pwc-archive/papers-with-abstracts huggingface dataset:
Quasi-Monte Carlo Variational Inference
Authors: Alexander Buchholz, Florian Wenzel, Stephan Mandt
Date: 2018-07-04 00:00:00
Proceeding: ICML 2018 7
Tasks: Variational Inference

Many machine learning problems involve Monte Carlo gradient estimators. As a
prominent example, we focus on Monte Carlo variational inference (MCVI) in this
paper. The performance of MCVI crucially depends on the variance of its
stochastic gradients. We propose variance reduction by means of Quasi-Monte
Carlo (QMC) sampling. QMC replaces N i.i.d. samples from a uniform probability
distribution by a deterministic sequence of samples of length N. This sequence
covers the underlying random variable space more evenly than i.i.d. draws,
reducing the variance of the gradient estimator. With our novel approach, both
the score function and the reparameterization gradient estimators lead to much
faster convergence. We also propose a new algorithm for Monte Carlo objectives,
where we operate with a constant learning rate and increase the number of QMC
samples per iteration. We prove that this way, our algorithm can converge
asymptotically at a faster rate than SGD. We furthermore provide theoretical
guarantees on QMC for Monte Carlo objectives that go beyond MCVI, and support
our findings by several experiments on large-scale data sets from various
domains.
VideoKifu, or the automatic transcription of a Go game
Authors: Mario Corsolini, Andrea Carta
Date: 2018-07-04 00:00:00

In two previous papers [arXiv:1508.03269, arXiv:1701.05419] we described the
techniques we employed for reconstructing the whole move sequence of a Go game.
That task was at first accomplished by means of a series of photographs,
manually shot, as explained during the scientific conference held within the
LIX European Go Congress (Liberec, CZ). The photographs were subsequently
replaced by a possibly unattended video live stream (provided by webcams,
videocameras, smartphones and so on) or, were the live stream not available, by
means of a pre-recorded video of the game itself, on condition that the goban
and the stones were clearly visible more often than not. As we hinted in the
latter paper, in the last two years we have improved both the algorithms
employed for reconstructing the grid and detecting the stones, making extensive
usage of the multicore capabilities offered by modern CPUs. Those capabilities
prompted us to develop some asynchronous routines, capable of double-checking
the position of the grid and the number and colour of any stone previously
detected, in order to get rid of minor errors possibly occurred during the main
analysis, and that may pass undetected especially in the course of an
unattended live streaming. Those routines will be described in details, as they
address some problems that are of general interest when reconstructing the move
sequence, for example what to do when large movements of the whole goban occur
(deliberate or not) and how to deal with captures of dead stones $-$ that could
be wrongly detected and recorded as "fresh" moves if not promptly removed.
The SEN1-2 Dataset for Deep Learning in SAR-Optical Data Fusion
Authors: Michael Schmitt, Lloyd Haydn Hughes, Xiao Xiang Zhu
Date: 2018-07-04 00:00:00
Tasks: Colorization, Deep Learning, Image Colorization

While deep learning techniques have an increasing impact on many technical
fields, gathering sufficient amounts of training data is a challenging problem
in remote sensing. In particular, this holds for applications involving data
from multiple sensors with heterogeneous characteristics. One example for that
is the fusion of synthetic aperture radar (SAR) data and optical imagery. With
this paper, we publish the SEN1-2 dataset to foster deep learning research in
SAR-optical data fusion. SEN1-2 comprises 282,384 pairs of corresponding image
patches, collected from across the globe and throughout all meteorological
seasons. Besides a detailed description of the dataset, we show exemplary
results for several possible applications, such as SAR image colorization,
SAR-optical image matching, and creation of artificial optical images from SAR
input data. Since SEN1-2 is the first large open dataset of this kind, we
believe it will support further developments in the field of deep learning for
remote sensing as well as multi-sensor data fusion.
Sequence-to-Sequence Data Augmentation for Dialogue Language Understanding
Authors: Yutai Hou, Yijia Liu, Wanxiang Che, Ting Liu
Date: 2018-07-04 00:00:00
Proceeding: COLING 2018 8
Tasks: Data Augmentation, Diversity, Text Augmentation

In this paper, we study the problem of data augmentation for language
understanding in task-oriented dialogue system. In contrast to previous work
which augments an utterance without considering its relation with other
utterances, we propose a sequence-to-sequence generation based data
augmentation framework that leverages one utterance's same semantic
alternatives in the training data. A novel diversity rank is incorporated into
the utterance representation to make the model produce diverse utterances and
these diversely augmented utterances help to improve the language understanding
module. Experimental results on the Airline Travel Information System dataset
and a newly created semantic frame annotation on Stanford Multi-turn,
Multidomain Dialogue Dataset show that our framework achieves significant
improvements of 6.38 and 10.04 F-scores respectively when only a training set
of hundreds utterances is represented. Case studies also confirm that our
method generates diverse utterances.
Wideband Time-Domain Digital Backpropagation via Subband Processing and Deep Learning
Authors: Christian Häger, Henry D. Pfister
Date: 2018-07-04 00:00:00

We propose a low-complexity sub-banded DSP architecture for digital
backpropagation where the walk-off effect is compensated using simple delay
elements. For a simulated 96-Gbaud signal and 2500 km optical link, our method
achieves a 2.8 dB SNR improvement over linear equalization.
TextSnake: A Flexible Representation for Detecting Text of Arbitrary Shapes
Authors: Shangbang Long, Jiaqiang Ruan, Wenjie Zhang, Xin He, Wenhao Wu, Cong Yao
Date: 2018-07-04 00:00:00
Proceeding: ECCV 2018 9
Tasks: Curved Text Detection, Scene Text Detection, Text Detection

Driven by deep neural networks and large scale datasets, scene text detection methods have progressed substantially over the past years, continuously refreshing the performance records on various standard benchmarks. However, limited by the representations (axis-aligned rectangles, rotated rectangles or quadrangles) adopted to describe text, existing methods may fall short when dealing with much more free-form text instances, such as curved text, which are actually very common in real-world scenarios. To tackle this problem, we propose a more flexible representation for scene text, termed as TextSnake, which is able to effectively represent text instances in horizontal, oriented and curved forms. In TextSnake, a text instance is described as a sequence of ordered, overlapping disks centered at symmetric axes, each of which is associated with potentially variable radius and orientation. Such geometry attributes are estimated via a Fully Convolutional Network (FCN) model. In experiments, the text detector based on TextSnake achieves state-of-the-art or comparable performance on Total-Text and SCUT-CTW1500, the two newly published benchmarks with special emphasis on curved text in natural images, as well as the widely-used datasets ICDAR 2015 and MSRA-TD500. Specifically, TextSnake outperforms the baseline on Total-Text by more than 40% in F-measure.
An Integration of Bottom-up and Top-Down Salient Cues on RGB-D Data: Saliency from Objectness vs. Non-Objectness
Authors: Nevrez Imamoglu, Wataru Shimoda, Chi Zhang, Yuming Fang, Asako Kanezaki, Keiji Yanai, Yoshifumi Nishida
Date: 2018-07-04 00:00:00
Tasks: Object, object-detection, Object Detection, RGB Salient Object Detection, Salient Object Detection

Bottom-up and top-down visual cues are two types of information that helps
the visual saliency models. These salient cues can be from spatial
distributions of the features (space-based saliency) or contextual /
task-dependent features (object based saliency). Saliency models generally
incorporate salient cues either in bottom-up or top-down norm separately. In
this work, we combine bottom-up and top-down cues from both space and object
based salient features on RGB-D data. In addition, we also investigated the
ability of various pre-trained convolutional neural networks for extracting
top-down saliency on color images based on the object dependent feature
activation. We demonstrate that combining salient features from color and dept
through bottom-up and top-down methods gives significant improvement on the
salient object detection with space based and object based salient cues. RGB-D
saliency integration framework yields promising results compared with the
several state-of-the-art-models.
Curiosity Driven Exploration of Learned Disentangled Goal Spaces
Authors: Adrien Laversanne-Finot, Alexandre Péré, Pierre-Yves Oudeyer
Date: 2018-07-04 00:00:00
Tasks: Diversity, Efficient Exploration, Representation Learning

Intrinsically motivated goal exploration processes enable agents to
autonomously sample goals to explore efficiently complex environments with
high-dimensional continuous actions. They have been applied successfully to
real world robots to discover repertoires of policies producing a wide
diversity of effects. Often these algorithms relied on engineered goal spaces
but it was recently shown that one can use deep representation learning
algorithms to learn an adequate goal space in simple environments. However, in
the case of more complex environments containing multiple objects or
distractors, an efficient exploration requires that the structure of the goal
space reflects the one of the environment. In this paper we show that using a
disentangled goal space leads to better exploration performances than an
entangled goal space. We further show that when the representation is
disentangled, one can leverage it by sampling goals that maximize learning
progress in a modular manner. Finally, we show that the measure of learning
progress, used to drive curiosity-driven exploration, can be used
simultaneously to discover abstract independently controllable features of the
environment.
Generating Synthetic but Plausible Healthcare Record Datasets
Authors: Laura Aviñó, Matteo Ruffini, Ricard Gavaldà
Date: 2018-07-04 00:00:00
Tasks: Clustering, Diagnostic

Generating datasets that "look like" given real ones is an interesting tasks
for healthcare applications of ML and many other fields of science and
engineering. In this paper we propose a new method of general application to
binary datasets based on a method for learning the parameters of a latent
variable moment that we have previously used for clustering patient datasets.
We compare our method with a recent proposal (MedGan) based on generative
adversarial methods and find that the synthetic datasets we generate are
globally more realistic in at least two senses: real and synthetic instances
are harder to tell apart by Random Forests, and the MMD statistic. The most
likely explanation is that our method does not suffer from the "mode collapse"
which is an admitted problem of GANs. Additionally, the generative models we
generate are easy to interpret, unlike the rather obscure GANs. Our experiments
are performed on two patient datasets containing ICD-9 diagnostic codes: the
publicly available MIMIC-III dataset and a dataset containing admissions for
congestive heart failure during 7 years at Hospital de Sant Pau in Barcelona.
Deep Autoencoder for Combined Human Pose Estimation and body Model Upscaling
Authors: Matthew Trumble, Andrew Gilbert, Adrian Hilton, John Collomosse
Date: 2018-07-04 00:00:00
Proceeding: ECCV 2018 9
Tasks: 3D Human Pose Estimation, Pose Estimation

We present a method for simultaneously estimating 3D human pose and body
shape from a sparse set of wide-baseline camera views. We train a symmetric
convolutional autoencoder with a dual loss that enforces learning of a latent
representation that encodes skeletal joint positions, and at the same time
learns a deep representation of volumetric body shape. We harness the latter to
up-scale input volumetric data by a factor of $4 \times$, whilst recovering a
3D estimate of joint positions with equal or greater accuracy than the state of
the art. Inference runs in real-time (25 fps) and has the potential for passive
human behaviour monitoring where there is a requirement for high fidelity
estimation of human body shape and pose.
Uncorrelated Feature Encoding for Faster Image Style Transfer
Authors: Minseong Kim, Jongju Shin, Myung-Cheol Roh, Hyun-Chul Choi
Date: 2018-07-04 00:00:00
Tasks: image-classification, Image Classification, Style Transfer

Recent fast style transfer methods use a pre-trained convolutional neural
network as a feature encoder and a perceptual loss network. Although the
pre-trained network is used to generate responses of receptive fields effective
for representing style and content of image, it is not optimized for image
style transfer but rather for image classification. Furthermore, it also
requires a time-consuming and correlation-considering feature alignment process
for image style transfer because of its inter-channel correlation. In this
paper, we propose an end-to-end learning method which optimizes an
encoder/decoder network for the purpose of style transfer as well as relieves
the feature alignment complexity from considering inter-channel correlation. We
used uncorrelation loss, i.e., the total correlation coefficient between the
responses of different encoder channels, with style and content losses for
training style transfer network. This makes the encoder network to be trained
to generate inter-channel uncorrelated features and to be optimized for the
task of image style transfer which maintained the quality of image style only
with a light-weighted and correlation-unaware feature alignment process.
Moreover, our method drastically reduced redundant channels of the encoded
feature and this resulted in the efficient size of structure of network and
faster forward processing speed. Our method can also be applied to cascade
network scheme for multiple scaled style transferring and allows user-control
of style strength by using a content-style trade-off parameter.
Factored Bandits
Authors: Julian Zimmert, Yevgeny Seldin
Date: 2018-07-04 00:00:00
Proceeding: NeurIPS 2018 12

We introduce the factored bandits model, which is a framework for learning
with limited (bandit) feedback, where actions can be decomposed into a
Cartesian product of atomic actions. Factored bandits incorporate rank-1
bandits as a special case, but significantly relax the assumptions on the form
of the reward function. We provide an anytime algorithm for stochastic factored
bandits and up to constants matching upper and lower regret bounds for the
problem. Furthermore, we show that with a slight modification the proposed
algorithm can be applied to utility based dueling bandits. We obtain an
improvement in the additive terms of the regret bound compared to state of the
art algorithms (the additive terms are dominating up to time horizons which are
exponential in the number of arms).
Empirical fixed point bifurcation analysis
Authors: Gergo Bohner, Maneesh Sahani
Date: 2018-07-04 00:00:00

In a common experimental setting, the behaviour of a noisy dynamical system
is monitored in response to manipulations of one or more control parameters.
Here, we introduce a structured model to describe parametric changes in
qualitative system behaviour via stochastic bifurcation analysis. In
particular, we describe an extension of Gaussian Process models of transition
maps, in which the learned map is directly parametrized by its fixed points and
associated local linearisations. We show that the system recovers the behaviour
of a well-studied one dimensional system from little data, then learn the
behaviour of a more realistic two dimensional process of mutually inhibiting
neural populations.
Diversity in Machine Learning
Authors: Zhiqiang Gong, Ping Zhong, Weidong Hu
Date: 2018-07-04 00:00:00
Tasks: BIG-bench Machine Learning, Camera Relocalization, Diversity, Image Segmentation, Machine Translation, object-detection, Object Detection, Semantic Segmentation

Machine learning methods have achieved good performance and been widely applied in various real-world applications. They can learn the model adaptively and be better fit for special requirements of different tasks. Generally, a good machine learning system is composed of plentiful training data, a good model training process, and an accurate inference. Many factors can affect the performance of the machine learning process, among which the diversity of the machine learning process is an important one. The diversity can help each procedure to guarantee a total good machine learning: diversity of the training data ensures that the training data can provide more discriminative information for the model, diversity of the learned model (diversity in parameters of each model or diversity among different base models) makes each parameter/model capture unique or complement information and the diversity in inference can provide multiple choices each of which corresponds to a specific plausible local optimal result. Even though the diversity plays an important role in machine learning process, there is no systematical analysis of the diversification in machine learning system. In this paper, we systematically summarize the methods to make data diversification, model diversification, and inference diversification in the machine learning process, respectively. In addition, the typical applications where the diversity technology improved the machine learning performance have been surveyed, including the remote sensing imaging tasks, machine translation, camera relocalization, image segmentation, object detection, topic modeling, and others. Finally, we discuss some challenges of the diversity technology in machine learning and point out some directions in future work.
Supervised Reinforcement Learning with Recurrent Neural Network for Dynamic Treatment Recommendation
Authors: Lu Wang, Wei zhang, Xiaofeng He, Hongyuan Zha
Date: 2018-07-04 00:00:00
Tasks: Recommendation Systems, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Dynamic treatment recommendation systems based on large-scale electronic
health records (EHRs) become a key to successfully improve practical clinical
outcomes. Prior relevant studies recommend treatments either use supervised
learning (e.g. matching the indicator signal which denotes doctor
prescriptions), or reinforcement learning (e.g. maximizing evaluation signal
which indicates cumulative reward from survival rates). However, none of these
studies have considered to combine the benefits of supervised learning and
reinforcement learning. In this paper, we propose Supervised Reinforcement
Learning with Recurrent Neural Network (SRL-RNN), which fuses them into a
synergistic learning framework. Specifically, SRL-RNN applies an off-policy
actor-critic framework to handle complex relations among multiple medications,
diseases and individual characteristics. The "actor" in the framework is
adjusted by both the indicator signal and evaluation signal to ensure effective
prescription and low mortality. RNN is further utilized to solve the
Partially-Observed Markov Decision Process (POMDP) problem due to the lack of
fully observed states in real world applications. Experiments on the publicly
real-world dataset, i.e., MIMIC-3, illustrate that our model can reduce the
estimated mortality, while providing promising accuracy in matching doctors'
prescriptions.
Polarity and Intensity: the Two Aspects of Sentiment Analysis
Authors: Leimin Tian, Catherine Lai, Johanna D. Moore
Date: 2018-07-04 00:00:00
Proceeding: WS 2018 7
Tasks: General Classification, Multimodal Sentiment Analysis, Multi-Task Learning, Sentiment Analysis, Vocal Bursts Valence Prediction

Current multimodal sentiment analysis frames sentiment score prediction as a
general Machine Learning task. However, what the sentiment score actually
represents has often been overlooked. As a measurement of opinions and
affective states, a sentiment score generally consists of two aspects: polarity
and intensity. We decompose sentiment scores into these two aspects and study
how they are conveyed through individual modalities and combined multimodal
models in a naturalistic monologue setting. In particular, we build unimodal
and multimodal multi-task learning models with sentiment score prediction as
the main task and polarity and/or intensity classification as the auxiliary
tasks. Our experiments show that sentiment analysis benefits from multi-task
learning, and individual modalities differ when conveying the polarity and
intensity aspects of sentiment.
Video Frame Interpolation by Plug-and-Play Deep Locally Linear Embedding
Authors: Anh-Duc Nguyen, Woojae Kim, Jongyoo Kim, Sang-Hoon Lee
Date: 2018-07-04 00:00:00
Tasks: Video Frame Interpolation

We propose a generative framework which takes on the video frame
interpolation problem. Our framework, which we call Deep Locally Linear
Embedding (DeepLLE), is powered by a deep convolutional neural network (CNN)
while it can be used instantly like conventional models. DeepLLE fits an
auto-encoding CNN to a set of several consecutive frames and embeds a linearity
constraint on the latent codes so that new frames can be generated by
interpolating new latent codes. Different from the current deep learning
paradigm which requires training on large datasets, DeepLLE works in a
plug-and-play and unsupervised manner, and is able to generate an arbitrary
number of frames. Thorough experiments demonstrate that without bells and
whistles, our method is highly competitive among current state-of-the-art
models.
Deep Saliency Hashing
Authors: Sheng Jin, Hongxun Yao, Xiaoshuai Sun, Shangchen Zhou, Lei Zhang, Xian-Sheng Hua
Date: 2018-07-04 00:00:00
Tasks: Deep Hashing, Quantization, Retrieval

In recent years, hashing methods have been proved to be effective and
efficient for the large-scale Web media search. However, the existing general
hashing methods have limited discriminative power for describing fine-grained
objects that share similar overall appearance but have subtle difference. To
solve this problem, we for the first time introduce the attention mechanism to
the learning of fine-grained hashing codes. Specifically, we propose a novel
deep hashing model, named deep saliency hashing (DSaH), which automatically
mines salient regions and learns semantic-preserving hashing codes
simultaneously. DSaH is a two-step end-to-end model consisting of an attention
network and a hashing network. Our loss function contains three basic
components, including the semantic loss, the saliency loss, and the
quantization loss. As the core of DSaH, the saliency loss guides the attention
network to mine discriminative regions from pairs of images. We conduct
extensive experiments on both fine-grained and general retrieval datasets for
performance evaluation. Experimental results on fine-grained datasets,
including Oxford Flowers-17, Stanford Dogs-120, and CUB Bird demonstrate that
our DSaH performs the best for fine-grained retrieval task and beats the
strongest competitor (DTQ) by approximately 10% on both Stanford Dogs-120 and
CUB Bird. DSaH is also comparable to several state-of-the-art hashing methods
on general datasets, including CIFAR-10 and NUS-WIDE.