from which we obtain PAC-Bayesian generalization bounds. Furthermore, from the PAC-Bayesian specialization to linear classifiers, we infer two learning algorithms, and we evaluate them on real data.
Unsupervised Iterative Deep Learning of Speech Features and Acoustic Tokens with Applications to Spoken Term Detection
Authors: Cheng-Tao Chung, Cheng-Yu Tsai, Chia-Hsiang Liu, Lin-shan Lee
Date: 2017-07-17 00:00:00

In this paper we aim to automatically discover high quality frame-level
speech features and acoustic tokens directly from unlabeled speech data. A
Multi-granular Acoustic Tokenizer (MAT) was proposed for automatic discovery of
multiple sets of acoustic tokens from the given corpus. Each acoustic token set
is specified by a set of hyperparameters describing the model configuration.
These different sets of acoustic tokens carry different characteristics for the
given corpus and the language behind, thus can be mutually reinforced. The
multiple sets of token labels are then used as the targets of a Multi-target
Deep Neural Network (MDNN) trained on frame-level acoustic features. Bottleneck
features extracted from the MDNN are then used as the feedback input to the MAT
and the MDNN itself in the next iteration. The multi-granular acoustic token
sets and the frame-level speech features can be iteratively optimized in the
iterative deep learning framework. We call this framework the Multi-granular
Acoustic Tokenizing Deep Neural Network (MATDNN). The results were evaluated
using the metrics and corpora defined in the Zero Resource Speech Challenge
organized at Interspeech 2015, and improved performance was obtained with a set
of experiments of query-by-example spoken term detection on the same corpora.
Visualization for the discovered tokens against the English phonemes was also
shown.
Eigenlogic: Interpretable Quantum Observables with applications to Fuzzy Behavior of Vehicular Robots
Authors: Zeno Toffano, François Dubois
Date: 2017-07-17 00:00:00

This work proposes a formulation of propositional logic, named Eigenlogic,
using quantum observables as propositions. The eigenvalues of these operators
are the truth-values and the associated eigenvectors the interpretations of the
propositional system. Fuzzy logic arises naturally when considering vectors
outside the eigensystem, the fuzzy membership function is obtained by the Born
rule of the logical observable.This approach is then applied in the context of
quantum robots using simple behavioral agents represented by Braitenberg
vehicles. Processing with non-classical logic such as multivalued logic, fuzzy
logic and the quantum Eigenlogic permits to enlarge the behavior possibilities
and the associated decisions of these simple agents.
Information-Flow Matting
Authors: Yağız Aksoy, Tunç Ozan Aydın, Marc Pollefeys
Date: 2017-07-17 00:00:00
Proceeding: CVPR 2017 7
Tasks: Image Matting

We present a novel, purely affinity-based natural image matting algorithm.
Our method relies on carefully defined pixel-to-pixel connections that enable
effective use of information available in the image. We control the information
flow from the known-opacity regions into the unknown region, as well as within
the unknown region itself, by utilizing multiple definitions of pixel
affinities. Among other forms of information flow, we introduce color-mixture
flow, which builds upon local linear embedding and effectively encapsulates the
relation between different pixel opacities. Our resulting novel linear system
formulation can be solved in closed-form and is robust against several
fundamental challenges of natural matting such as holes and remote intricate
structures. While our method is primarily designed as a standalone matting
tool, we show that it can also be used for regularizing mattes obtained by
sampling-based methods. The formulation is also extended to layer color
estimation and we show that the use of multiple channels of flow increases the
layer color quality. We also demonstrate our performance in green-screen keying
and analyze the characteristics of the utilized affinities.
Wide-Residual-Inception Networks for Real-time Object Detection
Authors: Youngwan Lee, Byeonghak Yim, Huien Kim, Eunsoo Park, Xuenan Cui, Taekang Woo, Hakil Kim
Date: 2017-02-04 00:00:00
Tasks: Object, object-detection, Object Detection, Real-Time Object Detection

Since convolutional neural network(CNN)models emerged,several tasks in
computer vision have actively deployed CNN models for feature extraction.
However,the conventional CNN models have a high computational cost and require
high memory capacity, which is impractical and unaffordable for commercial
applications such as real-time on-road object detection on embedded boards or
mobile platforms. To tackle this limitation of CNN models, this paper proposes
a wide-residual-inception (WR-Inception) network, which constructs the
architecture based on a residual inception unit that captures objects of
various sizes on the same feature map, as well as shallower and wider layers,
compared to state-of-the-art networks like ResNet. To verify the proposed
networks, this paper conducted two experiments; one is a classification task on
CIFAR-10/100 and the other is an on-road object detection task using a
Single-Shot Multi-box Detector(SSD) on the KITTI dataset.
Optimization by gradient boosting
Authors: Gérard Biau, Benoît Cadre
Date: 2017-07-17 00:00:00

Gradient boosting is a state-of-the-art prediction technique that
sequentially produces a model in the form of linear combinations of simple
predictors---typically decision trees---by solving an infinite-dimensional
convex optimization problem. We provide in the present paper a thorough
analysis of two widespread versions of gradient boosting, and introduce a
general framework for studying these algorithms from the point of view of
functional optimization. We prove their convergence as the number of iterations
tends to infinity and highlight the importance of having a strongly convex risk
functional to minimize. We also present a reasonable statistical context
ensuring consistency properties of the boosting predictors as the sample size
grows. In our approach, the optimization procedures are run forever (that is,
without resorting to an early stopping strategy), and statistical
regularization is basically achieved via an appropriate $L^2$ penalization of
the loss and strong convexity arguments.
A Strategy for an Uncompromising Incremental Learner
Authors: Ragav Venkatesan, Hemanth Venkateswara, Sethuraman Panchanathan, Baoxin Li
Date: 2017-05-02 00:00:00
Tasks: class-incremental learning, Class Incremental Learning, Continual Learning, Incremental Learning, Philosophy

Multi-class supervised learning systems require the knowledge of the entire
range of labels they predict. Often when learnt incrementally, they suffer from
catastrophic forgetting. To avoid this, generous leeways have to be made to the
philosophy of incremental learning that either forces a part of the machine to
not learn, or to retrain the machine again with a selection of the historic
data. While these hacks work to various degrees, they do not adhere to the
spirit of incremental learning. In this article, we redefine incremental
learning with stringent conditions that do not allow for any undesirable
relaxations and assumptions. We design a strategy involving generative models
and the distillation of dark knowledge as a means of hallucinating data along
with appropriate targets from past distributions. We call this technique,
phantom sampling.We show that phantom sampling helps avoid catastrophic
forgetting during incremental learning. Using an implementation based on deep
neural networks, we demonstrate that phantom sampling dramatically avoids
catastrophic forgetting. We apply these strategies to competitive multi-class
incremental learning of deep neural networks. Using various benchmark datasets
and through our strategy, we demonstrate that strict incremental learning could
be achieved. We further put our strategy to test on challenging cases,
including cross-domain increments and incrementing on a novel label space. We
also propose a trivial extension to unbounded-continual learning and identify
potential for future development.
Iris: A Conversational Agent for Complex Tasks
Authors: Ethan Fast, Binbin Chen, Julia Mendelsohn, Jonathan Bassen, Michael Bernstein
Date: 2017-07-17 00:00:00

Today's conversational agents are restricted to simple standalone commands.
In this paper, we present Iris, an agent that draws on human conversational
strategies to combine commands, allowing it to perform more complex tasks that
it has not been explicitly designed to support: for example, composing one
command to "plot a histogram" with another to first "log-transform the data".
To enable this complexity, we introduce a domain specific language that
transforms commands into automata that Iris can compose, sequence, and execute
dynamically by interacting with a user through natural language, as well as a
conversational type system that manages what kinds of commands can be combined.
We have designed Iris to help users with data science tasks, a domain that
requires support for command combination. In evaluation, we find that data
scientists complete a predictive modeling task significantly faster (2.6 times
speedup) with Iris than a modern non-conversational programming environment.
Iris supports the same kinds of commands as today's agents, but empowers users
to weave together these commands to accomplish complex goals.
Deep Learning to Attend to Risk in ICU
Authors: Phuoc Nguyen, Truyen Tran, Svetha Venkatesh
Date: 2017-07-17 00:00:00
Tasks: Decision Making, Deep Learning, ICU Mortality, Time Series, Time Series Analysis

Modeling physiological time-series in ICU is of high clinical importance.
However, data collected within ICU are irregular in time and often contain
missing measurements. Since absence of a measure would signify its lack of
importance, the missingness is indeed informative and might reflect the
decision making by the clinician. Here we propose a deep learning architecture
that can effectively handle these challenges for predicting ICU mortality
outcomes. The model is based on Long Short-Term Memory, and has layered
attention mechanisms. At the sensing layer, the model decides whether to
observe and incorporate parts of the current measurements. At the reasoning
layer, evidences across time steps are weighted and combined. The model is
evaluated on the PhysioNet 2012 dataset showing competitive and interpretable
results.
"Maximizing rigidity" revisited: a convex programming approach for generic 3D shape reconstruction from multiple perspective views
Authors: Pan Ji, Hongdong Li, Yuchao Dai, Ian Reid
Date: 2017-07-17 00:00:00
Proceeding: ICCV 2017 10
Tasks: 3D Reconstruction, 3D Shape Reconstruction

Rigid structure-from-motion (RSfM) and non-rigid structure-from-motion
(NRSfM) have long been treated in the literature as separate (different)
problems. Inspired by a previous work which solved directly for 3D scene
structure by factoring the relative camera poses out, we revisit the principle
of "maximizing rigidity" in structure-from-motion literature, and develop a
unified theory which is applicable to both rigid and non-rigid structure
reconstruction in a rigidity-agnostic way. We formulate these problems as a
convex semi-definite program, imposing constraints that seek to apply the
principle of minimizing non-rigidity. Our results demonstrate the efficacy of
the approach, with state-of-the-art accuracy on various 3D reconstruction
problems.