pwc-archive/papers-with-abstracts huggingface dataset:
A Structured Variational Autoencoder for Contextual Morphological Inflection
Authors: Lawrence Wolf-Sonkin, Jason Naradowsky, Sabrina J. Mielke, Ryan Cotterell
Date: 2018-06-10 00:00:00
Proceeding: ACL 2018 7
Tasks: Morphological Inflection, Variational Inference

Statistical morphological inflectors are typically trained on fully supervised, type-level data. One remaining open research question is the following: How can we effectively exploit raw, token-level data to improve their performance? To this end, we introduce a novel generative latent-variable model for the semi-supervised learning of inflection generation. To enable posterior inference over the latent variables, we derive an efficient variational inference procedure based on the wake-sleep algorithm. We experiment on 23 languages, using the Universal Dependencies corpora in a simulated low-resource setting, and find improvements of over 10% absolute accuracy in some cases.
Object Detection in Videos by High Quality Object Linking
Authors: Peng Tang, Chunyu Wang, Xinggang Wang, Wenyu Liu, Wen-Jun Zeng, Jingdong Wang
Date: 2018-01-30 00:00:00
Tasks: General Classification, Object, object-detection, Object Detection, Vocal Bursts Intensity Prediction

Compared with object detection in static images, object detection in videos
is more challenging due to degraded image qualities. An effective way to
address this problem is to exploit temporal contexts by linking the same object
across video to form tubelets and aggregating classification scores in the
tubelets. In this paper, we focus on obtaining high quality object linking
results for better classification. Unlike previous methods that link objects by
checking boxes between neighboring frames, we propose to link in the same
frame. To achieve this goal, we extend prior methods in following aspects: (1)
a cuboid proposal network that extracts spatio-temporal candidate cuboids which
bound the movement of objects; (2) a short tubelet detection network that
detects short tubelets in short video segments; (3) a short tubelet linking
algorithm that links temporally-overlapping short tubelets to form long
tubelets. Experiments on the ImageNet VID dataset show that our method
outperforms both the static image detector and the previous state of the art.
In particular, our method improves results by 8.8% over the static image
detector for fast moving objects.
Are All Languages Equally Hard to Language-Model?
Authors: Ryan Cotterell, Sabrina J. Mielke, Jason Eisner, Brian Roark
Date: 2018-06-10 00:00:00
Proceeding: NAACL 2018 6
Tasks: All, Language Modeling, Language Modelling, model

For general modeling methods applied to diverse languages, a natural question is: how well should we expect our models to work on languages with differing typological profiles? In this work, we develop an evaluation framework for fair cross-linguistic comparison of language models, using translated text so that all models are asked to predict approximately the same information. We then conduct a study on 21 languages, demonstrating that in some languages, the textual expression of the information is harder to predict with both $n$-gram and LSTM language models. We show complex inflectional morphology to be a cause of performance differences among languages.
Unsupervised Disambiguation of Syncretism in Inflected Lexicons
Authors: Ryan Cotterell, Christo Kirov, Sabrina J. Mielke, Jason Eisner
Date: 2018-06-10 00:00:00
Proceeding: NAACL 2018 6

Lexical ambiguity makes it difficult to compute various useful statistics of a corpus. A given word form might represent any of several morphological feature bundles. One can, however, use unsupervised learning (as in EM) to fit a model that probabilistically disambiguates word forms. We present such an approach, which employs a neural network to smoothly model a prior distribution over feature bundles (even rare ones). Although this basic model does not consider a token's context, that very property allows it to operate on a simple list of unigram type counts, partitioning each count among different analyses of that unigram. We discuss evaluation metrics for this novel task and report results on 5 languages.
Pólya Urn Latent Dirichlet Allocation: a doubly sparse massively parallel sampler
Authors: Alexander Terenin, Måns Magnusson, Leif Jonsson, David Draper
Date: 2017-04-12 00:00:00
Tasks: Topic Models

Latent Dirichlet Allocation (LDA) is a topic model widely used in natural language processing and machine learning. Most approaches to training the model rely on iterative algorithms, which makes it difficult to run LDA on big corpora that are best analyzed in parallel and distributed computational environments. Indeed, current approaches to parallel inference either don't converge to the correct posterior or require storage of large dense matrices in memory. We present a novel sampler that overcomes both problems, and we show that this sampler is faster, both empirically and theoretically, than previous Gibbs samplers for LDA. We do so by employing a novel P\'olya-urn-based approximation in the sparse partially collapsed sampler for LDA. We prove that the approximation error vanishes with data size, making our algorithm asymptotically exact, a property of importance for large-scale topic models. In addition, we show, via an explicit example, that - contrary to popular belief in the topic modeling literature - partially collapsed samplers can be more efficient than fully collapsed samplers. We conclude by comparing the performance of our algorithm with that of other approaches on well-known corpora.
An Enhanced Binary Particle-Swarm Optimization (E-BPSO) Algorithm for Service Placement in Hybrid Cloud Platforms
Authors: Wissem Abbes, Zied Kechaou, Amir Hussain, Abdulrahman M. Qahtani, Omar Aimutiry, Habib Dhahri, Adel M. ALIMI
Date: 2018-06-10 00:00:00
Tasks: Evolutionary Algorithms

Nowadays, hybrid cloud platforms stand as an attractive solution for organizations intending to implement combined private and public cloud applications, in order to meet their profitability requirements. However, this can only be achieved through the utilization of available resources while speeding up execution processes. Accordingly, deploying new applications entails dedicating some of these processes to a private cloud solution, while allocating others to the public cloud. In this context, the present work is set to help minimize relevant costs and deliver effective choices for an optimal service placement solution within minimal execution time. Several evolutionary algorithms have been applied to solve the service placement problem and are used when dealing with complex solution spaces to provide an optimal placement and often produce a short execution time. The standard BPSO algorithm is found to display a significant disadvantage, namely, of easily trapping into local optima, in addition to demonstrating a noticeable lack of robustness in dealing with service placement problems. Hence, to overcome critical shortcomings associated with the standard BPSO, an Enhanced Binary Particle Swarm Optimization (E-BPSO) algorithm is proposed, consisting of a modification of the particle position updating equation, initially inspired from the continuous PSO. Our proposed E-BPSO algorithm is shown to outperform state-of-the-art approaches in terms of both cost and execution time, using a real benchmark.
Cross-Dataset Adaptation for Visual Question Answering
Authors: Wei-Lun Chao, Hexiang Hu, Fei Sha
Date: 2018-06-10 00:00:00
Proceeding: CVPR 2018 6
Tasks: Domain Adaptation, Question Answering, Visual Question Answering, Visual Question Answering (VQA)

We investigate the problem of cross-dataset adaptation for visual question
answering (Visual QA). Our goal is to train a Visual QA model on a source
dataset but apply it to another target one. Analogous to domain adaptation for
visual recognition, this setting is appealing when the target dataset does not
have a sufficient amount of labeled data to learn an "in-domain" model. The key
challenge is that the two datasets are constructed differently, resulting in
the cross-dataset mismatch on images, questions, or answers.
  We overcome this difficulty by proposing a novel domain adaptation algorithm.
Our method reduces the difference in statistical distributions by transforming
the feature representation of the data in the target dataset. Moreover, it
maximizes the likelihood of answering questions (in the target dataset)
correctly using the Visual QA model trained on the source dataset. We
empirically studied the effectiveness of the proposed approach on adapting
among several popular Visual QA datasets. We show that the proposed method
improves over baselines where there is no adaptation and several other
adaptation methods. We both quantitatively and qualitatively analyze when the
adaptation can be mostly effective.
Learning Answer Embeddings for Visual Question Answering
Authors: Hexiang Hu, Wei-Lun Chao, Fei Sha
Date: 2018-06-10 00:00:00
Proceeding: CVPR 2018 6
Tasks: Question Answering, Transfer Learning, Visual Question Answering, Visual Question Answering (VQA)

We propose a novel probabilistic model for visual question answering (Visual
QA). The key idea is to infer two sets of embeddings: one for the image and the
question jointly and the other for the answers. The learning objective is to
learn the best parameterization of those embeddings such that the correct
answer has higher likelihood among all possible answers. In contrast to several
existing approaches of treating Visual QA as multi-way classification, the
proposed approach takes the semantic relationships (as characterized by the
embeddings) among answers into consideration, instead of viewing them as
independent ordinal numbers. Thus, the learned embedded function can be used to
embed unseen answers (in the training dataset). These properties make the
approach particularly appealing for transfer learning for open-ended Visual QA,
where the source dataset on which the model is learned has limited overlapping
with the target dataset in the space of answers. We have also developed
large-scale optimization techniques for applying the model to datasets with a
large number of answers, where the challenge is to properly normalize the
proposed probabilistic models. We validate our approach on several Visual QA
datasets and investigate its utility for transferring models across datasets.
The empirical results have shown that the approach performs well not only on
in-domain learning but also on transfer learning.
Smallify: Learning Network Size while Training
Authors: Guillaume Leclerc, Manasi Vartak, Raul Castro Fernandez, Tim Kraska, Samuel Madden
Date: 2018-06-10 00:00:00

As neural networks become widely deployed in different applications and on
different hardware, it has become increasingly important to optimize inference
time and model size along with model accuracy. Most current techniques optimize
model size, model accuracy and inference time in different stages, resulting in
suboptimal results and computational inefficiency. In this work, we propose a
new technique called Smallify that optimizes all three of these metrics at the
same time. Specifically we present a new method to simultaneously optimize
network size and model performance by neuron-level pruning during training.
Neuron-level pruning not only produces much smaller networks but also produces
dense weight matrices that are amenable to efficient inference. By applying our
technique to convolutional as well as fully connected models, we show that
Smallify can reduce network size by 35X with a 6X improvement in inference time
with similar accuracy as models found by traditional training techniques.
Stochastic seismic waveform inversion using generative adversarial networks as a geological prior
Authors: Lukas Mosser, Olivier Dubrule, Martin J. Blunt
Date: 2018-06-10 00:00:00
Tasks: Generative Adversarial Network

We present an application of deep generative models in the context of
partial-differential equation (PDE) constrained inverse problems. We combine a
generative adversarial network (GAN) representing an a priori model that
creates subsurface geological structures and their petrophysical properties,
with the numerical solution of the PDE governing the propagation of acoustic
waves within the earth's interior. We perform Bayesian inversion using an
approximate Metropolis-adjusted Langevin algorithm (MALA) to sample from the
posterior given seismic observations. Gradients with respect to the model
parameters governing the forward problem are obtained by solving the adjoint of
the acoustic wave equation. Gradients of the mismatch with respect to the
latent variables are obtained by leveraging the differentiable nature of the
deep neural network used to represent the generative model. We show that
approximate MALA sampling allows efficient Bayesian inversion of model
parameters obtained from a prior represented by a deep generative model,
obtaining a diverse set of realizations that reflect the observed seismic
response.
Being Negative but Constructively: Lessons Learnt from Creating Better Visual Question Answering Datasets
Authors: Wei-Lun Chao, Hexiang Hu, Fei Sha
Date: 2017-04-24 00:00:00
Proceeding: NAACL 2018 6
Tasks: Multiple-choice, Question Answering, Visual Question Answering, Visual Question Answering (VQA)

Visual question answering (Visual QA) has attracted a lot of attention
lately, seen essentially as a form of (visual) Turing test that artificial
intelligence should strive to achieve. In this paper, we study a crucial
component of this task: how can we design good datasets for the task? We focus
on the design of multiple-choice based datasets where the learner has to select
the right answer from a set of candidate ones including the target (\ie the
correct one) and the decoys (\ie the incorrect ones). Through careful analysis
of the results attained by state-of-the-art learning models and human
annotators on existing datasets, we show that the design of the decoy answers
has a significant impact on how and what the learning models learn from the
datasets. In particular, the resulting learner can ignore the visual
information, the question, or both while still doing well on the task. Inspired
by this, we propose automatic procedures to remedy such design deficiencies. We
apply the procedures to re-construct decoy answers for two popular Visual QA
datasets as well as to create a new Visual QA dataset from the Visual Genome
project, resulting in the largest dataset for this task. Extensive empirical
studies show that the design deficiencies have been alleviated in the remedied
datasets and the performance on them is likely a more faithful indicator of the
difference among learning models. The datasets are released and publicly
available via http://www.teds.usc.edu/website_vqa/.
Conditional Generative Adversarial and Convolutional Networks for X-ray Breast Mass Segmentation and Shape Classification
Authors: Vivek Kumar Singh, Santiago Romani, Hatem A. Rashwan, Farhan Akram, Nidhi Pandey, Md. Mostafa Kamal Sarker, Jordina Torrents Barrena, Saddam Abdulwahab, Adel Saleh, Miguel Arquez, Meritxell Arenas, Domenec Puig
Date: 2018-05-25 00:00:00
Tasks: General Classification

This paper proposes a novel approach based on conditional Generative
Adversarial Networks (cGAN) for breast mass segmentation in mammography. We
hypothesized that the cGAN structure is well-suited to accurately outline the
mass area, especially when the training data is limited. The generative network
learns intrinsic features of tumors while the adversarial network enforces
segmentations to be similar to the ground truth. Experiments performed on
dozens of malignant tumors extracted from the public DDSM dataset and from our
in-house private dataset confirm our hypothesis with very high Dice coefficient
and Jaccard index (>94% and >89%, respectively) outperforming the scores
obtained by other state-of-the-art approaches. Furthermore, in order to detect
portray significant morphological features of the segmented tumor, a specific
Convolutional Neural Network (CNN) have also been designed for classifying the
segmented tumor areas into four types (irregular, lobular, oval and round),
which provides an overall accuracy about 72% with the DDSM dataset.
All-in-one: Multi-task Learning for Rumour Verification
Authors: Elena Kochkina, Maria Liakata, Arkaitz Zubiaga
Date: 2018-06-10 00:00:00
Proceeding: COLING 2018 8
Tasks: All, General Classification, Multi-Task Learning, Rumour Detection, Stance Classification

Automatic resolution of rumours is a challenging task that can be broken down
into smaller components that make up a pipeline, including rumour detection,
rumour tracking and stance classification, leading to the final outcome of
determining the veracity of a rumour. In previous work, these steps in the
process of rumour verification have been developed as separate components where
the output of one feeds into the next. We propose a multi-task learning
approach that allows joint training of the main and auxiliary tasks, improving
the performance of rumour verification. We examine the connection between the
dataset properties and the outcomes of the multi-task learning models used.
Light field super resolution through controlled micro-shifts of light field sensor
Authors: M. Umair Mukati, Bahadir K. Gunturk
Date: 2017-09-27 00:00:00
Tasks: Super-Resolution, Translation

Light field cameras enable new capabilities, such as post-capture refocusing
and aperture control, through capturing directional and spatial distribution of
light rays in space. Micro-lens array based light field camera design is often
preferred due to its light transmission efficiency, cost-effectiveness and
compactness. One drawback of the micro-lens array based light field cameras is
low spatial resolution due to the fact that a single sensor is shared to
capture both spatial and angular information. To address the low spatial
resolution issue, we present a light field imaging approach, where multiple
light fields are captured and fused to improve the spatial resolution. For each
capture, the light field sensor is shifted by a pre-determined fraction of a
micro-lens size using an XY translation stage for optimal performance.
Deep Reinforcement Learning for Chinese Zero pronoun Resolution
Authors: Qingyu Yin, Yu Zhang, Wei-Nan Zhang, Ting Liu, William Yang Wang
Date: 2018-06-10 00:00:00
Proceeding: ACL 2018 7
Tasks: Chinese Zero Pronoun Resolution, Decision Making, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Deep neural network models for Chinese zero pronoun resolution learn semantic
information for zero pronoun and candidate antecedents, but tend to be
short-sighted---they often make local decisions. They typically predict
coreference chains between the zero pronoun and one single candidate antecedent
one link at a time, while overlooking their long-term influence on future
decisions. Ideally, modeling useful information of preceding potential
antecedents is critical when later predicting zero pronoun-candidate antecedent
pairs. In this study, we show how to integrate local and global decision-making
by exploiting deep reinforcement learning models. With the help of the
reinforcement learning agent, our model learns the policy of selecting
antecedents in a sequential manner, where useful information provided by
earlier predicted antecedents could be utilized for making later coreference
decisions. Experimental results on OntoNotes 5.0 dataset show that our
technique surpasses the state-of-the-art models.
Unsupervised Video-to-Video Translation
Authors: Dina Bashkirova, Ben Usman, Kate Saenko
Date: 2018-06-10 00:00:00
Proceeding: ICLR 2019 5
Tasks: Image-to-Image Translation, Translation, Unsupervised Image-To-Image Translation

Unsupervised image-to-image translation is a recently proposed task of
translating an image to a different style or domain given only unpaired image
examples at training time. In this paper, we formulate a new task of
unsupervised video-to-video translation, which poses its own unique challenges.
Translating video implies learning not only the appearance of objects and
scenes but also realistic motion and transitions between consecutive frames.We
investigate the performance of per-frame video-to-video translation using
existing image-to-image translation networks, and propose a spatio-temporal 3D
translator as an alternative solution to this problem. We evaluate our 3D
method on multiple synthetic datasets, such as moving colorized digits, as well
as the realistic segmentation-to-video GTA dataset and a new CT-to-MRI
volumetric images translation dataset. Our results show that frame-wise
translation produces realistic results on a single frame level but
underperforms significantly on the scale of the whole video compared to our
three-dimensional translation approach, which is better able to learn the
complex structure of video and motion and continuity of object appearance.
Attention-Based Guided Structured Sparsity of Deep Neural Networks
Authors: Amirsina Torfi, Rouzbeh A. Shirvani, Sobhan Soleymani, Nasser M. Nasrabadi
Date: 2018-02-13 00:00:00
Tasks: Network Pruning

Network pruning is aimed at imposing sparsity in a neural network
architecture by increasing the portion of zero-valued weights for reducing its
size regarding energy-efficiency consideration and increasing evaluation speed.
In most of the conducted research efforts, the sparsity is enforced for network
pruning without any attention to the internal network characteristics such as
unbalanced outputs of the neurons or more specifically the distribution of the
weights and outputs of the neurons. That may cause severe accuracy drop due to
uncontrolled sparsity. In this work, we propose an attention mechanism that
simultaneously controls the sparsity intensity and supervised network pruning
by keeping important information bottlenecks of the network to be active. On
CIFAR-10, the proposed method outperforms the best baseline method by 6% and
reduced the accuracy drop by 2.6x at the same level of sparsity.
Continuous-Time Visual-Inertial Odometry for Event Cameras
Authors: Elias Mueggler, Guillermo Gallego, Henri Rebecq, Davide Scaramuzza
Date: 2017-02-23 00:00:00
Tasks: Visual Odometry

Event cameras are bio-inspired vision sensors that output pixel-level
brightness changes instead of standard intensity frames. They offer significant
advantages over standard cameras, namely a very high dynamic range, no motion
blur, and a latency in the order of microseconds. However, due to the
fundamentally different structure of the sensor's output, new algorithms that
exploit the high temporal resolution and the asynchronous nature of the sensor
are required. Recent work has shown that a continuous-time representation of
the event camera pose can deal with the high temporal resolution and
asynchronous nature of this sensor in a principled way. In this paper, we
leverage such a continuous-time representation to perform visual-inertial
odometry with an event camera. This representation allows direct integration of
the asynchronous events with micro-second accuracy and the inertial
measurements at high frequency. The event camera trajectory is approximated by
a smooth curve in the space of rigid-body motions using cubic splines. This
formulation significantly reduces the number of variables in trajectory
estimation problems. We evaluate our method on real data from several scenes
and compare the results against ground truth from a motion-capture system. We
show that our method provides improved accuracy over the result of a
state-of-the-art visual odometry method for event cameras. We also show that
both the map orientation and scale can be recovered accurately by fusing events
and inertial data. To the best of our knowledge, this is the first work on
visual-inertial fusion with event cameras using a continuous-time framework.